Applications of Remote Sensing in Agricultural Soil and Crop Mapping

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Digital Agriculture".

Deadline for manuscript submissions: 25 January 2025 | Viewed by 4304

Special Issue Editors


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Guest Editor
USDA-ARS Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USA
Interests: crop mapping; crop condition monitoring; machine learning; geospatial analysis; web system development; irrigation management;
Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA 22030, USA
Interests: remote sensing; agro-geoinformatics; environmental modeling; geospatial information interoperability and standards; cyberinfrastructure; digital twin; AI/machine learning; image processing and analysis; pattern recognition; crop mapping
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Special Issue Information

Dear Colleagues,

This Special Issue will explore the multifaceted applications of remote sensing technologies for agricultural soil and crop mapping. Remote sensing has emerged as a powerful tool, offering unprecedented capabilities for monitoring, assessing, and managing agricultural landscapes. This Special Issue studies the latest advancements, methodologies, and case studies that showcase the diverse applications of remote sensing for enhancing precision agriculture and sustainable farming practices. Example topics addressed in this issue include, but are not limited to, advances in these fields:   

  • Advanced remote sensing techniques: we will explore cutting-edge methodologies and technologies employed in remote sensing, such as satellite imagery, unmanned aerial vehicles (UAVs), and hyperspectral imaging, as well as their roles in improving the accuracy and efficiency of agricultural mapping.
  • Integration with geospatial technologies: we will examine the synergies between remote sensing and geospatial technologies, such as geographic information systems (GIS) and global positioning systems (GPS), to provide comprehensive solutions for precise soil and crop mapping.
  • Crop mapping and health monitoring: We will investigate the use of remote sensing for early in-season crop type mapping, real-time monitoring of crop health, disease detection, and stress assessment. We will also highlight innovative approaches that contribute to the early identification and mitigation of potential crop productivity threats.
  • Soil quality assessment: We will address the application of remote sensing for assessing soil properties, nutrient levels, and moisture contents. We will also explore how these insights can be utilized for precision farming, optimizing resource allocation, and promoting sustainable soil management practices.
  • Data fusion and machine learning: We will explore the integration of remote sensing data with machine learning algorithms for data fusion and analysis. We will also showcase successful applications of artificial intelligence for extracting meaningful insights from large datasets to improve decision-making in agriculture.
  • Case studies and practical applications: We will present real-world case studies and success stories that demonstrate the practical implementation of remote sensing in agricultural settings. We will also highlight their impacts on yield optimization, resource efficiency, and overall sustainability.

For this Special Issue, we request contributions from researchers, scientists, and practitioners in the field to foster a deeper understanding of the role of remote sensing in transforming agricultural practices. By bringing together diverse perspectives and innovative approaches, we seek to contribute to the ongoing discourse on how to best leverage technology for the advancement of sustainable agriculture.

Dr. Haoteng Zhao
Dr. Chen Zhang
Guest Editors

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Keywords

  • remote sensing
  • soil moisture
  • soil properties
  • crop mapping
  • crop health monitoring
  • yield prediction
  • crop phenology
  • machine learning
  • data fusion

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Published Papers (5 papers)

