Topic Editors

Department of Natural Resources Management and Agricultural Engineering, Agricultural University of Athens, 75 Iera Odos, 11855 Athens, Greece
Laboratory of Remote Sensing, Spectroscopy and GIS, School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Department of Natural Resources Management and Agricultural Engineering, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece
Department of Natural Resources Management and Agricultural Engineering, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece

Remote Sensing and Geoinformatics in Agriculture and Environment Volume II

Abstract submission deadline
closed (30 April 2024)
Manuscript submission deadline
30 June 2024
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11910

Topic Information

Dear Colleagues,

Nowadays, the evolution of new technologies for spatial data collection—UAV drones, digital cameras, satellite data, sensors and more—their transmission in IoT technologies (Internet of Things), their emergence via Internet and their analysis through GIS provide enhanced capabilities and significant impetus for solving and confronting contemporary issues faced by agriculture, as well as by environmental sustainability. New innovative sensors carried on Earth observation instruments, tractors and field measuring devices are constantly collecting high-resolution, multitemporal and multispectral data, which supplement and integrate the data collected with more traditional approaches. GIS and other geospatial technologies shape these data into information that is accessible and interpretable by farmers and land managers to make efficient and informed decisions. At the same time, geospatial analyses of the human impact on the environment are crucial for a better understanding of the underlying relationships and processes. Advanced Earth observation technologies and geoinformatics are paving the way towards a better understanding of ecological and environmental interactions, identifying early indicators of environmental degradation and improving our capacity for risk assessment, timely forecast and response. Every single year brings much progress in remote sensing, GIS and spatial analysis methodologies and technologies in agriculture and environment. The goal of this Topic is to collect papers (original research articles and review papers) summarizing the contemporary progress and achievements of remote sensing and geoinformatics in agriculture and environment, and to highlight the recent advancements and novel applications in a wide spectrum of respective topics. This Topic will welcome manuscripts focusing on Earth observation, GIS and spatial analysis applications such as satellite data, geoinformatics and geospatial technologies, Web-GIS, GNSS and GPS, IoT, land information systems, spatial exploratory data analysis, spatial statistical models, spatial interpolation, geostatistics, neural networks and AI and the use of cloud services for the management of spatial data of large volume, in the following topics:

  • Land suitability classification;
  • Soil resources protection, land assessment and land use planning;
  • Water resources analysis, planning and management;
  • Ecosystem protection, restoration and management;
  • Forests evaluation and management;
  • Natural hazards, geohazards—natural disasters (floods, droughts, fires, landslides, etc.);
  • Spatial digital management of farms and agricultural holdings;
  • Precision agriculture, smart farming and data collection via spatial digital technologies;
  • Agricultural production and agricultural ecosystems monitoring;
  • Crop protection, pest and diseases management;
  • Weeds—invasive species;
  • Soil nutrients and fertility management;
  • Sustainable fishery through contemporary geospatial technologies’ application;
  • Livestock and pastures management;
  • Food security and food safety.

We look forward to receiving your original research articles and reviews.

Prof. Dr. Dionissios Kalivas
Dr. Thomas Alexandridis
Dr. Konstantinos X. Soulis
Dr. Emmanouil Psomiadis
Topic Editors

Keywords

  • Earth observation
  • spatial analysis
  • geoinformatics
  • GIS
  • remote sensing
  • precision agriculture
  • natural resources
  • environment

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Agriculture
agriculture
3.6 3.6 2011 17.7 Days CHF 2600 Submit
Drones
drones
4.8 6.1 2017 17.9 Days CHF 2600 Submit
Geomatics
geomatics
- - 2021 18.6 Days CHF 1000 Submit
Land
land
3.9 3.7 2012 14.8 Days CHF 2600 Submit
Remote Sensing
remotesensing
5.0 7.9 2009 23 Days CHF 2700 Submit

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Published Papers (9 papers)

