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Remote Sensing of Soil Condition Assessment and Degradation Drivers Monitoring

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: 15 June 2025 | Viewed by 14151

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Prof. Dr. Jianli Ding

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1. College of Geography and Remote sensing Science, Xinjiang University, Urumqi 830017, China
2. Xinjiang Institute of Technology, Aksu 843099, China
Interests: vegetation index; area information; arid areas; average angle; canopy structure; computational accuracy; curve approach; degrees centigrade; desert vegetation; drought; field measurement data; fractional vegetation cover; graphical method; grassland; greater area; ground objects; growing season; high consistency; high spatial resolution images; input parameters; interest in the field; kernel-driven models; kinds of plants; light-tight; man-made objects; mixed pixels; NDVI values; NIR bands; near-infrared spectroscopy; normalized difference vegetation index; objective information; radiation parameter; random sampling method; reference data; reflectance factor; reflectance values; root mean square error; sentinel-2 images; simulated data; soil adjusted vegetation index; solar angle; solar zenith angle; spectral curves; spectral features; spectral reflectance; supplemental material; sweet potato; unmanned aerial vehicles; valuable information; vegetation monitoring
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Prof. Dr. Danlin Yu

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Guest Editor

Department of Earth and Environmental Studies, Montclair State University, Montclair, NJ 07043, USA
Interests: urban remote sensing; geographic information science; system dynamics; complex system simulation; regional development
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Dr. Xiangyu Ge

E-Mail Website
Guest Editor

College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 800017, China
Interests: remote sensing satellite and UAV (multispectral and hyperspectral); soil salinity; digital soil mapping; land degradation; ecological hydrology; google earth engine
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Soil is an important foundation of life on Earth and is essential for the sustainable development of human society. Changes in soil conditions affect food production, land–atmosphere circulation, hydrological processes, ecosystem services, and human health. With the increasing impact of changing environments, soil conditions have undergone tremendous changes, resulting in a decline in soil productivity and regulatory capacity. Currently, at least half the world’s soils are degraded. This trend will lead to the loss of soil physical, chemical and biological characteristics, threatening the well-being of 3.2 billion people. Therefore, it is urgent to monitor and assess the soil condition and soil degradation. Remote sensing technology has become an important technology for observing various properties and states of soil. Remote sensing observation provides an opportunity for long-term dynamic monitoring and helps deepen our understanding of soil conditions, processes, changes and driving mechanisms.

This Special Issue encourages research on evaluating various processes, changes, driving mechanisms and future predictions related to soil properties, soil conditions and soil degradation through remote sensing (various platforms and electromagnetic spectrum). The integration of remote sensing knowledge with soil knowledge provides innovative knowledge for solving human–land relationship issues. Research areas may include (but are not limited to) the following：

Proximal/UAV/remote sensing monitor and assess soils (optical, microwave, thermal infrared, LIDAR, etc.);
Digital soil mapping;
Monitoring and assessment of soil variation;
Drivers of soil condition change and degradation;
Multi-source data fusion/synergy for soil monitoring;
Simulation of soil properties and degradation;
Future prediction of soil changes;
Coupling of human–land relationships;
Soils and sustainable development;
Soils and hydrological processes;
Soils and human activities;
Impact of changing soil conditions and degradation;
Soil erosion, pollution and salinization.

Prof. Dr. Jianli Ding
Prof. Dr. Danlin Yu
Dr. Xiangyu Ge
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

digital soil mapping
soil degradation
soil organic carbon
soil nutrients
soil variability
LULC
climate change
machine learning
remote sensing big data
scenario simulation

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Published Papers (7 papers)

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Research

33 pages, 17073 KiB

Open AccessArticle

Optimization of Multi-Source Remote Sensing Soil Salinity Estimation Based on Different Salinization Degrees

