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Mapping Essential Elements of Agricultural Land Using Remote Sensing

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 13417

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Special Issue Editors

Dr. Luo Liu

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Guest Editor

College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
Interests: crop system; crop mapping; deep learning
Special Issues, Collections and Topics in MDPI journals

Dr. Jilin Yang

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Guest Editor

College of Grassland Science and Technology, China Agricultural University, Beijing 100193, China
Interests: plant ecology remote sensing; vegetation phenology; remote sensing big data

Prof. Dr. Fei Li

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Institute of Grassland Research of CAAS, Hohhot 010010, China
Interests: integrated application of satellite-space-earth remote sensing technology; multi-scale remote sensing inversion of ecological parameters at global and regional scales; carbon and water cycle of vegetation; uav remote sensing monitoring of vegetation phenotype; machine learning and application development of big data
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Dr. Yaqun Liu

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Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Interests: land use/cover change; land use monitoring and simulation; agricultural remote sensing; agricultural land use; rural human–earth system
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Dr. Wenzhi Zhao

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Institute of Remote Sensing Science and Engineering, Faculty of Geographical Sciences, Beijing Normal University, Beijing, China
Interests: artificial intelligence; multi-source remote sensing fusion; crop dynamic monitoring

Special Issue Information

Dear Colleagues,

Agricultural remote sensing technology plays a crucial role in modern agriculture, especially in identifying and monitoring key elements of agricultural land including farmland, grassland and etc.. By utilizing remote sensing data, we can effectively classify, monitor, and manage agricultural land to enhance its yield and quality. Mapping and analyzing the essential elements of agricultural land is an important task, including soil types, vegetation cover, water resource distribution, climate variance, etc. By conducting remote sensing monitoring and mapping of agricultural land, precision agricultural management can be achieved, enhancing land utilization efficiency while reducing resource waste and environmental pollution. Moreover, it can provide scientific basis for agricultural decision-making, assisting farmers in formulating more rational planting plans to improve agricultural production stability and sustainability. In summary, excavate immense potential and promising prospects in mapping key elements of farmland.

Therefore, to better excavate immense potential and promising prospects in key elements of agricultural land by leveraging remote sensing technology, this Special Issue aims to invite original and innovative research on applications of multi-source remote sensing for mapping essential elements of agricultural land using mathematical statistics, machine learning, and deep learning methods, or other state-of-the-art approaches.

The research areas may include (but are not limited to) the following:

Agricultural land thematic information mapping;
Multi-sensor imagery fusion;
Near real-time remote sensing monitoring of plant growth
Water-food-energy-environment tradeoff and synergies in agricultural land supported by remote sensing;
Agricultural soil health diagnosis;
Machine learning or deep learning for near-real-time monitoring of plant growth.

Dr. Luo Liu
Dr. Jilin Yang
Prof. Dr. Fei Li
Dr. Yaqun Liu
Dr. Wenzhi Zhao
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

agricultural land mapping
agricultural land parameter retrieval
yield estimation or forecasts
biomass in agricultural land
water use efficiency in agricultural land
plant biodiversity in agricultural land
plant growth model
deep learning

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Published Papers (7 papers)

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Research

30 pages, 10463 KiB

Open AccessArticle

Enhancing Soil Moisture Prediction in Drought-Prone Agricultural Regions Using Remote Sensing and Machine Learning Approaches