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21 pages, 12535 KiB  
Article
Estimation of Maize Residue Cover Using Remote Sensing Based on Adaptive Threshold Segmentation and CatBoost Algorithm
by Nan Lin, Xunhu Ma, Ranzhe Jiang, Menghong Wu and Wenchun Zhang
Agriculture 2024, 14(5), 711; https://doi.org/10.3390/agriculture14050711 - 30 Apr 2024
Viewed by 564
Abstract
Maize residue cover (MRC) is an important parameter to quantify the degree of crop residue cover in the field and its spatial distribution characteristics. It is also a key indicator of conservation tillage. Rapid and accurate estimation of maize residue cover (MRC) and [...] Read more.
Maize residue cover (MRC) is an important parameter to quantify the degree of crop residue cover in the field and its spatial distribution characteristics. It is also a key indicator of conservation tillage. Rapid and accurate estimation of maize residue cover (MRC) and spatial mapping are of great significance to increasing soil organic carbon, reducing wind and water erosion, and maintaining soil and water. Currently, the estimation of maize residue cover in large areas suffers from low modeling accuracy and poor working efficiency. Therefore, how to improve the accuracy and efficiency of maize residue cover estimation has become a research hotspot. In this study, adaptive threshold segmentation (Yen) and the CatBoost algorithm are integrated and fused to construct a residue coverage estimation method based on multispectral remote sensing images. The maize planting areas in and around Sihe Town in Jilin Province, China, were selected as typical experimental regions, and the unmanned aerial vehicle (UAV) was employed to capture maize residue cover images of sample plots within the area. The Yen algorithm was applied to calculate and analyze maize residue cover. The successive projections algorithm (SPA) was used to extract spectral feature indices from Sentinel-2A multispectral images. Subsequently, the CatBoost algorithm was used to construct a maize residue cover estimation model based on spectral feature indices, thereby plotting the spatial distribution map of maize residue cover in the experimental area. The results show that the image segmentation based on the Yen algorithm outperforms traditional segmentation methods, with the highest Dice coefficient reaching 81.71%, effectively improving the accuracy of maize residue cover recognition in sample plots. By combining the spectral index calculation with the SPA algorithm, the spectral features of the images are effectively extracted, and the spectral feature indices such as NDTI and STI are determined. These indices are significantly correlated with maize residue cover. The accuracy of the maize residue cover estimation model built using the CatBoost model surpasses that of traditional machine learning models, with a maximum determination coefficient (R2) of 0.83 in the validation set. The maize residue cover estimation model constructed based on the Yen and CatBoost algorithms effectively enhances the accuracy and reliability of estimating maize residue cover in large areas using multispectral imagery, providing accurate and reliable data support and services for precision agriculture and conservation tillage. Full article
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25 pages, 18893 KiB  
Article
Understanding the Spatiotemporal Dynamics and Influencing Factors of the Rice–Crayfish Field in Jianghan Plain, China
by Fang Luo, Yiqing Zhang and Xiang Zhao
Remote Sens. 2024, 16(9), 1541; https://doi.org/10.3390/rs16091541 - 26 Apr 2024
Viewed by 455
Abstract
The rice–crayfish co-culture system, a representative of Agri-aqua food systems, has emerged worldwide as an effective strategy for enhancing agricultural land use efficiency and boosting sustainability, particularly in China and Southeast Asia. Despite its widespread adoption in China’s Jianghan Plain, the exact spatiotemporal [...] Read more.
The rice–crayfish co-culture system, a representative of Agri-aqua food systems, has emerged worldwide as an effective strategy for enhancing agricultural land use efficiency and boosting sustainability, particularly in China and Southeast Asia. Despite its widespread adoption in China’s Jianghan Plain, the exact spatiotemporal dynamics and factors influencing this practice in this region are yet to be clarified. Therefore, understanding the spatiotemporal dynamics and influencing factors of the rice–crayfish fields (RCFs) is crucial for promoting the rice–crayfish co-culture system, and optimizing land use policies. In this study, we identified the spatial distribution of RCF using Sentinel-2 images and land use spatiotemporal data to analyze its spatiotemporal dynamics during the period of 2016–2020. Additionally, we used the Multiscale Geographically Weighted Regression model to explore the key factors influencing RCF’s spatiotemporal changes. Our findings reveal that (1). the RCF area in Jianghan Plain expanded from 1216.04 km2 to 2429.76 km2 between 2016 and 2020, marking a 99.81% increase. (2). RCF in Jianghan