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Remote Sensing
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Real-Time Agricultural Monitoring from Remotely Sensed Data (Second Edition)
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Real-Time Agricultural Monitoring from Remotely Sensed Data (Second Edition)

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 December 2025 | Viewed by 3421

Special Issue Editors

Dr. Ruyin Cao

E-Mail Website
Guest Editor
School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: algorithm development; time-series remote sensing; vegetation phenology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Tao Cheng

E-Mail Website
Guest Editor
National Engineering & Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China
Interests: reflectance spectroscopy; quantitative remote sensing of vegetation; crop growth monitoring; crop mapping; smart farming
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ran Meng

E-Mail Website
Guest Editor
1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150008, China
2. National Key Laboratory of Smart Farming Technologies and Systems, Harbin 150008, China
Interests: multi-source remote sensing; vegetation dynamics; plant stress
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing data have been successfully used to investigate various agricultural activities, such as crop type mapping, crop phenology detection, soil moisture assessment, and crop growth monitoring. From a practical point of view, agricultural management requires timely and accurate crop and soil information provided by remote sensing data in real-time. Real-time agricultural monitoring is still impeded by limitations in remote sensing data quality, monitoring algorithms, and computing platforms.

In this context, a Special Issue entitled “Real-Time Agricultural Monitoring from Remotely Sensed Data” is being planned in the journal Remote Sensing. We welcome all research or review articles on agricultural monitoring as long as they focus on work carried out during the crop-growing season. In addition, methodology papers on processing within-season remote sensing data (e.g., time-series data) are also welcome. This Special Issue has a broad range of topics, including crop monitoring (e.g., crop type classification, crop phenology detection, crop phenotyping, crop yield prediction) and agricultural condition investigations (e.g., agricultural drought, biotic/abiotic stresses). It should be noted that remotely sensed data from satellites, drones, or field instruments should be among the main data sources.

We look forward to receiving your contributions.

Prof. Dr. Ruyin Cao
Prof. Dr. Tao Cheng
Prof. Dr. Ran Meng
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

  • agricultural remote sensing
  • crop types
  • crop phenotype
  • crop yield
  • in-season
  • phenotyping
  • plant stress
  • precision agriculture
  • time-series data
  • within-season

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Related Special Issue

Published Papers (3 papers)

