Applications of Machine Learning and Remote Sensing in Soil and Water Conservation

Kim, Ye Inn; Park, Woo Hyeon; Shin, Yongchul; Park, Jin-Woo; Engel, Bernie; Yun, Young-Jo; Jang, Won Seok

doi:10.3390/hydrology11110183

Open AccessFeature PaperReview

Applications of Machine Learning and Remote Sensing in Soil and Water Conservation

by

Ye Inn Kim

¹,

Woo Hyeon Park

¹,

Yongchul Shin

²,

Jin-Woo Park

³,

Bernie Engel

⁴,

Young-Jo Yun

^5,*

and

Won Seok Jang

^5,*

¹

Department of Landscape Architecture, Kangwon National University, Chuncheon 24341, Republic of Korea

²

Department of Agricultural Civil Engineering, Kyungpook National University, Daegu 41566, Republic of Korea

³

Division of Forest Sciences, College of Forest and Environmental Sciences, Kangwon National University, Chuncheon 24341, Republic of Korea

⁴

Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907, USA

⁵

Department of Ecological Landscape Architecture Design, Kangwon National University, Chuncheon 24341, Republic of Korea

^*

Authors to whom correspondence should be addressed.

Hydrology 2024, 11(11), 183; https://doi.org/10.3390/hydrology11110183

Submission received: 30 September 2024 / Revised: 23 October 2024 / Accepted: 25 October 2024 / Published: 30 October 2024

(This article belongs to the Special Issue Big Data and Machine Learning in Hydrology: Recent Advances and Trends)

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Abstract

The application of machine learning (ML) and remote sensing (RS) in soil and water conservation has become a powerful tool. As analytical tools continue to advance, the variety of ML algorithms and RS sources has expanded, providing opportunities for more sophisticated analyses. At the same time, researchers are required to select appropriate technologies based on the research objectives, topic, and scope of the study area. In this paper, we present a comprehensive review of the application of ML algorithms and RS that has been implemented to advance research in soil and water conservation. The key contribution of this review paper is that it provides an overview of current research areas within soil and water conservation and their effectiveness in improving prediction accuracy and resource management in categorized subfields, including soil properties, hydrology and water resources, and wildfire management. We also highlight challenges and future directions based on limitations of ML and RS applications in soil and water conservation. This review aims to serve as a reference for researchers and decision-makers by offering insights into the effectiveness of ML and RS applications in the fields of soil and water conservation.

Keywords:

machine learning; remote sensing; soil conservation; water conservation; environmental analysis; data-driven decision-making; resource management

1. Introduction

Soil and water play a pivotal role in various ecological processes, including nutrient cycling, water filtration, and habitat provision, which collectively support biodiversity and ecosystem stability. Soils contribute to the cycling of carbon, nitrogen, and phosphorus critical for plant growth and ecosystem productivity [1,2,3]. Water is essential for maintaining hydrological cycles, regulating temperature, and sustaining terrestrial and aquatic habitats [4,5]. These resources are fundamental to ecosystem resilience and functionality, impacting not only natural processes but also human activities such as agriculture and urban development [6,7].

In addition to their ecological significance, soil and water resources are crucial for sustainable agricultural practices and food security [8]. However, their integrity is increasingly being compromised by anthropogenic factors including climate change, population growth, deforestation, and unsustainable land-use practices [9]. Climate change can exacerbate soil erosion, disrupt nutrient cycles, and affect water availability by altering precipitation patterns and increasing the frequency of extreme weather events [10]. Population growth and urban expansion place additional pressures on these resources, leading to overexploitation, pollution, and habitat loss [11]. Deforestation can further undermine soil structure and reduce the land’s capacity to retain water, while poor land-management practices can accelerate soil degradation and water contamination [12,13].

Addressing these challenges requires a comprehensive approach to soil and water conservation that encompasses a range of strategies aimed at mitigating the negative effects of these stressors [14,15]. Effective soil conservation involves practices such as erosion control, moisture retention through irrigation management and organic amendments, and sustainable land-use planning. Similarly, water conservation encompasses measures to enhance water quality, improve storage capacity, and promote efficient usage. These conservation practices are important not only for sustaining ecosystem health and agricultural productivity but also for supporting broader environmental management goals, including wildfire mitigation and recovery.

Traditionally, numerical models have been essential tools in soil and water conservation [16,17]. However, their reliance on a limited set of variables and specific assumptions often results in prediction accuracy being heavily dependent on given input data [18,19,20,21]. Additionally, they may fail to account for uncertainties in the detection of climate change [18]. Thus, advanced tools that can complement or even replace traditional numerical models are needed. Integration of machine learning (ML) and remote sensing (RS) data presents a promising solution to limitations of traditional methods in soil and water conservation [22].

Advances in usage of RS data provide extensive spatial and temporal data, capturing environmental changes with high precision across large areas [23,24]. RS techniques such as multispectral and hyperspectral imaging, LiDAR, and synthetic aperture radar (SAR) enable the collection of data on various environmental parameters, including soil moisture, vegetation cover, land-surface temperature, and water quality. When combined with ML techniques such as Random Forest (RF), Support Vector Machines (SVMs), Convolutional Neural Networks (CNNs), and Gradient Boosting Machines (GBMs), these data can be analyzed to identify patterns, make predictions, and develop more effective conservation strategies [25,26]. The ability of ML to process vast amounts of RS data enables extraction of valuable insights from complex, unstructured datasets, leading to improved accuracy in predicting soil and water resource changes. Additionally, the fusion of ML and RS allows for real-time monitoring and assessment, offering dynamic and responsive tools for decision-making in conservation practices [27,28]. This integrated approach can enhance resource optimization, increase the precision of policy implementation, and facilitate data-driven decision-making in soil and water management.

As analytical tools continue to advance, the variety of ML algorithms and RS sources has expanded, offering new opportunities for more sophisticated and nuanced analyses [29]. This growing diversity allows researchers to tailor their methodological approaches more precisely to the specific needs of their research objectives, environmental conditions, and the scale of the study area. However, selecting the most appropriate technologies has become increasingly complex, requiring researchers to carefully evaluate trade-offs between spatial resolution, temporal frequency, data availability, and computational complexity [30]. For example, ML algorithms such as RF and SVMs offer robust predictions but differ significantly in their data requirements and performance based on dataset size and structure. Similarly, RS platforms such as UAVs, MODIS, and Landsat provide varied spatial and temporal coverage, demanding strategic selection to align with specific conservation goals.

The objective of this review is to explore the integration of ML and RS technologies within soil and water conservation research. As the variety of algorithms and data sources grows, this review aims to demonstrate how these advanced tools can address the challenges posed by traditional numerical models. Specifically, it provides a comparative evaluation to guide the selection of appropriate ML algorithms and RS platforms for distinct environmental contexts, balancing trade-offs between spatial resolution, temporal frequency, data availability, and computational complexity.

This review highlights the application of these technologies across various subfields, including soil-erosion control, water-quality monitoring, and moisture retention, demonstrating their effectiveness in improving prediction accuracy and enhancing resource management. Additionally, this paper will discuss future research directions, focusing on integrating ML with RS and developing innovative solutions for sustainable soil and water conservation. The scope includes a detailed examination of how these advanced tools can be applied across diverse conservation challenges and implications for future policy and practice.

2. Materials and Methods

Searching/Classification Methodology

The literature review focused on collecting published journals that applied ML algorithms and RS data to conduct research related to soil and water conservation, as summarized in Figure 1.

To identify relevant publications, we utilized the web-based bibliographic database “ScienceDirect” to identify relevant publications, using specific keywords such as “machine learning”, “remote sensing”, “soil conservation”, and “water conservation”. This search resulted in the identification of 195 studies conducted across 45 countries, as shown in Figure 2.

Studies conducted in China, the United States, and Iran showed the highest frequencies, with smaller but notable frequencies in Australia, Canada, Russia, and several European countries. Studies conducted in other locations around the world, including parts of Africa, South America, and South Asia, were also marked, indicating their relative frequency in the data. Figure 3 illustrates trends in reviewed publications across four key research areas from 2006 to 2024. Wildfire management exhibited a dramatic increase in publications, peaking in 2022, likely due to the increasing impact of climate change, advances in ML and RS technologies, and increased global awareness driven by initiatives such as the UN’s SDGs and the Paris Agreement. Research studies on soil properties, hydrology, and water resources also peaked around the same time, although they showed a more gradual rise and fall.

The research subjects of 195 studies were identified through a thorough screening of both abstracts and methodologies, focusing on the specific objects where ML or RS technologies were applied. These subjects were categorized into 34 distinct research topics, referred to as “subcategorized subjects”, including soil conductivity, soil salinity, soil organic carbon (SOC), soil aggregate stability, soil chemistry, soil degradation, soil erodibility, soil matric potential, soil mercury, soil moisture, soil nutrients, soil total nitrogen, soil respiration, soil stiffness, soil texture, soil types, soil organic matter, soil water content and evapotranspiration, groundwater level, streamflow, surface water, water storage, sediment concentration, algal blooms, Secchi disk depth, sediment discharge, water quality, turbidity, evapotranspiration, flash flood water depth, inundation status, ocean surface CO_2, wildfire prediction, wildfire monitoring, and wildfire recovery, as shown in Table 1. These subcategorized subjects were then reclassified into four research fields: (1) soil properties; (2) hydrology and water resources; and (3) wildfire management. While some subcategorized subjects of collected studies were closely related and ambiguous to distinguish, the classification focused on the objective of this study. The research field of soil properties had the highest number of publications, followed by wildfire management and hydrology and water resources. Publications were distributed as follows: 93 papers (48%) on soil properties, 52 papers (27%) on wildfire management, and 50 papers (25%) on hydrology and water resources, as shown in Figure 4.

3. Results and Discussion

3.1. Types and Frequencies of RS Data Used in Soil and Water Conservation Research

From the 195 studies collected, a total of 41 different types of RS techniques were identified (Appendix A, Table A2). Figure 5 depicts the number of publications that utilized each RS datum, highlighting only those RS data used more than twice across the 195 studies. The following RS data were used only once each: AGRS, AVIRIS-NG, GF-1, Triplesat, PALSAR-2, Terra, ZH-1, ETM+, SVC, NLCD, Himawari-8, TDC, AMSR-E, MERIS, MERRA-2, Chinese Environmental 1A satellite, GOES-16, TM, SAR, and SPOT-4. This study analyzed types and frequencies of RS data used across different research fields in soil and water conservation. RS data were classified based on four research fields. Below is an overview of the most used RS data types within each field. Table 2 summarizes the number of publications and the top three most used RS data in different environmental research fields, including soil properties, hydrology and water resources, and wildfire management. For soil properties, Landsat 8 was the most used (32 times). Its high-resolution images and multispectral capabilities are particularly effective for evaluating soil characteristics. In hydrology and water resources, Landsat 8 was again predominant. It was used 18 times for tracking changes in water bodies, flood monitoring, and resource management. Lastly, in wildfire management, MODIS was the leading algorithm. It was used 20 times, offering vital support for real-time fire monitoring and management across damaged areas.

Landsat 8, MODIS, and Sentinel-2 are essential satellites, each serving different research purposes due to differences in their spatial and temporal resolution. Landsat 8, with its 30 m resolution, is well suited to analyzing small-scale features such as soil moisture and water flow, making it ideal for studies focused on soil properties and hydrological processes [61,73,141,147,154]. MODIS, by contrast, offers a lower spatial resolution of 250 to 1000 m but compensates with a higher temporal resolution, revisiting the same location twice daily. This makes MODIS highly effective for monitoring large-scale and dynamic events such as wildfires, as it captures wide areas quickly and detects heat through infrared bands [179,192,201]. Sentinel-2, with a 10 to 20 m resolution and a five-day revisit period, provides a middle ground between Landsat and MODIS, supporting both detailed analyses and temporal monitoring of environmental changes [41,64,140,148,184]. While each satellite has distinct strengths, their limitations also shape their use cases. Landsat’s high spatial resolution makes it ideal for localized studies, but its 16-day revisit period can limit its utility for rapidly evolving events. MODIS, with frequent observations, supports continuous monitoring but lacks the spatial detail needed for small-scale analyses. Sentinel-2 offers more frequent data than Landsat and higher resolution than MODIS, but its relatively short historical archive compared to that of the Landsat program, which dates to the 1970s, limits its utility for long-term trend analysis.

These RS datasets provide valuable spatial and temporal coverage across different scales. MODIS delivers daily global data, enabling real-time monitoring of dynamic processes such as wildfire progression [178,201,215]. Landsat 8 and Sentinel-2 provide high-resolution imagery, suitable for time-series analysis of hydrological events or soil [58,59]. For example, Landsat’s long historical record supports decades-long environmental monitoring, making it a critical tool for tracking soil erosion and land-use changes. Sentinel-2 and MODIS complement these capabilities by offering consistent datasets for more recent years, facilitating trend analysis and predictive modeling in fields like wildfire management and soil conservation [73,148,156]. In summary, the choice of satellite depends on the research objectives and trade-offs between spatial and temporal resolution. MODIS is preferred for monitoring large-scale, rapidly evolving events, while Landsat is better suited for detailed studies of soil properties. Sentinel-2 serves as a versatile option, balancing both spatial and temporal resolution. The variation in usage rates reflects the unique advantages and limitations of each satellite, highlighting the importance of thoughtful selection to meet specific research needs. For instance, ML techniques have enhanced the utility of RS data by supporting predictive modeling across various fields, including wildfire detection and soil-moisture estimation. However, the choice of ML algorithms must also consider the availability of data, computational requirements, and the specific environmental factors being modeled.

