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Advanced in Remote Sensing Approaches for Agricultural Monitoring at Field and Regional Scale

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: 15 April 2025 | Viewed by 6568

Special Issue Editors

Dr. Jose Antonio Dominguez-Gómez

E-Mail Website
Guest Editor
Ingeniería Topográfica y Cartografía, Universidad Politécnica de Madrid, Madrid, Spain
Interests: GIS; optical and radar remote sensing; UAV; satellite images; photogrammetry; precision agriculture
Special Issues, Collections and Topics in MDPI journals
Dr. Jitka Kumhálová

E-Mail Website
Guest Editor
Faculty of Engineering, Department Machinery Utilization, Czech University of Life Sciences Prague, Prague, Czech Republic
Interests: GIS; optical remote sensing; UAV; satellite images; photogrammetry; precision agriculture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The introduction to the Special Issue “Advancements in Remote Sensing Approaches for Agricultural Monitoring at Field and Regional Scale” provides a vital scientific background, underscoring the paramount importance of this research area. In the ever-evolving landscape of agriculture, the integration of cutting-edge technologies is crucial for enhancing precision and sustainability. Remote sensing emerges as a linchpin in this pursuit, acting as a transformative tool that enables a comprehensive understanding of agricultural systems.

The precision agriculture paradigm, incorporating techniques such as photogrammetry, UAVs, and artificial intelligence, aligns seamlessly with the capabilities of remote sensing technologies. Satellite-, drone-, and field instrument-derived data play a pivotal role in unraveling the complexities of agricultural monitoring, providing invaluable insights into yield variations, crop health, and overall ecosystem dynamics.

This introduction sets the stage for a deeper exploration of how remote sensing acts as a catalyst for innovation in the agricultural domain. By establishing the synergy between the chosen thematic focus and the capabilities of remote sensing, this Special Issue aims to propel advancements that contribute to the evolution of sustainable and ecologically conscious agricultural practices at both field and regional scales.

The incorporation of all new techniques in the use of remote sensing and in the knowledge of agriculture at the local level, with new sensors that make remote sensing behave as a source of temporal data and learning in monitoring systems using artificial intelligence, makes it necessary to exchange all types of experience with the different sensors installed on any type of platform and in this way advance ecological and sustainable agriculture.

This dedicated Special Issue, titled “Advancements in Remote Sensing Approaches for Agricultural Monitoring at Field and Regional Scales”, is currently in the planning stages for publication in the Remote Sensing journal. We invite submissions of research or review articles related to agricultural monitoring, with a specific emphasis on ecological and sustainable farming practices. Contributions should prominently feature remotely sensed data derived from satellites, drones, or field instruments as primary data sources. Researchers and experts in the field are encouraged to contribute their insights and findings to enhance our understanding of the application of remote sensing in the context of agriculture. Your valuable submissions will contribute to the comprehensive exploration of cutting-edge techniques and methodologies in the realm of agricultural monitoring.

Dr. Jose Antonio Dominguez-Gómez
Dr. Jitka Kumhálová
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

  • precision agriculture
  • photogrammetry
  • UAV
  • yield monitoring
  • crop monitoring
  • agricultural monitoring
  • artificial intelligence

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Published Papers (6 papers)