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Research

21 pages, 3434 KiB  
Article
Identifying Changes and Their Drivers in Paddy Fields of Northeast China: Past and Future
by Xuhua Hu, Yang Xu, Peng Huang, Dan Yuan, Changhong Song, Yingtao Wang, Yuanlai Cui and Yufeng Luo
Agriculture 2024, 14(11), 1956; https://doi.org/10.3390/agriculture14111956 - 31 Oct 2024
Abstract
Northeast China plays a crucial role as a major grain-producing region, and attention to its land use and land cover changes (LUCC), especially farmland changes, are crucial to ensure food security and promote sustainable development. Based on the Moderate Resolution Imaging Spectroradiometer (MODIS) [...] Read more.
Northeast China plays a crucial role as a major grain-producing region, and attention to its land use and land cover changes (LUCC), especially farmland changes, are crucial to ensure food security and promote sustainable development. Based on the Moderate Resolution Imaging Spectroradiometer (MODIS) data and a decision tree model, land types, especially those of paddy fields in Northeast China from 2000 to 2020, were extracted, and the spatiotemporal changes in paddy fields and their drivers were analyzed. The development trends of paddy fields under different future scenarios were explored alongside the Coupled Model Intercomparison Project Phase 6 (CMIP6) data. The findings revealed that the kappa coefficients of land use classification from 2000 to 2020 reached 0.761–0.825, with an overall accuracy of 80.5–87.3%. The proposed land classification method can be used for long-term paddy field monitoring in Northeast China. The LUCC in Northeast China is dominated by the expansion of paddy fields. The centroids of paddy fields gradually shifted toward the northeast by a distance of 292 km, with climate warming being the main reason for the shift. Under various climate scenarios, the temperature in Northeast China and its surrounding regions is projected to rise. Each scenario is anticipated to meet the temperature conditions necessary for the northeastward expansion of paddy fields. This study provides support for ensuring sustainable agricultural development in Northeast China. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Agricultural Soil and Crop Mapping)
18 pages, 6721 KiB  
Article
Rice Yield Estimation Using Machine Learning and Feature Selection in Hilly and Mountainous Chongqing, China
by Li Fan, Shibo Fang, Jinlong Fan, Yan Wang, Linqing Zhan and Yongkun He
Agriculture 2024, 14(9), 1615; https://doi.org/10.3390/agriculture14091615 - 14 Sep 2024
Viewed by 862
Abstract
To investigate effective techniques for estimating rice production in hilly and mountainous areas, in this study, we collected yield data at the field level, agro-meteorological data, and Sentinel-2/MSI remote sensing data in Chongqing, China, between 2020 and 2023. The integral values of vegetation [...] Read more.
To investigate effective techniques for estimating rice production in hilly and mountainous areas, in this study, we collected yield data at the field level, agro-meteorological data, and Sentinel-2/MSI remote sensing data in Chongqing, China, between 2020 and 2023. The integral values of vegetation indicators from the rice greening up to heading–filling stages were determined using the Newton–trapezoidal integration method. Using correlation analysis and importance analysis of permutation features, the effects of agro-meteorological variables and vegetation index integrals on rice yield were assessed. The chosen characteristics were then combined with three machine learning techniques—random forest (RF), support vector machine (SVM), and partial least squares regression (PLSR)—to create six rice yield estimate models. The results showed that combined vegetation indices were more effective than indices used in separate development phases. Specifically, the correlation coefficients between the integral values of eight vegetation indices from rice greening up to heading–filling stages and rice yield were all above 0.65. By introducing agro-meteorological factors as new independent variables and combining them with vegetation indices as input parameters, the predictive capability of the model was evaluated. The results showed that the performance of PLSR remained stable, while the prediction accuracies of SVM and RF improved by 13% to 21.5%. After feature selection, the inversion performance of all three machine learning models improved, with the RF model coupled with variables selected during permutation feature importance analysis achieving the optimal inversion effect, which was characterized by a coefficient of determination of 0.85, a root mean square error of 529.1 kg/hm2, and a mean relative error of 5.63%. This study provides technical support for improving the accuracy of remote sensing-based crop yield estimation in hilly and mountainous regions, facilitating precise agricultural management and informing agrarian decision making. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Agricultural Soil and Crop Mapping)
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19 pages, 8921 KiB  
Article
A Method for Cropland Layer Extraction in Complex Scenes Integrating Edge Features and Semantic Segmentation
by Yihang Lu, Lin Li, Wen Dong, Yizhen Zheng, Xin Zhang, Jinzhong Zhang, Tao Wu and Meiling Liu
Agriculture 2024, 14(9), 1553; https://doi.org/10.3390/agriculture14091553 - 8 Sep 2024
Viewed by 691
Abstract
Cultivated land is crucial for food production and security. In complex environments like mountainous regions, the fragmented nature of the cultivated land complicates rapid and accurate information acquisition. Deep learning has become essential for extracting cultivated land but faces challenges such as edge [...] Read more.
