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Remote Sensing for Agricultural Water Management (RSAWM) (Second Edition)
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Remote Sensing for Agricultural Water Management (RSAWM) (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: 30 November 2024 | Viewed by 3872

Special Issue Editors

Dr. Ramona Magno

E-Mail Website
Guest Editor
Institute of Bioeconomy, National Research Council, 50019 Sesto Fiorentino, Italy
Interests: drought monitoring and early warning systems; climate change/variability at national and international levels; forest management; land use/land cover characterization; desertification
Special Issues, Collections and Topics in MDPI journals
Dr. Paolo Filippucci

E-Mail Website
Guest Editor
Research Institute for Geo-Hydrological Protection, National Research Council, Perugia, Italy
Interests: remote sensing; soil moisture; rainfall; river discharge; irrigation; high resolution

Special Issue Information

Dear Colleagues,

Global warming is changing precipitation patterns, exacerbating extreme events such as droughts, floodings, heat waves and windstorms. This intensification, along with the impact of human activities, threatens water availability and quality, as well as soil fertility, putting a strain on the resilience of the agriculture sector. Monitoring and managing water resources is becoming more and more necessary to avoid wasting this precious resource. In this context, the availability of detailed information on the water cycle is crucial to plan a sustainable water allocation between different competing users and guide farmers and authorities in the adoption of more effective water saving practices.

Such information can be acquired using new technologies and remote sensing instruments, and exploiting advances in computing performance, data processing and modeling are also the key elements.

This Special Issue solicits papers dealing with innovative strategies, approaches, techniques and tools able to build a comprehensive water management framework to encompass and link all the phases, from the drivers to the impacts and responses.

A multi-data-driven approach based on high-resolution multi-platform and multi-mission EO data that integrate climate, soil, hydrogeological, vegetation and water use (e.g., irrigation practices) metrics is highly recommended, together with the use of innovative machine learning methodologies.

Dr. Ramona Magno
Dr. Paolo Filippucci
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

  • high resolution
  • EO data
  • machine learning
  • multi-data-driven approach
  • water balance
  • water consumption
  • soil
  • climate
  • vegetation
  • irrigation

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Related Special Issue

Published Papers (4 papers)