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Research

13 pages, 9487 KiB  
Article
Cotton Yield Prediction via UAV-Based Cotton Boll Image Segmentation Using YOLO Model and Segment Anything Model (SAM)
by Janvita Reddy, Haoyu Niu, Jose L. Landivar Scott, Mahendra Bhandari, Juan A. Landivar, Craig W. Bednarz and Nick Duffield
Remote Sens. 2024, 16(23), 4346; https://doi.org/10.3390/rs16234346 - 21 Nov 2024
Viewed by 177
Abstract
Accurate cotton yield prediction is essential for optimizing agricultural practices, improving storage management, and efficiently utilizing resources like fertilizers and water, ultimately benefiting farmers economically. Traditional yield estimation methods, such as field sampling and cotton weighing, are time-consuming and labor intensive. Emerging technologies [...] Read more.
Accurate cotton yield prediction is essential for optimizing agricultural practices, improving storage management, and efficiently utilizing resources like fertilizers and water, ultimately benefiting farmers economically. Traditional yield estimation methods, such as field sampling and cotton weighing, are time-consuming and labor intensive. Emerging technologies provide a solution by offering farmers advanced forecasting tools that can significantly enhance production efficiency. In this study, the authors employ segmentation techniques on cotton crops collected using unmanned aerial vehicles (UAVs) to predict yield. The authors apply Segment Anything Model (SAM) for semantic segmentation, combined with You Only Look Once (YOLO) object detection, to enhance the cotton yield prediction model performance. By correlating segmentation outputs with yield data, we implement a linear regression model to predict yield, achieving an R2 value of 0.913, indicating the model’s reliability. This approach offers a robust framework for cotton yield prediction, significantly improving accuracy and supporting more informed decision-making in agriculture. Full article
(This article belongs to the Special Issue Advanced in Remote Sensing Approaches for Agricultural Monitoring at Field and Regional Scale)
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24 pages, 6494 KiB  
Article
Reconstruction of Fine-Spatial-Resolution FY-3D-Based Vegetation Indices to Achieve Farmland-Scale Winter Wheat Yield Estimation via Fusion with Sentinel-2 Data
by Xijia Zhou, Tao Wang, Wei Zheng, Mingwei Zhang and Yuanyuan Wang
Remote Sens. 2024, 16(22), 4143; https://doi.org/10.3390/rs16224143 - 6 Nov 2024
Viewed by 559
Abstract
The spatial resolution (250–1000 m) of the FY-3D MERSI is too coarse for agricultural monitoring at the farmland scale (20–30 m). To achieve the winter wheat yield (WWY) at the farmland scale, based on FY-3D, a method framework is developed in this work. [...] Read more.
The spatial resolution (250–1000 m) of the FY-3D MERSI is too coarse for agricultural monitoring at the farmland scale (20–30 m). To achieve the winter wheat yield (WWY) at the farmland scale, based on FY-3D, a method framework is developed in this work. The enhanced deep convolutional spatiotemporal fusion network (EDCSTFN) was used to perform a spatiotemporal fusion on the 10 day interval FY-3D and Sentinel-2 vegetation indices (VIs), which were compared with the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). In addition, a BP neural network was built to calculate the farmland-scale WWY based on the fused VIs, and the Aqua MODIS gross primary productivity product was used as ancillary data for WWY estimation. The results reveal that both the EDCSTFN and ESTARFM achieve satisfactory precision in the fusion of the Sentinel-2 and FY-3D VIs; however, when the period of spatiotemporal data fusion is relatively long, the EDCSTFN can achieve greater precision than ESTARFM. Finally, the WWY estimation results based on the fused VIs show remarkable correlations with the WWY data at the county scale and provide abundant spatial distribution details about the WWY, displaying great potential for accurate farmland-scale WWY estimations based on reconstructed fine-spatial-temporal-resolution FY-3D data. Full article
(This article belongs to the Special Issue Advanced in Remote Sensing Approaches for Agricultural Monitoring at Field and Regional Scale)
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21 pages, 10239 KiB  
Article
Accurate Estimation of Gross Primary Production of Paddy Rice Cropland with UAV Imagery-Driven Leaf Biochemical Model
by Xiaolong Hu, Liangsheng Shi, Lin Lin, Shenji Li, Xianzhi Deng, Jinmin Li, Jiang Bian, Chenye Su, Shuai Du, Tinghan Wang, Yujie Wang and Zhitao Zhang
Remote Sens. 2024, 16(20), 3906; https://doi.org/10.3390/rs16203906 - 21 Oct 2024
Viewed by 774
Abstract
Accurate estimation of gross primary production (GPP) of paddy rice fields is essential for understanding cropland carbon cycles, yet remains challenging due to spatial heterogeneity. In this study, we integrated high-resolution unmanned aerial vehicle (UAV) imagery into a leaf biochemical properties-based model for [...] Read more.
Accurate estimation of gross primary production (GPP) of paddy rice fields is essential for understanding cropland carbon cycles, yet remains challenging due to spatial heterogeneity. In this study, we integrated high-resolution unmanned aerial vehicle (UAV) imagery into a leaf biochemical properties-based model for improving GPP estimation. The key parameter, maximum carboxylation rate at the top of the canopy (Vcmax,025), was quantified using various spatial information representation methods, including mean (μref) and standard deviation (σref) of reflectance, gray-level co-occurrence matrix (GLCM)-based features, local binary pattern histogram (LBPH), and convolutional neural networks (CNNs). Our models were evaluated using a two-year eddy covariance (EC) system and UAV measurements. The result shows that incorporating spatial information can vastly improve the accuracy of Vcmax,025 and GPP estimation. CNN methods achieved the best Vcmax,025 estimation, with an R of 0.94, an RMSE of 19.44 μmol m−2 s−1, and an MdAPE of 11%, and further produced highly accurate GPP estimates, with an R of 0.92, an RMSE of 6.5 μmol m−2 s−1, and an MdAPE of 23%. The μref-GLCM texture features and μref-LBPH joint-driven models also gave promising results. However, σref contributed less to Vcmax,025 estimation. The Shapley value analysis revealed that the contribution of input features varied considerably across different models. The CNN model focused on nir and red-edge bands and paid much attention to the subregion with high spatial heterogeneity. The μref-LBPH joint-driven model mainly prioritized reflectance information. The μref-GLCM-based features joint-driven model emphasized the role of GLCM texture indices. As the first study to leverage the spatial information from high-resolution UAV imagery for GPP estimation, our work underscores the critical role of spatial information and provides new insight into monitoring the carbon cycle. Full article
(This article belongs to the Special Issue Advanced in Remote Sensing Approaches for Agricultural Monitoring at Field and Regional Scale)
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18 pages, 8322 KiB  
Article
At Which Overpass Time Do ECOSTRESS Observations Best Align with Crop Health and Water Rights?
by Benjamin D. Goffin, Carlos Calvo Cortés-Monroy, Fernando Neira-Román, Diya D. Gupta and Venkataraman Lakshmi
Remote Sens. 2024, 16(17), 3174; https://doi.org/10.3390/rs16173174 - 28 Aug 2024
Viewed by 701
Abstract
Agroecosystems are facing the adverse effects of climate change. This study explored how the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) can give new insight into irrigation allocation and plant health. Leveraging the global coverage and 70-m spatial resolution of the [...] Read more.
Agroecosystems are facing the adverse effects of climate change. This study explored how the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) can give new insight into irrigation allocation and plant health. Leveraging the global coverage and 70-m spatial resolution of the Evaporative Stress Index (ESI) from ECOSTRESS, we processed over 200 overpasses and examined patterns over 3 growing seasons across the Maipo River Basin of Central Chile, which faces exacerbated water stress. We found that ECOSTRESS ESI varies substantially based on the overpass time, with ESI values being systematically higher in the morning and lower in the afternoon. We also compared variations in ESI against spatial patterns in the environment. To that end, we analyzed the vegetation greenness sensed from Landsat 8 and compiled the referential irrigation allocation from Chilean water regulators. Consistently, we found stronger correlations between these variables and ESI in the morning time (than in the afternoon). Based on our findings, we discussed new insights and potential applications of ECOSTRESS ESI in support of improved agricultural monitoring and sustainable water management. Full article
(This article belongs to the Special Issue Advanced in Remote Sensing Approaches for Agricultural Monitoring at Field and Regional Scale)
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18 pages, 3862 KiB  
Article
Spatial Distribution of Cropping Systems in South Asia Using Time-Series Satellite Data Enriched with Ground Data
by Murali Krishna Gumma, Pranay Panjala, Sunil K. Dubey, Deepak K. Ray, C. S. Murthy, Dakshina Murthy Kadiyala, Ismail Mohammed and Yamano Takashi
Remote Sens. 2024, 16(15), 2733; https://doi.org/10.3390/rs16152733 - 26 Jul 2024
Viewed by 2204
Abstract
A cropping system practice is the sequential cultivation of crops in different crop seasons of a year. Cropping system practices determine the land productivity and sustainability of agriculture in regions and, therefore, information on cropping systems of different regions in the form of [...] Read more.
A cropping system practice is the sequential cultivation of crops in different crop seasons of a year. Cropping system practices determine the land productivity and sustainability of agriculture in regions and, therefore, information on cropping systems of different regions in the form of maps and statistics form critical inputs in crop planning for optimal use of resources. Although satellite-based crop mapping is widely practiced, deriving cropping systems maps using satellites is less reported. Here, we developed moderate-resolution maps of the major cropping systems of South Asia for the year 2014–2015 using multi-temporal satellite data together with a spectral matching technique (SMT) developed with an extensive set of field observation data supplemented with expert-identified crops in high-resolution satellite images. We identified and mapped 27 major cropping systems of South Asia at 250 m spatial resolution. The rice-wheat cropping system is the dominant system, followed by millet-wheat and soybean-wheat. The map showing the cropping system practices of regions opens up many use cases related to the agriculture performance of the regions. Comparison of such maps of different time periods offers insights on sensitive regions and analysis of such maps in conjunction with resources maps such as climate, soil, etc., enables optimization of resources vis-à-vis enhancing land productivity. Thus, the current study offers new opportunities to revisit the cropping system practices and redesign the same to meet the challenges of food security and climate resilient agriculture. Full article
(This article belongs to the Special Issue Advanced in Remote Sensing Approaches for Agricultural Monitoring at Field and Regional Scale)
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Graphical abstract

