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Remote Sensing
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Hyperspectral Sensors for Soil Parameters and Crop Parameters Retrieval
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Hyperspectral Sensors for Soil Parameters and Crop Parameters Retrieval

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: 15 June 2025 | Viewed by 6750

Special Issue Editors

Dr. Minfeng Xing

E-Mail Website
Guest Editor
School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: the synthetic aperture radar image processing; the application of unmanned aerial vehicle; quantitative estimation of land surface variables from satellite remote sensing and on integration of multiple data sources with numerical models
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jinfei Wang

E-Mail Website
Guest Editor
Department of Geography and Environment, University of Western Ontario, London, ON N6A5C2, Canada
Interests: algorithms for automatic linear and other man-made feature detection from images; methods for GIS feature extraction and lane use/cover change detection in urban environment using multispectral and hyperspectral data; methods for object oriented information extraction from high resolution remotely sensed imagery; applications of radar/optical remote sensing and GIS for environmental change analysis near large rivers/mountains and in marsh and mangrove wetlands
Special Issues, Collections and Topics in MDPI journals
Dr. Qisheng He

E-Mail Website
Guest Editor
College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
Interests: quantitative remote sensing; vegetation remote sensing; data fusion; data assimilation; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The utilization of hyperspectral sensors in remote sensing has opened up avenues for the extraction of soil properties and the analysis of crop health. This technology, using a wide range of electromagnetic spectrum, provides continuous spectral data that can improve our understanding of soil properties, crop growth conditions, and changes. Further, it can help in accurate retrieval of biophysical and biochemical parameters of soil and crops, thereby significantly contributing to precision agriculture and sustainable farming practices. Therefore, the burgeoning use of hyperspectral sensors in remote sensing provides unprecedented possibilities for comprehensive monitoring of soil and crop parameters. In light of these advancements, we are inviting researchers to contribute their latest findings and research work.

The aim of this special issue is to convene and showcase the most recent developments, innovations, and applications of hyperspectral sensors in retrieving soil and crop parameters. This includes, but is not limited to, the use of hyperspectral sensors mounted on various platforms such as stationary setups, mobile units, Unmanned Aerial Vehicles (UAVs), aircraft, and satellites at different spatial and temporal scales.

This special Issue, “Hyperspectral Sensors for Soil Parameters and Crop Parameters Retrieval”, encourages submissions that discuss novel techniques or approaches leveraging hyperspectral sensors to retrieve soil parameters such as soil moisture, organic matter, mineral content, and other soil physicochemical properties. Similarly, contributions are welcomed on the estimation or inversion of crop parameters, including growth conditions, nutritional status, disease detection, and productivity. These could span different spatial and temporal scales, covering small farm-level studies to global agricultural landscapes.

Dr. Minfeng Xing
Prof. Dr. Jinfei Wang
Dr. Qisheng He
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

  • hyperspectral imaging
  • soil parameter retrieval
  • crop parameter retrieval
  • remote sensing technologies
  • precision agriculture
  • sustainable farming
  • soil physicochemical properties
  • crop health monitoring
  • unmanned aerial vehicles (UAVs)
  • biophysical and biochemical parameter estimation

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Published Papers (4 papers)