Plain evolved toward a more contiguous and clustered spatial pattern, suggesting a clear industrial agglomeration in this area. (3). The expansion of the RCFs was majorly influenced by its landscape and local agricultural conditions. Significantly, the Aggregation and Landscape Shape Indexes positively impacted this expansion, whereas proximity to rural areas and towns had a negative impact. This study provides a solid foundation for promoting the rice–crayfish co-culture system and sustainably developing related industries. To ensure the sustainable development of rice–crayfish co-culture industries in Jianghan Plain, we recommend that local governments optimize the spatial layout of rural settlements, improve transportation infrastructure, and enhance regional agricultural water sources and irrigation system construction, all in line with the national strategy of rural revitalization and village planning. Additionally, promoting the concentration and contiguity of RCF through land consolidation can achieve efficient development of these industries. Full article
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25 pages, 11619 KiB  
Article
Mapping Soybean Planting Areas in Regions with Complex Planting Structures Using Machine Learning Models and Chinese GF-6 WFV Data
by Bao She, Jiating Hu, Linsheng Huang, Mengqi Zhu and Qishuo Yin
Agriculture 2024, 14(2), 231; https://doi.org/10.3390/agriculture14020231 - 31 Jan 2024
Cited by 1 | Viewed by 749
Abstract
To grasp the spatial distribution of soybean planting areas in time is the prerequisite for the work of growth monitoring, crop damage assessment and yield estimation. The research on remote sensing identification of soybean conducted in China mainly focuses on the major producing [...] Read more.
To grasp the spatial distribution of soybean planting areas in time is the prerequisite for the work of growth monitoring, crop damage assessment and yield estimation. The research on remote sensing identification of soybean conducted in China mainly focuses on the major producing areas in Northeast China, while paying little attention to the Huang-Huai-Hai region and the Yangtze River Basin, where the complex planting structures and fragmented farmland landscape bring great challenges to soybean mapping in these areas. This study used Chinese GF-6 WFV imagery acquired during the pod-setting stage of soybean in the 2019 growing season, and two counties i.e., Guoyang situated in the northern plain of Anhui Province and Mingguang located in the Jianghuai hilly regionwere selected as the study areas. Three machine learning algorithms were employed to establish soybean identification models, and the distribution of soybean planting areas in the two study areas was separately extracted. This study adopted a stepwise hierarchical extraction strategy. First, a set of filtering rules was established to eliminate non-cropland objects, so the targets of subsequent work could thereby focus on field vegetation. The focal task of this study involved the selection of well-behaved features and classifier. In addition to the 8 spectral bands, a variety of texture features, color space components, and vegetation indices were employed, and the ReliefF algorithm was applied to evaluate the importance of each candidate feature. Then, a SFS (Sequential Forward Selection) method was applied to conduct feature selection, which was performed coupled with three candidate classifiers, i.e., SVM, RF and BPNN to screen out the features conductive to soybean mapping. The accuracy evaluation results showed that, the soybean identification model generated from SVM algorithm and corresponding feature subset outperformed RF and BPNN in both two study areas. The Kappa coefficients of the ground samples in Guoyang ranged from 0.69 to 0.80, while those in Mingguang fell within the range of 0.71 to 0.76. The near-infrared band (B4) and red edge bands (B5 and B6), the ‘Mean’ texture feature and the vegetation indices, i.e., EVI, SAVI and CIgreen, demonstrated advantages in soybean identification. The feature selection operation achieved a balance between extraction accuracy and data volume, and the accuracy level could also meet practical requirements, showing a good application prospect. This method and findings of this study may serve as a reference for research on soybean identification in areas with similar planting structures, and the detailed soybean map can provide an objective and reliable basis for local agricultural departments to carry out agricultural production management and policy formulation. Full article
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21 pages, 6253 KiB  
Article
Comparative Analysis of Algorithms to Cleanse Soil Micro-Relief Point Clouds
by Simone Ott, Benjamin Burkhard, Corinna Harmening, Jens-André Paffenholz and Bastian Steinhoff-Knopp
Geomatics 2023, 3(4), 501-521; https://doi.org/10.3390/geomatics3040027 - 26 Nov 2023
Viewed by 775
Abstract
