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Remote Sensing Application in Augmenting Water and Fertilizer Utilization for...
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Remote Sensing Application in Augmenting Water and Fertilizer Utilization for Sustainable Agriculture

A special issue of Plants (ISSN 2223-7747). This special issue belongs to the section "Plant Nutrition".

Deadline for manuscript submissions: closed (30 June 2025) | Viewed by 836

Special Issue Editors

Prof. Dr. Junying Chen

E-Mail Website
Guest Editor
College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, China
Interests: application of remote sensing technology in agriculture; reclaimed water irrigation; water-saving agriculture
Dr. Wenquan Gu

E-Mail Website
Guest Editor
State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China
Interests: intelligent regulation of water resources system; uncertainty and risk analysis of water resources system; agricultural remote sensing; hydrological model
Dr. Xiaochun Zhang

E-Mail Website
Guest Editor
State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China
Interests: remote sensing data fusion modelling; remote sensing evapotranspiration and disaster monitoring

Special Issue Information

Dear Colleagues,

The growth of the global population and the intensification of climate change have resulted in water scarcity and ecological degradation becoming significant constraints to sustainable agricultural development. The scientific and efficient utilization of water and fertilizer in agricultural production to ensure the stability and sustainability of agricultural production has become a prominent area of global research.

Remote sensing technology, as a non-destructive monitoring technology, can provide large-scale, real-time and continuous observations for monitoring crop health and the physicochemical properties of soil, assessing the impact of soil moisture and nutrients on crop yields and the regional environment, and optimizing on-farm irrigation and fertilizer application strategies to ensure food security and environmentally compatible development.

We welcome contributions regarding the innovative application of remote sensing technology in sustainable agricultural practices, with a particular focus on the efficient use of water and fertilizer. The objective is to investigate the potential use of remote sensing technology in advancing precision agriculture and enhancing the sustainability and efficiency of resource use.

Prof. Dr. Junying Chen
Dr. Wenquan Gu
Dr. Xiaochun Zhang
Guest Editors

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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

  • remote sensing
  • water and fertilizer utilization
  • sustainable agriculture
  • crop growth condition

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Published Papers (2 papers)

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Research

24 pages, 5039 KiB  
Article
Advanced Estimation of Winter Wheat Leaf’s Relative Chlorophyll Content Across Growth Stages Using Satellite-Derived Texture Indices in a Region with Various Sowing Dates
by Jingyun Chen, Quan Yin, Jianjun Wang, Weilong Li, Zhi Ding, Pei Sun Loh, Guisheng Zhou and Zhongyang Huo
Plants 2025, 14(15), 2297; https://doi.org/10.3390/plants14152297 - 25 Jul 2025
Viewed by 275
Abstract
Accurately estimating leaves’ relative chlorophyll contents (widely represented by Soil and Plant Analysis Development (SPAD) values) across growth stages is crucial for assessing crop health, particularly in regions characterized by varying sowing dates. Unlike previous studies focusing on high-resolution UAV imagery or specific [...] Read more.
Accurately estimating leaves’ relative chlorophyll contents (widely represented by Soil and Plant Analysis Development (SPAD) values) across growth stages is crucial for assessing crop health, particularly in regions characterized by varying sowing dates. Unlike previous studies focusing on high-resolution UAV imagery or specific growth stages, this research incorporates satellite-derived texture indices (TIs) into a SPAD value estimation model applicable across multiple growth stages (from tillering to grain-filling). Field experiments were conducted in Jiangsu Province, China, where winter wheat sowing dates varied significantly from field to field. Sentinel-2 imagery was employed to extract vegetation indices (VIs) and TIs. Following a two-step variable selection method, Random Forest (RF)-LassoCV, five machine learning algorithms were applied to develop estimation models. The newly developed model (SVR-RBFVIs+TIs) exhibited robust estimation performance (R2 = 0.8131, RMSE = 3.2333, RRMSE = 0.0710, and RPD = 2.3424) when validated against independent SPAD value datasets collected from fields with varying sowing dates. Moreover, this optimal model also exhibited a notable level of transferability at another location with different sowing times, wheat varieties, and soil types from the modeling area. In addition, this research revealed that despite the lower resolution of satellite imagery compared to UAV imagery, the incorporation of TIs significantly improved estimation accuracies compared to the sole use of VIs typical in previous studies. Full article
(This article belongs to the Special Issue Remote Sensing Application in Augmenting Water and Fertilizer Utilization for Sustainable Agriculture)
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22 pages, 6134 KiB  
Article
The Evaluation of Small-Scale Field Maize Transpiration Rate from UAV Thermal Infrared Images Using Improved Three-Temperature Model
by Xiaofei Yang, Zhitao Zhang, Qi Xu, Ning Dong, Xuqian Bai and Yanfu Liu
Plants 2025, 14(14), 2209; https://doi.org/10.3390/plants14142209 - 17 Jul 2025
Viewed by 306
Abstract
Transpiration is the dominant process driving water loss in crops, significantly influencing their growth, development, and yield. Efficient monitoring of transpiration rate (Tr) is crucial for evaluating crop physiological status and optimizing water management strategies. The three-temperature (3T) model has potential for rapid [...] Read more.
Transpiration is the dominant process driving water loss in crops, significantly influencing their growth, development, and yield. Efficient monitoring of transpiration rate (Tr) is crucial for evaluating crop physiological status and optimizing water management strategies. The three-temperature (3T) model has potential for rapid estimation of transpiration rates, but its application to low-altitude remote sensing has not yet been further investigated. To evaluate the performance of 3T model based on land surface temperature (LST) and canopy temperature (TC) in estimating transpiration rate, this study utilized an unmanned aerial vehicle (UAV) equipped with a thermal infrared (TIR) camera to capture TIR images of summer maize during the nodulation-irrigation stage under four different moisture treatments, from which LST was extracted. The Gaussian Hidden Markov Random Field (GHMRF) model was applied to segment the TIR images, facilitating the extraction of TC. Finally, an improved 3T model incorporating fractional vegetation coverage (FVC) was proposed. The findings of the study demonstrate that: (1) The GHMRF model offers an effective approach for TIR image segmentation. The mechanism of thermal TIR segmentation implemented by the GHMRF model is explored. The results indicate that when the potential energy function parameter β value is 0.1, the optimal performance is provided. (2) The feasibility of utilizing UAV-based TIR remote sensing in conjunction with the 3T model for estimating Tr has been demonstrated, showing a significant correlation between the measured and the estimated transpiration rate (Tr-3TC), derived from TC data obtained through the segmentation and processing of TIR imagery. The correlation coefficients (r) were 0.946 in 2022 and 0.872 in 2023. (3) The improved 3T model has demonstrated its ability to enhance the estimation accuracy of crop Tr rapidly and effectively, exhibiting a robust correlation with Tr-3TC. The correlation coefficients for the two observed years are 0.991 and 0.989, respectively, while the model maintains low RMSE of 0.756 mmol H2O m−2 s−1 and 0.555 mmol H2O m−2 s−1 for the respective years, indicating strong interannual stability. Full article
(This article belongs to the Special Issue Remote Sensing Application in Augmenting Water and Fertilizer Utilization for Sustainable Agriculture)
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