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Research

24 pages, 12865 KB  
Article
Mapping Crop Types and Cropping Patterns Using Multiple-Source Satellite Datasets in Subtropical Hilly and Mountainous Region of China
by Yaoliang Chen, Zhiying Xu, Hongfeng Xu, Zhihong Xu, Dacheng Wang and Xiaojian Yan
Remote Sens. 2025, 17(13), 2282; https://doi.org/10.3390/rs17132282 - 3 Jul 2025
Viewed by 1088
Abstract
A timely and accurate distribution of crop types and cropping patterns provides a crucial reference for the management of agriculture and food security. However, accurately mapping crop types and cropping patterns in subtropical hilly and mountainous areas often face challenges such as mixed [...] Read more.
A timely and accurate distribution of crop types and cropping patterns provides a crucial reference for the management of agriculture and food security. However, accurately mapping crop types and cropping patterns in subtropical hilly and mountainous areas often face challenges such as mixed pixels resulted from fragmented patches and difficulty in obtaining optical satellites due to a frequently cloudy and rainy climate. Here we propose a crop type and cropping pattern mapping framework in subtropical hilly and mountainous areas, considering multiple sources of satellites (i.e., Landsat 8/9, Sentinel-2, and Sentinel-1 images and GF 1/2/7). To develop this framework, six types of variables from multi-sources data were applied in a random forest classifier to map major summer crop types (singe-cropped rice and double-cropped rice) and winter crop types (rapeseed). Multi-scale segmentation methods were applied to improve the boundaries of the classified results. The results show the following: (1) Each type of satellite data has at least one variable selected as an important feature for both winter and summer crop type classification. Apart from the endmember variables, the other five extracted variable types are selected by the RF classifier for both winter and summer crop classifications. (2) SAR data can capture the key information of summer crops when optical data is limited, and the addition of SAR data can significantly improve the accuracy as to summer crop types. (3) The overall accuracy (OA) of both summer and winter crop type mapping exceeded 95%, with clear and relatively accurate cropland boundaries. Area evaluation showed a small bias in terms of the classified area of rapeseed, single-cropped rice, and double-cropped rice from statistical records. (4) Further visual examination of the spatial distribution showed a better performance of the classified crop types compared to three existing products. The results suggest that the proposed method has great potential in accurately mapping crop types in a complex subtropical planting environment. Full article
(This article belongs to the Special Issue Real-Time Agricultural Monitoring from Remotely Sensed Data (Second Edition))
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23 pages, 3792 KB  
Article
Investigating the Mechanisms of Hyperspectral Remote Sensing for Belowground Yield Traits in Potato Plants
by Wenqian Chen, Yurong Huang, Wei Tan, Yujia Deng, Cuihong Yang, Xiguang Zhu, Jian Shen and Nanfeng Liu
Remote Sens. 2025, 17(12), 2097; https://doi.org/10.3390/rs17122097 - 19 Jun 2025
Cited by 2 | Viewed by 838
Abstract
Potatoes, as the world’s fourth-largest staple crop, are vital for global food security. Efficient methods for assessing yield and quality are essential for policy-making and optimizing production. Traditional yield assessment techniques remain destructive, labor-intensive, and unsuitable for large-scale monitoring. While remote sensing has [...] Read more.
Potatoes, as the world’s fourth-largest staple crop, are vital for global food security. Efficient methods for assessing yield and quality are essential for policy-making and optimizing production. Traditional yield assessment techniques remain destructive, labor-intensive, and unsuitable for large-scale monitoring. While remote sensing has offered a promising alternative, current approaches largely depend on empirical correlations rather than physiological mechanisms. This limitation arises because potato tubers grow underground, rendering their traits invisible to aboveground sensors. This study investigated the mechanisms underlying hyperspectral remote sensing for assessing belowground yield traits in potatoes. Field experiments with four cultivars and five nitrogen treatments were conducted to collect foliar biochemistries (chlorophyll, nitrogen, and water and dry matter content), yield traits (tuber yield, fresh/dry weight, starch, protein, and water content), and leaf spectra. Two approaches were developed for predicting belowground yield traits: (1) a direct method linking leaf spectra to yield via statistical models and (2) an indirect method using structural equation modeling (SEM) to link foliar biochemistry to yield. The SEM analysis revealed that foliar nitrogen exhibited negative effects on tuber fresh weight (path coefficient b = −0.57), yield (−0.37), and starch content (−0.30). Similarly, leaf water content negatively influenced tuber water content (0.52), protein (−0.27), and dry weight (−0.42). Conversely, chlorophyll content showed positive associations with both tuber protein (0.59) and dry weight (0.56). Direct models (PLSR, SVR, and RFR) achieved higher accuracy for yield (R2 = 0.58–0.84) than indirect approaches (R2 = 0.16–0.45), though the latter provided physiological insights. The reduced accuracy in indirect methods primarily stemmed from error propagation within the SEM framework. Future research should scale these leaf-level mechanisms to canopy observations and integrate crop growth models to improve robustness across environments. This work advances precision agriculture by clarifying spectral–yield linkages in potato systems, offering a framework for hyperspectral-based yield prediction. Full article
(This article belongs to the Special Issue Real-Time Agricultural Monitoring from Remotely Sensed Data (Second Edition))
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24 pages, 9008 KB  
Article
Estimation of Aboveground Biomass of Chinese Milk Vetch Based on UAV Multi-Source Map Fusion
by Chaoyang Zhang, Qiang Zhu, Zhenghuan Fu, Chu Yuan, Mingjian Geng and Ran Meng
Remote Sens. 2025, 17(4), 699; https://doi.org/10.3390/rs17040699 - 18 Feb 2025
Cited by 1 | Viewed by 935
Abstract
Chinese milk vetch (CMV), as a typical green manure in southern China, plays an important role in improving soil quality and partially substituting nitrogen chemical fertilizers for rice production. Accurately estimating the aboveground biomass (AGB) of CMV is crucial for quantifying the biological [...] Read more.
Chinese milk vetch (CMV), as a typical green manure in southern China, plays an important role in improving soil quality and partially substituting nitrogen chemical fertilizers for rice production. Accurately estimating the aboveground biomass (AGB) of CMV is crucial for quantifying the biological nitrogen fixation amount (BNFA) and assessing its viability as a nitrogen fertilizer alternative. However, the traditional estimation methods have low efficiency in field-scale evaluations. Recently, unmanned aerial vehicle (UAV) remote sensing technology has been widely adopted for AGB estimation. This study utilized UAV-based multispectral and RGB imagery to extract spectral (Sp), textural (Tex), and structural features (Str), comparing various feature combinations in AGB estimation for CMV. The results indicated that the fusion of spectral, textural, and structural features indicated optimal estimation performance across all feature combinations, resulting in R2 values of 0.89 and 0.83 for model cross-validation and spatial transferability validation, respectively. The inclusion of textural and spectral features notably improved AGB estimation, indicated an increase of 0.15 and 0.14 in R2 values for model cross-validation and spatial transferability validation, respectively, compared with relying on spectral features only. Estimation based exclusively on structural features resulted in R2 values of 0.65 and 0.52 for model cross-validation and spatial transferability validation, respectively. The present study establishes a rapid and extensive approach to evaluate the BNFA of CMV at the full blooming stage utilizing the optimal AGB estimation model, which will provide an effective calculation method for chemical fertilizer reduction. Full article
(This article belongs to the Special Issue Real-Time Agricultural Monitoring from Remotely Sensed Data (Second Edition))
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