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Remote Sensing Technology for Agricultural and Land Management

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Environmental Sensing".

Deadline for manuscript submissions: 25 May 2026 | Viewed by 12611

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Prof. Dr. Paul Krause

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Computer Science Research Centre, University of Surrey, Guildford GU2 7XH, UK
Interests: machine learning; good old fashioned AI; ecological modelling; medical AI; AI and environmental modelling/monitoring
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Special Issue Information

Dear Colleagues,

With climate change and biodiversity crises, we need to rethink the way we perform agriculture and land management. Working with nature rather than trying to control it has the potential to increase carbon capture and biodiversity in agricultural units without sacrificing productivity. With regard to the management of the wider landscape, practices in “rewilding” are showing their potential to enhance the ecosystem services such as flood control, water quality, pollination, and amenity that wild spaces provide.

We are still learning how to manage this change effectively. Continuous assessment of land holdings is needed in order to ensure that the management goals are being met. This can only be achieved cost-effectively at scale through the use of remote sensing. This Special Issue will focus on techniques and case studies that build an experience base for the use of remote sensing to manage agricultural units and natural spaces in an environmentally positive and sustainable way.

Submissions of research papers, case studies, and review articles are welcome.

Prof. Dr. Paul Krause
Guest Editor

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Keywords

sensor networks
Internet of Things
environmental monitoring
satellite image analysis
ecological intensification
conservation agriculture
ecosystem services
biodiversity assessment

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Published Papers (7 papers)

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Research

24 pages, 9623 KB

Open AccessArticle

Significant Land Cover Transitions and Regional Acceleration at the Continental Scale of Africa over the Last Four Decades

by Hidayat Ullah, Wilson Kalisa, Shawkat Ali, Delong Kong and Jiahua Zhang

Sensors 2026, 26(8), 2318; https://doi.org/10.3390/s26082318 - 9 Apr 2026

Viewed by 328

Abstract

Land cover (LC) change is reshaping terrestrial ecosystems and profoundly impacting sustainable development in Africa, yet the long-term, continental-scale spatiotemporal dynamics of these shifts remain obscured. To address the above issue, this study systematically explores the spatiotemporal dynamics of LC across Africa from 1985 to 2022 by leveraging the fine-resolution remote-sensing-derived GLC_FCS30D LC dataset within a stratified Intensity Analysis framework. To decompose landscape changes into interval, category, and transition levels across five climatic sub-regions of Africa, we systematically evaluate the temporal consistency of land systems. This hierarchical approach disentangles systematic transition pathways from random fluctuations, thereby revealing the distinct regional regimes governing continental transformation of LC. Our results ultimately show a strong LC change acceleration in Africa after 2010, mainly in Southern, Eastern, and Western Africa, which together made up 80 to 90% of the continent’s LC dynamics. During the whole study period, shrubland and grassland had the highest gross turnover due to their high bidirectional volatility. Intensity-wise, forest remained inactive even though it was a persistent net loser to crop in East Africa (2010–2020), to shrub in Southern Africa (1990–2022), and to wetland in West Africa during the post-2000 intervals. Wetland had a major change in dynamics from historical growth during 1985–1990 to systematic decline in 2015–2022. Cropland increased by systematically targeting shrubland and grassland, mainly in East Africa. Additionally, the Sahel contributed 40% of continental grassland to bare area transitions, despite some recovery of grassland in the region. These findings show that aggregate net-change metrics obscure the volatility in African LC; therefore, distinct regional regimes such as agricultural expansion and forest degradation necessitate spatially differentiated management strategies. Full article

(This article belongs to the Special Issue Remote Sensing Technology for Agricultural and Land Management)