18 pages, 2332 KiB  
Article
Early Season Forecasting of Corn Yield at Field Level from Multi-Source Satellite Time Series Data
by Johann Desloires, Dino Ienco and Antoine Botrel
Remote Sens. 2024, 16(9), 1573; https://doi.org/10.3390/rs16091573 - 28 Apr 2024
Viewed by 1358
Abstract
Crop yield forecasting during an ongoing season is crucial to ensure food security and commodity markets. For this reason, here, a scalable approach to forecast corn yields at the field-level using machine learning and satellite imagery from Sentinel-2 and Landsat missions is proposed. [...] Read more.
Crop yield forecasting during an ongoing season is crucial to ensure food security and commodity markets. For this reason, here, a scalable approach to forecast corn yields at the field-level using machine learning and satellite imagery from Sentinel-2 and Landsat missions is proposed. The model, evaluated on 1319 corn fields in the U.S. Corn Belt from 2017 to 2022, integrates biophysical parameters from Sentinel-2, Land Surface Temperature (LST) from Landsat, and agroclimatic data from ERA5 reanalysis dataset. Resampling the time series over thermal time significantly enhances predictive performance. The addition of LST to our model further improves in-season yield forecasting, through its capacity to detect early drought, which is not immediately visible to optical sensors such as the Sentinel-2. Finally, we propose a new two-stage machine learning strategy to mitigate early season partially available data. It consists in extending the current time series on the basis of complete historical data and adapting the model inference according to the crop progress. Full article
(This article belongs to the Special Issue Advanced in Remote Sensing Approaches for Agricultural Monitoring at Field and Regional Scale)
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