by Xizhuoma Zha, Shaofeng Jia, Yan Han, Wenbin Zhu and Aifeng Lv

Remote Sens. 2025, 17(2), 181; https://doi.org/10.3390/rs17020181 - 7 Jan 2025

Cited by 1 | Viewed by 1233

Abstract

The North China Plain is a crucial agricultural region in China, but irregular precipitation patterns have led to significant water shortages. To address this, analyzing the high-resolution dynamics of root-zone soil moisture transport is essential for optimizing irrigation strategies and improving water resource efficiency. The Richards equation is a robust model for describing soil moisture transport dynamics across multiple soil layers, yet its application at large spatial scales is hindered by its sensitivity to boundary conditions and model parameters. This study introduces a novel approach that, for the first time, employs a continuous time series of near-surface soil moisture as the upper boundary condition in the Richards equation to estimate high-resolution root-zone soil moisture in the North China Plain, thus enabling its large-scale application. Singular spectrum analysis (SSA) was first applied to reconstruct site-specific time series, filling in missing and singular values. Leveraging observational data from 617 monitoring sites across the North China Plain and multiple spatial covariates, we developed a machine learning model to estimate near-surface soil moisture at a 1 km resolution. This high-resolution, continuous near-surface soil moisture series then served as the upper boundary condition for the Richards equation, facilitating the estimation of root-zone soil moisture across the region. The results indicated that the machine learning model achieved a correlation coefficient (R) of 0.92 for estimating spatial near-surface soil moisture. Analysis of spatial covariates showed that atmospheric forcing factors, particularly temperature and evaporation, had the most substantial impact on model performance, followed by static factors such as latitude, longitude, and soil texture. With a continuous time series of near-surface soil moisture, the Richards equation method accurately predicted multi-layer soil moisture and demonstrated its applicability for large-scale spatial use. The model yielded R values of 0.97, 0.78, 0.618, and 0.43, with RMSEs of 0.024, 0.06, 0.08, and 0.11, respectively, for soil layers at depths of 10 cm, 20 cm, 40 cm, and 100 cm across the North China Plain. Full article

(This article belongs to the Special Issue Mapping Essential Elements of Agricultural Land Using Remote Sensing)

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20 pages, 21022 KiB

Open AccessArticle

Decoupling the Impacts of Climate Change and Human Activities on Terrestrial Vegetation Carbon Sink

by Shuheng Dong, Wanxia Ren, Xiaobin Dong, Fan Lei, Xue-Chao Wang, Linglin Xie and Xiafei Zhou

Remote Sens. 2024, 16(23), 4417; https://doi.org/10.3390/rs16234417 - 26 Nov 2024

Cited by 4 | Viewed by 1109

Abstract

Net ecosystem productivity (NEP) plays a vital role in quantifying the carbon exchange between the atmosphere and terrestrial ecosystems. Understanding the effects of dominant driving forces and their respective contribution rates on NEP can aid in the effective management of terrestrial carbon sinks, especially in rapidly urbanizing coastal areas where climate change (CC) and human activities (HA) occur frequently. Combining MODIS NPP products and meteorological data from 2000 to 2020, this paper established a Modis NPP-Soil heterotrophic respiration (R_h) model to estimate the magnitude of NEP in China’s coastal zone (CCZ). Hotspot analysis, variation trend, partial correlation, and residual analysis were applied to explore the spatiotemporal patterns of NEP and the contributions of CC and HA to the dynamics of NEP. We also explored the changes in NEP in different land use types. It was found that there is a clear north–south difference in the spatial pattern of NEP in CCZ, with Zhejiang Province serving as the main watershed for this difference. In addition, NEP in most regions showed an improvement trend, especially in the Beijing–Tianjin–Hebei region and Shandong Province, but the pixel values of NEP here were generally not as high as that in most southern provinces. According to the types of driving forces, the improvement of NEP in these regions primarily results from the synergistic effects of CC and HA. NEP changes in provinces south of Zhejiang are mainly dominated by single-factor-driven degradation. The area where HA contributes to the increase in NEP is much larger than that of CC. From the perspective of land use types, forests and farmland are the dominant contributors to the magnitude of NEP in CCZ. Full article

(This article belongs to the Special Issue Mapping Essential Elements of Agricultural Land Using Remote Sensing)

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28 pages, 6134 KiB

Open AccessArticle

Enhanced Blue Band Vegetation Index (The Re-Modified Anthocyanin Reflectance Index (RMARI)) for Accurate Farmland Shelterbelt Extraction

by Xinle Zhang, Jiming Liu, Linghua Meng, Chuan Qin, Zeyu An, Yihao Wang and Huanjun Liu