by Huifang Chen, Jingwei Wu and Chi Xu

Remote Sens. 2025, 17(7), 1315; https://doi.org/10.3390/rs17071315 - 7 Apr 2025

Viewed by 345

Abstract

The timely and accurate monitoring of regional soil salinity is crucial for the sustainable development of land and the stability of the ecological environment in arid and semi-arid regions. However, due to the spatiotemporal heterogeneity of soil properties and environmental conditions, improving the accuracy of soil salinization monitoring remains challenging. This study aimed to explore whether partitioned modeling based on salinization degrees during both the bare soil and vegetation cover periods can enhance the accuracy of regional soil salinity prediction. Specifically, this study integrated in situ hyperspectral data and satellite multispectral data using spectral response functions. Subsequently, machine learning methods such as random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM), and multiple linear regression (MLR) were employed, in combination with sensitive spectral indices, to develop a multi-source remote sensing soil salinity estimation model optimized for different salinization degrees (mild or lower salinization vs. moderate or higher salinization). The performance of this partitioned modeling approach was then compared with an overall modeling approach that does not distinguish between salinization degrees to determine the optimal modeling strategy. The results highlight the effectiveness of considering regional soil salinization degrees in enhancing the sensitivity of spectral indices to soil salinity and improving modeling accuracy. Classifying salinization degrees helps identify spectral variable combinations that are more sensitive to the construction of soil salinity content (SSC) models, positively impacting soil salinity estimation. The partitioned modeling strategy outperformed the overall modeling strategy in both accuracy and stability, with R² values reaching 0.84 and 0.80 and corresponding RMSE values of 0.1646% and 0.1710% during the bare soil and vegetation cover periods, respectively. This study proposes an optimized modeling strategy based on regional salinization degrees, providing scientific evidence and technical support for the precise assessment and effective management of soil salinization. Full article

(This article belongs to the Special Issue Remote Sensing of Soil Condition Assessment and Degradation Drivers Monitoring)

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19 pages, 4444 KiB

Open AccessArticle

Weighted Variable Optimization-Based Method for Estimating Soil Salinity Using Multi-Source Remote Sensing Data: A Case Study in the Weiku Oasis, Xinjiang, China

by Zhuohan Jiang, Zhe Hao, Jianli Ding, Zhiguo Miao, Yukun Zhang, Alimira Alimu, Xin Jin, Huiling Cheng and Wen Ma

Remote Sens. 2024, 16(17), 3145; https://doi.org/10.3390/rs16173145 - 26 Aug 2024

Cited by 4 | Viewed by 1563

Abstract

Soil salinization is a significant global threat to sustainable agricultural development, with soil salinity serving as a crucial indicator for evaluating soil salinization. Remote sensing technology enables large-scale inversion of soil salinity, facilitating the monitoring and assessment of soil salinization levels, thus supporting the prevention and management of soil salinization. This study employs multi-source remote sensing data, selecting 8 radar polarization combinations, 10 spectral indices, and 3 topographic factors to form a feature variable dataset. By applying a normalized weighted variable optimization method, highly important feature variables are identified. AdaBoost, LightGBM, and CatBoost machine learning methods are then used to develop soil salinity inversion models and evaluate their performance. The results indicate the following: (1) There is generally a strong correlation between radar polarization combinations and vegetation indices, and a very high correlation between various vegetation indices and the salinity index S3. (2) The top five feature variables, in order of importance, are Aspect, VH², Normalized Difference Moisture Index (NDMI), VH, and Vegetation Moisture Index (VMI). (3) The method of normalized weighted importance scoring effectively screens important variables, reducing the number of input feature variables while enhancing the model’s inversion accuracy. (4) Among the three machine learning models, CatBoost performs best overall in soil salt content (SSC) prediction. Combined with the top five feature variables, CatBoost achieves the highest prediction accuracy (R² = 0.831, RMSE = 2.653, MAE = 1.034) in the prediction phase. This study provides insights for the further development and application of methods for collaborative inversion of soil salinity using multi-source remote sensing data. Full article

(This article belongs to the Special Issue Remote Sensing of Soil Condition Assessment and Degradation Drivers Monitoring)

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15 pages, 5662 KiB

Open AccessArticle

Optimal Mapping of Soil Erodibility in a Plateau Lake Watershed: Empirical Models Empowered by Machine Learning

by Jiaxue Wang, Yujiao Wei, Zheng Sun, Shixiang Gu, Shihan Bai, Jinming Chen, Jing Chen, Yongsheng Hong and Yiyun Chen

Remote Sens. 2024, 16(16), 3017; https://doi.org/10.3390/rs16163017 - 17 Aug 2024

Cited by 3 | Viewed by 1274

Abstract

Soil erodibility (K) refers to the inherent ability of soil to withstand erosion. Accurate estimation and spatial prediction of K values are vital for assessing soil erosion and managing land resources. However, as most K-value estimation models are empirical, they suffer from significant extrapolation uncertainty, and traditional studies on spatial prediction focusing on individual empirical K values have neglected to explore the spatial pattern differences between various empirical models. This work proposed a universal framework for selecting an optimal soil-erodibility map using empirical models enhanced by machine learning. Specifically, three empirical models, namely, the erosion-productivity impact calculator model (K_EPIC), the Shirazi model (K_Shirazi), and the Torri model (K_Torri) were used to estimate K values. Random Forest (RF) and Gradient-Boosting Decision Tree (GBDT) algorithms were employed to develop prediction models, which led to the creation of three K-value maps. The spatial distribution of K values and associated environmental covariates were also investigated across varying empirical models. Results showed that RF achieved the highest accuracy, with R² of K_EPIC, K_Shirazi, and K_Torri increasing by 46%, 34%, and 22%, respectively, compared to GBDT. And distinctions among environmental variables that shape the spatial patterns of empirical models have been identified. The K_EPIC and K_Shirazi are influenced by soil porosity and soil moisture. The K_Torri is more sensitive to soil moisture conditions and terrain location. More importantly, our study has highlighted disparities in the spatial patterns across the three K-value maps. Considering the data distribution, spatial distribution, and measured K values, the K_Torri model outperformed others in estimating soil erodibility in the plateau lake watershed. This study proposed a framework that aimed to create optimal soil-erodibility maps and offered a scientific and accurate K-value estimation method for the assessment of soil erosion. Full article