Detecting changes in soil micro-relief in farmland helps to understand degradation processes like sheet erosion. Using the high-resolution technique of terrestrial laser scanning (TLS), we generated point clouds of three 2 × 3 m plots on a weekly basis from May to mid-June [...] Read more.
Detecting changes in soil micro-relief in farmland helps to understand degradation processes like sheet erosion. Using the high-resolution technique of terrestrial laser scanning (TLS), we generated point clouds of three 2 × 3 m plots on a weekly basis from May to mid-June in 2022 on cultivated farmland in Germany. Three well-known applications for eliminating vegetation points in the generated point cloud were tested: Cloth Simulation Filter (CSF) as a filtering method, three variants of CANUPO as a machine learning method, and ArcGIS PointCNN as a deep learning method, a sub-category of machine learning using deep neural networks. We assessed the methods with hard criteria such as F1 score, balanced accuracy, height differences, and their standard deviations to the reference surface, resulting in data gaps and robustness, and with soft criteria such as time-saving capacity, accessibility, and user knowledge. All algorithms showed a low performance at the initial measurement epoch, increasing with later epochs. While most of the results demonstrate a better performance of ArcGIS PointCNN, this algorithm revealed an exceptionally low performance in plot 1, which is describable by the generalization gap. Although CANUPO variants created the highest amount of data gaps, we recommend that CANUPO include colour values in combination with CSF. Full article
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23 pages, 4937 KiB  
Review
Quantifying Aboveground Grass Biomass Using Space-Borne Sensors: A Meta-Analysis and Systematic Review
by Reneilwe Maake, Onisimo Mutanga, George Chirima and Mbulisi Sibanda
Geomatics 2023, 3(4), 478-500; https://doi.org/10.3390/geomatics3040026 - 18 Oct 2023
Viewed by 1263
Abstract
Recently, the move from cost-tied to open-access data has led to the mushrooming of research in pursuit of algorithms for estimating the aboveground grass biomass (AGGB). Nevertheless, a comprehensive synthesis or direction on the milestones achieved or an overview of how these models [...] Read more.
Recently, the move from cost-tied to open-access data has led to the mushrooming of research in pursuit of algorithms for estimating the aboveground grass biomass (AGGB). Nevertheless, a comprehensive synthesis or direction on the milestones achieved or an overview of how these models perform is lacking. This study synthesises the research from decades of experiments in order to point researchers in the direction of what was achieved, the challenges faced, as well as how the models perform. A pool of findings from 108 remote sensing-based AGGB studies published from 1972 to 2020 show that about 19% of the remote sensing-based algorithms were tested in the savannah grasslands. An uneven annual publication yield was observed with approximately 36% of the research output from Asia, whereas countries in the global south yielded few publications (<10%). Optical sensors, particularly MODIS, remain a major source of satellite data for AGGB studies, whilst studies in the global south rarely use active sensors such as Sentinel-1. Optical data tend to produce low regression accuracies that are highly inconsistent across the studies compared to radar. The vegetation indices, particularly the Normalised Difference Vegetation Index (NDVI), remain as the most frequently used predictor variable. The predictor variables such as the sward height, red edge position and backscatter coefficients produced consistent accuracies. Deciding on the optimal algorithm for estimating the AGGB is daunting due to the lack of overlap in the grassland type, location, sensor types, and predictor variables, signalling the need for standardised remote sensing techniques, including data collection methods to ensure the transferability of remote sensing-based AGGB models across multiple locations. Full article
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26 pages, 13995 KiB  
Article
Evaluation of C and X-Band Synthetic Aperture Radar Derivatives for Tracking Crop Phenological Development
by Marta Pasternak and Kamila Pawłuszek-Filipiak
Remote Sens. 2023, 15(20), 4996; https://doi.org/10.3390/rs15204996 - 17 Oct 2023
Viewed by 2371
Abstract
Due to the expanding population and the constantly changing climate, food production is now considered a crucial concern. Although passive satellite remote sensing has already demonstrated its capabilities in accurate crop development monitoring, its limitations related to sunlight and cloud cover significantly restrict [...] Read more.