3.2. Types and Frequencies of ML Algorithm Used in Soil and Water Conservation Research

A total of 50 distinct ML algorithms were identified, and their usage frequencies are illustrated in Figure 6. Algorithms that appeared more than twice are shown, while those not depicted to prevent excessive clutter, each used only once, include RTM, ARD, BAGGING, BDT, SA, SCA-Elman, SoLIM, SOM, SR, PSO-SVR, B-CART, CBR, DR, DELM, GAN, BAYE, LGBM, GSC, GRNN, PCR, PKR, RPART, MR-CNN, LMM, EBP, ETR, ELR, EM, EFS, EPR, DBN, DRF, DMP, LDA, MDN, MLPR, MT, Nue-SICR, SICR, FR, FCN, FNN, AdaBag, BST, M5P, YOLO, and IF. The number of publications across research fields related to RS data and the top three most used algorithms along with their frequency of usage are shown in Table 3. The soil properties field had the highest number of publications, with RF being the most prevalent algorithm. Similarly, hydrology and water resources and wildfire management also showed a preference for RF as the leading algorithm. In cases where algorithms were used with the same frequency, they were ranked equally. The total number of algorithm usages does not necessarily match the total number of publications due to the use of multiple algorithms in some studies.

RF, ANN, and SVMs are among the most widely used ML algorithms across environmental research. Each algorithm offers distinct advantages, but their limitations also influence their applicability in specific contexts. RF is an ensemble learning method that constructs multiple decision trees during training and aggregates their outputs for prediction. This approach enhances accuracy and mitigates overfitting by averaging the results of numerous trees, which is particularly effective for managing the noisy and high-dimensional datasets common in environmental studies. RF is frequently used because it can identify feature importance, helping researchers determine the most influential variables affecting environmental processes, such as water-quality prediction [130,156,159], soil-moisture estimation [93,96,106], and soil-salinity mapping [41,50]. Its robustness and capacity to handle large datasets make it useful for various ecological and hydrological applications. However, RF can become computationally demanding when applied to large datasets with many trees, and it may struggle with extrapolation beyond the range of the training data, which limits its predictive capability in unseen scenarios. ANNs excel at modeling complex, nonlinear relationships, which are essential in dynamic environmental systems. Comprising interconnected neurons that adjust weights through iterative training, ANNs optimize predictions for complex variables. This makes them particularly effective for tasks such as groundwater-level mapping [125,218] and soil-respiration prediction [113]. However, ANNs require large training datasets to generalize effectively, posing challenges in data-scarce environments. Additionally, ANNs are computationally intensive and often criticized as “black-box” models, as their internal workings are not easily interpretable, which can limit their acceptance in certain applications where model transparency is critical. SVMs are effective for both classification and regression tasks, especially when dealing with small to medium-sized datasets. They work by finding the optimal hyperplane that separates data points into different classes, making them particularly useful for applications such as soil-salinity prediction [44,219] and water-quality monitoring [140]. SVMs perform well with high-dimensional data and are less prone to overfitting compared to other models. However, their performance can degrade with very large datasets, and they can be sensitive to the choice of hyperparameters, which may require significant tuning to achieve optimal performance.

In summary, the choice of ML algorithm depends on the specific research objective, data availability, and computational resources. RF is preferred for its robustness and ability to manage high-dimensional data, while ANNs are advantageous for modeling nonlinear relationships but require substantial data and computational power. SVMs offer versatility in handling small to medium-sized datasets but may require careful tuning for optimal results. Understanding the strengths and limitations of these algorithms is crucial to their effective application in environmental research.

3.3. Field-Specific Observations

3.3.1. Soil Properties

In the research field of soil properties, numerous studies have combined RS data from Landsat 8 and Sentinel-2 with RF [22,44,73]. Landsat 8 and Sentinel-2 are widely utilized for mapping and monitoring soil properties at regional and global scales due to their high-resolution multispectral imagery. These satellites provide data that can be used to derive indicators related to soil properties such as organic carbon content, soil moisture, and soil texture. ML algorithms such as RF and SVMs are particularly effective in this field because they can handle large and complex datasets and model nonlinear relationships between RS-derived variables and soil attributes. RF is especially useful for processing large amounts of data and analyzing complex patterns that link spectral information with soil properties, while SVMs are often employed to classify and predict soil properties by maximizing the margin between different types of soil data. The integration of RS data and ML algorithms enables more accurate and efficient prediction and mapping of soil characteristics, which is essential for sustainable land management, agriculture, and environmental conservation.

3.3.2. Hydrology and Water Resources

In hydrology and water resources, studies focus on predicting river flow, groundwater levels, and water quality. Landsat 8 and Sentinel-2 are frequently used for their ability to capture high-resolution spatial and temporal data relevant to water bodies and terrain. These satellites provide key insights into variables like surface-water extent, vegetation cover, and soil moisture, directly influencing hydrological processes. RF and SVR are widely applied ML algorithms due to their robustness in handling nonlinear hydrological patterns.

RF is effective at processing large datasets identifying river flow and groundwater patterns, while SVR is effective at predicting continuous variables, such as water quality. For instance, the authors of [136] use Landsat 8 and Sentinel-2 to monitor water quality in wetlands, and the authors of [167] apply RF, SVR, and XGBoost with Sentinel data to predict flash flood water depth. These integrations of RS and ML enhance predictive accuracy, aiding sustainable water management and planning.

3.3.3. Wildfire Management

In the research field of wildfire management, ML techniques are extensively used to predict and monitor wildfire occurrence. MODIS, known for its daily global coverage, is one of the most frequently utilized RS data sources in this domain. MODIS provides critical information for real-time monitoring and historical analysis of wildfires, enabling the detection of active fires, mapping of burn scars, and assessment of the extent of fire-affected areas [192,194,195,200]. Its frequent revisit times are particularly useful for tracking wildfire progression and immediate impacts [201]. Integrating MODIS data with ML algorithms such as RF and SVMs strengthens predictive capabilities, supports risk mitigation, and improves post-fire recovery efforts, contributing to more effective wildfire management strategies [180,204,205]. For instance, MODIS data and ML techniques have been used for fire-susceptibility mapping, real-time tracking, and post-wildfire recovery assessments [177,215,216]. This synergy between RS and ML plays a crucial role in developing sustainable wildfire management frameworks.

4. Challenges and Limitations

4.1. Data-Related Challenges

One of the fundamental challenges in the application of ML to soil and water conservation lies in the availability, quality, and consistency of RS data. RS data are often characterized by varying spatial, spectral, and temporal resolutions, which can introduce significant variability into datasets used for model training and validation. For instance, while Landsat can provide data with moderate (15~120 m) spatial resolution and a long temporal record, Sentinel-2 offers higher spatial resolution (10~60 m) but with a shorter historical dataset. The integration of these diverse data sources can be problematic, as differences in resolution, sensor characteristics, and data-acquisition periods can lead to discrepancies that need to be harmonized. In addition, inconsistent or incomplete datasets are a common issue, particularly in regions with limited historical monitoring or where cloud cover frequently obstructs satellite observations. These data gaps can introduce biases into ML models, leading to inaccurate results for prediction. For example, if training data are not representative of the full range of environmental conditions, the model may fail to generalize effectively, resulting in poor performance when applied to new or unseen conditions. Moreover, preprocessing of RS data, including tasks such as georeferencing, atmospheric correction, and resampling to a common spatial and temporal grid, can be technically demanding and resource-intensive. Harmonization of data from multiple sensors requires advanced techniques, such as data fusion and cross-calibration, to ensure consistency of inputs for ML models.

4.2. Limitations in Technology and Implementation

The implementation of ML in the context of soil and water conservation is often constrained by the availability of computational resources and the inherent complexity of the algorithms employed. High-dimensional datasets, which contain numerous variables across extensive temporal data, require substantial computational power. High-performance computing clusters or cloud-based solutions are essential for processing these datasets efficiently, yet access to such infrastructure can be limited in many research contexts. Storing large volumes of RS data also presents challenges, as traditional storage systems may struggle to manage the scale and complexity involved. Furthermore, the complexity of ML algorithms, particularly advanced methods such as CNN and RNN, requires not only computational resources but also specialized expertise. These models often involve a high number of hyperparameters that need careful tuning, which can pose barriers in resource-constrained environments lacking access to skilled personnel and computational infrastructure. This complexity can be a significant barrier to the adoption of ML in resource-limited settings, where access to both infrastructure and skilled personnel might be limited. Model interpretability is another a significant concern in environmental applications. Many ML models, especially those classified as “black-box” models, offer limited insights into underlying decision-making processes, which can hinder their acceptance and use in policy-making or by stakeholders. Decision-makers often require not only accurate predictions but also an understanding of the rationale, which can be challenging to provide with complex ML models. Finally, scalability limits the broader applicability of ML models across diverse geographic regions with availability constraints.

5. Conclusions and Future Directions

Effective algorithm and RS data selection are important in environmental studies, as both must align with research objectives, data characteristics, and problem complexity. Algorithm selection is determined by the complexity of interactions among variables and the need for interpretability. RF is widely preferred due to its robustness in modeling nonlinear relationships and handling complex variable interactions, making it effective for tasks like soil-property analysis and wildfire prediction. SVMs are particularly effective when working with high-dimensional datasets, excelling in classification and regression, as seen in water-quality monitoring. ANN and MLP are well suited to capturing intricate nonlinear patterns, making them valuable for predicting complex environmental phenomena. However, each algorithm presents trade-offs between performance, computational requirements, and ease of interpretation. Thus, optimizing algorithm selection remains a key priority. Comparative evaluations across diverse environmental applications will offer deeper insights into how algorithms perform under different conditions. These evaluations will also guide the development of methodologies, ensuring that models are not only accurate but also sufficiently interpretable to be integrated into decision-making processes effectively.

Similarly, RS data selection plays a crucial role in achieving reliable predictions. The appropriate RS data source depends on the spatial, temporal, and thematic resolution required by the research objective. For instance, Landsat and Sentinel satellites provide multispectral imagery suitable for monitoring long-term environmental changes, such as soil degradation or water quality. MODIS, with its high temporal frequency, is ideal for dynamic phenomena such as wildfire progression. Meanwhile, UAVs offer high spatial resolution and flexibility, enabling localized studies such as soil-moisture mapping. The decision to use a particular RS platform should carefully balance the trade-offs between spatial resolution, revisit frequency, and data availability to align with the study’s specific requirements.

The adoption of ML and RS technologies in soil and water conservation has significant implications for climate change adaptation and sustainable resource management. However, achieving meaningful conservation outcomes will require interdisciplinary collaboration between ML specialists, environmental scientists, and policy-makers. To bridge the gap between technological advancements and practical application, capacity-building initiatives should empower stakeholders with the necessary knowledge and skills to implement ML-driven solutions effectively. Ultimately, the careful selection of algorithms and RS data, supported by robust interdisciplinary frameworks, will enable innovative solutions to address the complex challenges of soil and water conservation, driving more sustainable and adaptive environmental management practices.

To further enhance the effectiveness of these solutions, advancements in big data analytics and cloud computing will address the computational and storage challenges that currently limit the full-scale adoption of ML methods in environmental conservation. As these technologies become more accessible, researchers will be better equipped to process large datasets efficiently. Additionally, the development of explainable AI models will promote transparency, allowing non-experts to understand and trust ML outputs, which is essential for effective integration into policy and management strategies. Future research should also explore the potential of hybrid models, combining multiple ML algorithms with diverse RS datasets. Such models can leverage the strengths of various techniques and datasets to enhance prediction accuracy and offer a more holistic understanding of environmental processes. Integrating ground-based sensor networks with RS data will further improve real-time monitoring and forecasting capabilities, helping stakeholders respond proactively to environmental changes.

Author Contributions

Conceptualization, W.S.J. and Y.-J.Y.; methodology, W.S.J., Y.I.K. and W.H.P.; validation, W.S.J., W.H.P., J.-W.P. and Y.S.; formal analysis, Y.-J.Y.; investigation, W.H.P.; resources, Y.S.; writing—original draft preparation, Y.I.K.; writing—review and editing, Y.I.K., W.H.P. and B.E.; visualization, Y.I.K.; supervision, W.S.J. and Y.-J.Y.; project administration, W.S.J.; funding acquisition, W.S.J. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the research project Developing of S–P–C experts for field-adaptive forest fire management (S: smart, P: professional, C: confluence) (RS-2024-00402624), funded by the Korea Forest Service.

Data Availability Statement

The data used in this study are contained within the article. Additional data are available upon request from the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. A list of all the abbreviated ML algorithms used in the paper.