(This article belongs to the Special Issue Remote Sensing of Soil Condition Assessment and Degradation Drivers Monitoring)

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30 pages, 10273 KiB

Open AccessArticle

Downscale Inversion of Soil Moisture during Vegetation Growth Period in Ebinur Lake Watershed

by Hongzhi Xiao, Jinjie Wang, Jianli Ding, Xiang Li and Keyu Chen

Remote Sens. 2024, 16(6), 983; https://doi.org/10.3390/rs16060983 - 11 Mar 2024

Viewed by 1512

Abstract

Soil moisture content is an important measure of soil health, and high-precision soil moisture trend analysis is essential for understanding regional ecological quality in the context of climate change, flood monitoring, and water cycle processes. However, in the arid regions of Central Asia, where data are severely lacking, obtaining high-spatial-resolution, continuous soil moisture data is difficult due to the scarcity of stations. Moreover, because soil moisture is easily affected by evaporation time, surface morphology, and anthropogenic factors, mature theoretical models or empirical or semiempirical models to measure soil moisture are also lacking. To investigate the distribution and trend of soil moisture in the Ebinur Lake water, in this study, microwave remote sensing and visible remote sensing data were selected as inputs, and the Global Land Data Assimilation System (GLDAS-2.2) data products were downscaled using the GTWR model, which increased the spatial scale from 27,830 m × 27,830 m to 30 m × 30 m. The phenomena involved in the soil moisture change cycle, spatial distribution, temporal variation, and internal randomness distance were analyzed in the study area through wavelet analysis, Theil–Sen trend analysis, the Mann–Kendall (MK) test, and a variogram. This study obtained high-resolution continuous soil moisture data in the arid and data-scarce region in Central Asia, thus broadening the field of multisource remote sensing analysis and providing a theoretical basis for the construction of precision agriculture in northwest China. Full article

(This article belongs to the Special Issue Remote Sensing of Soil Condition Assessment and Degradation Drivers Monitoring)

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23 pages, 6486 KiB

Open AccessArticle

Assessing the Potential of UAV-Based Multispectral and Thermal Data to Estimate Soil Water Content Using Geophysical Methods

by Yunyi Guan and Katherine Grote

Remote Sens. 2024, 16(1), 61; https://doi.org/10.3390/rs16010061 - 22 Dec 2023

Cited by 13 | Viewed by 2766

Abstract

Knowledge of the soil water content (SWC) is important for many aspects of agriculture and must be monitored to maximize crop yield, efficiently use limited supplies of irrigation water, and ensure optimal nutrient management with minimal environmental impact. Single-location sensors are often used to monitor SWC, but a limited number of point measurements is insufficient to measure SWC across most fields since SWC is typically very heterogeneous. To overcome this difficulty, several researchers have used data acquired from unmanned aerial vehicles (UAVs) to predict the SWC by using machine learning on a limited number of point measurements acquired across a field. While useful, these methods are limited by the relatively small number of SWC measurements that can be acquired with conventional measurement techniques. This study uses UAV-based data and thousands of SWC measurements acquired using geophysical methods at two different depths and before and after precipitation to predict the SWC using the random forest method across a vineyard in the central United States. Both multispectral data (five reflectance bands and eleven vegetation indices calculated from these bands) and thermal UAV-based data were acquired, and the importance of different reflectance data and vegetation indices in the prediction of SWC was analyzed. Results showed that when both thermal and multispectral data were used to estimate SWC, the thermal data contributed the most to prediction accuracy, although multispectral data were also important. Reflectance data contributed as much or more to prediction accuracy than most vegetation indices. SWC measurements that had a larger sample size and greater penetration depth (~30 cm sampling depth) were more accurately predicted than smaller and shallower SWC estimates (~18 cm sampling depth). The timing of SWC estimation was also important; higher accuracy predictions were achieved in wetter soils than in drier soils, and a light precipitation event also improved prediction accuracy. Full article