Due to the expanding population and the constantly changing climate, food production is now considered a crucial concern. Although passive satellite remote sensing has already demonstrated its capabilities in accurate crop development monitoring, its limitations related to sunlight and cloud cover significantly restrict real-time temporal monitoring resolution. Considering synthetic aperture radar (SAR) technology, which is independent of the Sun and clouds, SAR remote sensing can be a perfect alternative to passive remote sensing methods. However, a variety of SAR sensors and delivered SAR indices present different performances in such context for different vegetation species. Therefore, this work focuses on comparing various SAR-derived indices from C-band and (Sentinel-1) and X-band (TerraSAR-X) data with the in situ information (phenp; pgy development, vegetation height and soil moisture) in the context of tracking the phenological development of corn, winter wheat, rye, canola, and potato. For this purpose, backscattering coefficients in VV and VH polarizations (σVV0, σVH0), interferometric coherence, and the dual pol radar vegetation index (DpRVI) were calculated. To reduce noise in time series data and evaluate which filtering method presents a higher usability in SAR phenology tracking, signal filtering, such as Savitzky–Golay and moving average, with different parameters, were employed. The achieved results present that, for various plant species, different sensors (Sentinel-1 or TerraSAR-X) represent different performances. For instance, σVH0 of TerraSAR-X offered higher consistency with corn development (r = 0.81), while for canola σVH0 of Sentinel-1 offered higher performance (r = 0.88). Generally, σVV0, σVH0 performed better than DpRVI or interferometric coherence. Time series filtering makes it possible to increase an agreement between phenology development and SAR-delivered indices; however, the Savitzky–Golay filtering method is more recommended. Besides phenological development, high correspondences can be found between vegetation height and some of SAR indices. Moreover, in some cases, moderate correlation was found between SAR indices and soil moisture. Full article
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21 pages, 13167 KiB  
Article
The Retrieval of Forest and Grass Fractional Vegetation Coverage in Mountain Regions Based on Spatio-Temporal Transfer Learning
by Yuxuan Huang, Xiang Zhou, Tingting Lv, Zui Tao, Hongming Zhang, Ruoxi Li, Mingjian Zhai and Houyu Liang
Remote Sens. 2023, 15(19), 4857; https://doi.org/10.3390/rs15194857 - 7 Oct 2023
Viewed by 928
Abstract
The vegetation cover of forests and grasslands in mountain regions plays a crucial role in regulating climate at both regional and global scales. Thus, it is necessary to develop accurate methods for estimating and monitoring fractional vegetation cover (FVC) in mountain areas. However, [...] Read more.
The vegetation cover of forests and grasslands in mountain regions plays a crucial role in regulating climate at both regional and global scales. Thus, it is necessary to develop accurate methods for estimating and monitoring fractional vegetation cover (FVC) in mountain areas. However, the complex topographic and climate factors pose significant challenges to accurately estimating the FVC of mountain forests and grassland. Existing remote sensing products, FVC retrieval methods, and FVC samples may fail to meet the required accuracy standards. In this study, we propose a method based on spatio-temporal transfer learning for the retrieval of FVC in mountain forests and grasslands, using the mountain region of Huzhu County, Qinghai Province, as the study area. The method combines simulated FVC samples, Sentinel-2 images, and mountain topographic factor data to pre-train LSTM and 1DCNN models and subsequently transfer the models to HJ-2A/B remote sensing images. The results of the study indicated the following: (1) The FVC samples generated by the proposed method (R2 = 0.7536, RMSE = 0.0596) are more accurate than those generated by the dichotomy method (R2 = 0.4997, RMSE = 0.1060) based on validation with ground truth data. (2) The LSTM model performed better than the 1DCNN model: the average R2 of the two models was 0.9275 and 0.8955; the average RMSE was 0.0653 and 0.0735. (3) Topographic features have a significant impact on FVC retrieval results, particularly in relatively high-altitude mountain regions (DEM > 3000 m) or non-growing seasons (May and October). Therefore, the proposed method has better potential in FVC fine spatio-temporal retrieval of high-resolution mountainous remote sensing images. Full article
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27 pages, 4630 KiB  
Article
Using Machine-Learning Algorithms to Predict Soil Organic Carbon Content from Combined Remote Sensing Imagery and Laboratory Vis-NIR Spectral Datasets
by Hayfa Zayani, Youssef Fouad, Didier Michot, Zeineb Kassouk, Nicolas Baghdadi, Emmanuelle Vaudour, Zohra Lili-Chabaane and Christian Walter
Remote Sens. 2023, 15(17), 4264; https://doi.org/10.3390/rs15174264 - 30 Aug 2023
Cited by 2 | Viewed by 2243
Abstract
Understanding spatial and temporal variability in soil organic carbon (SOC) content helps simultaneously assess soil fertility and several parameters that are strongly associated with it, such as structural stability, nutrient cycling, biological activity, and soil aeration. Therefore, it appears necessary to monitor SOC [...] Read more.
