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Remote Sensing
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Cropland and Yield Mapping with Multi-source Remote Sensing
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Cropland and Yield Mapping with Multi-source Remote Sensing

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: 31 May 2025 | Viewed by 6630

Special Issue Editors

Dr. Wen Zhuo

E-Mail Website
Guest Editor
School of Earth, Environment & Society, McMaster University, Hamilton, ON L8S 4K1, Canada
Interests: agricultural models; data assimilation; crop yield estimation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Shibo Fang

E-Mail Website
Guest Editor
State Key Laboratory of Sever Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Interests: remote sensing; meteorology; agriculture; environment; climatology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yi Xie

E-Mail Website
Guest Editor
College of Geographical Sciences, Shanxi Normal University, Taiyuan 030031, China
Interests: crop growth monitoring; yield estimation and prediction; multi-source remote sensing data fusion
Dr. Xiaochuang Yao

E-Mail Website
Guest Editor
College of Land Science and Technology, China Agricultural University, Beijing 100081, China
Interests: spatial big data technology; machine learning; deep learning; crop mapping
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Accurate and timely information on cropland distribution and crop yield estimation or in-season forecasting can be used to support government agricultural decision making, assist in agricultural management practices, and optimize resource use. With the rapid development of the radiometric, spatial, temporal, and spectral resolutions of remote sensing technology, the integration of multi-source remote sensing is a good way to enhance the spatial resolution, improve data accuracy, capture a broader range of environmental variables, and enable the comprehensive monitoring and analysis of landscapes at various scales. Therefore, to better understand the challenges and opportunities presented by integrating multi-source remotely sensed observations for agricultural applications (especially for cropland or crop yield mapping), this Special Issue aims to invite original and innovative research on applications of multi-source remote sensing for croplands, the crop yield, and crop-type mapping, or crop parameter retrieval using data assimilation algorithms, machine learning, and deep learning methods, or other state-of-the-art approaches. The research areas may include (but are not limited to) the following:

  • Crop yield estimation or forecasting;
  • Farmland or crop-type mapping;
  • Multi-sensor imagery fusion;
  • Spatially explicit crop model development, implementation, and validation;
  • Model data assimilation algorithms, systems, and uncertainty;
  • Multi-source data for retrieving crop parameters;
  • Machine learning or deep learning for agricultural studies.

Dr. Wen Zhuo
Prof. Dr. Shibo Fang
Prof. Dr. Yi Xie
Dr. Xiaochuang Yao
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

  • yield mapping
  • crop yield estimation or forecasts
  • cropland mapping
  • crop parameter retrieval
  • multi-source imagery
  • crop growth model
  • Google Earth Engine
  • data assimilation
  • machine learning
  • deep learning

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Published Papers (5 papers)