ML	Full Name
ABR	Adaptive Boosting Regression
AdaBag	Boosting and Bagging
AdaBoost	Boosted Classifier
ANFIS	Adaptive Neuro Fuzzy Inference System
ANN	Artificial Neural Network
ARD	Automatic Relevance Determination
BAGGING	Bootstrap Aggregating Regression
BAYE	Bayesian
B-CART	Bagged Classification and Regression Trees
BDT	Bagging Decision Tree
BPNN	Back Propagation Neural Network
BRTs	Boosted Regression Trees
BST	Extreme Gradient Boosting Tree
CART	Classification and Regression Trees
CB	Cubist
CBR	Catboost Regression
CNN	Convolutional Neural Network
DBN	Deep Belief Network
DELM	Deep Extreme Learning Machine
DL	Deep Learning
DMP	Dense Multilayer Perceptron
DNN	Deep Neural Networks
DR	Dmine Regression
DRF	Distributed Random Forest
DTr	Decision Tree
EBP	Error Back Propagation
EFS	Exhaustive Feature Selection
ELM	Extreme Learning Machine
ELR	Extreme Learning Machine Regression
EM	Evaluation metrics
EN	Elastic Net
EPR	Evolutionary Polynomial Regression
ERT	Extremely Randomized Tree
ETR	Extreme Tree Regression
FCN	Fully Connected Network
FNN	Feed Forward Neural Networks
FR	Frequency Ratio
GAN	Generative Adversarial Networks
GB	Gradient Boosting
GBDT	Gradient Boosted Decision Tree
GBM	Gradient Boosting Machine
GBR	Gradient Boosting Regression
GBRT	Gradient Boosting Regression Tree
GEP	Genetic Expression Programming
GLM	Generalized Linear Model
GPR	Gaussian Process Regression
GRNN	General Regression Neural Network
GSC	Generalized Synthetic Control
Isolation Forest	Isolation Forest
KNN	K-Nearest Neighbors
La-R	Lasso Regression
LARS	Least Angle Regression
LDA	Linear Discriminant Analysis
LGBM	Light Gradient Boosting Machine
Li-R	Linear Regression
LMM	Linear Mixed-Effects Model
Lo-R	Logistic Regression
LSTM	Long Short-Term Memory
M5P	M5-Pruned
MARS	Multivariate Adaptive Regression Spline
MaxEnt	Maximum Entropy Model
MDN	Mixture Density Network
MLP	Multilayer Perceptron
MLPR	Multilayer Perceptron Regression
MLR	Multiple Linear Regression
MR-CNN	Mask Region-Based Convolutional Neural Network
MT	M5 Model Tree
NB	Naïve Bayes
Neu-SICR	Neural Network-Satellite and in situ Sensor Collaborated Reconstruction
NN	Neural Networks
NNET	Feed-Forward Neural Network
OLS	Ordinary Least Squares
PCR	Principal Component Regression
PKR	Polynomial Kernel Regression
PLS	Partial Least Squares
PLSR	Partial Least Squares Regression
PSO-SVR	Particle Swarm Optimization and Support Vector Machine
QR	Quantile Regression Forest
RBFN	Radial Basin Function Neural Network
RF	Random Forest
RNN	Recurrent Neural Network
RPART	Recursive Partitioning and Regression Trees
RR	Ridge Regression
RT	Regression Tree
RTM	Radiative Transfer Models
RVR	Relevance Vector Regression
SA	Sensitivity Analysis
SCA-Elman	Sine Cosine Algorithm-Elman
SGB	Stochastic Gradient Boosting
SICR	Sensor Collaborated Reconstruction
SLR	Stepwise Linear Regression
SoLIM	Soil–Landscape Inference Model (Fuzzy Logic)
SOM	Self-Organizing Maps
SR	Simple Regression
SVM	Support Vector Machine
SVR	Support Vector Regression
XGB	EXtreme Gradient Boosting
XGBR	EXtreme Gradient Boosting Regression
YOLO	You Only Look Once

Table A2. Descriptions of RS techniques implemented in reviewed publications. In the description of RS techniques related to satellites, the resolution, launching entity, and key features were included.

RS Techniques		Descriptions
Satellite	ALOS-2 *	- 3 m, 6 m, 10 m - JAXA (Japan Aerospace Exploration Agency) * - ALOS-2 is a Japanese Earth observation satellite equipped with a SAR sensor for monitoring land-surface changes and natural hazards regardless of weather conditions such as clouds or rain ****
	Chinese Environmental 1A	- 30~60 m - CRESDA (China Centre for Resources Satellite Data and Application) - China’s Earth observation satellite for monitoring environmental elements such as air, water, soil, and vegetation
	GF-1	- 2 m, 8 m - China National Space Administration (CNSA) - A high-resolution ground-observation satellite from China’s GaoFen series, known for its advanced imaging capabilities to monitor urban areas, natural resources, and environmental changes
	GOES-16	- 0.5~2 km - NOAA (National Oceanic and Atmospheric Administration) - U.S. geostationary weather satellite that monitors weather conditions over North America in real time
	Himawari-8	- 0.5 km, 1 km, 2 km - JMA (Japan Meteorological Agency) - A geostationary satellite for weather monitoring in the Asia–Pacific region, Himawari-8 provides continuous and detailed weather observations
	Landsat 4, 5	- 30 m, 80 m, 120 m - NASA (The National Aeronautics and Space Administration)/USGS (The United States Geological Survey) - A satellite designed to observe the Earth’s land surface and monitor changes in land use and natural resources, both of which have officially ended their missions
	Landsat 7	- 15 m, 30 m, 60 m - NASA (The National Aeronautics and Space Administration)/USGS (The United States Geological Survey) - A satellite used for Earth observation, capable of capturing detailed imagery to monitor environmental changes, land use, and natural resources, and continues to provide valuable data since its launch in 1999
	Landsat 8, 9	- 15 m, 30 m, 100 m - NASA (The National Aeronautics and Space Administration)/USGS (The United States Geological Survey) - The latest satellites in the Landsat series, launched in 2013 and 2021, providing high-quality Earth surface data for monitoring environmental changes, land use, and natural resources
	RADARSAT	- 1~100 m - CSA (Canadian Space Agency) - A Canadian-operated SAR (Synthetic Aperture Radar) satellite known for its capability to provide detailed, all-weather, day-and-night imagery for global environmental monitoring, disaster response, and resource management
	RapidEye	- 5 m, 6.5 m - BlackBridge Networks - A satellite constellation that provides multispectral imagery, designed for applications in agriculture, forest management, and other areas
	Sentinel-1	- 5~40 m - ESA (European Space Agency) - A satellite sensor using Synthetic Aperture Radar (SAR) to observe the Earth in all weather conditions, both day and night, providing continuous and detailed surface monitoring
	Sentinel-2	- 10 m, 20 m, 60 m - ESA (European Space Agency) - A satellite sensor providing high-resolution multispectral images, useful for agriculture, forestry, and land-cover monitoring
	Sentinel-3	- 300 m, 500 m, 1 km - ESA (European Space Agency) - A satellite sensor used to monitor sea and land surface temperatures, colors, and ocean conditions, equipped with multiple instruments for comprehensive Earth observation and environmental monitoring
	SMAP	- 3 km, 10 km, 40 km - NASA (The National Aeronautics and Space Administration) - A satellite primarily used for observing soil moisture and freeze/thaw conditions, crucial for climate research and agricultural monitoring, providing detailed data to support environmental and climate studies
	SPOT-4	- 10 m, 20 m, 60 m - CNES (Centre National d’Etudes Spatiales) - SPOT-4 is an Earth observation satellite that carries HRVIR, HRG, and VEGETATION sensors with an additional shortwave infrared band for agriculture, forestry, and environmental monitoring
	SPOT-7	- 1.5 m, 6 m - Airbus Defense and Space - SPOT-7 is the latest satellite in the SPOT series, providing high-resolution Earth observation imagery with a NAOMI sensor that significantly improves spatial resolution
	SRTM	- 30 m, 90 m - NASA (The National Aeronautics and Space Administration) - A global satellite mission that collects elevation data to create 3D terrain models of Earth’s surface
	Terra	- 250 m, 500 m, 1 km - NASA (The National Aeronautics and Space Administration) - A satellite that provides comprehensive observations of the Earth’s environment, collecting data on the atmosphere, land, oceans, and energy systems to support environmental monitoring and research
	Triplesat	- 0.8 m, 3.2 m - 21AT (The Twenty-First Century Aerospace Technology) - A high-resolution Earth observation satellite widely used for commercial purposes, offering detailed imagery for applications in agriculture, urban planning, and resource management
	WorldView-3	- 0.31 m, 1.24 m - DigitalGlobe - A commercial high-resolution Earth observation satellite that provides high-quality imagery data for use in a variety of industries
	ZH-1	- 10 m - CNSA (the China National Space Administration/ASI (the Italian Space Agency) - A high-resolution Earth observation satellite from China, designed for detailed imaging to monitor urban areas, natural resources, and environmental changes
AGRS		- A technology that utilizes aircraft and drones to gather detailed information about the Earth’s surface and geology, providing valuable data for various applications
AMSR-E		- A microwave radiometer that monitors various aspects of the Earth’s water cycle, including precipitation, cloud water, water vapor, sea-surface winds, sea-surface temperature, ice, snow, and soil moisture
AVIRIS-NG		- An airborne hyperspectral imaging sensor that captures detailed information across a wide range of spectral bands, including visible and infrared wavelengths
ETM+		- A sensor on board the Landsat 7 satellite that is an enhanced version of ETM with a total of eight spectral bands
Thermal infrared		- A remote-sensing technology that measures surface temperatures by observing thermal infrared emissions, providing data for climate studies, weather monitoring, and environmental analysis
Leica ADS80		- A high-resolution digital camera used for aerial photogrammetry, capturing detailed images from the air
LiDAR		- A remote-sensing technology that uses lasers to precisely measure the 3D structure of terrain, providing detailed topographic data for applications in mapping, forestry, and environmental monitoring
MERIS		- A sensor with high spectral and radiometric resolution and dual spatial resolution that studies the Earth’s water cycle, including precipitation, cloud water, water vapor, sea-surface winds, sea-surface temperatures, ice, snow, and soil moisture
MODIS		- A satellite sensor that captures comprehensive global data, viewing the entire Earth’s surface every one to two days with high temporal resolution
PALSAR-2		- An L-band SAR mounted on the ALOS-2 satellite, providing detailed surface information globally with high precision for applications in terrain-mapping, disaster-monitoring, and environmental assessment
SAR		- A technology that uses electromagnetic waves to observe the Earth’s surface, enabling data collection in all weather conditions and at any time, providing reliable information for environmental monitoring and disaster management
SVC		- A spectroradiometer that measures the reflectance spectra of the Earth’s surface, enabling detailed analysis of material composition and characterization for applications in geology, agriculture, and environmental monitoring
TDC		- A thermal infrared sensor, which measures infrared radiation to detect temperature variations on the Earth’s surface
Hyperspectral Imager		- An instrument that measures hundreds of narrow wavelength bands, precisely analyzing the material composition of the Earth’s surface
TM		- A sensor on the Landsat 4 and 5 satellites with seven spectral bands, TM (Thematic Mapper) is optimized to collect detailed information on land surface characteristics
UAS/UAV		- Remote sensing platforms using drones, including UASs (Unmanned Aerial Systems) and UAVs (Unmanned Aerial Vehicles), employed to collect high spatial resolution data for various applications such as mapping, agriculture, and environmental monitoring
VIIRS		- A satellite sensor that observes the Earth’s atmosphere, oceans, and land, providing valuable data for climate research, disaster monitoring, and various environmental applications with comprehensive multispectral imaging

Note: Each asterisk level corresponds to a specific category (* Satellite name, ** resolution of satellite, *** launching entity, and **** key features).