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Research

23 pages, 14007 KiB  
Article
Influence of Land Use and Land Cover Changes and Precipitation Patterns on Groundwater Storage in the Mississippi River Watershed: Insights from GRACE Satellite Data
by Padmanava Dash, Sushant Shekhar, Varun Paul and Gary Feng
Remote Sens. 2024, 16(22), 4285; https://doi.org/10.3390/rs16224285 - 17 Nov 2024
Viewed by 347
Abstract
Growing human demands are placing significant pressure on groundwater resources, causing declines in many regions. Identifying areas where groundwater levels are declining due to human activities is essential for effective resource management. This study investigates the influence of land use and land cover, [...] Read more.
Growing human demands are placing significant pressure on groundwater resources, causing declines in many regions. Identifying areas where groundwater levels are declining due to human activities is essential for effective resource management. This study investigates the influence of land use and land cover, crop types, and precipitation patterns on groundwater level trends across the Mississippi River Watershed (MRW), USA. Groundwater storage changes from 2003 to 2015 were estimated using data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission. A spatiotemporal analysis was conducted at four scales: the entire MRW, groundwater regimes based on groundwater level change rates, 31 states within the MRW, and six USGS hydrologic unit code (HUC)-2 watersheds. The results indicate that the Lower Mississippi region experienced the fastest groundwater decline, with a Sen’s slope of −0.07 cm/year for the mean equivalent water thickness, which was attributed to intensive groundwater-based soybean farming. By comparing groundwater levels with changes in land use, crop types, and precipitation, trends driven by human activities were identified. This work underscores the ongoing relevance of GRACE data and the GRACE Follow-On mission, launched in 2018, which continues to provide vital data for monitoring groundwater storage. These insights are critical for managing groundwater resources and mitigating human impacts on the environment. Full article
(This article belongs to the Special Issue Remote Sensing for Agricultural Water Management (RSAWM) (Second Edition))
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15 pages, 4826 KiB  
Article
Assessing Evapotranspiration Changes in Response to Cropland Expansion in Tropical Climates
by Leonardo Laipelt, Julia Brusso Rossi, Bruno Comini de Andrade, Morris Scherer-Warren and Anderson Ruhoff
Remote Sens. 2024, 16(18), 3404; https://doi.org/10.3390/rs16183404 - 13 Sep 2024
Viewed by 709
Abstract
The expansion of cropland in tropical regions has significantly accelerated in recent decades, triggering an escalation in water demand and changing the total water loss to the atmosphere (evapotranspiration). Additionally, the increase in areas dedicated to agriculture in tropical climates coincides with an [...] Read more.
The expansion of cropland in tropical regions has significantly accelerated in recent decades, triggering an escalation in water demand and changing the total water loss to the atmosphere (evapotranspiration). Additionally, the increase in areas dedicated to agriculture in tropical climates coincides with an increased frequency of drought events, leading to a series of conflicts among water users. However, detailed studies on the impacts of changes in water use due to agriculture expansion, including irrigation, are still lacking. Furthermore, the higher presence of clouds in tropical environments poses challenges for the availability of high-resolution data for vegetation monitoring via satellite images. This study aims to analyze 37 years of agricultural expansion using the Landsat collection and a satellite-based model (geeSEBAL) to assess changes in evapotranspiration resulting from cropland expansion in tropical climates, focusing on the São Marcos River Basin in Brazil. It also used a methodology for estimating daily evapotranspiration on days without satellite images. The results showed a 34% increase in evapotranspiration from rainfed areas, mainly driven by soybean cultivation. In addition, irrigated areas increased their water use, despite not significantly changing water use at the basin scale. Conversely, natural vegetation areas decreased their evapotranspiration rates by 22%, suggesting possible further implications with advancing changes in land use and land cover. Thus, this study underscores the importance of using satellite-based evapotranspiration estimates to enhance our understanding of water use across different land use types and scales, thereby improving water management strategies on a large scale. Full article
(This article belongs to the Special Issue Remote Sensing for Agricultural Water Management (RSAWM) (Second Edition))
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21 pages, 11743 KiB  
Article
Assessing Future Ecological Sustainability Shaped by Shared Socioeconomic Pathways: Insights from an Arid Farming–Pastoral Zone of China
by Jiachen Ji, Sunxun Zhang, Tingting Zhou, Fan Zhang, Tianqi Zhao, Xinying Wu, Yanan Zhuo, Yue Zhang and Naijing Lu
Remote Sens. 2024, 16(16), 2894; https://doi.org/10.3390/rs16162894 - 8 Aug 2024
Viewed by 1086
Abstract
Ecological sustainability quantifies the capacity of an ecological system to sustain its health while fulfilling human survival needs and supporting future development. An accurate projection of ecological dynamics for sustainability is crucial for decision-makers to comprehend potential risks. However, the intricate interplay between [...] Read more.
Ecological sustainability quantifies the capacity of an ecological system to sustain its health while fulfilling human survival needs and supporting future development. An accurate projection of ecological dynamics for sustainability is crucial for decision-makers to comprehend potential risks. However, the intricate interplay between climate change and human activity has hindered comprehensive assessments of future ecological sustainability, leaving it inadequately investigated thus far. This study aimed to assess future ecological sustainability shaped by the Shared Socioeconomic Pathways (SSPs) using remote sensing data from a typical arid farming–pastoral zone located at the northern foot of Yinshan Mountain (NFYM), Inner Mongolia, China. Five machine learning models were employed to evaluate the relationship between ecological sustainability and its driving factors. The results indicate that (1) overall ecological sustainability initially decreased and then increased during 2003–2022; (2) the Geophysical Fluid Dynamics Laboratory Earth System Model version 4 (GFDL-ESM4) mode and random forest model demonstrated the best performance in climate and ecological sustainability simulations; and (3) the annual change rates of ecological sustainability from 2023 to 2099 are projected to be +0.45%, −0.05%, and −0.46% per year under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, respectively, suggesting that stringent environmental policies can effectively enhance ecological sustainability. The proposed framework can assist decision-makers in understanding ecological changes under different SSPs and calls for strategies to enhance ecosystem resilience in the NFYM and similar regions. Full article
(This article belongs to the Special Issue Remote Sensing for Agricultural Water Management (RSAWM) (Second Edition))
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23 pages, 9319 KiB  
Article
Drought Monitoring and Prediction in Agriculture: Employing Earth Observation Data, Climate Scenarios and Data Driven Methods; a Case Study: Mango Orchard in Tamale, Ghana
by Marius Hobart, Michael Schirrmann, Abdul-Halim Abubakari, Godwin Badu-Marfo, Simone Kraatz and Mohammad Zare
Remote Sens. 2024, 16(11), 1942; https://doi.org/10.3390/rs16111942 - 28 May 2024
Viewed by 1089
Abstract
The study focused on the prediction of the Temperature Vegetation Dryness Index (TVDI), an agricultural drought index, for a Mango orchard in Tamale, Ghana. It investigated the temporal relationship between the meteorological drought index, Standardized Precipitation Index (SPI), and TVDI. The SPI was [...] Read more.
The study focused on the prediction of the Temperature Vegetation Dryness Index (TVDI), an agricultural drought index, for a Mango orchard in Tamale, Ghana. It investigated the temporal relationship between the meteorological drought index, Standardized Precipitation Index (SPI), and TVDI. The SPI was calculated based on utilizing precipitation data from the World Meteorological Organization (WMO) database (2010–2022) and CMIP6 projected precipitation data (2023–2050) from 35 climate models representing various Shared Socioeconomic Pathway (SSP) climate change scenarios. Concurrently, TVDI was derived from Landsat 8/9 satellite imagery, validated using thermal data obtained from unmanned aerial vehicle (UAV) surveys. A comprehensive cross-correlation analysis between TVDI and SPI was conducted to identify lag times between these indices. Building on this temporal relationship, the TVDI was modeled as a function of SPI, with varying lag times as inputs to the Wavelet-Adaptive Neuro-Fuzzy Inference System (Wavelet-ANFIS). This innovative approach facilitated robust predictions of TVDI as an agricultural drought index, specifically relying on SPI as a predictor of meteorological drought occurrences for the years 2023–2050. The research outcome provides practical insights into the dynamic nature of drought conditions in the Tamale mango orchard region. The results indicate significant water stress projected for different time frames: 186 months for SSP126, 183 months for SSP245, and 179 months for both SSP370 and SSP585. This corresponds to a range of 55–57% of the projected months. These insights are crucial for formulating proactive and sustainable strategies for agricultural practices. For instance, implementing supplemental irrigation systems or crop adaptations can be effective measures. The anticipated outcomes contribute to a nuanced understanding of drought impacts, facilitating informed decision-making for agricultural planning and resource allocation. Full article
(This article belongs to the Special Issue Remote Sensing for Agricultural Water Management (RSAWM) (Second Edition))
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