Remote Sens. 2024, 16(19), 3680; https://doi.org/10.3390/rs16193680 - 2 Oct 2024

Viewed by 1343

Abstract

Farmland shelterbelts are aimed at farmland protection and productivity improvement, environmental protection and ecological balance, as well as land use planning and management. Farmland shelterbelts play a vital role in determining the structural integrity and overall effectiveness of farmland, and assessing the dynamic changes within these protective forests accurately and swiftly is essential to maintaining their protective functions as well as for policy formulation and effectiveness evaluation in relevant departments. Traditional methods for extracting farmland shelterbelt information have faced significant challenges due to the large workload required and the inconsistencies in the accuracy of existing methods. For example, the existing vegetation index extraction methods often have significant errors, which remain unresolved. Therefore, developing a more efficient extraction method with greater accuracy is imperative. This study focused on Youyi Farm in Heilongjiang Province, China, utilizing satellite data with spatial resolutions ranging from 0.8 m (GF-7) to 30 m (Landsat). By taking into account the growth cycles of farmland shelterbelts and variations in crop types, the optimal temporal window for extraction is identified based on phenological analysis. The study introduced a new index—the Re-Modified Anthocyanin Reflectance Index (RMARI)—which is an improvement on existing vegetation indexes, such as the NDVI and the improved original ARI. Both the accuracy and extraction results showed significant improvements, and the feasibility of the RMARI was confirmed. The study proposed four extraction schemes for farmland shelterbelts: (1) spectral feature extraction, (2) extraction using vegetation indexes, (3) random forest extraction, and (4) RF combined with characteristic index bands. The extraction process was implemented on the GEE platform, and results from different spatial resolutions were compared. Results showed that (1) the bare soil period in May is the optimal time period for extracting farmland shelterbelts; (2) the RF method combined with characteristic index bands produces the best extraction results, effectively distinguishing shelterbelts from other land features; (3) the RMARI reduces background noise more effectively than the NDVI and ARI, resulting in more comprehensive extraction outcomes; and (4) among the satellite images analyzed—GF-7, Planet, Sentinel-2, and Landsat OLI 8—GF-7 achieves the highest extraction accuracy (with a Kappa coefficient of 0.95 and an OA of 0.97), providing the most detailed textural information. However, comprehensive analysis suggests that Sentinel-2 is more suitable for large-scale farmland shelterbelt information extraction. This study provides new approaches and technical support for periodic dynamic forestry surveys, providing valuable reference points for agricultural ecological research. Full article

(This article belongs to the Special Issue Mapping Essential Elements of Agricultural Land Using Remote Sensing)

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17 pages, 13631 KiB

Open AccessArticle

Ensemble Machine Learning on the Fusion of Sentinel Time Series Imagery with High-Resolution Orthoimagery for Improved Land Use/Land Cover Mapping

by Mukti Ram Subedi, Carlos Portillo-Quintero, Nancy E. McIntyre, Samantha S. Kahl, Robert D. Cox, Gad Perry and Xiaopeng Song

Remote Sens. 2024, 16(15), 2778; https://doi.org/10.3390/rs16152778 - 30 Jul 2024

Cited by 1 | Viewed by 2766

Abstract

In the United States, several land use and land cover (LULC) data sets are available based on satellite data, but these data sets often fail to accurately represent features on the ground. Alternatively, detailed mapping of heterogeneous landscapes for informed decision-making is possible using high spatial resolution orthoimagery from the National Agricultural Imagery Program (NAIP). However, large-area mapping at this resolution remains challenging due to radiometric differences among scenes, landscape heterogeneity, and computational limitations. Various machine learning (ML) techniques have shown promise in improving LULC maps. The primary purposes of this study were to evaluate bagging (Random Forest, RF), boosting (Gradient Boosting Machines [GBM] and extreme gradient boosting [XGB]), and stacking ensemble ML models. We used these techniques on a time series of Sentinel 2A data and NAIP orthoimagery to create a LULC map of a portion of Irion and Tom Green counties in Texas (USA). We created several spectral indices, structural variables, and geometry-based variables, reducing the dimensionality of features generated on Sentinel and NAIP data. We then compared accuracy based on random cross-validation without accounting for spatial autocorrelation and target-oriented cross-validation accounting for spatial structures of the training data set. Comparison of random and target-oriented cross-validation results showed that autocorrelation in the training data offered overestimation ranging from 2% to 3.5%. The XGB-boosted stacking ensemble on-base learners (RF, XGB, and GBM) improved model performance over individual base learners. We show that meta-learners are just as sensitive to overfitting as base models, as these algorithms are not designed to account for spatial information. Finally, we show that the fusion of Sentinel 2A data with NAIP data improves land use/land cover classification using geographic object-based image analysis. Full article