References

Pereira, P.; Bogunovic, I.; Muñoz-Rojas, M.; Brevik, E.C. Soil ecosystem services, sustainability, valuation and management. Curr. Opin. Environ. Sci. Health 2018, 5, 7–13. [Google Scholar] [CrossRef]
Trap, J.; Bonkowski, M.; Plassard, C.; Villenave, C.; Blanchart, E. Ecological importance of soil bacterivores for ecosystem functions. Plant Soil 2016, 398, 1–24. [Google Scholar] [CrossRef]
Whitford, W.G. The importance of the biodiversity of soil biota in arid ecosystems. Biodivers. Conserv. 1996, 5, 185–195. [Google Scholar] [CrossRef]
Allan, R.P.; Barlow, M.; Byrne, M.P.; Cherchi, A.; Douville, H.; Fowler, H.J.; Gan, T.Y.; Pendergrass, A.G.; Rosenfel, D.; Swann, A.; et al. Advances in understanding large-scale responses of the water cycle to climate change. Ann. N. Y. Acad. Sci. 2020, 1472, 49–75. [Google Scholar] [CrossRef]
Peixoto, J.P.; Kettani, M.A. The control of the water cycle. Sci. Am. 1973, 228, 46–63. [Google Scholar] [CrossRef]
Lal, R. Soil conservation and ecosystem services. Int. Soil Water Conserv. Res. 2014, 2, 36–47. [Google Scholar] [CrossRef]
Neary, D.G.; Ryan, K.C.; DeBano, L.F. Wildland fire in ecosystems: Effects of fire on soils and water. Gen. Tech. Rep. 2005, 4, 42. [Google Scholar]
Ali, M.; Talukder, M. Increasing water productivity in crop production—A synthesis. Agric. Water Manag. 2008, 95, 1201–1213. [Google Scholar] [CrossRef]
Akhtar, N.; Syakir Ishak, M.I.; Bhawani, S.A.; Umar, K. Various natural and anthropogenic factors responsible for water quality degradation: A review. Water 2021, 13, 2660. [Google Scholar] [CrossRef]
Bungau, S.; Behl, T.; Aleya, L.; Bourgeade, P.; Aloui-Sossé, B.; Purza, A.L.; Abid, A.; Samuel, A.D. Expatiating the impact of anthropogenic aspects and climatic factors on long-term soil monitoring and management. Environ. Sci. Pollut. Res. 2021, 28, 30528–30550. [Google Scholar] [CrossRef]
Zhang, X.; Chen, J.; Tan, M.; Sun, Y. Assessing the impact of urban sprawl on soil resources of Nanjing city using satellite images and digital soil databases. Catena 2007, 69, 16–30. [Google Scholar] [CrossRef]
Hajabbasi, M.A.; Jalalian, A.; Karimzadeh, H.R. Deforestation effects on soil physical and chemical properties, Lordegan, Iran. Plant Soil 1997, 190, 301–308. [Google Scholar] [CrossRef]
Syamsih, D. Impacts of Deforestation on Soil Quality and Water Resources in Tropical Forest Areas of Sumatra. J. Horiz. 2024, 1, 16–22. [Google Scholar] [CrossRef]
Blanco-Canqui, H.; Lal, R. Soil and water conservation. In Principles of Soil Conservation and Management, 1st ed.; Springer: Dordrecht, The Netherlands, 2008; pp. 1–19. [Google Scholar]
Côté, I.M.; Darling, E.S.; Brown, C.J. Interactions among ecosystem stressors and their importance in conservation. Proc. R. Soc. B Biol. Sci. 2016, 283, 20152592. [Google Scholar] [CrossRef]
Gassman, P.W.; Reyes, M.R.; Green, C.H.; Arnold, J. The soil and water assessment tool: Historical development, applications, and future research directions. Trans. ASABE 2007, 50, 1211–1250. [Google Scholar] [CrossRef]
Mansell, R.S.; Ma, L.; Ahuja, L.R.; Bloom, S.A. Adaptive grid refinement in numerical models for water flow and chemical transport in soil: A review. Vadose Zone J. 2002, 1, 222–238. [Google Scholar] [CrossRef]
Kattenberg, A.; Giorgi, F.; Grassl, H.; Meehl, G.A.; Mitchell, J.F.B.; Stouffer, R.J.; Tokioka, T.; Weaver, A.J.; Wigley, T.M.L. Climate models–projections of future climate. In Climate Change 1995, The Science of Climate Change: Contribution of Working Group I to the Second Assessment Report of the Intergovernmental Panel on Climate Change; Houghton, J.T., Meira Filbo, B.G., Callander, B.A., Harris, N., Kattenberg, A., Maskell, K., Eds.; Cambridge University Press: Cambridge, UK, 1996; pp. 285–357. [Google Scholar]
Kavetski, D.; Franks, S.W.; Kuczera, G. Confronting input uncertainty in environmental modelling. Calibration Watershed Models 2003, 6, 49–68. [Google Scholar]
Matott, L.S.; Babendreier, J.E.; Purucker, S.T. Evaluating uncertainty in integrated environmental models: A review of concepts and tools. Water Resour. Res. 2009, 45. [Google Scholar] [CrossRef]
Refsgaard, J.C.; van der Sluijs, J.P.; Højberg, A.L.; Vanrolleghem, P.A. Uncertainty in the environmental modelling process—A framework and guidance. Environ. Model. Softw. 2007, 22, 1543–1556. [Google Scholar] [CrossRef]
Zhong, S.; Zhang, K.; Bagheri, M.; Burken, J.G.; Gu, A.; Li, B.; Ma, X.; Marrone, B.L.; Ren, Z.J.; Schrier, J.; et al. Machine learning: New ideas and tools in environmental science and engineering. Environ. Sci. Technol. 2021, 55, 12741–12754. [Google Scholar] [CrossRef]
Huang, C.; Chen, Y.; Zhang, S.; Wu, J. Detecting, extracting, and monitoring surface water from space using optical sensors: A review. Rev. Geophys. 2018, 56, 333–360. [Google Scholar] [CrossRef]
Rogan, J.; Chen, D. Remote sensing technology for mapping and monitoring land-cover and land-use change. Prog. Plan. 2004, 61, 301–325. [Google Scholar] [CrossRef]
Florath, J.; Keller, S. Supervised machine learning approaches on multispectral remote sensing data for a combined detection of fire and burned area. Remote Sens. 2022, 14, 657. [Google Scholar] [CrossRef]
Sun, A.Y.; Scanlon, B.R. How can Big Data and machine learning benefit environment and water management: A survey of methods, applications, and future directions. Environ. Res. Lett. 2019, 14, 073001. [Google Scholar] [CrossRef]
Glasgow, H.B.; Burkholder, J.M.; Reed, R.E.; Lewitus, A.J.; Kleinman, J.E. Real-time remote monitoring of water quality: A review of current applications, and advancements in sensor, telemetry, and computing technologies. J. Exp. Mar. Biol. Ecol. 2004, 300, 409–448. [Google Scholar] [CrossRef]
Wang, S.; Guan, K.; Zhang, C.; Lee, D.; Margenot, A.J.; Ge, Y.; Peng, J.; Zhou, W.; Zhou, Q.; Huang, Y. Using soil library hyperspectral reflectance and machine learning to predict soil organic carbon: Assessing potential of airborne and spaceborne optical soil sensing. Remote Sens. Environ. 2022, 271, 112914. [Google Scholar] [CrossRef]
Detsikas, S.E.; Petropoulos, G.P.; Kalogeropoulos, K.; Faraslis, I. Combining Low-Cost UAV Imagery with Machine Learning Classifiers for Accurate Land Use/Land Cover Mapping. Earth 2024, 5, 244–254. [Google Scholar] [CrossRef]
Zhang, Y.; Liu, J.; Shen, W. A review of ensemble learning algorithms used in remote sensing applications. Appl. Sci. 2022, 12, 8654. [Google Scholar] [CrossRef]
Khanal, S.; Fulton, J.; Klopfenstein, A.; Douridas, N.; Shearer, S. Integration of high resolution remotely sensed data and machine learning techniques for spatial prediction of soil properties and corn yield. Comput. Electron. Agric. 2018, 153, 213–225. [Google Scholar] [CrossRef]
Dixon, D.J.; Zhu, Y.; Brown, C.F.; Jin, Y. Satellite detection of canopy-scale tree mortality and survival from California wildfires with spatio-temporal deep learning. Remote Sens. Environ. 2023, 298, 113842. [Google Scholar] [CrossRef]
Pastick, N.J.; Wylie, B.K.; Rigge, M.B.; Dahal, D.; Boyte, S.P.; Jones, M.O.; Allredm, B.W.; Parajuli, S.; Wu, Z. Rapid monitoring of the abundance and spread of exotic annual grasses in the western United States using remote sensing and machine learning. AGU Adv. 2021, 2, e2020AV000298. [Google Scholar] [CrossRef]
Saygın, F.; Aksoy, H.; Alaboz, P.; Dengiz, O. Different approaches to estimating soil properties for digital soil map integrated with machine learning and remote sensing techniques in a sub-humid ecosystem. Environ. Monit. Assess. 2023, 195, 1061. [Google Scholar] [CrossRef] [PubMed]
Rezaei, M.; Mousavi, S.R.; Rahmani, A.; Zeraatpisheh, M.; Rahmati, M.; Pakparvar, M.; Mahjenabadi, V.A.J.; Seuntjens, P.; Cornelis, W. Incorporating machine learning models and remote sensing to assess the spatial distribution of saturated hydraulic conductivity in a light-textured soil. Comput. Electron. Agric. 2023, 209, 107821. [Google Scholar] [CrossRef]
Jia, P.; Zhang, J.; He, W.; Hu, Y.; Zeng, R.; Zamanian, K.; Jia, K.; Zhao, X. Combination of hyperspectral and machine learning to invert soil electrical conductivity. Remote Sens. 2022, 14, 2602. [Google Scholar] [CrossRef]
Wang, N.; Xue, J.; Peng, J.; Biswas, A.; He, Y.; Shi, Z. Integrating remote sensing and landscape characteristics to estimate soil salinity using machine learning methods: A case study from Southern Xinjiang, China. Remote Sens. 2020, 12, 4118. [Google Scholar] [CrossRef]
Mohamed, S.A.; Metwaly, M.M.; Metwalli, M.R.; AbdelRahman, M.A.; Badreldin, N. Integrating active and passive remote sensing data for mapping soil salinity using machine learning and feature selection approaches in arid regions. Remote Sens. 2023, 15, 1751. [Google Scholar] [CrossRef]
Wang, Z.; Zhang, F.; Zhang, X.; Chan, N.W.; Ariken, M.; Zhou, X.; Wang, Y. Regional suitability prediction of soil salinization based on remote-sensing derivatives and optimal spectral index. Sci. Total Environ. 2021, 775, 145807. [Google Scholar] [CrossRef] [PubMed]
Cui, J.; Chen, X.; Han, W.; Cui, X.; Ma, W.; Li, G. Estimation of soil salt content at different depths using UAV multi-spectral remote sensing combined with machine learning algorithms. Remote Sens. 2023, 15, 5254. [Google Scholar] [CrossRef]
Naimi, S.; Ayoubi, S.; Zeraatpisheh, M.; Dematte, J.A.M. Ground observations and environmental covariates integration for mapping of soil salinity: A machine learning-based approach. Remote Sens. 2021, 13, 4825. [Google Scholar] [CrossRef]
Wang, S.; Chen, Y.; Wang, M.; Li, J. Performance comparison of machine learning algorithms for estimating the soil salinity of salt-affected soil using field spectral data. Remote Sens. 2019, 11, 2605. [Google Scholar] [CrossRef]
Erkin, N.; Zhu, L.; Gu, H.; Tusiyiti, A. Method for predicting soil salinity concentrations in croplands based on machine learning and remote sensing techniques. J. Appl. Remote Sens. 2019, 13, 034520. [Google Scholar] [CrossRef]
Kaplan, G.; Gašparović, M.; Alqasemi, A.S.; Aldhaheri, A.; Abuelgasim, A.; Ibrahim, M. Soil salinity prediction using Machine Learning and Sentinel–2 Remote Sensing Data in Hyper–Arid areas. Phys. Chem. Earth Parts A/B/C 2023, 130, 103400. [Google Scholar] [CrossRef]
Jiang, X.; Duan, H.; Liao, J.; Guo, P.; Huang, C.; Xue, X. Estimation of soil salinization by machine learning algorithms in different arid regions of northwest China. Remote Sens. 2022, 14, 347. [Google Scholar] [CrossRef]
Haq, Y.U.; Shahbaz, M.; Asif, H.S.; Al-Laith, A.; Alsabban, W.H. Spatial mapping of soil salinity using machine learning and remote sensing in Kot Addu, Pakistan. Sustainability 2023, 15, 12943. [Google Scholar] [CrossRef]
Wang, J.; Ding, J.; Yu, D.; Teng, D.; He, B.; Chen, X.; Ge, X.; Zhang, Z.; Wang, Y.; Yang, W.; et al. Machine learning-based detection of soil salinity in an arid desert region, Northwest China: A comparison between Landsat-8 OLI and Sentinel-2 MSI. Sci. Total Environ. 2020, 707, 136092. [Google Scholar] [CrossRef]
Zhang, H.; Fu, X.; Zhang, Y.; Qi, Z.; Zhang, H.; Xu, Z. Mapping Multi-Depth Soil Salinity Using Remote Sensing-Enabled Machine Learning in the Yellow River Delta, China. Remote Sens. 2023, 15, 5640. [Google Scholar] [CrossRef]
Wei, Q.; Nurmemet, I.; Gao, M.; Xie, B. Inversion of soil salinity using multisource remote sensing data and particle swarm machine learning models in Keriya Oasis, northwestern China. Remote Sens. 2022, 14, 512. [Google Scholar] [CrossRef]
Hoa, P.V.; Giang, N.V.; Binh, N.A.; Hai, L.V.H.; Pham, T.D.; Hasanlou, M.; Tien Bui, D. Soil salinity mapping using SAR sentinel-1 data and advanced machine learning algorithms: A case study at Ben Tre Province of the Mekong River Delta (Vietnam). Remote Sens. 2019, 11, 128. [Google Scholar] [CrossRef]
Das, A.; Bhattacharya, B.K.; Setia, R.; Jayasree, G.; Das, B.S. A novel method for detecting soil salinity using AVIRIS-NG imaging spectroscopy and ensemble machine learning. ISPRS J. Photogramm. Remote Sens. 2023, 200, 191–212. [Google Scholar] [CrossRef]
Nguyen, H.D.; Van, C.P.; Nguyen, T.G.; Dang, D.K.; Pham, T.T.N.; Nguyen, Q.H.; Bui, Q.T. Soil salinity prediction using hybrid machine learning and remote sensing in Ben Tre province on Vietnam’s Mekong River Delta. Environ. Sci. Pollut. Res. 2023, 30, 74340–74357. [Google Scholar] [CrossRef]
Zhao, W.; Zhou, C.; Zhou, C.; Ma, H.; Wang, Z. Soil salinity inversion model of oasis in arid area based on UAV multispectral remote sensing. Remote Sens. 2022, 14, 1804. [Google Scholar] [CrossRef]
Aksoy, S.; Yildirim, A.; Gorji, T.; Hamzehpour, N.; Tanik, A.; Sertel, E. Assessing the performance of machine learning algorithms for soil salinity mapping in Google Earth Engine platform using Sentinel-2A and Landsat-8 OLI data. Adv. Space Res. 2022, 69, 1072–1086. [Google Scholar] [CrossRef]
Ma, G.; Ding, J.; Han, L.; Zhang, Z.; Ran, S. Digital mapping of soil salinization based on Sentinel-1 and Sentinel-2 data combined with machine learning algorithms. Reg. Sustain. 2021, 2, 177–188. [Google Scholar] [CrossRef]
Kalambukattu, J.G.; Johns, B.; Kumar, S.; Raj, A.D.; Ellur, R. Temporal remote sensing based soil salinity mapping in Indo-Gangetic plain employing machine-learning techniques. Proc. Indian Natl. Sci. Acad. 2023, 89, 290–305. [Google Scholar] [CrossRef]
Zhang, T.; Li, Y.; Wang, M. Remote sensing-based prediction of organic carbon in agricultural and natural soils influenced by salt and sand mining using machine learning. J. Environ. Manag. 2024, 352, 120107. [Google Scholar] [CrossRef]