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Research

21 pages, 4440 KiB  
Article
Automatic Grape Cluster Detection Combining YOLO Model and Remote Sensing Imagery
by Ana María Codes-Alcaraz, Nicola Furnitto, Giuseppe Sottosanti, Sabina Failla, Herminia Puerto, Carmen Rocamora-Osorio, Pedro Freire-García and Juan Miguel Ramírez-Cuesta
Remote Sens. 2025, 17(2), 243; https://doi.org/10.3390/rs17020243 - 11 Jan 2025
Viewed by 1119
Abstract
Precision agriculture has recently experienced significant advancements through the use of technologies such as unmanned aerial vehicles (UAVs) and satellite imagery, enabling more efficient and precise agricultural management. Yield estimation from these technologies is essential for optimizing resource allocation, improving harvest logistics, and [...] Read more.
Precision agriculture has recently experienced significant advancements through the use of technologies such as unmanned aerial vehicles (UAVs) and satellite imagery, enabling more efficient and precise agricultural management. Yield estimation from these technologies is essential for optimizing resource allocation, improving harvest logistics, and supporting decision-making for sustainable vineyard management. This study aimed to evaluate grape cluster numbers estimated by using YOLOv7x in combination with images obtained by UAVs from a vineyard. Additionally, the capability of several vegetation indices calculated from Sentinel-2 and PlanetScope satellites to estimate grape clusters was evaluated. The results showed that the application of the YOLOv7x model to RGB images acquired from UAVs was able to accurately predict grape cluster numbers (R2 value and RMSE value of 0.64 and 0.78 clusters vine−1). On the contrary, vegetation indexes derived from Sentinel-2 and PlanetScope satellites were found not able to predict grape cluster numbers (R2 lower than 0.23), probably due to the fact that these indexes are more related to vegetation vigor, which is not always related to yield parameters (e.g., cluster number). This study suggests that the combination of high-resolution UAV images, multispectral satellite images, and advanced detection models like YOLOv7x can significantly improve the accuracy of vineyard management, resulting in more efficient and sustainable agriculture. Full article
(This article belongs to the Special Issue Cropland and Yield Mapping with Multi-source Remote Sensing)
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23 pages, 11236 KiB  
Article
A Refined Terrace Extraction Method Based on a Local Optimization Model Using GF-2 Images
by Guobin Kan, Jie Gong, Bao Wang, Xia Li, Jing Shi, Yutao Ma, Wei Wei and Jun Zhang
Remote Sens. 2025, 17(1), 12; https://doi.org/10.3390/rs17010012 - 24 Dec 2024
Viewed by 655
Abstract
Terraces are an important form of surface modification, and their spatial distribution data are of utmost importance for ensuring food and water security. However, the extraction of terrace patches faces challenges due to the complexity of the terrain and limitations in remote sensing [...] Read more.
Terraces are an important form of surface modification, and their spatial distribution data are of utmost importance for ensuring food and water security. However, the extraction of terrace patches faces challenges due to the complexity of the terrain and limitations in remote sensing (RS) data. Therefore, there is an urgent need for advanced technology models that can accurately extract terraces. High-resolution RS data allows for detailed characterization of terraces by capturing more precise surface features. Moreover, leveraging deep learning (DL) models with local adaptive improvements can further enhance the accuracy of interpretation by exploring latent information. In this study, we employed five models: ResU-Net, U-Net++, RVTransUNet, XDeepLabV3+, and ResPSPNet as DL models to extract fine patch terraces from GF-2 images. We then integrated morphological, textural, and spectral features to optimize the extraction process by addressing issues related to low adhesion and edge segmentation performance. The model structure and loss function were adjusted accordingly to achieve high-quality terrace mapping results. Finally, we utilized multi-source RS data along with terrain elements for correction and optimization to generate a 1 m resolution terrace distribution map in the Zuli River Basin (TDZRB). Evaluation results after correction demonstrate that our approach achieved an OA, F1-Score, and MIoU of 96.67%, 93.94%, and 89.37%, respectively. The total area of terraces in the Zuli River Basin was calculated at 2557 ± 117.96 km2 using EM with our model methodology; this accounts for approximately 41.74% ± 1.93% of the cultivated land area within the Zuli River Basin. Therefore, obtaining accurate information on patch terrace distribution serves as essential foundational data for terrace ecosystem research and government decision-making. Full article
(This article belongs to the Special Issue Cropland and Yield Mapping with Multi-source Remote Sensing)
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27 pages, 5787 KiB  
Article
Large-Scale Maize Condition Mapping to Support Agricultural Risk Management
by Edina Birinyi, Dániel Kristóf, Roland Hollós, Zoltán Barcza and Anikó Kern
Remote Sens. 2024, 16(24), 4672; https://doi.org/10.3390/rs16244672 - 14 Dec 2024
Viewed by 1074
Abstract
Crop condition mapping and yield loss detection are highly relevant scientific fields due to their economic importance. Here, we report a new, robust six-category crop condition mapping methodology based on five vegetation indices (VIs) using Sentinel-2 imagery at a 10 m spatial resolution. [...] Read more.