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21 pages, 5982 KB

Open AccessArticle

Evaluating Geostationary Satellite-Based Approaches for NDVI Gap Filling in Polar-Orbiting Satellite Observations

by Han-Sol Ryu, Sung-Joo Yoon, Jinyeong Kim and Tae-Ho Kim

Sensors 2026, 26(5), 1731; https://doi.org/10.3390/s26051731 - 9 Mar 2026

Viewed by 419

Abstract

The Normalized Difference Vegetation Index (NDVI) derived from polar-orbiting satellites is widely used for vegetation monitoring; however, its temporal continuity is often limited by cloud contamination and fixed revisit cycles. To address this limitation, this study investigates the feasibility of using geostationary satellite observations to enhance the spatial completeness of Sentinel-2 NDVI at its standard revisit intervals through cloud gap-filling applications. Geostationary Ocean Color Imager II (GOCI-II) data (250 m) was used as input, while Sentinel-2 Multispectral Instrument (MSI) NDVI (10 m) served as the reference dataset. To enable cross-sensor integration, a data-driven transformation framework was developed to convert GOCI-II NDVI into MSI-like NDVI while preserving dominant spatial variation patterns rather than pursuing strict pixel-level super-resolution. The transformed NDVI was assessed through spatial comparisons and statistical metrics, including correlation coefficient, mean absolute error, root mean square error (RMSE), normalized RMSE, and structural similarity index measure. Results show that geostationary-derived NDVI captures broad spatial organization and field-scale variability observed in MSI NDVI. Building on this cross-scale consistency, cloud gap-filling experiments demonstrate that temporally adjacent transformed NDVI scenes maintain consistent variation patterns, supporting their complementary use for compensating cloud-induced gaps. Although reduced contrast and magnitude-dependent biases remain, primarily due to the large spatial resolution difference and sub-pixel heterogeneity, an intermediate-resolution (80 m) sensitivity analysis indicates improved stability when the resolution gap is reduced. Overall, these findings highlight the practical potential of integrating geostationary and polar-orbiting observations to improve NDVI spatial continuity in cloud-prone regions. Full article

(This article belongs to the Special Issue Remote Sensing Technology for Agricultural and Land Management)

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24 pages, 9966 KB

Open AccessArticle

A Cross-Layer Feature Fusion Framework with Hierarchical Interaction for Remote Sensing Change Detection

by Xin Meng, Chuanbiao Qiu, Chong Liu and Yanli Xu

Sensors 2026, 26(4), 1176; https://doi.org/10.3390/s26041176 - 11 Feb 2026

Viewed by 446

Abstract

The rapid progress of remote sensing (RS) and computer vision has greatly advanced change detection (CD), and hybrid architectures combining Transformers and convolutional neural networks (CNNs) have shown strong potential in recent years. Nevertheless, reliable CD for very high-resolution (VHR) imagery remains challenging due to large appearance variations across acquisition times, complex background clutter, and target structural diversity. These factors often hinder the modeling of fine edge textures, the maintenance of feature continuity, and the suppression of false changes caused by illumination fluctuations. To address these issues, this paper proposes a Cross-layer Feature Fusion Framework (CLFF) that achieves more accurate and stable change detection by explicitly enhancing the collaborative fusion capability of multi-layer features. The core component of this framework is the Multi-level Interaction Perception Block (MP-Block), which organizes effective interactions among features of different semantic levels. Based on the embedded Multi-branch Interaction Fusion Mechanism (MIFM), the MP-Block accomplishes collaborative refinement and reorganization of cross-layer features through two parallel paths for feature reconstruction and recalibration: the Response-aware Feature Reconstruction Branch (RFRB) and Adaptive Channel Group Fusion Branch (ACGF). Additionally, a lightweight position-aware attention module is introduced to adaptively modulate spatial responses, further suppressing background interference and highlighting key information related to changes. This method effectively mitigates the limitations of traditional CNNs, such as limited receptive fields and insufficient multi-layer feature interaction, while significantly enhancing the ability to collaboratively model multi-layer contextual information. To verify its effectiveness, systematic experiments were conducted on four widely used change detection benchmark datasets: LEVIR, WHU, SYSU and HRCUS. The results show that, compared to corresponding baseline models, CLFF achieves performance improvements of 1.35%, 2.78%, 3.54% and 4.85% in the IoU metric, respectively. Full article

(This article belongs to the Special Issue Remote Sensing Technology for Agricultural and Land Management)

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23 pages, 7709 KB

Open AccessArticle

Spatiotemporal Land Use Change Detection Through Automated Sampling and Multi-Feature Composite Analysis: A Case Study of the Ebinur Lake Basin

by Yi Yang, Liang Zhao, Ya Guo, Shihua Liu, Xiang Qin, Yixiao Li and Xiaoqiong Jiang