Understanding spatial and temporal variability in soil organic carbon (SOC) content helps simultaneously assess soil fertility and several parameters that are strongly associated with it, such as structural stability, nutrient cycling, biological activity, and soil aeration. Therefore, it appears necessary to monitor SOC regularly and investigate rapid, non-destructive, and cost-effective approaches for doing so, such as proximal and remote sensing. To increase the accuracy of predictions of SOC content, this study evaluated combining remote sensing time series with laboratory spectral measurements using machine and deep-learning algorithms. Partial least squares (PLS) regression, random forest (RF), and deep neural network (DNN) models were developed using Sentinel-2 (S2) time series of 58 sampling points of bare soil and according to three approaches. In the first approach, only S2 bands were used to calibrate and compare the performance of the models. In the second, S2 indices, Sentinel-1 (S1) indices, and S1 soil moisture were added separately during model calibration to evaluate their effects individually and then together. In the third, we added the laboratory indices incrementally and tested their influence on model accuracy. Using only S2 bands, the DNN model outperformed the PLS and RF models (ratio of performance to the interquartile distance RPIQ = 0.79, 1.36 and 1.67, respectively). Additional information improved performances only for model calibration, with S1 soil moisture yielding the most stable improvement among three iterations. Including equivalent indices of the S2 indices calculated using soil spectra obtained under laboratory conditions improved prediction of SOC, and the use of only two indices achieved good validation performances for the RF and DNN models (mean RPIQ = 2.01 and 1.77, respectively). Full article
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20 pages, 19839 KiB  
Article
Radiometric Compensation for Occluded Crops Imaged Using High-Spatial-Resolution Unmanned Aerial Vehicle System
by Naledzani Ndou, Kgabo Humphrey Thamaga, Yonela Mndela and Adolph Nyamugama
Agriculture 2023, 13(8), 1598; https://doi.org/10.3390/agriculture13081598 - 12 Aug 2023
Cited by 2 | Viewed by 1354
Abstract
Crop characterization is considered a prerequisite to devising effective strategies for ensuring successful implementation of sustainable agricultural management strategies. As such, remote-sensing technology has opened an exciting horizon for crop characterization at reasonable spatial, spectral, and temporal scales. However, the presence of shadows [...] Read more.
Crop characterization is considered a prerequisite to devising effective strategies for ensuring successful implementation of sustainable agricultural management strategies. As such, remote-sensing technology has opened an exciting horizon for crop characterization at reasonable spatial, spectral, and temporal scales. However, the presence of shadows on croplands tends to distort radiometric properties of the crops, subsequently limiting the retrieval of crop-related information. This study proposes a simple and reliable approach for radiometrically compensating crops under total occlusion using brightness-based compensation and thresholding approaches. Unmanned aerial vehicle (UAV) imagery was used to characterize crops at the experimental site. In this study, shadow was demarcated through the computation and use of mean spectral radiance values as the threshold across spectral channels of UAV imagery. Several image classifiers, viz., k-nearest neighbor (KNN), maximum likelihood, multilayer perceptron (MLP), and image segmentation, were used to categorize land features, with a view to determine the areal coverage of crops prior to the radiometric compensation process. Radiometric compensation was then performed to restore radiometric properties of land features under occlusion by performing brightness tuning on the RGB imagery. Radiometric compensation results revealed maize and soil as land features subjected to occlusion. The relative error of the mean results for radiance comparison between lit and occluded regions revealed 26.47% deviation of the restored radiance of occluded maize from that of lit maize. On the other hand, the reasonable REM value of soil was noted to be 50.92%, implying poor radiometric compensation results. Postradiometric compensation classification results revealed increases in the areal coverage of maize cultivars and soil by 40.56% and 12.37%, respectively, after being radiometrically compensated, as predicted by the KNN classifier. The maximum likelihood, MLP, and segmentation classifiers predicted increases in area covered with maize of 18.03%, 22.42%, and 30.64%, respectively. Moreover, these classifiers also predicted increases in the area covered with soil of 1.46%, 10.05%, and 14.29%, respectively. The results of this study highlight the significance of brightness tuning and thresholding approaches in radiometrically compensating occluded crops. Full article
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