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Agricultural Water Management Using Geospatial Technologies

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

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 7257

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Special Issue Editors

Dr. Xihua Yang

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Principal Research Scientist, New South Wales Department of Planning, Industry and Environment, University Technology Sydney, P.O. Box 624, Parramatta, NSW 2150, Australia
Interests: remote sensing and geospatial information system (GIS) applications in agriculture and soil erosion modeling
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Prof. Dr. Alfredo Huete

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School of Life Sciences, University of Technology Sydney, Ultimo, NSW 2007, Australia
Interests: biophysical remote sensing; terrestrial ecohydrology; land surface phenology; carbon and water fluxes; geostationary and low earth observations; time series analyses; climate change impacts; vegetation health and ecosystem resilience; ecological forecasting
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Prof. Dr. Yong Xue

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Guest Editor

School of Environment Science and Geoinformatics, China University of Mining and Technology, Xuzhou 221116, China
Interests: quantitative remote sensing; geocomputation; aerosol optical depth; thermal inertia modeling; heat exchange calculation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The use of remote sensing and spatial-related technologies and their application in agricultural drainage water management (DWM) is growing rapidly. Along with other techniques, such as geographic information systems (GIS), spatial data analytics, advanced machine learning-based tools, remote sensing provides a better alternative to the conventional techniques in the monitoring and assessment of water resources and agricultural DWM. It allows us to develop cost-effective tools for the delineation and evaluation of the factors affecting DWM, including geomorphology, hydrology, climate conditions, and the socio-economic and institutional environment. The aim of the present Special Issue is to cover the relevant topics, trends, and best practices in agricultural DWM monitoring and modeling so that the timing and the amount of water discharged from agricultural drainage systems can be better managed.

We would like to invite you to contribute by submitting articles about your recent research, experimental work, reviews, and/or case studies related to agricultural drainage management. Contributions may include, but are not limited to, the applications of remote sensing and GIS in the following topics:

Rainfall–runoff modeling;
Development and application of a water balance model;
Multi-model approach for stream flow simulation;
Multi-criteria analysis for construction of agricultural drinage network;
Machine learning and geo-computation in agricultural drinage management;
The effects of climate change on water resources planning and management and sustainable agricultural drainage systems;
Water demand–supply–constraint analysis in irrigated agriculture;
Irrigation performance indicators
Scenario analysis of drainage management practices;
Groundwater recharge estimation;
Remote sensing for water storage and environmental water usage;
Soil erosion, non-point source pollution, and water quality modeling at agricultural watersheds;
High-resolution remote sensing applications in agricultural drainage management;
UAV and laser scanning techniques and their applications in agricultural drainage management;
Drainage water disposal;
Mobile mapping for agricultural drainage networks.

Dr. Xihua Yang
Prof. Dr. Alfredo R. Huete
Prof. Dr. Yong Xue
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

Drainage water management Drinage network
Surface runoff
Water balance
Remote sensing
GIS
Water resources

Published Papers (3 papers)

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Research

21 pages, 7294 KiB

Open AccessArticle

NDVI Response to Satellite-Estimated Antecedent Precipitation in Dryland Pastures

by Carlos Brieva, Patricia M. Saco, Steven G. Sandi, Sebastián Mora and José F. Rodríguez

Remote Sens. 2023, 15(14), 3615; https://doi.org/10.3390/rs15143615 - 20 Jul 2023

Viewed by 881

Abstract

Precipitation is a critical driver of vegetation productivity and dynamics in dryland environments, especially in areas with intense livestock farming. Availability and access to accurate, reliable, and timely rainfall data are essential for natural resources management, environmental monitoring, and informing hydrological rainfall-runoff models. Gauged precipitation data in drylands are often scarce, fragmented, and with low spatial resolution; therefore, satellite-estimated precipitation becomes a valuable dataset for overcoming this constraint. Using statistical indices, we compared satellite-derived precipitation data from four products (CHIRPS, GPM, TRMM, and PERSIANN-CDR) against gauged data at different temporal scales (daily, monthly, and yearly). Spatial correlations were calculated for GPM and CHIRPS estimates against interpolated gauged precipitation. We then estimated NDVI response to Antecedent Accumulated Precipitation (AAP) for 1, 3, 6, 9, and 12 months of four major vegetation types typical of the region. Statistical metrics varied with temporal scales being highest and acceptable for periods of 1 month or 1 year. At monthly scale GPM presented the best Pearson’s Correlation Coefficient (r), Root Mean Square Error (RMSE) and RMSE-observations standard deviation ratio (RSR) and CHIRPS resulted in lower Mean Error (ME) and Bias. On an annual basis CHIRPS showed the best adjustment for all indicators except for r. NDVI responses to 3 months of AAP were significant for all vegetation types in the study area. The findings of this study show that estimated precipitation data from GPM and CHIRPS satellites are accurate and valuable as a tool for analysing the relationships between precipitation and vegetation in the drylands of Mendoza. Full article

