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Remote Sensing
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Enhancing Vegetation and Water Use Management Through Earth Observation
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Enhancing Vegetation and Water Use Management Through Earth Observation

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 1913

Special Issue Editors

Prof. Dr. Fei Zhang

E-Mail Website
Guest Editor
College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, China
Interests: remote sensing; water environment; landscape pattern; land use/land cover; modelling building; data reconstruction; image fusion
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaoping Wang

E-Mail Website
Guest Editor
College of Natural Resources and Environment, Northwest A&F University, Yangling 712100, China
Interests: remote sensing; terrestrial water-carbon cycle; environment monitoring and management
Special Issues, Collections and Topics in MDPI journals
Dr. Brian Alan Johnson

E-Mail Website
Guest Editor
Natural Resources and Ecosystem Services, Institute for Global Environmental Strategies, Kanagawa 240-0115, Japan
Interests: geographic information systems (GIS); remote sensing; spatial modeling; and data mining for urban and environmental analysis and planning; mapping urban land cover (green space, impervious surfaces, etc.); monitoring forest health using fine resolution satellite imagery
Special Issues, Collections and Topics in MDPI journals
Dr. Xu Ma

E-Mail Website
Guest Editor
College of Geography and Remote Sensing Science, Xinjiang University, Urumqi 830046, China
Interests: mathematical modeling on remote sensing; radiative transfer of vegetation; inversion of fractional vegetation cover; monitoring of biological crusts; short-lived vegetation

Special Issue Information

Dear Colleagues,

Amidst the confluence of climate warming and anthropogenic activities, the global water cycle undergoes significant changes, exacerbating the disparity between water resource supply and demand. The terrestrial water cycle, a complex nonlinear system, necessitates a comprehensive understanding of its dynamics to effectively manage limited water resources. At the same time, in the context of anthropogenic activities, regional water cycle research must consider the impacts of human activities, notably alterations in vegetation and water usage, particularly in agricultural irrigation.

Remote sensing plays a vital role in monitoring vegetation dynamics and managing water resources by providing comprehensive spatial and temporal information. These technologies enable rapid and precise assessments, facilitating informed decision-making for sustainable land and water management practices. This special issue aims to explore the latest satellite technologies for monitoring regional vegetation and water resources. Topics of interest span spatial and temporal variations in regional water resources (e.g., precipitation, groundwater, runoff, lake water, soil water content, etc.) and vegetation attributes (e.g., coverage rate, leaf area index, and biomass), alongside investigations into the coupling mechanism of regional water resources and vegetation.

Thus, we invite contributions exploring multi-source remote sensing data integration, multi-scale methodologies, and studies focused on vegetation and water use management, among other issues.

Remote sensing studies on the coupling mechanisms of regional water resources and vegetation are becoming one of the most active disciplines in earth sciences, providing many opportunities and advances for watershed ecohydrology and other geographical disciplines. Remote Sensing is a world-renowned journal, and this special issue will attract more readers.

The special issue is focused on the following: vegetation health, water availability, vegetation stress, drought sustainable water management, agricultural water, multi-source data integration, leaf area index, biomass.

Prof. Dr. Fei Zhang
Dr. Xiaoping Wang
Dr. Brian Alan Johnson
Dr. Xu Ma
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

  • remote sensing observation of land surface parameters
  • soil water content
  • vegetation parameters
  • water resources
  • evapotranspiration
  • ecological process model
  • hydrological model

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Published Papers (3 papers)