Cultivated land is crucial for food production and security. In complex environments like mountainous regions, the fragmented nature of the cultivated land complicates rapid and accurate information acquisition. Deep learning has become essential for extracting cultivated land but faces challenges such as edge detail loss and limited adaptability. This study introduces a novel approach that combines geographical zonal stratification with the temporal characteristics of medium-resolution remote sensing images for identifying cultivated land. The methodology involves geographically zoning and stratifying the study area, and then integrating semantic segmentation and edge detection to analyze remote sensing images and generate initial extraction results. These results are refined through post-processing with medium-resolution imagery classification to produce a detailed map of the cultivated land distribution. The method achieved an overall extraction accuracy of 95.07% in Tongnan District, with specific accuracies of 92.49% for flat cultivated land, 96.18% for terraced cultivated land, 93.80% for sloping cultivated land, and 78.83% for forest intercrop land. The results indicate that, compared to traditional methods, this approach is faster and more accurate, reducing both false positives and omissions. This paper presents a new methodological framework for large-scale cropland mapping in complex scenarios, offering valuable insights for subsequent cropland extraction in challenging environments. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Agricultural Soil and Crop Mapping)
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30 pages, 11567 KiB  
Article
Gini Coefficient-Based Feature Learning for Unsupervised Cross-Domain Classification with Compact Polarimetric SAR Data
by Xianyu Guo, Junjun Yin, Kun Li and Jian Yang
Agriculture 2024, 14(9), 1511; https://doi.org/10.3390/agriculture14091511 - 3 Sep 2024
Viewed by 661
Abstract
Remote sensing image classification usually needs many labeled samples so that the target nature can be fully described. For synthetic aperture radar (SAR) images, variations of the target scattering always happen to some extent due to the imaging geometry, weather conditions, and system [...] Read more.
Remote sensing image classification usually needs many labeled samples so that the target nature can be fully described. For synthetic aperture radar (SAR) images, variations of the target scattering always happen to some extent due to the imaging geometry, weather conditions, and system parameters. Therefore, labeled samples in one image could not be suitable to represent the same target in other images. The domain distribution shift of different images reduces the reusability of the labeled samples. Thus, exploring cross-domain interpretation methods is of great potential for SAR images to improve the reuse rate of existing labels from historical images. In this study, an unsupervised cross-domain classification method is proposed that utilizes the Gini coefficient to rank the robust and stable polarimetric features in both the source and target domains (GRFST) such that an unsupervised domain adaptation (UDA) can be achieved. This method selects the optimal features from both the source and target domains to alleviate the domain distribution shift. Both fully polarimetric (FP) and compact polarimetric (CP) SAR features are explored for crop-domain terrain type classification. Specifically, the CP mode refers to the hybrid dual-pol mode with an arbitrary transmitting ellipse wave. This is the first attempt in the open literature to investigate the representing abilities of different CP modes for cross-domain terrain classification. Experiments are conducted from four aspects to demonstrate the performance of CP modes for cross-data, cross-scene, and cross-crop type classification. Results show that the GRFST-UDA method yields a classification accuracy of 2% to 12% higher than the traditional UDA methods. The degree of scene similarity has a certain impact on the accuracy of cross-domain crop classification. It was also found that when both the FP and circular CP SAR data are used, stable, promising results can be achieved. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Agricultural Soil and Crop Mapping)
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21 pages, 10573 KiB  
Article
Spatial Mapping of Soil CO2 Flux in the Yellow River Delta Farmland of China Using Multi-Source Optical Remote Sensing Data
by Wenqing Yu, Shuo Chen, Weihao Yang, Yingqiang Song and Miao Lu
Agriculture 2024, 14(9), 1453; https://doi.org/10.3390/agriculture14091453 - 25 Aug 2024
Viewed by 868
Abstract
The spatial prediction of soil CO2 flux is of great significance for assessing regional climate change and high-quality agricultural development. Using a single satellite to predict soil CO2 flux is limited by climatic conditions and land cover, resulting in low prediction [...] Read more.
The spatial prediction of soil CO2 flux is of great significance for assessing regional climate change and high-quality agricultural development. Using a single satellite to predict soil CO2 flux is limited by climatic conditions and land cover, resulting in low prediction accuracy. To this end, this study proposed a strategy of multi-source spectral satellite coordination and selected seven optical satellite remote sensing data sources (i.e., GF1-WFV, GF6-WFV, GF4-PMI, CB04-MUX, HJ2A-CCD, Sentinel 2-L2A, and Landsat 8-OLI) to extract auxiliary variables (i.e., vegetation indices and soil texture features). We developed a tree-structured Parzen estimator (TPE)-optimized extreme gradient boosting (XGBoost) model for the prediction and spatial mapping of soil CO2 flux. SHapley additive explanation (SHAP) was used to analyze the driving effects of auxiliary variables on soil CO2 flux. A scatter matrix correlation analysis showed that the distributions of auxiliary variables and soil CO2 flux were skewed, and the linear correlations between them (r < 0.2) were generally weak. Compared with single-satellite variables, the TPE-XGBoost model based on multiple-satellite variables significantly improved the prediction accuracy (RMSE = 3.23 kg C ha−1 d−1, R2 = 0.73), showing a stronger fitting ability for the spatial variability of soil CO2 flux. The spatial mapping results of soil CO2 flux based on the TPE-XGBoost model revealed that the high-flux areas were mainly concentrated in eastern and northern farmlands. The SHAP analysis revealed that PC2 and the TCARI of Sentinel 2-L2A and the TVI of HJ2A-CCD had significant positive driving effects on the prediction accuracy of soil CO2 flux. The above results indicate that the integration of multiple-satellite data can enhance the reliability and accuracy of spatial predictions of soil CO2 flux, thereby supporting regional agricultural sustainable development and climate change response strategies. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Agricultural Soil and Crop Mapping)
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