Remote Sensing and Geoinformatics in Agriculture and Environment Volume II

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

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 (8 papers)

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All Agriculture Drones Geomatics Land Remote Sensing

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25 pages, 18893 KiB  

Open AccessArticle

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 231

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

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment Volume II)

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25 pages, 11619 KiB  

Open AccessArticle

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 674

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 CI_green, 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

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment Volume II)

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21 pages, 6253 KiB  

Open AccessArticle

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 694

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

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment Volume II)

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23 pages, 4937 KiB  

Open AccessReview

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 1179

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

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment Volume II)

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26 pages, 13995 KiB  

Open AccessEditor’s ChoiceArticle

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 2269

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 $({\sigma }_{VV}^0$, ${\sigma }_{VH}^0$), 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, ${\sigma }_{VH}^0$ of TerraSAR-X offered higher consistency with corn development (r = 0.81), while for canola ${\sigma }_{VH}^0$ of Sentinel-1 offered higher performance (r = 0.88). Generally, ${\sigma }_{VV}^0$, ${\sigma }_{VH}^0$ 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

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment Volume II)

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21 pages, 13167 KiB  

Open AccessArticle

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 - 07 Oct 2023

Viewed by 841

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

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment Volume II)

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27 pages, 4630 KiB  

Open AccessArticle

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 1 | Viewed by 2091

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

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment Volume II)

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20 pages, 19839 KiB  

Open AccessArticle

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 1 | Viewed by 1276

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

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment Volume II)

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