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Advances in Remote Sensing for Crop Monitoring and Food Security
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Advances in Remote Sensing for Crop Monitoring and Food Security

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: 30 September 2025 | Viewed by 4629

Special Issue Editors

Dr. Lili Xu

E-Mail Website
Guest Editor
College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China
Interests: remote sensing of environment; land use and land cover change; precision agriculture; crop monitoring; time series analysis; fractional vegetation cover; food security.
Dr. Yingying Dong

E-Mail Website
Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: hyperspectral imaging; remote sensing image processing; geophysical techniques; agriculture; data assimilation; diseases; geochemistry; geophysical signal processing; neural nets; nitrogen; pest control
Special Issues, Collections and Topics in MDPI journals
Dr. Ran Huang

E-Mail Website
Guest Editor
School of Automation, Hangzhou Dianzi University, Hangzhou, China
Interests: thermal remote sensing; vegetation remote sensing; crop classification; smart agriculture
Prof. Dr. Hao Wu

E-Mail Website
Guest Editor
College of Urban and Environment Sciences, Huazhong Normal University, Wuhan 430079, China
Interests: land cover classification; land use modelling; agricultural system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the era of smart agriculture, remote sensing techniques play an increasingly important role in the intelligent and high-quality development of agriculture, providing key technical support for efficient food production and food security. In recent years, multi-source remote sensing data from satellites and Unmanned Aerial Vehicle (UAV) platforms have offered long-term observational information with high spatial, temporal, and spectral resolution. This information complements ground investigations, greatly enriching our multi-scale understanding of crop growth processes and agricultural practices. Many advanced techniques and applications have emerged in remote sensing for crop monitoring and food security, utilizing physics-based models, empirical models, and machine learning algorithms.

This Special Issue focuses on methodologies and practices in agricultural remote sensing for food security. We welcome novel techniques and applications for monitoring cropland areas, crop growth processes, and crop loss-related abiotic/biotic stresses, as well as advanced practices aimed at improving the efficiency of crop planting and production management. This Special Issue will provide important technical and methodological support for field management throughout the entire growth period of crops, precise water control and fertilization, high-precision yield estimation, and support stable and high yields. We are soliciting papers on, but not limited to, the following topics:

  • Cropland change detection;
  • Satellite-based cropland identification and classification;
  • UAV-based high-resolution mapping of agricultural fields;
  • Quantitative inversion of crop structural and biochemical parameters;
  • Crop growth monitoring based on temporal analysis;
  • Disease, pests, lodging, and weeds monitoring for crops;
  • Interactions between extreme climate events and crops;
  • Crop yield assessment.

Dr. Lili Xu
Dr. Yingying Dong
Dr. Ran Huang
Prof. Dr. Hao Wu
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

  • crop monitoring
  • land use change
  • high-resolution imagery
  • precision agriculture
  • phenology
  • growth stages
  • vegetation indices
  • crop stress detection
  • yield models
  • machine learning algorithms

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Published Papers (5 papers)