(This article belongs to the Special Issue Mapping Essential Elements of Agricultural Land Using Remote Sensing)

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23 pages, 15641 KiB

Open AccessArticle

Impacts of the Middle Route of the South-to-North Water Diversion Project on Land Surface Temperature and Fractional Vegetation Coverage in the Danjiang River Basin

by Shidong Wang, Yuanyuan Liu, Jianhua Guo, Jinping Liu and Huabin Chai

Remote Sens. 2024, 16(14), 2665; https://doi.org/10.3390/rs16142665 - 21 Jul 2024

Cited by 1 | Viewed by 1543

Abstract

The Middle Route of the South-to-North Water Diversion Project is a critical infrastructure that ensures optimal water resource distribution across river basins and safeguards the livelihood of people in China. This study investigated its effects on the land surface temperature (LST) and fractional vegetation coverage (FVC) in the Danjiang River Basin. Moreover, it examined the spatial and temporal patterns of this project, providing a scientific basis for the safe supply of water and ecological preservation. We used the improved interpolation of mean anomaly (IMA) method based on the digital elevation model (DEM) to reconstruct LST while FVC was estimated using the image element dichotomous model. Our findings indicated a general increase in the average LST in the Danjiang River Basin post-project implementation. During both wet and dry seasons, the cooling effect was primarily observed in the south-central region during the daytime, with extreme values of 6.1 °C and 5.9 °C. Conversely, during the nighttime, the cooling effect was more prevalent in the northern region, with extreme values of 3.0 °C and 2.3 °C. In contrast, the warming effect during both seasons was predominantly located in the northern region during the daytime, with extreme values of 5.3 °C and 5.5 °C. At night, the warming effect was chiefly observed in the south-central region, with extreme values of 5.8 °C and 5.9 °C. FVC displayed a seasonal trend, with higher values in the wet season and overall improvement over time. Statistical analysis revealed a negative correlation between vegetation change and daytime temperature variations in both periods (r = −0.184, r = −0.195). Furthermore, a significant positive correlation existed between vegetation change and nighttime temperature changes (r = 0.315, r = 0.328). Overall, the project contributed to regulating LST, fostering FVC development, and enhancing ecological stability in the Danjiang River Basin. Full article

(This article belongs to the Special Issue Mapping Essential Elements of Agricultural Land Using Remote Sensing)

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20 pages, 12739 KiB

Open AccessArticle

Assessment of Land Suitability Potential Using Ensemble Approaches of Advanced Multi-Criteria Decision Models and Machine Learning for Wheat Cultivation

by Kamal Nabiollahi, Ndiye M. Kebonye, Fereshteh Molani, Mohammad Hossein Tahari-Mehrjardi, Ruhollah Taghizadeh-Mehrjardi, Hadi Shokati and Thomas Scholten

Remote Sens. 2024, 16(14), 2566; https://doi.org/10.3390/rs16142566 - 12 Jul 2024