Sensors 2025, 25(14), 4314; https://doi.org/10.3390/s25144314 - 10 Jul 2025

Viewed by 854

Abstract

Land use change plays a pivotal role in understanding surface processes and environmental dynamics, exerting considerable influence on regional ecosystem management. Traditional monitoring approaches, which often rely on manual sampling and single spectral features, exhibit limitations in efficiency and accuracy. This study proposes an innovative technical framework that integrates automated sample generation, multi-feature optimization, and classification model refinement to enhance the accuracy of land use classification and enable detailed spatiotemporal analysis in the Ebinur Lake Basin. By integrating Landsat data with multi-temporal European Space Agency (ESA) products, we acquired 14,000 pixels of 2021 land use samples, with multi-temporal spectral features enabling robust sample transfer to 12028 pixels in 2011 and 10,997 pixels in 2001. Multi-temporal composite data were reorganized and reconstructed to form annual and monthly feature spaces that integrate spectral bands, indices, terrain, and texture information. Feature selection based on the Gini coefficient and Out-Of-Bag Error (OOBE) reduced the original 48 features to 23. In addition, an object-oriented Gradient Boosting Decision Tree (GBDT) model was employed to perform accurate land use classification. A systematic evaluation confirmed the effectiveness of the proposed framework, achieving an overall accuracy of 93.17% and a Kappa coefficient of 92.03%, while significantly reducing noise in the classification maps. Based on land use classification results from three different periods, the spatial distribution and pattern changes of major land use types in the region over the past two decades were investigated through analyses of ellipses, centroid shifts, area changes, and transition matrices. This automated framework effectively enhances automation, offering technical support for accurate large-area land use classification. Full article

(This article belongs to the Special Issue Remote Sensing Technology for Agricultural and Land Management)

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26 pages, 17954 KB

Open AccessArticle

A Large-Scale Agricultural Land Classification Method Based on Synergistic Integration of Time Series Red-Edge Vegetation Index and Phenological Features

by Huansan Zhao, Chunyan Chang, Zhuoran Wang and Gengxing Zhao

Sensors 2025, 25(2), 503; https://doi.org/10.3390/s25020503 - 16 Jan 2025

Cited by 6 | Viewed by 2131

Abstract

Agricultural land classification plays a pivotal role in food security and ecological sustainability, yet achieving accurate large-scale mapping remains challenging. This study presents methodological innovations through a multi-level feature enhancement framework that transcends traditional time series analysis. Using Shandong Province, northern China’s agricultural heartland, as a case study, we first established a foundation with time series red-edge vegetation indices (REVI) from Sentinel-2 imagery, uniquely combining the normalized difference red edge index (NDRE₇₀₅) and plant senescence reflectance index (PSRI). Moving beyond conventional time series analysis, we innovatively amplified key temporal characteristics through newly designed spatial feature parameters (SFPs) and phenological feature parameters (PFPs). This strategic enhancement of critical temporal points significantly improved classification performance by capturing subtle spatial patterns and phenological transitions that are often overlooked in traditional approaches. The study yielded three significant findings: (1) The synergistic application of NDRE₇₀₅ and PSRI significantly outperformed single-index approaches, demonstrating the effectiveness of our dual-index strategy; (2) The integration of SFPs and PFPs with time series REVI markedly enhanced feature discrimination at crucial growth stages, with PFPs showing superior capability in distinguishing agricultural land types through amplified phenological signatures; (3) Our optimal classification scheme (FC6), leveraging both enhanced spatial and phenological features, achieved remarkable accuracy (93.21%) with a Kappa coefficient of 0.9159, representing improvements of 4.83% and 0.0538, respectively, over the baseline approach. This comprehensive framework successfully mapped 120,996 km² of agricultural land, differentiating winter wheat–summer maize rotation areas (39.44%), single-season crop fields (36.16%), orchards (14.49%), and facility vegetable fields (9.91%). Our approach advances the field by introducing a robust, scalable methodology that not only utilizes the full potential of time series data but also strategically enhances critical temporal features for improved classification accuracy, particularly valuable for regions with complex farming systems and diverse crop patterns. Full article

(This article belongs to the Special Issue Remote Sensing Technology for Agricultural and Land Management)