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Research

28 pages, 7506 KiB  
Article
Impact of Plateau Grassland Degradation on Ecological Suitability: Revealing Degradation Mechanisms and Dividing Potential Suitable Areas with Multi Criteria Models
by Yi Chai, Lin Xu, Yong Xu, Kun Yang, Rao Zhu, Rui Zhang and Xiaxing Li
Remote Sens. 2025, 17(15), 2539; https://doi.org/10.3390/rs17152539 (registering DOI) - 22 Jul 2025
Abstract
The Qinghai–Tibetan Plateau (QTP), often referred to as the “Third Pole” of the world, harbors alpine grassland ecosystems that play an essential role as global carbon sinks, helping to mitigate the pace of climate change. Nonetheless, alterations in natural environmental conditions coupled with [...] Read more.
The Qinghai–Tibetan Plateau (QTP), often referred to as the “Third Pole” of the world, harbors alpine grassland ecosystems that play an essential role as global carbon sinks, helping to mitigate the pace of climate change. Nonetheless, alterations in natural environmental conditions coupled with escalating human activities have disrupted the seasonal growth cycles of grasslands, thereby intensifying degradation processes. To date, the key drivers and lifecycle dynamics of Grassland Depletion across the QTP remain contentious, limiting our comprehension of its ecological repercussions and regulatory mechanisms. This study comprehensively investigates grassland degradation on the Qinghai–Tibetan Plateau, analyzing its drivers and changes in ecological suitability during the growing season. By integrating natural factors (e.g., precipitation and temperature) and anthropogenic influences (e.g., population density and grazing intensity), it examines observational data from over 160 monitoring stations collected between the 1980s and 2020. The findings reveal three distinct phases of grassland degradation: an acute degradation phase in 1990 (GDI, Grassland Degradation Index = 2.53), a partial recovery phase from 1996 to 2005 (GDI < 2.0) during which the proportion of degraded grassland decreased from 71.85% in 1990 to 51.22% in 2005, and a renewed intensification of degradation after 2006 (GDI > 2.0), with degraded grassland areas reaching 56.39% by 2020. Among the influencing variables, precipitation emerged as the most significant driver, interacting closely with anthropogenic factors such as grazing practices and population distribution. Specifically, the combined impacts of precipitation with population density, grazing pressure, and elevation were particularly notable, yielding interaction q-values of 0.796, 0.767, and 0.752, respectively. Our findings reveal that while grasslands exhibit superior carbon sink potential relative to forests, their productivity and ecological functionality are undergoing considerable declines due to the compounded effects of multiple interacting factors. Consequently, the spatial distribution of ecologically suitable zones has contracted significantly, with the remaining high-suitability regions concentrating in the “twin-star” zones of Baingoin and Zanda grasslands, areas recognized as focal points for future ecosystem preservation. Furthermore, the effects of climate change and intensifying anthropogenic activity have driven the reduction in highly suitable grassland areas, shrinking from 41,232 km2 in 1990 to 24,485 km2 by 2020, with projections indicating a further decrease to only 2844 km2 by 2060. This study sheds light on the intricate mechanisms behind Grassland Depletion, providing essential guidance for conservation efforts and ecological restoration on the QTP. Moreover, it offers theoretical underpinnings to support China’s carbon neutrality and peak carbon emission goals. Full article
(This article belongs to the Special Issue Enhancing Vegetation and Water Use Management Through Earth Observation)
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23 pages, 7915 KiB  
Article
Beyond Algorithm Updates: A Systematic Validation of GPM DPR-V07 over China’s Multiscale Topography
by Jia Song, Haiwei Zhang, Yi Lyu, Hao Wu, Fei Zhang, Xu Ma and Bin Yong
Remote Sens. 2025, 17(14), 2410; https://doi.org/10.3390/rs17142410 - 12 Jul 2025
Viewed by 297
Abstract
The Global Precipitation Measurement (GPM) mission’s Dual-Frequency Precipitation Radar (DPR) serves as a critical benchmark for calibrating satellite-based precipitation products, with its retrieval quality directly governing the accuracy of global precipitation estimates. While the updated version 07 (DPR-V07) algorithm introduces substantial refinements over [...] Read more.
The Global Precipitation Measurement (GPM) mission’s Dual-Frequency Precipitation Radar (DPR) serves as a critical benchmark for calibrating satellite-based precipitation products, with its retrieval quality directly governing the accuracy of global precipitation estimates. While the updated version 07 (DPR-V07) algorithm introduces substantial refinements over its predecessor (DPR-V06), systematic evaluations of its operational advancements in precipitation monitoring remain limited. This study utilizes ground-based rain gauge data from Mainland China (2015–2018) to assess improvements of DPR-V07 over its predecessor’s (DPR-V06) effects. The results indicate that DPR-V07 reduces the high-altitude precipitation underestimation by 5% (vs. V06) in the west (W) and corrects the elevation-linked overestimation via an improved terrain sensitivity. The seasonal analysis demonstrates winter-specific advancements of DPR-V07, with a 3–8% reduction in the miss bias contributing to a lower total bias. However, the algorithm updates yield unintended trade-offs: the High-Sensitivity Scan (HS) mode exhibits significant detection performance degradation, particularly in east (E) and midwest (M) regions, with Critical Success Index (CSI) values decreasing by approximately 0.12 compared to DPR-V06. Furthermore, summer error components show a minimal improvement, suggesting unresolved challenges in warm-season retrieval physics. This study establishes a systematic framework for evaluating precipitation retrieval advancements, providing critical insights for future satellite algorithm development and operational applications in hydrometeorological monitoring. Full article
(This article belongs to the Special Issue Enhancing Vegetation and Water Use Management Through Earth Observation)
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26 pages, 11094 KiB  
Article
Evapotranspiration Disaggregation Using an Integrated Indicating Factor Based on Slope Units
by Linjiang Wang, Bingfang Wu, Weiwei Zhu, Abdelrazek Elnashar, Nana Yan and Zonghan Ma
Remote Sens. 2025, 17(7), 1201; https://doi.org/10.3390/rs17071201 - 28 Mar 2025
Viewed by 596
Abstract
This study proposes an evapotranspiration (ET) disaggregation model based on slope units. Different slope units are first delineated based on digital elevation model data with high spatial resolution. Key factors influencing ET variability across topographies, such as radiation, vegetation, and moisture, are integrated [...] Read more.
This study proposes an evapotranspiration (ET) disaggregation model based on slope units. Different slope units are first delineated based on digital elevation model data with high spatial resolution. Key factors influencing ET variability across topographies, such as radiation, vegetation, and moisture, are integrated using Sentinel-2 and DEM data to construct an indicating factor. A slope-scale ET disaggregation model is developed using ETWatch data (1 km resolution) and the integrated factor, yielding reliable 10 m resolution ET data that reflect slope-scale variations. The validation in Huairou and Baotianman shows coefficients of determination of 0.9 and 0.91, respectively, and root mean square errors of 0.45 mm and 0.47 mm. Compared to the original 1 km resolution ET data, the disaggregated results show improved accuracy, with R2 values increasing by 1% (Huairou) and 2% (Baotianman) and RMSE decreasing by 21% and 13%, respectively. This model offers a novel approach for estimating forest evapotranspiration in mountainous areas and significant potential for water resource management and sustainable land–water allocation. Full article
(This article belongs to the Special Issue Enhancing Vegetation and Water Use Management Through Earth Observation)
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