Cited by 8 | Viewed by 1903

Abstract

Land suitability assessment, as an important process in modern agriculture, involves the evaluation of numerous aspects such as soil properties, climate, relief, hydrology and socio-economic aspects. The aim of this study was to evaluate the suitability of soils for wheat cultivation in the Gavshan region, Iran, as the country is facing the task of becoming self-sufficient in wheat. Various methods were used to evaluate the land, such as multi-criteria decision-making (MCDM), which is proving to be important for land use planning. MCDM and machine learning (ML) are useful for decision-making processes because they use complicated spatial data and methods that are widely available. Using a geomorphological map, seventy soil profiles were selected and described, and ten soil properties and wheat yields were determined. Three MCDM approaches, including the technique of preference ordering by similarity to the ideal solution (TOPSIS), gray relational analysis (GRA), and simple additive weighting (SAW), were used and evaluated. The criteria weights were extracted using Shannon’s entropy method. Random forest (RF) model and auxiliary variables (remote sensing data, terrain data, and geomorphological maps) were used to represent the land suitability values. Spatial autocorrelation analysis as a statistical method was applied to analyze the spatial variability of the spatial data. Slope, CEC (cation exchange capacity), and OC (organic carbon) were the most important factors for wheat cultivation. The spatial autocorrelation between the key criteria (slope, CEC, and OC) and wheat yield confirmed these results. These results also showed a significant correlation between the land suitability values of TOPSIS, GRA, and SAW and wheat yield (0.74, 0.72, and 0.57, respectively). The spatial distribution of land suitability values showed that the areas classified as good according to TOPSIS and GRA were larger than those classified as moderate and weak according to the SAW approach. These results were also confirmed by the autocorrelation of the MCDM techniques with wheat yield. In addition, the RF model showed its effectiveness in processing complex spatial data and improved the accuracy of land suitability assessment. In this study, by integrating advanced MCDM techniques and ML, an applicable land evaluation approach for wheat cultivation was proposed, which can improve the accuracy of land suitability and be useful for considering sustainability principles in land management. Full article

(This article belongs to the Special Issue Mapping Essential Elements of Agricultural Land Using Remote Sensing)

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16 pages, 8535 KiB

Open AccessArticle

Effects of Soil Moisture and Atmospheric Vapor Pressure Deficit on the Temporal Variability of Productivity in Eurasian Grasslands

by Tianyou Zhang, Yandan Liu, Yusupukadier Zimini, Liuhuan Yuan and Zhongming Wen

Remote Sens. 2024, 16(13), 2368; https://doi.org/10.3390/rs16132368 - 28 Jun 2024

Viewed by 1789

Abstract

The grasslands in high-latitude areas are sensitive to climate warming and drought. However, the drought stress effect on the long-term variability of grassland productivity at the continental scale still hinders our understanding. Based on aboveground net primary production (ANPP) surveys, satellite remote sensing Normalized Difference Vegetation Index (NDVI), and meteorological data, we comprehensively analyzed three Aridity metrics and their effect on ANPP in Eurasian grassland from 1982 to 2020. Our results showed that the ANPP had an overall uptrend from 1982 to 2020, increasing most in the Tibetan Plateau alpine steppe subregion (TPSSR). Among three Aridity indicators, vapor pressure deficit (VPD) had an overall uptrend, while the trend of Aridity and soil moisture (SM) was insignificant from 1982 to 2020. Soil drought had negative effects on ANPP for all Eurasian grassland, while the atmospheric VPD had a positive effect on ANPP for TPSSR and the Mongolian Plateau steppe subregion (MPSSR), but a negative effect for the Black Sea–Kazakhstan steppe subregion (BKSSR) which was the driest subregion. SM had been the predominant driving factor for the interannual variability of ANPP in MPSSR since 1997. The increasing VPD had facilitated grassland productivity in alpine grasslands due to its cascading effect with an increasing temperature after 2000. The cascading effects networks of climate factors—drought factors (VPD, Aridity, and SM)—ANPP (CDA–CENet) indicated that SM was the predominant driving factor of the interannual variability of ANPP in MPSSR and BKSSR, and the dominance of SM had enhanced after the year 1997. The inhibitory effect of VPD on ANPP transformed into a facilitating effect after 1997, and the facilitating effect of SM is weakening in TPSSR. Full article

(This article belongs to the Special Issue Mapping Essential Elements of Agricultural Land Using Remote Sensing)

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