(This article belongs to the Special Issue Remote Sensing of Soil Condition Assessment and Degradation Drivers Monitoring)

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17 pages, 3441 KiB

Open AccessArticle

Improving the Accuracy of Soil Organic Carbon Estimation: CWT-Random Frog-XGBoost as a Prerequisite Technique for In Situ Hyperspectral Analysis

by Jixiang Yang, Xinguo Li and Xiaofei Ma

Remote Sens. 2023, 15(22), 5294; https://doi.org/10.3390/rs15225294 - 9 Nov 2023

Cited by 12 | Viewed by 2455

Abstract

Rapid and accurate measurement of the soil organic carbon (SOC) content is a pre-condition for sustainable grain production and land development, and contributes to carbon neutrality in the agricultural industry. To provide technical support for the development and utilization of land resources, the SOC content can be estimated using Vis-NIR diffuse reflectance spectroscopy. However, the spectral redundancy and co-linearity issues of Vis-NIR spectra pose extreme challenges for spectral analysis and model construction. This study compared the effects of different pre-processing methods and feature variable algorithms on the estimation of the SOC content. To this end, in situ hyperspectral data and soil samples were collected from the lakeside oasis of Bosten Lake in Xinjiang, China. The results showed that the combination of continuous wavelet transform (CWT)-random frog could rapidly estimate the SOC content with excellent estimation accuracy (R² of 0.65–0.86). The feature variable selection algorithm effectively improved the estimation accuracy (average improvement of (0.30–0.48); based on their ability to improve model estimation on average, the algorithms can be ranked as follows: particle swarm optimization (PSO) > ant colony optimization (ACO) > random frog > Boruta > simulated annealing (SA) > successive projections algorithm (SPA). The CWT-XGBoost model based on random frog showed the best results, with R² = 0.86, RMSE = 2.44, and RPD = 2.78. The feature bands accounted for only 0.57% of the Vis-NIR bands, and the most important sensitive bands were distributed at 755–1195 nm, 1602 nm, 1673 nm, and 2213 nm. These findings are of significance for the extraction of precise information on lakeside oases in arid areas, which would aid in achieving human–land sustainability. Full article

(This article belongs to the Special Issue Remote Sensing of Soil Condition Assessment and Degradation Drivers Monitoring)

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19 pages, 10476 KiB

Open AccessArticle

Fine Resolution Mapping of Soil Organic Carbon in Croplands with Feature Selection and Machine Learning in Northeast Plain China

by Xianglin Zhang, Jie Xue, Songchao Chen, Nan Wang, Tieli Xie, Yi Xiao, Xueyao Chen, Zhou Shi, Yuanfang Huang and Zhiqing Zhuo

Remote Sens. 2023, 15(20), 5033; https://doi.org/10.3390/rs15205033 - 20 Oct 2023

Cited by 3 | Viewed by 2163

Abstract

Unsustainable human management has negative effects on cropland soil organic carbon (SOC), causing a decrease in soil health and the emission of greenhouse gas. Due to contiguous fields, large-scale mechanized operations are widely used in the Northeast China Plain, which greatly improves production efficiency while decreasing the soil quality, especially for SOC. Therefore, an up-to-date SOC map is needed to estimate soil health after long-term cultivation to inform better land management. Using Quantile Regression Forest, a total of 396 soil samples from 132 sampling sites at three soil depth intervals and 40 environmental covariates (e.g., Landsat 8 spectral indices, and WorldClim 2 and MODIS products) selected by the Boruta feature selection algorithm were used to map the spatial distribution of SOC in the cropland of the Northeast Plain at a 90 m spatial resolution. The results showed that SOC increased overall from the southern area to the northern area, with an average of 17.34 g kg⁻¹ in the plough layer (PL) and 13.92 g kg⁻¹ in the compacted layer (CL). At the vertical scale, SOC decreased, with depths getting deeper. The average decrease in SOC from PL to CL was 3.41 g kg⁻¹. Climate (i.e., average temperature, daytime and nighttime land surface temperature, and mean temperature of driest quarter) was the dominant controlling factor, followed by position (i.e., oblique geographic coordinate at 105°), and organism (i.e., the average and variance of net primary productivity in the non-crop period). The average uncertainty was 1.04 in the PL and 1.07 in the CL. The high uncertainty appeared in the area with relatively scattered fields, high altitudes, and complex landforms. This study updated the 90 m resolution cropland SOC maps at spatial and vertical scales, which clarifies the influence of mechanized operations and provides a reference for soil conservation policy-making. Full article

(This article belongs to the Special Issue Remote Sensing of Soil Condition Assessment and Degradation Drivers Monitoring)

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