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22 pages, 11023 KiB  
Article
Comparing Satellite-Derived and Model-Based Surface Soil Moisture for Spring Barley Yield Prediction in Central Europe
by Felix Reuß, Mariette Vreugdenhil, Emanuel Bueechi and Wolfgang Wagner
Remote Sens. 2025, 17(8), 1394; https://doi.org/10.3390/rs17081394 - 14 Apr 2025
Viewed by 553
Abstract
Surface soil moisture (SSM) has proven to be an important variable for the yield prediction of main crops like maize and wheat, but its value for spring barley, the third most cultivated crop in Europe, has not yet been evaluated. This study assesses [...] Read more.
Surface soil moisture (SSM) has proven to be an important variable for the yield prediction of main crops like maize and wheat, but its value for spring barley, the third most cultivated crop in Europe, has not yet been evaluated. This study assesses how much of spring barley yield variability can be explained by the commonly used model and satellite-based global SSM products ERA5 SWVL1 and H SAF. A Feed Forward Neural Network, SSM time series, and reference yield data are used to predict spring barley yield at NUTS level for Austria, Czechia, and Germany. A random train-test split is used to assess the explained variability and a cross-validation at the NUTS level for the spatial evaluation. The results indicate the following: (1) ERA5 SWVL1 achieved an R2 of 0.37, H SAF an R2 of 0.33; (2) Both products achieved the lowest RMSE and MAE in Czechia, high RMSE and MAE values are observed in Eastern Germany. (3) ERA5 SWVL1 performed better in areas with low sensitivity for microwaves like the Alpine region, but both products achieved similar results in 80% of the NUTS regions. These findings contribute to better utilization of SSM and more accurate yield predictions for spring barley and similar crops. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Food Security)
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25 pages, 26721 KiB  
Article
Effective Cultivated Land Extraction in Complex Terrain Using High-Resolution Imagery and Deep Learning Method
by Zhenzhen Liu, Jianhua Guo, Chenghang Li, Lijun Wang, Dongkai Gao, Yali Bai and Fen Qin
Remote Sens. 2025, 17(5), 931; https://doi.org/10.3390/rs17050931 - 6 Mar 2025
Cited by 1 | Viewed by 707
Abstract
The accurate extraction of cultivated land information is crucial for optimizing regional farmland layouts and enhancing food supply. To address the problem of low accuracy in existing cultivated land products and the poor applicability of cultivated land extraction methods in fragmented, small parcel [...] Read more.
The accurate extraction of cultivated land information is crucial for optimizing regional farmland layouts and enhancing food supply. To address the problem of low accuracy in existing cultivated land products and the poor applicability of cultivated land extraction methods in fragmented, small parcel agricultural landscapes and complex terrain mapping, this study develops an advanced cultivated land extraction model for the western part of Henan Province, China, utilizing Gaofen-2 (GF-2) imagery and an improved U-Net architecture to achieve a 1 m resolution regional mapping in complex terrain. We obtained optimal input data for the U-Net model by fusing spectral features and vegetation index features from remote sensing images. We evaluated and validated the effectiveness of the proposed method from multiple perspectives and conducted a cultivated land change detection and agricultural landscape fragmentation assessment in the study area. The experimental results show that the proposed method achieved an F1 score of 89.55% for the entire study area, with an F1 score ranging from 83.84% to 90.44% in the hilly or transitional zones. Compared to models that solely rely on spectral features, the feature selection-based model demonstrates superior performance in hilly and adjacent mountainous regions, with improvements of 4.5% in Intersection over Union (IoU). Cultivated land mapping results show that 83.84% of the cultivated land parcels are smaller than 0.64 hectares. From 2017 to 2022, the overall cultivated land area decreased by 15.26 km2, with the most significant reduction occurring in the adjacent hilly areas, where the land parcels are small and fragmented. This trend highlights the urgent need for effective land management strategies to address fragmentation and prevent further loss of cultivated land in these areas. We anticipate that the findings can contribute to precision agriculture management and agricultural modernization in complex terrains of the world. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Food Security)
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16 pages, 8588 KiB  
Article
A Novel Approach for Farmland Size Estimation in Small-Scale Agriculture Using Edge Counting and Remote Sensing
by Jingnan Du, Sucheng Xu, Jinshan Li, Jiakun Duan and Wu Xiao
Remote Sens. 2024, 16(16), 2981; https://doi.org/10.3390/rs16162981 - 14 Aug 2024
Cited by 1 | Viewed by 1388
Abstract
Accurate and timely information on farmland size is crucial for agricultural development, resource management, and other related fields. However, there is currently no mature method for estimating farmland size in smallholder farming areas. This is due to the small size of farmland plots [...] Read more.