Odebiri, O.; Mutanga, O.; Odindi, J.; Naicker, R. Modelling soil organic carbon stock distribution across different land-uses in South Africa: A remote sensing and deep learning approach. ISPRS J. Photogramm. Remote Sens. 2022, 188, 351–362. [Google Scholar] [CrossRef]
Zhang, X.; Xue, J.; Chen, S.; Wang, N.; Shi, Z.; Huang, Y.; Zhuo, Z. Digital mapping of soil organic carbon with machine learning in dryland of Northeast and North plain China. Remote Sens. 2022, 14, 2504. [Google Scholar] [CrossRef]
Emadi, M.; Taghizadeh-Mehrjardi, R.; Cherati, A.; Danesh, M.; Mosavi, A.; Scholten, T. Predicting and mapping of soil organic carbon using machine learning algorithms in Northern Iran. Remote Sens. 2020, 12, 2234. [Google Scholar] [CrossRef]
John, K.; Abraham, I.I.; Kebonye, N.M.; Ayito, E.O.; Agyeman, C.P.; Afu, S.M. Using machine learning algorithms to estimate soil organic carbon variability with environmental variables and soil nutrient indicators in an alluvial soil. Land 2020, 9, 487. [Google Scholar] [CrossRef]
Meng, X.; Bao, Y.; Wang, Y.; Zhang, X.; Liu, H. An advanced soil organic carbon content prediction model via fused temporal-spatial-spectral (TSS) information based on machine learning and deep learning algorithms. Remote Sens. Environ. 2022, 280, 113166. [Google Scholar] [CrossRef]
Zeraatpisheh, M.; Ayoubi, S.; Jafari, A.; Tajik, S.; Finke, P. Digital mapping of soil properties using multiple machine learning in a semi-arid region, central Iran. Geoderma 2019, 338, 445–452. [Google Scholar] [CrossRef]
Zhou, T.; Geng, Y.; Chen, J.; Pan, J.; Haase, D.; Lausch, A. High-resolution digital mapping of soil organic carbon and soil total nitrogen using DEM derivatives, Sentinel-1 and Sentinel-2 data based on machine learning algorithms. Sci. Total Environ. 2020, 729, 138244. [Google Scholar] [CrossRef] [PubMed]
Zhou, T.; Geng, Y.; Ji, C.; Xu, X.; Wang, H.; Pan, J.; Lausch, A. Prediction of soil organic carbon and the C:N ratio on a national scale using machine learning and satellite data: A comparison between Sentinel-2, Sentinel-3 and Landsat-8 images. Sci. Total Environ. 2021, 755, 142661. [Google Scholar] [CrossRef] [PubMed]
Abdoli, P.; Khanmirzaei, A.; Hamzeh, S.; Rezaei, S.; Moghimi, S. Use of remote sensing data to predict soil organic carbon in some agricultural soils of Iran. Remote Sens. Appl. Soc. Environ. 2023, 30, 100969. [Google Scholar] [CrossRef]
Paul, S.S.; Coops, N.C.; Johnson, M.S.; Krzic, M.; Chandna, A.; Smukler, S.M. Mapping soil organic carbon and clay using remote sensing to predict soil workability for enhanced climate change adaptation. Geoderma 2020, 363, 114177. [Google Scholar] [CrossRef]
Hamzehpour, N.; Shafizadeh-Moghadam, H.; Valavi, R. Exploring the driving forces and digital mapping of soil organic carbon using remote sensing and soil texture. Catena 2019, 182, 104141. [Google Scholar] [CrossRef]
Matinfar, H.R.; Maghsodi, Z.; Mousavi, S.R.; Rahmani, A. Evaluation and Prediction of Topsoil organic carbon using Machine learning and hybrid models at a Field-scale. Catena 2021, 202, 105258. [Google Scholar] [CrossRef]
Xie, B.; Ding, J.; Ge, X.; Li, X.; Han, L.; Wang, Z. Estimation of soil organic carbon content in the Ebinur Lake wetland, Xinjiang, China, based on multisource remote sensing data and ensemble learning algorithms. Sensors 2022, 22, 2685. [Google Scholar] [CrossRef]
Wang, B.; Waters, C.; Orgill, S.; Gray, J.; Cowie, A.; Clark, A.; Liu, L.D. High resolution mapping of soil organic carbon stocks using remote sensing variables in the semi-arid rangelands of eastern Australia. Sci. Total Environ. 2018, 630, 367–378. [Google Scholar] [CrossRef]
Xing, C.; Chen, N.; Zhang, X.; Gong, J. A machine learning based reconstruction method for satellite remote sensing of soil moisture images with in situ observations. Remote Sens. 2017, 9, 484. [Google Scholar] [CrossRef]
Zeraatpisheh, M.; Ayoubi, S.; Mirbagheri, Z.; Mosaddeghi, M.R.; Xu, M. Spatial prediction of soil aggregate stability and soil organic carbon in aggregate fractions using machine learning algorithms and environmental variables. Geoderma Reg. 2021, 27, e00440. [Google Scholar] [CrossRef]
Mahjenabadi, V.A.J.; Mousavi, S.R.; Rahmani, A.; Karami, A.; Rahmani, H.A.; Khavazi, K.; Rezaei, M. Digital mapping of soil biological properties and wheat yield using remotely sensed, soil chemical data and machine learning approaches. Comput. Electron. Agric. 2022, 197, 106978. [Google Scholar] [CrossRef]
Rukhovich, D.I.; Koroleva, P.V.; Rukhovich, D.D.; Rukhovich, A.D. Recognition of the bare soil using deep machine learning methods to create maps of arable soil degradation based on the analysis of multi-temporal remote sensing data. Remote Sens. 2022, 14, 2224. [Google Scholar] [CrossRef]
Alexakis, D.D.; Tapoglou, E.; Vozinaki, A.E.K.; Tsanis, I.K. Integrated use of satellite remote sensing, artificial neural networks, field spectroscopy, and GIS in estimating crucial soil parameters in terms of soil erosion. Remote Sens. 2019, 11, 1106. [Google Scholar] [CrossRef]
Fernández, D.; Adermann, E.; Pizzolato, M.; Pechenkin, R.; Rodríguez, C.G.; Taravat, A. Comparative analysis of machine learning algorithms for soil erosion modelling based on remotely sensed data. Remote Sens. 2023, 15, 482. [Google Scholar] [CrossRef]
Do, T.A.T.; Tran, H.D.; Do, A.N.T. Classifying forest cover and mapping forest fire susceptibility in Dak Nong province, Vietnam utilizing remote sensing and machine learning. Ecol. Inform. 2024, 79, 102392. [Google Scholar]
Saygın, F.; Aksoy, H.; Alaboz, P.; Birol, M.; Dengiz, O. Estimation of soil erodability parameters based on different machine algorithms integrated with remote sensing techniques. Int. J. Environ. Sci. Technol. 2024, 12, 9527–9540. [Google Scholar] [CrossRef]
Maia, R.F.; Lurbe, C.B.; Hornbuckle, J. Machine learning approach to estimate soil matric potential in the plant root zone based on remote sensing data. Front. Plant Sci. 2022, 13, 931491. [Google Scholar] [CrossRef]
Suleymanov, A.; Suleymanov, R.; Kulagin, A.; Yurkevich, M. Mercury Prediction in Urban Soils by Remote Sensing and Relief Data Using Machine Learning Techniques. Remote Sens. 2023, 15, 3158. [Google Scholar] [CrossRef]
Senanayake, I.P.; Yeo, I.Y.; Walker, J.P.; Willgoose, G.R. Estimating catchment scale soil moisture at a high spatial resolution: Integrating remote sensing and machine learning. Sci. Total Environ. 2021, 776, 145924. [Google Scholar] [CrossRef]
Mao, T.; Shangguan, W.; Li, Q.; Li, L.; Zhang, Y.; Huang, F.; Li, J.; Liu, W.; Zhang, R. A spatial downscaling method for remote sensing soil moisture based on random forest considering soil moisture memory and mass conservation. Remote Sens. 2022, 14, 3858. [Google Scholar] [CrossRef]
Babaeian, E.; Paheding, S.; Siddique, N.; Devabhaktuni, V.K.; Tuller, M. Estimation of root zone soil moisture from ground and remotely sensed soil information with multisensor data fusion and automated machine learning. Remote Sens. Environ. 2021, 260, 112434. [Google Scholar] [CrossRef]
Sun, H.; Cui, Y. Evaluating downscaling factors of microwave satellite soil moisture based on machine learning method. Remote Sens. 2021, 13, 133. [Google Scholar] [CrossRef]
Senyurek, V.; Lei, F.; Boyd, D.; Kurum, M.; Gurbuz, A.C.; Moorhead, R. Machine learning-based CYGNSS soil moisture estimates over ISMN sites in CONUS. Remote Sens. 2020, 12, 1168. [Google Scholar] [CrossRef]
Araya, S.N.; Fryjoff-Hung, A.; Anderson, A.; Viers, J.H.; Ghezzehei, T.A. Advances in soil moisture retrieval from multispectral remote sensing using unoccupied aircraft systems and machine learning techniques. Hydrol. Earth Syst. Sci. 2021, 25, 2739–2758. [Google Scholar] [CrossRef]
Kisekka, I.; Peddinti, S.R.; Kustas, W.P.; McElrone, A.J.; Bambach-Ortiz, N.; McKee, L.; Bastiaanssen, W. Spatial–temporal modeling of root zone soil moisture dynamics in a vineyard using machine learning and remote sensing. Irrig. Sci. 2022, 40, 761–777. [Google Scholar] [CrossRef]
Greifeneder, F.; Notarnicola, C.; Wagner, W. A machine learning-based approach for surface soil moisture estimations with google earth engine. Remote Sens. 2021, 13, 2099. [Google Scholar] [CrossRef]
Liu, Y.; Yang, Y.; Jing, W.; Yue, X. Comparison of different machine learning approaches for monthly satellite-based soil moisture downscaling over Northeast China. Remote Sens. 2017, 10, 31. [Google Scholar] [CrossRef]
Zeng, L.; Hu, S.; Xiang, D.; Zhang, X.; Li, D.; Li, L.; Zhang, T. Multilayer soil moisture mapping at a regional scale from multisource data via a machine learning method. Remote Sens. 2019, 11, 284. [Google Scholar] [CrossRef]
Zhao, J.; Zhang, C.; Min, L.; Guo, Z.; Li, N. Retrieval of farmland surface soil moisture based on feature optimization and machine learning. Remote Sens. 2022, 14, 5102. [Google Scholar] [CrossRef]
Abowarda, A.S.; Bai, L.; Zhang, C.; Long, D.; Li, X.; Huang, Q.; Sun, Z. Generating surface soil moisture at 30 m spatial resolution using both data fusion and machine learning toward better water resources management at the field scale. Remote Sens. Environ. 2021, 255, 112301. [Google Scholar] [CrossRef]
Wang, S.; Wu, Y.; Li, R.; Wang, X. Remote sensing-based retrieval of soil moisture content using stacking ensemble learning models. Land Degrad. Dev. 2023, 34, 911–925. [Google Scholar] [CrossRef]
Im, J.; Park, S.; Rhee, J.; Baik, J.; Choi, M. Downscaling of AMSR-E soil moisture with MODIS products using machine learning approaches. Environ. Earth Sci. 2016, 75, 1–19. [Google Scholar] [CrossRef]
Karthikeyan, L.; Mishra, A.K. Multi-layer high-resolution soil moisture estimation using machine learning over the United States. Remote Sens. Environ. 2021, 266, 112706. [Google Scholar] [CrossRef]
Araya, S.N.; Fryjoff-Hung, A.; Anderson, A.; Viers, J.H.; Ghezzehei, T.A. Machine learning based soil moisture retrieval from unmanned aircraft system multispectral remote sensing. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA, 26 September–2 October 2020. [Google Scholar]
Chen, Q.; Miao, F.; Wang, H.; Xu, Z.X.; Tang, Z.; Yang, L.; Qi, S. Downscaling of satellite remote sensing soil moisture products over the Tibetan Plateau based on the random forest algorithm: Preliminary results. Earth Space Sci. 2020, 7, e2020EA001265. [Google Scholar] [CrossRef]
Nouraki, A.; Golabi, M.; Albaji, M.; Naseri, A.; Homayouni, S. Spatial-temporal modeling of soil moisture using optical and thermal remote sensing data and machine learning algorithms. Iran. J. Soil Water Res. 2023, 54, 637–653. [Google Scholar]
Chen, L.; Xing, M.; He, B.; Wang, J.; Shang, J.; Huang, X.; Xu, M. Estimating soil moisture over winter wheat fields during growing season using machine-learning methods. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2021, 14, 3706–3718. [Google Scholar] [CrossRef]
Hajdu, I.; Yule, I.; Dehghan-Shear, M.H. Modelling of near-surface soil moisture using machine learning and multi-temporal sentinel 1 images in New Zealand. In Proceedings of the IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 22–27 July 2018. [Google Scholar]
Santi, E.; Dabboor, M.; Pettinato, S.; Paloscia, S. Combining machine learning and compact polarimetry for estimating soil moisture from C-band SAR data. Remote Sens. 2019, 11, 2451. [Google Scholar] [CrossRef]
Park, S.; Im, J.; Park, S.; Rhee, J. AMSR2 soil moisture downscaling using multisensor products through machine learning approach. In Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium, Milan, Italy, 26–31 July 2015. [Google Scholar]
Bueno, M.; Baca García, C.; Montoya, N.; Rau, P.; Loayza, H. Watershed scale soil moisture estimation model using machine learning and remote sensing in a data-scarce context. Sci. Agropecu. 2024, 15, 103–120. [Google Scholar] [CrossRef]
Cui, Y.; Chen, X.; Xiong, W.; He, L.; Lv, F.; Fan, W.; Luo, Z.; Hong, Y. A soil moisture spatial and temporal resolution improving algorithm based on multi-source remote sensing data and GRNN model. Remote Sens. 2020, 12, 455. [Google Scholar] [CrossRef]
Chen, S.; She, D.; Zhang, L.; Guo, M.; Liu, X. Spatial downscaling methods of soil moisture based on multisource remote sensing data and its application. Water 2019, 11, 1401. [Google Scholar] [CrossRef]
Tong, C.; Wang, H.; Magagi, R.; Goïta, K.; Wang, K. Spatial gap-filling of SMAP soil moisture pixels over Tibetan plateau via machine learning versus geostatistics. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2021, 14, 9899–9912. [Google Scholar] [CrossRef]
Nadeem, A.A.; Zha, Y.; Shi, L.; Ali, S.; Wang, X.; Zafar, Z.; Afzal, Z.; Tariq, M.A.U.R. Spatial downscaling and gap-filling of SMAP soil moisture to high resolution using MODIS surface variables and machine learning approaches over ShanDian River Basin, China. Remote Sens. 2023, 15, 812. [Google Scholar] [CrossRef]
Zhang, W.; Zhu, L.; Zhuang, Q.; Chen, D.; Sun, T. Mapping cropland soil nutrients contents based on multi-spectral remote sensing and machine learning. Agriculture 2023, 13, 1592. [Google Scholar] [CrossRef]
Song, Y.Q.; Zhao, X.; Su, H.Y.; Li, B.; Hu, Y.M.; Cui, X.S. Predicting spatial variations in soil nutrients with hyperspectral remote sensing at regional scale. Sensors 2018, 18, 3086. [Google Scholar] [CrossRef] [PubMed]