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29 pages, 13171 KB

Open AccessArticle

Enhancing Coffee Agroforestry Systems Suitability Using Geospatial Analysis and Sentinel Satellite Data in Gedeo Zone, Ethiopia

by Wondifraw Nigussie, Husam Al-Najjar, Wanchang Zhang, Eshetu Yirsaw, Worku Nega, Zhijie Zhang and Bahareh Kalantar

Sensors 2024, 24(19), 6287; https://doi.org/10.3390/s24196287 - 28 Sep 2024

Cited by 5 | Viewed by 4434

Abstract

The Gedeo zone agroforestry systems are the main source of Ethiopia’s coffee beans. However, land-use and suitability analyses are not well documented due to complex topography, heterogeneous agroforestry, and lack of information. This research aimed to map the coffee coverage and identify land suitability for coffee plantations using remote sensing, Geographic Information Systems (GIS), and the Analytical Hierarchy Process (AHP) in the Gedeo zone, Southern Ethiopia. Remote sensing classifiers often confuse agroforestry and plantations like coffee cover with forest cover because of their similar spectral signatures. Mapping shaded coffee in Gedeo agroforestry using optical or multispectral remote sensing is challenging. To address this, the study identified and mapped coffee coverage from Sentinel-1 data with a decibel (dB) value matched to actual coffee coverage. The actual field data were overlaid on Sentinel-1, which was used to extract the raster value. Pre-processing, classification, standardization, and reclassification of thematic layers were performed to find potential areas for coffee plantation. Hierarchy levels of the main criteria were formed based on climatological, edaphological, physiographic, and socioeconomic factors. These criteria were divided into 14 sub-criteria, reclassified based on their impact on coffee growing, with their relative weights derived using AHP. From the total study area of 1356.2 km², the mapped coffee coverage is 583 km². The outcome of the final computed factor weight indicated that average annual temperature and mean annual rainfall are the primary factors, followed by annual mean maximum temperature, elevation, annual mean minimum temperature, soil pH, Land Use/Land Cover (LULC), soil texture, Cation Exchange Capacity (CEC), slope, Soil Organic Matter (SOM), aspect, distance to roads, and distance to water, respectively. The identified coffee plantation potential land suitability reveals unsuitable (413 km²), sub-suitable (596.1 km²), and suitable (347.1 km²) areas. This study provides comprehensive spatial details for Ethiopian cultivators, government officials, and agricultural extension specialists to select optimal coffee farming locations, enhancing food security and economic prosperity. Full article

(This article belongs to the Special Issue Remote Sensing Technology for Agricultural and Land Management)

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28 pages, 16525 KB

Open AccessArticle

DMAF-NET: Deep Multi-Scale Attention Fusion Network for Hyperspectral Image Classification with Limited Samples

by Hufeng Guo and Wenyi Liu

Sensors 2024, 24(10), 3153; https://doi.org/10.3390/s24103153 - 15 May 2024

Cited by 7 | Viewed by 2786

Abstract

In recent years, deep learning methods have achieved remarkable success in hyperspectral image classification (HSIC), and the utilization of convolutional neural networks (CNNs) has proven to be highly effective. However, there are still several critical issues that need to be addressed in the HSIC task, such as the lack of labeled training samples, which constrains the classification accuracy and generalization ability of CNNs. To address this problem, a deep multi-scale attention fusion network (DMAF-NET) is proposed in this paper. This network is based on multi-scale features and fully exploits the deep features of samples from multiple levels and different perspectives with an aim to enhance HSIC results using limited samples. The innovation of this article is mainly reflected in three aspects: Firstly, a novel baseline network for multi-scale feature extraction is designed with a pyramid structure and densely connected 3D octave convolutional network enabling the extraction of deep-level information from features at different granularities. Secondly, a multi-scale spatial–spectral attention module and a pyramidal multi-scale channel attention module are designed, respectively. This allows modeling of the comprehensive dependencies of coordinates and directions, local and global, in four dimensions. Finally, a multi-attention fusion module is designed to effectively combine feature mappings extracted from multiple branches. Extensive experiments on four popular datasets demonstrate that the proposed method can achieve high classification accuracy even with fewer labeled samples. Full article

(This article belongs to the Special Issue Remote Sensing Technology for Agricultural and Land Management)

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