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Research

23 pages, 5050 KiB  
Article
Soil Organic Carbon Prediction and Mapping in Morocco Using PRISMA Hyperspectral Imagery and Meta-Learner Model
by Yassine Bouslihim, Abdelkrim Bouasria, Budiman Minasny, Fabio Castaldi, Andree Mentho Nenkam, Ali El Battay and Abdelghani Chehbouni
Remote Sens. 2025, 17(8), 1363; https://doi.org/10.3390/rs17081363 - 11 Apr 2025
Viewed by 673
Abstract
Accurate mapping of soil organic carbon (SOC) supports sustainable land management practices and carbon accounting initiatives for mitigating climate change impacts. This study presents a novel meta-learner framework that combines multiple machine learning algorithms and spectra processing algorithms to optimize SOC prediction using [...] Read more.
Accurate mapping of soil organic carbon (SOC) supports sustainable land management practices and carbon accounting initiatives for mitigating climate change impacts. This study presents a novel meta-learner framework that combines multiple machine learning algorithms and spectra processing algorithms to optimize SOC prediction using the PRISMA hyperspectral satellite imagery in the Doukkala plain of Morocco. The framework employs a two-layer structure of prediction models. The first layer consists of Random Forest (RF), Support Vector Regression (SVR), and Partial Least Squares Regression (PLSR). These base models were configured using data smoothing, transformation, and spectral feature selection techniques, based on a 70/30% data split. The second layer utilizes a ridge regression model as a meta-learner to integrate predictions from the base models. Results indicated that RF and SVR performance improved primarily with feature selection, while PLSR was most influenced by data smoothing. The meta-learner approach outperformed individual base models, achieving an average relative improvement of 48.8% over single models, with an R2 of 0.65, an RMSE of 0.194%, and an RPIQ of 2.247. This study contributes to the development of methodologies for predicting and mapping soil properties using PRISMA hyperspectral data. Full article
(This article belongs to the Special Issue Hyperspectral Sensors for Soil Parameters and Crop Parameters Retrieval)
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16 pages, 5342 KiB  
Article
Optimization of Grassland Carrying Capacity with Grass Quality Indicators Through GF5B Hyperspectral Images
by Xuejun Cheng, Maoxin Liao, Shuangyin Zhang, Siying Wang, Yiyun Chen and Teng Fei
Remote Sens. 2024, 16(24), 4807; https://doi.org/10.3390/rs16244807 - 23 Dec 2024
Cited by 1 | Viewed by 852
Abstract
The accurate estimation of grassland carrying capacity (GCC) in the alpine grasslands of the Changjiang River source region is crucial for managing livestock loads and ensuring ecological security on the Qinghai-Tibetan Plateau. Previous remote sensing methods have predominantly focused on yield indicators, often [...] Read more.
The accurate estimation of grassland carrying capacity (GCC) in the alpine grasslands of the Changjiang River source region is crucial for managing livestock loads and ensuring ecological security on the Qinghai-Tibetan Plateau. Previous remote sensing methods have predominantly focused on yield indicators, often neglecting quality indicators, which hampers precise GCC estimation. Here, we collected 25 samples from the Dangqu basin, analyzing various grass parameters including yield, crude protein (CP), neutral detergent fiber (NDF), and acid detergent fiber (ADF). Then, we developed models to optimize GCC using quality indicators derived from GF5B images, assessing performance through Pearson correlation coefficient (R2), root mean square error (RMSE), and relative root mean square error (rRMSE). Results were found to show an average yield of 61.26 g/m2, with CP, ADF, and NDF ranging from 5.81% to 18.75%, 45.47% to 58.80%, and 27.50% to 31.81%, respectively. Spectra in the near-infrared range, such as 1918 nm, and spectral indices improved the accuracy of the hyperspectral inversion of grass parameters. The GCC increased from 0.51 SU·hm−2 to 0.63 SU·hm−2 post-optimization, showing an increasing trend from northwest to southeast. This study enhances GCC estimation accuracy, aiding in reasonable livestock management and effective ecological preservation. Full article
(This article belongs to the Special Issue Hyperspectral Sensors for Soil Parameters and Crop Parameters Retrieval)
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20 pages, 23235 KiB  
Article
A Cross-Resolution Surface Net Radiative Inversion Based on Transfer Learning Methods
by Shuqi Miao, Qisheng He, Liujun Zhu, Mingxiao Yu, Yuhan Gu and Mingru Zhou
Remote Sens. 2024, 16(13), 2450; https://doi.org/10.3390/rs16132450 - 3 Jul 2024
Cited by 2 | Viewed by 1400
Abstract
Net radiation (Rn) is a key component of the Earth’s energy balance. With the rise of deep learning technology, remote sensing technology has made significant progress in the acquisition of large-scale surface parameters. However, the generally low spatial resolution of net radiation data [...] Read more.