Crop condition mapping and yield loss detection are highly relevant scientific fields due to their economic importance. Here, we report a new, robust six-category crop condition mapping methodology based on five vegetation indices (VIs) using Sentinel-2 imagery at a 10 m spatial resolution. We focused on maize, the most drought-affected crop in the Carpathian Basin, using three selected years of data (2017, 2022, and 2023). Our methodology was validated at two different spatial scales against independent reference data. At the parcel level, we used harvester-derived precision yield data from six maize parcels. The agreement between the yield category maps and those predicted from the crop condition time series by our Random Forest model was 84.56%, while the F1 score was 0.74 with a two-category yield map. Using a six-category yield map, the accuracy decreased to 48.57%, while the F1 score was 0.42. The parcel-level analysis corroborates the applicability of the method on large scales. Country-level validation was conducted for the six-category crop condition map against official county-scale census data. The proportion of areas with the best and worst crop condition categories in July explained 64% and 77% of the crop yield variability at the county level, respectively. We found that the inclusion of the year 2022 (associated with a severe drought event) was important, as it represented a strong baseline for the scaling. The study’s novelty is also supported by the inclusion of damage claims from the Hungarian Agricultural Risk Management System (ARMS). The crop condition map was compared with these claims, with further quantitative analysis confirming the method’s applicability. This method offers a cost-effective solution for assessing damage claims and can provide early yield loss estimates using only remote sensing data. Full article
(This article belongs to the Special Issue Cropland and Yield Mapping with Multi-source Remote Sensing)
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27 pages, 6702 KiB  
Article
Assimilating Satellite-Based Biophysical Variables Data into AquaCrop Model for Silage Maize Yield Estimation Using Water Cycle Algorithm
by Elahe Akbari, Ali Darvishi Boloorani, Jochem Verrelst and Stefano Pignatti
Remote Sens. 2024, 16(24), 4665; https://doi.org/10.3390/rs16244665 - 13 Dec 2024
Viewed by 1156
Abstract
Accurate crop yield estimation is critical to successful agricultural operations. Current crop growth models often overlook the spatial and geographic components of the lands, leading to suboptimal yield estimates. To address this issue, assimilation of satellite vegetation products into these models can account [...] Read more.
Accurate crop yield estimation is critical to successful agricultural operations. Current crop growth models often overlook the spatial and geographic components of the lands, leading to suboptimal yield estimates. To address this issue, assimilation of satellite vegetation products into these models can account for spatial variations in the land and improve estimation accuracy. In this paper, the AquaCrop model, a water-driven crop growth model, was selected for recalibration and assimilation of satellite-derived biophysical products due to its simplicity and lack of computational complexity. To this end, field samples of soil (sampled before cultivation) and crop features were collected during the growing season of silage maize. Digital hemisphere photography (DHP) and destructive sampling methods were used for measuring fraction vegetation cover (fCover) and biomass in Qaleh-Now County, southern Tehran, in 2019. Based on our proposed workflow in previous studies, a Gaussian process regression–particle swarm optimization (GPR-PSO) algorithm and global sensitivity analysis were applied to retrieve the fCover and biomass from Sentinel-2 satellite data and to identify the most sensitive parameters in the AquaCrop model, respectively. Here, we propose the use of an optimization water cycle algorithm (WCA) instead of a PSO algorithm as an assimilation method for the parameter calibration of AquaCrop. This study also focused on using both fCover and biomass state variables simultaneously in the model, as opposed to only the fCover, and found that using both variables led to significantly higher calibration accuracy. The WCA method outperformed the PSO method in AquaCrop’s calibration, leading to more accurate results on maize yield estimates. It has enhanced results, decreasing RMSE values by 3.8 and 4.7 ton/ha, RRMSE by 6.4% and 10%, and increasing R2 by 0.17 and 0.35 for model calibration and validation, respectively. These results suggest that assimilating satellite-derived data and optimizing the calibration process through WCA can significantly improve the accuracy of crop yield estimations in water-driven crop growth models, highlighting the potential of this approach for precision agriculture. Full article
(This article belongs to the Special Issue Cropland and Yield Mapping with Multi-source Remote Sensing)
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20 pages, 5703 KiB  
Article
Spatiotemporal Evolution Disparities of Vegetation Trends over the Tibetan Plateau under Climate Change
by Jieru Ma, Hong-Li Ren, Xin Mao, Minghong Liu, Tao Wang and Xudong Ma
Remote Sens. 2024, 16(14), 2585; https://doi.org/10.3390/rs16142585 - 14 Jul 2024
Cited by 1 | Viewed by 1538
Abstract
The Tibetan Plateau has experienced profound climate change with significant implication for spatial vegetation greenness. However, the spatiotemporal disparities of long-term vegetation trends in response to observed climate change remain unclear. Based on remote-sensing vegetation images indicated by the normalized difference vegetation index [...] Read more.
The Tibetan Plateau has experienced profound climate change with significant implication for spatial vegetation greenness. However, the spatiotemporal disparities of long-term vegetation trends in response to observed climate change remain unclear. Based on remote-sensing vegetation images indicated by the normalized difference vegetation index (NDVI) from two long-term combined datasets, GIMMS and MODIS, we identified two spatiotemporal evolution patterns (SEPs) in long-term vegetation anomalies across the Tibetan Plateau. This new perspective integrates spatial and temporal NDVI changes during the growing seasons over the past four decades. Notably, the dipole evolution pattern that rotates counterclockwise from May to September accounted for 62.8% of the spatial mean amplitude of vegetation trends, dominating the spatiotemporal disparities. This dominant pattern trend is attributed to simultaneous effects of spatial warming and rising CO2, which accounted for 75% and 15%, respectively, along with a lagged effect of dipole precipitation, accounting for 6%. Overall, wetting and warming promote greening evolution in the northern Tibetan Plateau, while slight drying and warming favor browning evolution in the southern Tibetan Plateau. These findings provide insights into the combined effects of climate change on spatiotemporal vegetation trends and inform future adaptive strategies in fragile regions. Full article
(This article belongs to the Special Issue Cropland and Yield Mapping with Multi-source Remote Sensing)
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