(This article belongs to the Special Issue Agricultural Water Management Using Geospatial Technologies)

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17 pages, 8297 KiB

Open AccessArticle

Long-Term Gully Erosion and Its Response to Human Intervention in the Tableland Region of the Chinese Loess Plateau

by Jiaxi Wang, Yan Zhang, Jiayong Deng, Shuangwu Yu and Yiyang Zhao

Remote Sens. 2021, 13(24), 5053; https://doi.org/10.3390/rs13245053 - 13 Dec 2021

Cited by 11 | Viewed by 2456

Abstract

The gully erosion process is influenced by both natural conditions and human activities on the tableland region, the Chinese Loess Plateau, which is a densely populated agricultural area with unique topography. For the purpose of assessing long-term gully growth rates, the influencing factors and potential of gully growth, KH-4B satellite images, Quickbird-2 images, and unmanned aerial vehicle (UAV) images were used to assess gully erosion from 1969 to 2019. The effects of runoff, topography and human activities were analyzed with information derived from historical and present images. Ninety-five investigated gullies were classified into four types: 45 growing, 25 stable, 21 infilled and four excavated gullies. The rates (R_A) of 45 growing gullies ranged from 0.50 to 20.94 m²·yr⁻¹, with an average of 5.66 m²·yr⁻¹ from 1969 to 2010. The present drainage area, local slope, average drainage slope, annual runoff, and ratio of the terraced area were all significantly different between the stable and growing gullies. The long-term gully growth rate could be estimated using a nonlinear regression model with annual runoff (Q_a) and the slope of the drainage area (S_d) as predictors (R_A = 0.301Q_a^0.562S_d, R² = 0.530). Based on the S_g-A and S_g-Q_a relationship that was used to reveal the threshold conditions for gully growth, all growing gullies still have the potential to keep growing, but soil and water conservation measures, including terraces, could change the threshold condition by reducing the effective drainage area. The results of this study could be helpful for preventing further gully erosion by dealing with gullies far above the threshold line. Full article

(This article belongs to the Special Issue Agricultural Water Management Using Geospatial Technologies)

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18 pages, 4881 KiB

Open AccessArticle

Adaptive Determination of the Flow Accumulation Threshold for Extracting Drainage Networks from DEMs

by Wei Zhang, Wenkai Li, Hugo A. Loaiciga, Xiuguo Liu, Shuya Liu, Shengjie Zheng and Han Zhang

Remote Sens. 2021, 13(11), 2024; https://doi.org/10.3390/rs13112024 - 21 May 2021

Cited by 5 | Viewed by 2165

Abstract

Selecting the flow accumulation threshold (FAT) plays a central role in extracting drainage networks from Digital Elevation Models (DEMs). This work presents the MR-AP (Multiple Regression and Adaptive Power) method for choosing suitable FAT when extracting drainage from DEMs. This work employs 36 sample sub-basins in Hubei (China) province. Firstly, topography, the normalized difference vegetation index (NDVI), and water storage change are used in building multiple regression models to calculate the drainage length. Power functions are fit to calculate the FAT of each sub-basin. Nine randomly chosen regions served as test sub-basins. The results show that: (1) water storage change and NDVI have high correlation with the drainage length, and the coefficient of determination (R²) ranges between 0.85 and 0.87; (2) the drainage length obtained from the Multiple Regression model using water storage change, NDVI, and topography as influence factors is similar to the actual drainage length, featuring a coefficient of determination (R²) equal to 0.714; (3) the MR-AP method calculates suitable FATs for each sub-basin in Hubei province, with a drainage length error equal to 5.13%. Moreover, drainage network extraction by the MR-AP method mainly depends on the water storage change and the NDVI, thus being consistent with the regional water-resources change. Full article

(This article belongs to the Special Issue Agricultural Water Management Using Geospatial Technologies)

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