Hengl, T.; Leenaars, J.G.; Shepherd, K.D.; Walsh, M.G.; Heuvelink, G.B.; Mamo, T.; Tilahun, H.; Berkhout, E.; Cooper, M.; Fegraus, E.; et al. Soil nutrient maps of Sub-Saharan Africa: Assessment of soil nutrient content at 250 m spatial resolution using machine learning. Nutr. Cycl. Agroecosystems 2017, 109, 77–102. [Google Scholar] [CrossRef]
Kaur, G.; Das, K.; Hazra, J. Soil nutrients prediction using remote sensing data in western india: An evaluation of machine learning models. In Proceedings of the IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA, 26 September–2 October 2020. [Google Scholar]
Liu, Y.; Lin, J.; Yue, H. Soil respiration estimation in desertified mining areas based on UAV remote sensing and machine learning. Earth Sci. Inform. 2023, 16, 3433–3448. [Google Scholar] [CrossRef]
Ewing, J.; Oommen, T.; Jayakumar, P.; Alger, R. Characterizing soil stiffness using thermal remote sensing and machine learning. Remote Sens. 2021, 13, 2306. [Google Scholar] [CrossRef]
Forkuor, G.; Hounkpatin, O.K.; Welp, G.; Thiel, M. High resolution mapping of soil properties using remote sensing variables in south-western Burkina Faso: A comparison of machine learning and multiple linear regression models. PLoS ONE 2017, 12, e0170478. [Google Scholar] [CrossRef] [PubMed]
Mirzaeitalarposhti, R.; Shafizadeh-Moghadam, H.; Taghizadeh-Mehrjardi, R.; Demyan, M.S. Digital soil texture mapping and spatial transferability of machine learning models using sentinel-1, sentinel-2, and terrain-derived covariates. Remote Sens. 2022, 14, 5909. [Google Scholar] [CrossRef]
Zhai, Y.; Thomasson, J.A.; Boggess, J.E., III; Sui, R. Soil texture classification with artificial neural networks operating on remote sensing data. Comput. Electron. Agric. 2006, 54, 53–68. [Google Scholar] [CrossRef]
Haq, Y.U.; Shahbaz, M.; Asif, H.S.; Al-Laith, A.; Alsabban, W.; Aziz, M.H. Identification of soil type in Pakistan using remote sensing and machine learning. PeerJ Comput. Sci. 2022, 8, e1109. [Google Scholar] [CrossRef] [PubMed]
Heil, J.; Jörges, C.; Stumpe, B. Fine-scale mapping of soil organic matter in agricultural soils using UAVs and machine learning. Remote Sens. 2022, 14, 3349. [Google Scholar] [CrossRef]
Zhang, M.; Zhang, M.; Yang, H.; Jin, Y.; Zhang, X.; Liu, H. Mapping regional soil organic matter based on sentinel-2a and modis imagery using machine learning algorithms and google earth engine. Remote Sens. 2021, 13, 2934. [Google Scholar] [CrossRef]
Chen, D.; Chang, N.; Xiao, J.; Zhou, Q.; Wu, W. Mapping dynamics of soil organic matter in croplands with MODIS data and machine learning algorithms. Sci. Total Environ. 2019, 669, 844–855. [Google Scholar] [CrossRef]
Wu, T.; Luo, J.; Dong, W.; Sun, Y.; Xia, L.; Zhang, X. Geo-object-based soil organic matter mapping using machine learning algorithms with multi-source geo-spatial data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2019, 12, 1091–1106. [Google Scholar] [CrossRef]
Filgueiras, R.; Almeida, T.S.; Mantovani, E.C.; Dias, S.H.B.; Fernandes-Filho, E.I.; da Cunha, F.F.; Venancio, L.P. Soil water content and actual evapotranspiration predictions using regression algorithms and remote sensing data. Agric. Water Manag. 2020, 241, 106346. [Google Scholar] [CrossRef]
Lee, J.Y.; Raza, M.; Kwon, K.D. Land use and land cover changes in the Haean Basin of Korea: Impacts on soil erosion. Epis. J. Int. Geosci. 2019, 42, 17–32. [Google Scholar] [CrossRef]
Guo, D.; Saft, M.; Hou, X.; Webb, J.A.; Hairsine, P.B.; Western, A.W. How does wildfire and climate variability affect streamflow in forested catchments? A regional study in eastern Australia. J. Hydrol. 2023, 625, 129979. [Google Scholar]
Rahaman, M.H.; Masroor, M.; Sajjad, H. Integrating remote sensing derived indices and machine learning algorithms for precise extraction of small surface water bodies in the lower Thoubal river watershed, India. J. Clean. Prod. 2023, 422, 138563. [Google Scholar] [CrossRef]
Acharya, T.D.; Subedi, A.; Lee, D.H. Evaluation of machine learning algorithms for surface water extraction in a Landsat 8 scene of Nepal. Sensors 2019, 19, 2769. [Google Scholar] [CrossRef] [PubMed]
Yang, H.; Guo, H.; Dai, W.; Nie, B.; Qiao, B.; Zhu, L. Bathymetric mapping and estimation of water storage in a shallow lake using a remote sensing inversion method based on machine learning. Int. J. Digit. Earth 2022, 15, 789–812. [Google Scholar] [CrossRef]
Dehkordi, A.T.; Ghasemi, H.; Zoej, M.J.V. Machine learning-based estimation of suspended sediment concentration along Missouri River using remote sensing imageries in Google Earth Engine. In Proceedings of the 2021 7th International Conference on Signal Processing and Intelligent Systems, Tehran, Iran, 29–30 December 2021. [Google Scholar]
Izadi, M.; Sultan, M.; Kadiri, R.E.; Ghannadi, A.; Abdelmohsen, K. A remote sensing and machine learning-based approach to forecast the onset of harmful algal bloom. Remote Sens. 2021, 13, 3863. [Google Scholar] [CrossRef]
Zhang, Y.; Shi, K.; Sun, X.; Zhang, Y.; Li, N.; Wang, W.; Zhou, Y.; Zhi, W.; Liu, M.; Li, Y.; et al. Improving remote sensing estimation of Secchi disk depth for global lakes and reservoirs using machine learning methods. GIScience Remote Sens. 2022, 59, 1367–1383. [Google Scholar] [CrossRef]
Mohsen, A.; Kovács, F.; Kiss, T. Remote sensing of sediment discharge in rivers using Sentinel-2 images and machine-learning algorithms. Hydrology 2022, 9, 88. [Google Scholar] [CrossRef]
Mondal, I.; Hossain, S.A.; Roy, S.K.; Karmakar, J.; Jose, F.; De, T.K.; Nguyen, T.T.; Elkhrachy, I.; Nguyen, N.M. Assessing intra and interannual variability of water quality in the Sundarban mangrove dominated estuarine ecosystem using remote sensing and hybrid machine learning models. J. Clean. Prod. 2024, 442, 140889. [Google Scholar] [CrossRef]
Song, W.; Yinglan, A.; Wang, Y.; Fang, Q.; Tang, R. Study on remote sensing inversion and temporal-spatial variation of Hulun lake water quality based on machine learning. J. Contam. Hydrol. 2024, 260, 104282. [Google Scholar] [CrossRef]
Yang, W.; Fu, B.; Li, S.; Lao, Z.; Deng, T.; He, W.; He, H.; Chen, Z. Monitoring multi-water quality of internationally important karst wetland through deep learning, multi-sensor and multi-platform remote sensing images: A case study of Guilin, China. Ecol. Indic. 2023, 154, 110755. [Google Scholar] [CrossRef]
Qiao, Z.; Sun, S.; Jiang, Q.O.; Xiao, L.; Wang, Y.; Yan, H. Retrieval of total phosphorus concentration in the surface water of miyun reservoir based on remote sensing data and machine learning algorithms. Remote Sens. 2021, 13, 4662. [Google Scholar] [CrossRef]
Borovskaya, R.; Krivoguz, D.; Chernyi, S.; Kozhurin, E.; Khorosheltseva, V.; Zinchenko, E. Surface water salinity evaluation and identification for using remote sensing data and machine learning approach. J. Mar. Sci. Eng. 2022, 10, 257. [Google Scholar] [CrossRef]
Ahmed, M.; Mumtaz, R.; Anwar, Z.; Shaukat, A.; Arif, O.; Shafait, F. A multi–step approach for optically active and inactive water quality parameter estimation using deep learning and remote sensing. Water 2022, 14, 2112. [Google Scholar] [CrossRef]
Zhu, X.; Guo, H.; Huang, J.J.; Tian, S.; Xu, W.; Mai, Y. An ensemble machine learning model for water quality estimation in coastal area based on remote sensing imagery. J. Environ. Manag. 2022, 323, 116187. [Google Scholar] [CrossRef] [PubMed]
Leggesse, E.S.; Zimale, F.A.; Sultan, D.; Enku, T.; Srinivasan, R.; Tilahun, S.A. Predicting optical water quality indicators from remote sensing using machine learning algorithms in tropical highlands of Ethiopia. Hydrology 2023, 10, 110. [Google Scholar] [CrossRef]
Zhao, X.; Xu, H.; Ding, Z.; Wang, D.; Deng, Z.; Wang, Y.; Wu, T.; Li, W.; Lu, Z.; Wang, G. Comparing deep learning with several typical methods in prediction of assessing chlorophyll-a by remote sensing: A case study in Taihu Lake, China. Water Supply 2021, 21, 3710–3724. [Google Scholar] [CrossRef]
Sun, X.; Zhang, Y.; Shi, K.; Zhang, Y.; Li, N.; Wang, W.; Huang, X.; Qin, B. Monitoring water quality using proximal remote sensing technology. Sci. Total Environ. 2022, 803, 149805. [Google Scholar] [CrossRef]
Qian, J.; Liu, H.; Qian, L.; Bauer, J.; Xue, X.; Yu, G.; He, Q.; Zhou, Q.; Bi, Y.; Norra, S. Water quality monitoring and assessment based on cruise monitoring, remote sensing, and deep learning: A case study of Qingcaosha Reservoir. Front. Environ. Sci. 2022, 10, 979133. [Google Scholar] [CrossRef]
Hoyek, A.; Arias-Rodriguez, L.F.; Perosa, F. Holistic approach for estimating water quality ecosystem services of danube floodplains: Field measures, remote sensing, and machine learning. Hydrobiology 2022, 1, 211–231. [Google Scholar] [CrossRef]
Wang, X.; Zhang, F.; Ding, J. Evaluation of water quality based on a machine learning algorithm and water quality index for the Ebinur Lake Watershed, China. Sci. Rep. 2017, 7, 12858. [Google Scholar]
Li, N.; Ning, Z.; Chen, M.; Wu, D.; Hao, C.; Zhang, D.; Bai, R.; Liu, H.; Chen, X.; Li, W.; et al. Satellite and machine learning monitoring of optically inactive water quality variability in a tropical river. Remote Sens. 2022, 14, 5466. [Google Scholar] [CrossRef]
Peterson, K.T.; Sagan, V.; Sloan, J.J. Deep learning-based water quality estimation and anomaly detection using Landsat-8/Sentinel-2 virtual constellation and cloud computing. GIScience Remote Sens. 2020, 57, 510–525. [Google Scholar] [CrossRef]
Najafzadeh, M.; Basirian, S. Evaluation of river water quality index using remote sensing and artificial intelligence models. Remote Sens. 2023, 15, 2359. [Google Scholar] [CrossRef]
Maier, P.M.; Keller, S. Machine learning regression on hyperspectral data to estimate multiple water parameters. In Proceedings of the 2018 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, Amsterdam, The Netherlands, 23–26 September 2018. [Google Scholar]
Arias-Rodriguez, L.F.; Duan, Z.; Sepúlveda, R.; Martinez-Martinez, S.I.; Disse, M. Monitoring water quality of valle de bravo reservoir, mexico, using entire lifespan of meris data and machine learning approaches. Remote Sens. 2020, 12, 1586. [Google Scholar] [CrossRef]
Cao, Q.; Yu, G.; Sun, S.; Dou, Y.; Li, H.; Qiao, Z. Monitoring water quality of the Haihe River based on ground-based hyperspectral remote sensing. Water 2021, 14, 22. [Google Scholar] [CrossRef]
Xiao, Y.; Guo, Y.; Yin, G.; Zhang, X.; Shi, Y.; Hao, F.; Fu, Y. UAV multispectral image-based urban river water quality monitoring using stacked ensemble machine learning algorithms—A case study of the Zhanghe river, China. Remote Sens. 2022, 14, 3272. [Google Scholar] [CrossRef]
Cao, Z.; Ma, R.; Duan, H.; Pahlevan, N.; Melack, J.; Shen, M.; Xue, K. A machine learning approach to estimate chlorophyll-a from Landsat-8 measurements in inland lakes. Remote Sens. Environ. 2020, 248, 111974. [Google Scholar] [CrossRef]
Adusei, Y.Y.; Quaye-Ballard, J.; Adjaottor, A.A.; Mensah, A.A. Spatial prediction and mapping of water quality of Owabi reservoir from satellite imageries and machine learning models. Egypt. J. Remote Sens. Space Sci. 2021, 24, 825–833. [Google Scholar] [CrossRef]
Arias-Rodriguez, L.F.; Tüzün, U.F.; Duan, Z.; Huang, J.; Tuo, Y.; Disse, M. Global water quality of inland waters with harmonized landsat-8 and sentinel-2 using cloud-computed machine learning. Remote Sens. 2023, 15, 1390. [Google Scholar] [CrossRef]
Blix, K.; Pálffy, K.R.; Tóth, V.; Eltoft, T. Remote sensing of water quality parameters over Lake Balaton by using Sentinel-3 OLCI. Water 2018, 10, 1428. [Google Scholar] [CrossRef]
Cai, J.; Chen, J.; Dou, X.; Xing, Q. Using machine learning algorithms with in situ hyperspectral reflectance data to assess comprehensive water quality of urban rivers. IEEE Trans. Geosci. Remote Sens. 2022, 60, 1–13. [Google Scholar] [CrossRef]
Souza, A.P.; Oliveira, B.A.; Andrade, M.L.; Starling, M.C.V.; Pereira, A.H.; Maillard, P.l.; Maillard, P.; Nogueira, K.; dos Santos, J.A.; Amorim, C.C.; et al. Integrating remote sensing and machine learning to detect turbidity anomalies in hydroelectric reservoirs. Sci. Total Environ. 2023, 902, 165964. [Google Scholar] [CrossRef]
Ma, Y.; Song, K.; Wen, Z.; Liu, G.; Shang, Y.; Lyu, L.; Du, J.; Yang, Q.; Li, S.; Tao, H.; et al. Remote sensing of turbidity for lakes in northeast China using Sentinel-2 images with machine learning algorithms. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2021, 14, 9132–9146. [Google Scholar] [CrossRef]
Rubin, H.J.; Lutz, D.A.; Steele, B.G.; Cottingham, K.L.; Weathers, K.C.; Ducey, M.J.; Palace, M.; Johnson, K.M.; Chipman, J.W. Remote sensing of lake water clarity: Performance and transferability of both historical algorithms and machine learning. Remote Sens. 2021, 13, 1434. [Google Scholar] [CrossRef]
Zeng, W.; Xu, K.; Cheng, S.; Zhao, L.; Yang, K. Regional Remote Sensing of Lake Water Transparency Based on Google Earth Engine: Performance of Empirical Algorithm and Machine Learning. Appl. Sci. 2023, 13, 4007. [Google Scholar] [CrossRef]
Magrì, S.; Ottaviani, E.; Prampolini, E.; Besio, G.; Fabiano, B.; Federici, B. Application of machine learning techniques to derive sea water turbidity from Sentinel-2 imagery. Remote Sens. Appl. Soc. Environ. 2023, 30, 100951. [Google Scholar] [CrossRef]
Lu, Q.; Si, W.; Wei, L.; Li, Z.; Xia, Z.; Ye, S.; Xia, Y. Retrieval of water quality from UAV-borne hyperspectral imagery: A comparative study of machine learning algorithms. Remote Sens. 2021, 13, 3928. [Google Scholar] [CrossRef]