Net radiation (Rn) is a key component of the Earth’s energy balance. With the rise of deep learning technology, remote sensing technology has made significant progress in the acquisition of large-scale surface parameters. However, the generally low spatial resolution of net radiation data and the relative scarcity of surface flux site data at home and abroad limit the potential of deep learning methods in constructing high spatial resolution net radiation models. To address this challenge, this study proposes an innovative approach of a multi-scale transfer learning framework, which assumes that composite models at different spatial scales are similar in structure and parameters, thus enabling the training of accurate high-resolution models using fewer samples. In this study, the Heihe River Basin was taken as the study area and the Rn products of the Global Land Surface Satellite (GLASS) were selected as the target for coarse model training. Based on the dense convolutional network (DenseNet) architecture, 25 deep learning models were constructed to learn the spatial and temporal distribution patterns of GLASS Rn products by combining multi-source data, and a 5 km coarse resolution net radiation model was trained. Subsequently, the parameters of the pre-trained coarse-resolution model were fine-tuned with a small amount of measured ground station data to achieve the transfer from the 5 km coarse-resolution model to the 1 km high-resolution model, and a daily high-resolution net radiation model with 1 km resolution for the Heihe River Basin was finally constructed. The results showed that the bias, R2, and RMSE of the high-resolution net radiation model obtained by transfer learning were 0.184 W/m2, 0.924, and 24.29 W/m2, respectively, which was better than those of the GLASS Rn products. The predicted values were highly correlated with the measured values at the stations and the fitted curves were closer to the measured values at the stations than those of the GLASS Rn products, which further demonstrated that the transfer learning method could capture the soil moisture and temporal variation of net radiation. Finally, the model was used to generate 1 km daily net radiation products for the Heihe River Basin in 2020. This study provides new perspectives and methods for future large-scale and long-time-series estimations of surface net radiation. Full article
(This article belongs to the Special Issue Hyperspectral Sensors for Soil Parameters and Crop Parameters Retrieval)
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22 pages, 14590 KiB  
Article
Early Detection of Drought Stress in Durum Wheat Using Hyperspectral Imaging and Photosystem Sensing
by Bishal Roy, Vasit Sagan, Alifu Haireti, Maria Newcomb, Roberto Tuberosa, David LeBauer and Nadia Shakoor
Remote Sens. 2024, 16(1), 155; https://doi.org/10.3390/rs16010155 - 30 Dec 2023
Cited by 5 | Viewed by 3016
Abstract
Wheat, being the third largest U.S. crop and the principal food grain, faces significant risks from climate extremes such as drought. This necessitates identifying and developing methods for early water-stress detection to prevent yield loss and improve water-use efficiency. This study investigates the [...] Read more.
Wheat, being the third largest U.S. crop and the principal food grain, faces significant risks from climate extremes such as drought. This necessitates identifying and developing methods for early water-stress detection to prevent yield loss and improve water-use efficiency. This study investigates the potential of hyperspectral imaging to detect the early stages of drought stress in wheat. The goal is to utilize this technology as a tool for screening and selecting drought-tolerant wheat genotypes in breeding programs. Additionally, this research aims to systematically evaluate the effectiveness of various existing sensors and methods for detecting early stages of water stress. The experiment was conducted in a durum wheat experimental field trial in Maricopa, Arizona, in the spring of 2019 and included well-watered and water-limited treatments of a panel of 224 replicated durum wheat genotypes. Spectral indices derived from hyperspectral imagery were compared against other plant-level indicators of water stress such as Photosystem II (PSII) and relative water content (RWC) data derived from proximal sensors. Our findings showed a 12% drop in photosynthetic activity in the most affected genotypes when compared to the least affected. The Leaf Water Vegetation Index 1 (LWVI1) highlighted differences between drought-resistant and drought-susceptible genotypes. Drought-resistant genotypes retained 43.36% more water in leaves under well-watered conditions compared to water-limited conditions, while drought-susceptible genotypes retained only 15.69% more. The LWVI1 and LWVI2 indices, aligned with the RWC measurements, revealed a strong inverse correlation in the susceptible genotypes, underscoring their heightened sensitivity to water stress in earlier stages. Several genotypes previously classified based on their drought resistance showed spectral indices deviating from expectations. Results from this research can aid farmers in improving crop yields by informing early management practices. Moreover, this research offers wheat breeders insights into the selection of drought-tolerant genotypes, a requirement that is becoming increasingly important as weather patterns continue to change. Full article
(This article belongs to the Special Issue Hyperspectral Sensors for Soil Parameters and Crop Parameters Retrieval)
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