Accurate and timely information on farmland size is crucial for agricultural development, resource management, and other related fields. However, there is currently no mature method for estimating farmland size in smallholder farming areas. This is due to the small size of farmland plots in these areas, which have unclear boundaries in medium and high-resolution satellite imagery, and irregular shapes that make it difficult to extract complete boundaries using morphological rules. Automatic farmland mapping algorithms using remote sensing data also perform poorly in small-scale farming areas. To address this issue, this study proposes a farmland size evaluation index based on edge frequency (ECR). The algorithm utilizes the high temporal resolution of Sentinel-2 satellite imagery to compensate for its spatial resolution limitations. First, all Sentinel-2 images from one year are used to calculate edge frequencies, which can divide farmland areas into low-value farmland interior regions, medium-value non-permanent edges, and high-value permanent edges (PE). Next, the Otsu’s thresholding algorithm is iteratively applied twice to the edge frequencies to first extract edges and then permanent edges. The ratio of PE to cropland (ECR) is then calculated. Using the North China Plain and Northeast China Plain as study areas, and comparing with existing farmland size datasets, the appropriate estimation radius for ECR was determined to be 1600 m. The study found that the peak ECR value for the Northeast China Plain was 0.085, and the peak value for the North China Plain was 0.105. The overall distribution was consistent with the reference dataset. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Food Security)
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14 pages, 6513 KiB  
Technical Note
Development of a Distance-Adaptive Gaussian Fitting Method for Scheimpflug LiDAR-Based Plant Phenotyping
by Kaihua Wu, Lei Chen, Kaijie Shao, Fengnong Chen and Hongze Lin
Remote Sens. 2025, 17(9), 1604; https://doi.org/10.3390/rs17091604 - 30 Apr 2025
Viewed by 157
Abstract
Lidar has emerged as a pivotal technique within the booming field of plant phenotyping, which has seen significant advancements in recent years. Beyond the conventional LiDAR systems that determine distance based on time-of-flight principles, Scheimpflug LiDAR, an emerging technique proposed within the past [...] Read more.
Lidar has emerged as a pivotal technique within the booming field of plant phenotyping, which has seen significant advancements in recent years. Beyond the conventional LiDAR systems that determine distance based on time-of-flight principles, Scheimpflug LiDAR, an emerging technique proposed within the past decade, has also expanded its field to plant phenotyping. However, early applications of Scheimpflug LiDAR were predominantly focused on aerosol detection, where stringent requirements for range resolution were not paramount. In this paper, a detailed description of a Scheimpflug LiDAR designed for plant phenotyping is proposed. Furthermore, to ensure high-precision scanning of plant targets, a distance-adaptive Gaussian fitting methodology is proposed to improve the spatial precision from 0.1781 m to 0.044 m at 10 m, compared with the traditional maximum method. The results indicate that the point cloud data acquired through our method yield more precise phenotyping outcomes, such as diameter at breast height (DBH) and plant height. This paves the way for further application of the Scheimpflug LiDAR on growth stages monitoring and precision agriculture. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Food Security)
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13 pages, 9176 KiB  
Technical Note
Evaluating Sentinel-2 for Monitoring Drought-Induced Crop Failure in Winter Cereals
by Adrià Descals, Karen Torres, Aleixandre Verger and Josep Peñuelas
Remote Sens. 2025, 17(2), 340; https://doi.org/10.3390/rs17020340 - 20 Jan 2025
Viewed by 1072
Abstract
Extreme climate events can threaten food production and disrupt supply chains. For instance, the 2023 drought in Catalonia caused large areas of winter cereals to wilt and die early, yielding no grain. This study examined whether Sentinel-2 can detect total crop losses of [...] Read more.
Extreme climate events can threaten food production and disrupt supply chains. For instance, the 2023 drought in Catalonia caused large areas of winter cereals to wilt and die early, yielding no grain. This study examined whether Sentinel-2 can detect total crop losses of winter cereals using ground truth data on crop failure. The methodology explored which Sentinel-2 phenological and greenness variables could best predict three drought impact classes: normal growth, moderate impact, and high impact, where the crop failed to produce grain. The results demonstrate that winter cereals affected by drought exhibit a premature decline in several vegetation indices. As a result, the best predictors for detecting total crop losses were metrics associated with the later stages of crop development. Specifically, the mean Normalized Difference Vegetation Index (NDVI) for the first half of May showed the highest correlation with drought impact classes (R2 = 0.66). This study is the first to detect total crop losses at the plantation level using field data combined with Sentinel-2 imagery. It also offers insights into rapid monitoring methods for crop failure, an event likely to become more frequent as the climate warms. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Food Security)
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