Mosre, J.; Suárez, F. Actual evapotranspiration estimates in arid cold regions using machine learning algorithms with in situ and remote sensing data. Water 2021, 13, 870. [Google Scholar] [CrossRef]
Loggenberg, K.; Strever, A.; Greyling, B.; Poona, N. Modelling water stress in a Shiraz vineyard using hyperspectral imaging and machine learning. Remote Sens. 2018, 10, 202. [Google Scholar] [CrossRef]
Elkhrachy, I. Flash flood water depth estimation using SAR images, digital elevation models, and machine learning algorithms. Remote Sens. 2022, 14, 440. [Google Scholar] [CrossRef]
Zhang, L.; Hu, Q.; Tang, Z. Using Sentinel-2 imagery and machine learning algorithms to assess the inundation status of Nebraska conservation easements during 2018–2021. Remote Sens. 2022, 14, 4382. [Google Scholar] [CrossRef]
Fu, Z.; Hu, L.; Chen, Z.; Zhang, F.; Shi, Z.; Hu, B.; Du, Z.; Liu, R. Estimating spatial and temporal variation in ocean surface pCO₂ in the Gulf of Mexico using remote sensing and machine learning techniques. Sci. Total Environ. 2020, 745, 140965. [Google Scholar] [CrossRef]
Singha, C.; Swain, K.C.; Moghimi, A.; Foroughnia, F.; Swain, S.K. Integrating geospatial, remote sensing, and machine learning for climate-induced forest fire susceptibility mapping in Similipal Tiger Reserve, India. For. Ecol. Manag. 2024, 555, 121729. [Google Scholar] [CrossRef]
Kalantar, B.; Ueda, N.; Idrees, M.O.; Janizadeh, S.; Ahmadi, K.; Shabani, F. Forest fire susceptibility prediction based on machine learning models with resampling algorithms on remote sensing data. Remote Sens. 2020, 12, 3682. [Google Scholar] [CrossRef]
Piralilou, S.T.; Einali, G.; Ghorbanzadeh, O.; Nachappa, T.G.; Gholamnia, K.; Blaschke, T.; Ghamisi, P. A Google Earth Engine approach for wildfire susceptibility prediction fusion with remote sensing data of different spatial resolutions. Remote Sens. 2022, 14, 672. [Google Scholar] [CrossRef]
Xie, L.; Zhang, R.; Zhan, J.; Li, S.; Shama, A.; Zhan, R.; Wang, T.; Lv, J.; Bao, X.; Wu, R. Wildfire risk assessment in Liangshan Prefecture, China based on an integration machine learning algorithm. Remote Sens. 2022, 14, 4592. [Google Scholar] [CrossRef]
Cheng, S.; Jin, Y.; Harrison, S.P.; Quilodrán-Casas, C.; Prentice, I.C.; Guo, Y.K.; Arcucci, R. Parameter flexible wildfire prediction using machine learning techniques: Forward and inverse modelling. Remote Sens. 2022, 14, 3228. [Google Scholar] [CrossRef]
Sulova, A.; Arsanjani, J.J. Exploratory analysis of driving force of wildfires in Australia: An application of machine learning within Google Earth engine. Remote Sens. 2020, 13, 10. [Google Scholar] [CrossRef]
Saim, A.A.; Aly, M.H. Machine learning for modeling wildfire susceptibility at the state level: An example from Arkansas, USA. Geographies 2022, 2, 31–47. [Google Scholar] [CrossRef]
Pérez-Porras, F.J.; Triviño-Tarradas, P.; Cima-Rodríguez, C.; Meroño-de-Larriva, J.E.; García-Ferrer, A.; Mesas-Carrascosa, F.J. Machine learning methods and synthetic data generation to predict large wildfires. Sensors 2021, 21, 3694. [Google Scholar] [CrossRef]
McCandless, T.C.; Kosovic, B.; Petzke, W. Enhancing wildfire spread modelling by building a gridded fuel moisture content product with machine learning. Mach. Learn. Sci. Technol. 2020, 1, 035010. [Google Scholar] [CrossRef]
Rafaqat, W.; Iqbal, M.; Kanwal, R.; Song, W. Study of driving factors using machine learning to determine the effect of topography, climate, and fuel on wildfire in pakistan. Remote Sens. 2022, 14, 1918. [Google Scholar] [CrossRef]
Agrawal, N.; Nelson, P.V.; Low, P.V. A Novel Approach for Predicting Large Wildfires Using Machine Learning Towards Environmental Justice via Environmental Remote Sensing and Atmospheric Reanalysis Data across the United States. Remote Sens. 2023, 15, 5501. [Google Scholar] [CrossRef]
Jiang, W.; Qiao, Y.; Zheng, X.; Zhou, J.; Jiang, J.; Meng, Q.; Su, G.; Zhong, S.; Wang, F. Wildfire risk assessment using deep learning in Guangdong Province, China. Int. J. Appl. Earth Obs. Geoinf. 2024, 128, 103750. [Google Scholar] [CrossRef]
Shirazi, Z.; Wang, L.; Bondur, V.G. Modeling Conditions Appropriate for Wildfire in South East China—A Machine Learning Approach. Front. Earth Sci. 2021, 9, 622307. [Google Scholar] [CrossRef]
Valdez, M.C.; Chang, K.T.; Chen, C.F.; Chiang, S.H.; Santos, J.L. Modelling the spatial variability of wildfire susceptibility in Honduras using remote sensing and geographical information systems. Geomat. Nat. Hazards Risk 2017, 8, 876–892. [Google Scholar] [CrossRef]
Heisig, J.; Olson, E.; Pebesma, E. Predicting wildfire fuels and hazard in a central European temperate forest using active and passive remote sensing. Fire 2022, 5, 29. [Google Scholar] [CrossRef]
Zhao, Y.; Ma, J.; Li, X.; Zhang, J. Saliency detection and deep learning-based wildfire identification in UAV imagery. Sensors 2018, 18, 712. [Google Scholar] [CrossRef]
Wang, G.; Li, H.; Li, P.; Lang, X.; Feng, Y.; Ding, Z.; Xie, S. M4SFWD: A Multi-Faceted synthetic dataset for remote sensing forest wildfires detection. Expert Syst. Appl. 2024, 248, 123489. [Google Scholar] [CrossRef]
Velayati, A.H.; Boloorani, A.D.; Kiavarz, M.; Samani, N.N.; Alavipanah, S.K. Spatiotemporal analysis of wildfire in the Tigris and Euphrates basin: A remote sensing based wildfire potential mapping. Remote Sens. Appl. Soc. Environ. 2024, 34, 101150. [Google Scholar] [CrossRef]
Pelletier, N.; Millard, K.; Darling, S. Wildfire likelihood in Canadian treed peatlands based on remote-sensing time-series of surface conditions. Remote Sens. Environ. 2023, 296, 113747. [Google Scholar] [CrossRef]
Jin, S.; Wang, T.; Huang, H.; Zheng, X.; Li, T.; Guo, Z. A self-adaptive wildfire detection algorithm by fusing physical and deep learning schemes. Int. J. Appl. Earth Obs. Geoinf. 2024, 127, 103671. [Google Scholar] [CrossRef]
Zhang, Q.; Zhu, J.; Huang, Y.; Yuan, Q.; Zhang, L. Beyond being wise after the event: Combining spatial, temporal and spectral information for Himawari-8 early-stage wildfire detection. Int. J. Appl. Earth Obs. Geoinf. 2023, 124, 103506. [Google Scholar] [CrossRef]
Van, L.N.; Tran, V.N.; Nguyen, G.V.; Yeon, M.; Do, M.T.T.; Lee, G. Enhancing wildfire mapping accuracy using mono-temporal Sentinel-2 data: A novel approach through qualitative and quantitative feature selection with explainable AI. Ecol. Inform. 2024, 81, 102601. [Google Scholar] [CrossRef]
Huot, F.; Hu, R.L.; Goyal, N.; Sankar, T.; Ihme, M.; Chen, Y.F. Next day wildfire spread: A machine learning dataset to predict wildfire spreading from remote-sensing data. IEEE Trans. Geosci. Remote Sens. 2022, 60, 1–13. [Google Scholar] [CrossRef]
dos Santos, S.M.B.; Duverger, S.G.; Bento-Gonçalves, A.; Franca-Rocha, W.; Vieira, A.; Teixeira, G. Remote sensing applications for mapping large wildfires based on machine learning and time series in northwestern Portugal. Fire 2023, 6, 43. [Google Scholar] [CrossRef]
Iban, M.C.; Sekertekin, A. Machine learning based wildfire susceptibility mapping using remotely sensed fire data and GIS: A case study of Adana and Mersin provinces, Turkey. Ecol. Inform. 2022, 69, 101647. [Google Scholar] [CrossRef]
Mohajane, M.; Costache, R.; Karimi, F.; Pham, Q.B.; Essahlaoui, A.; Nguyen, H.; Laneve, G.; Oudija, F. Application of remote sensing and machine learning algorithms for forest fire mapping in a Mediterranean area. Ecol. Indic. 2021, 129, 107869. [Google Scholar] [CrossRef]
Janiec, P.; Gadal, S. A comparison of two machine learning classification methods for remote sensing predictive modeling of the forest fire in the North-Eastern Siberia. Remote Sens. 2020, 12, 4157. [Google Scholar] [CrossRef]
Poon, P.K.; Kinoshita, A.M. Estimating evapotranspiration in a post-fire environment using remote sensing and machine learning. Remote Sens. 2018, 10, 1728. [Google Scholar] [CrossRef]
Nur, A.S.; Kim, Y.J.; Lee, C.W. Creation of wildfire susceptibility maps in plumas national forest using InSAR coherence, deep learning, and metaheuristic optimization approaches. Remote Sens. 2022, 14, 4416. [Google Scholar] [CrossRef]
Sim, S.; Kim, W.; Lee, J.; Kang, Y.; Im, J.; Kwon, C.; Kim, S. Wildfire severity mapping using sentinel satellite data based on machine learning approaches. Korean J. Remote Sens. 2020, 36, 1109–1123. [Google Scholar]
Ghorbanzadeh, O.; Valizadeh Kamran, K.; Blaschke, T.; Aryal, J.; Naboureh, A.; Einali, J.; Bian, J. Spatial prediction of wildfire susceptibility using field survey GPS data and machine learning approaches. Fire 2019, 2, 43. [Google Scholar] [CrossRef]
Ghorbanzadeh, O.; Kamran, K.V.; Blaschke, T.; Aryal, J.; Naboureh, A.; Einali, J.; Bian, J. Comparisons of diverse machine learning approaches for wildfire susceptibility mapping. Symmetry 2020, 12, 604. [Google Scholar] [CrossRef]
Napoli, M.D.; Marsiglia, P.; Martire, D.D.; Ramondini, M.; Ullo, S.L.; Calcaterra, D. Landslide susceptibility assessment of wildfire burnt areas through earth-observation techniques and a machine learning-based approach. Remote Sens. 2020, 12, 2505. [Google Scholar] [CrossRef]
Zikiou, N.; Rushmeier, H.; Capel, M.I.; Kandakji, T.; Rios, N.; Lahdir, M. Remote Sensing and Machine Learning for Accurate Fire Severity Mapping in Northern Algeria. Remote Sens. 2024, 16, 1517. [Google Scholar] [CrossRef]
Bahadori, N.; Razavi-Termeh, S.V.; Sadeghi-Niaraki, A.; Al-Kindi, K.M.; Abuhmed, T.; Nazeri, B.; Choi, S.M. Wildfire susceptibility mapping using deep learning algorithms in two satellite imagery dataset. Forests 2023, 14, 1325. [Google Scholar] [CrossRef]
Shmuel, A.; Heifetz, E. Global wildfire susceptibility mapping based on machine learning models. Forests 2022, 13, 1050. [Google Scholar] [CrossRef]
Hamilton, D.A.; Brothers, K.L.; Jones, S.D.; Colwell, J.; Winters, J. Wildland fire tree mortality mapping from hyperspatial imagery using machine learning. Remote Sens. 2021, 13, 290. [Google Scholar] [CrossRef]
Schreck, J.S.; Petzke, W.; Jiménez, P.A.; Brummet, T.; Knievel, J.C.; James, E.; Kosović, B.; Gagne, D.J. Machine Learning and VIIRS Satellite Retrievals for Skillful Fuel Moisture Content Monitoring in Wildfire Management. Remote Sens. 2023, 15, 3372. [Google Scholar] [CrossRef]
Syifa, M.; Panahi, M.; Lee, C.W. Mapping of post-wildfire burned area using a hybrid algorithm and satellite data: The case of the camp fire wildfire in California, USA. Remote Sens. 2020, 12, 623. [Google Scholar] [CrossRef]
Oliver, J.A.; Pivot, F.C.; Tan, Q.; Cantin, A.S.; Wooster, M.J.; Johnston, J.M. A machine learning approach to waterbody segmentation in thermal infrared imagery in support of tactical wildfire mapping. Remote Sens. 2022, 14, 2262. [Google Scholar] [CrossRef]
Ban, Y.; Zhang, P.; Nascetti, A.; Bevington, A.R.; Wulder, M.A. Near real-time wildfire progression monitoring with Sentinel-1 SAR time series and deep learning. Sci. Rep. 2020, 10, 1322. [Google Scholar] [CrossRef] [PubMed]
Cui, Q.; Zhang, F.; Fu, S.; Wei, X.; Ma, Y.; Wu, K. High Spatiotemporal Resolution PM2. 5 concentration estimation with machine learning algorithm: A Case Study for Wildfire in California. Remote Sens. 2022, 14, 1635. [Google Scholar] [CrossRef]
Serra-Burriel, F.; Delicado, P.; Prata, A.T.; Cucchietti, F.M. Estimating heterogeneous wildfire effects using synthetic controls and satellite remote sensing. Remote Sens. Environ. 2021, 265, 112649. [Google Scholar] [CrossRef]
Nolè, A.; Rita, A.; Spatola, M.F.; Borghetti, M. Biogeographic variability in wildfire severity and post-fire vegetation recovery across the European forests via remote sensing-derived spectral metrics. Sci. Total Environ. 2022, 823, 153807. [Google Scholar] [CrossRef]
Guerra-Hernández, J.; Pereira, J.M.; Stovall, A.; Pascual, A. Impact of fire severity on forest structure and biomass stocks using NASA GEDI data. Insights from the 2020 and 2021 wildfire season in Spain and Portugal. Sci. Remote Sens. 2024, 9, 100134. [Google Scholar] [CrossRef]
Chen, R.; He, B.; Li, Y.; Fan, C.; Yin, J.; Zhang, H.; Zhang, Y. Estimation of potential wildfire behavior characteristics to assess wildfire danger in southwest China using deep learning schemes. J. Environ. Manag. 2024, 351, 120005. [Google Scholar] [CrossRef] [PubMed]
Ramsey, S.; Reinke, K.; Jones, S. Mapping the recovery of Mountain Ash (Eucalyptus regnans) and Alpine Ash (E. delegatensis) using satellite remote sensing and a machine learning classifier. Remote Sens. Appl. Soc. Environ. 2024, 36, 101274. [Google Scholar] [CrossRef]
Fernández-Guisuraga, J.M.; Fernández-Manso, A.; Quintano, C.; Fernández-García, V.; Cerrillo, A.; Marqués, G.; Cascallana, G.; Calvo, L. FIREMAP: Cloud-based software to automate the estimation of wildfire-induced ecological impacts and recovery processes using remote sensing techniques. Ecol. Inform. 2024, 81, 102591. [Google Scholar] [CrossRef]
Lee, S.; Hyun, Y.; Lee, S.; Lee, M.J. Groundwater potential mapping using remote sensing and GIS-based machine learning techniques. Remote Sens. 2020, 12, 1200. [Google Scholar] [CrossRef]
Wang, J.; Peng, J.; Li, H.; Yin, C.; Liu, W.; Wang, T.; Zhang, H. Soil salinity mapping using machine learning algorithms with the Sentinel-2 MSI in arid areas, China. Remote Sens. 2021, 13, 305. [Google Scholar] [CrossRef]
Bouslihim, Y.; Rochdi, A.; Aboutayeb, R.; Amrani-Paaza, N.E.; Miftah, A.; Hssaini, L. Soil aggregate stability mapping using remote sensing and GIS-based machine learning technique. Front. Earth Sci. 2021, 9, 748859. [Google Scholar] [CrossRef]

Figure 1. An overview of the study’s process, highlighting the search methodology, identification of research topics, employed ML algorithms and RS data, and classification and sub-classification criteria, as well as key discussion points [31,32,33].

Figure 2. A world map depicting 47 countries where research has been conducted, marked by “X”. Colored circles represent the number of publications in each country, with the corresponding numbers shown in the legend.

Figure 3. Number of publications from 2006 to 2024 in four research areas: soil properties; hydrology and water resources; and wildfire management. A notable peak in publications on wildfire management occurred around 2022.

Figure 4. Distribution of 195 research studies across four research fields. This pie chart illustrates the percentage of publications dedicated to each field: soil properties (48%); hydrology and water resources (27%); and wildfire management (25%).

Figure 5. Frequency of usage for RS data in soil and water conservation research. A total of 41 RS techniques were identified. Only those used more than twice are shown in the figure. RS data used only once are not shown.

Figure 6. Frequency of usage for ML algorithms in soil and water conservation research. RF shows the highest frequency of usage. Only algorithms used more than twice are shown in the figure. RS data used only once are not shown.

Table 1. Classification of 195 research studies into four research fields based on their subcategorized subjects. (This table lists the number of publications associated with each research field and specific subcategorized subjects within them. The four fields include soil properties, hydrology and water resources, and wildfire management, encompassing a total of 34 subcategorized subjects).

Research Fields	Subcategorized Subjects	Number of Publications
Soil properties	Soil conductivity [34,35,36], soil salinity [28,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56], SOC [57,58,59,60,61,62,63,64,65,66,67,68,69,70,71], soil aggregate stability [72,73], soil chemistry [31,74], soil degradation [75], soil erodibility [76,77,78,79], soil matric potential [80], soil mercury [81], soil moisture [82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108], soil nutrients [109,110,111], soil total nitrogen [112], soil respiration [113], soil stiffness [114], soil texture [115,116,117], soil types [118], soil organic matter [119,120,121,122], soil water content and evapotranspiration [123]	93
Hydrology and water resources	Groundwater level [124,125], streamflow [126], surface water [127,128], water storage [129], sediment concentration [130], algal blooms [131], Secchi disk depth [132], sediment discharge [133], water quality [134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158], turbidity [159,160,161,162,163,164], evapotranspiration [165,166], flash flood water depth [167], inundation status [168], ocean surface CO₂ [169]	50
Wildfire management	Wildfire prediction [32,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190], wildfire monitoring [25,33,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215], wildfire recovery [216,217]	52

Table 2. Overview of the number of publications and the top three most used RS data in different research fields, including soil properties, hydrology and water resources, and wildfire management.

Research Fields	Number of Publications	Top Three Most Used RS Data
Research Fields	Number of Publications	Algorithms	Frequency of Usage
Soil properties	93	(1) Landsat 8	32
		(2) Sentinel-2	28
		(3) MODIS	22
Hydrology and water resources	50	(1) Landsat 8	18
		(2) Sentinel-2	16
		(3) Rapid Eye	7
Wildfire management	52	(1) MODIS	20
		(2) Sentinel-2	15
		(3) Landsat 8	10

Table 3. Overview of the number of publications and the top three most used ML algorithms in different research fields, including soil properties, hydrology and water resources, and wildfire management.

Research Fields	Number of Publications	Top Three Most Used RS Data
Research Fields	Number of Publications	Algorithms	Frequency of Usage
Soil properties	93	(1) RF	67
		(2) ANN	23
		(3) SVM	21
Hydrology and water resources	50	(1) RF	32
		(2) SVM, SVR	14
		(3) XGB	9
Wildfire management	52	(1) RF	30
		(2) SVM	16
		(3) MLP	7

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Kim, Y.I.; Park, W.H.; Shin, Y.; Park, J.-W.; Engel, B.; Yun, Y.-J.; Jang, W.S. Applications of Machine Learning and Remote Sensing in Soil and Water Conservation. Hydrology 2024, 11, 183. https://doi.org/10.3390/hydrology11110183

AMA Style

Kim YI, Park WH, Shin Y, Park J-W, Engel B, Yun Y-J, Jang WS. Applications of Machine Learning and Remote Sensing in Soil and Water Conservation. Hydrology. 2024; 11(11):183. https://doi.org/10.3390/hydrology11110183

Chicago/Turabian Style

Kim, Ye Inn, Woo Hyeon Park, Yongchul Shin, Jin-Woo Park, Bernie Engel, Young-Jo Yun, and Won Seok Jang. 2024. "Applications of Machine Learning and Remote Sensing in Soil and Water Conservation" Hydrology 11, no. 11: 183. https://doi.org/10.3390/hydrology11110183

APA Style

Kim, Y. I., Park, W. H., Shin, Y., Park, J.-W., Engel, B., Yun, Y.-J., & Jang, W. S. (2024). Applications of Machine Learning and Remote Sensing in Soil and Water Conservation. Hydrology, 11(11), 183. https://doi.org/10.3390/hydrology11110183

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Applications of Machine Learning and Remote Sensing in Soil and Water Conservation

Abstract

1. Introduction

2. Materials and Methods

Searching/Classification Methodology

3. Results and Discussion

3.1. Types and Frequencies of RS Data Used in Soil and Water Conservation Research

3.2. Types and Frequencies of ML Algorithm Used in Soil and Water Conservation Research

3.3. Field-Specific Observations

3.3.1. Soil Properties

3.3.2. Hydrology and Water Resources

3.3.3. Wildfire Management

4. Challenges and Limitations

4.1. Data-Related Challenges

4.2. Limitations in Technology and Implementation

5. Conclusions and Future Directions

Author Contributions

Funding

Data Availability Statement

Conflicts of Interest

Appendix A

References

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