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Remote Sensing
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Remote Sensing for Water Resources Assessment in Agriculture
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Remote Sensing for Water Resources Assessment in Agriculture

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: closed (31 December 2021) | Viewed by 23259

Special Issue Editors

Dr. Ram L. Ray

E-Mail Website
Guest Editor
College of Agriculture and Human Sciences, Prairie View A&M University (PVAMU), Prairie View, TX 77446, USA
Interests: natural hazards (e.g., landslide and flood) and risk analysis using GIS/remote sensing and spatial statistical analysis; fluvial geomorphology; flood risk analysis; flood hazards; soil water dynamics; water resource management; hydrologic modeling; remote sensing; climate change; carbon sequestration; soil moisture dynamics; drought; precision agriculture and land use/land cover change
Special Issues, Collections and Topics in MDPI journals
Dr. Sudhir K. Singh

E-Mail Website
Guest Editor
K. Banerjee Center of Atmospheric & Ocean Studies, IIDS, Nehru Science Center, University of Allahabad, UP, Allahabad, India
Interests: agriculture; climate change; land use/land cover change dynamics; remote sensing; soil moisture; water quality; hydrological modeling; water resource management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Water is a renewable resource, but its availability is limited. Water resources play a crucial role in economic and social development because of their significant impact on municipal water supplies, industry, and agricultural production. Especially in semi-arid and drought-prone areas, land-use and climate change have a major impact on surface and groundwater resources, which are primary sources of irrigated agriculture. Water resource assessment, which includes soil moisture, surface water, groundwater, and evapotranspiration, is important for sustainable agriculture in a changing climate. Remote sensing data integrated with in situ observation and modeling can be used to address some of the critical issues of agricultural water resource management focusing on the conservation and management of water resources. This Special Issue of Remote Sensing will collect articles (original research articles, review articles, and case studies) to provide insights into the applications of remote sensing data and remote sensing GIS-based techniques to address critical issues of agricultural water resource management, which includes assessment, monitoring, and modeling, of water resources, and water-related extremes (e.g., flood, and drought) at numerous spatial and temporal scales.

This open-access Special Issue invites high-quality and innovative scientific articles, which include innovative and cutting-edge research on the application of remote sensing techniques and data from any platform (ground sensing, satellite, aircraft, drones, etc.) to the study of critical water-related issues in agriculture. Potential topics include, but are not limited to, the following:

  • Water resource assessment in agriculture
  • Role of satellite-based soil moisture in agriculture
  • Remote sensing and agricultural drought
  • Impact of agriculture on water quality
  • Hydrologic modeling and remote sensing to agricultural water resource management
  • Ground sensing and remote sensing of evapotranspiration
  • Precision agriculture
  • Computer application in agriculture
  • Big data analytics in agriculture
  • Multi- to hyper-spectral sensing in agriculture
  • Impact of climate change on agriculture
Assoc. Prof. Ram L Ray
Assist. Prof. Sudhir K. Singh
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

  • Agriculture
  • Big data analytics in agriculture
  • Climate change
  • Computer application in agriculture
  • Evapotranspiration
  • Flood and drought
  • Groundwater
  • Hydrologic and crop modeling
  • Hydrometeorology
  • Irrigation
  • Multi- to hyper-spectral
  • Precision agriculture
  • Remote sensing
  • Soil moisture
  • Synthetic aperture radar
  • Time series analysis
  • Unmanned aerial vehicle (UAV)
  • Water resource management

Published Papers (5 papers)

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Research

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22 pages, 9614 KiB  
Article
An Exponential Filter Model-Based Root-Zone Soil Moisture Estimation Methodology from Multiple Datasets
by Yanqing Yang, Zhenxin Bao, Houfa Wu, Guoqing Wang, Cuishan Liu, Jie Wang and Jianyun Zhang
Remote Sens. 2022, 14(8), 1785; https://doi.org/10.3390/rs14081785 - 7 Apr 2022
Cited by 4 | Viewed by 1698
Abstract
Modern smart agriculture initiative presents more requests for soil moisture (SM) monitoring over large agricultural areas. Remote sensing techniques facilitate high-resolution surface SM (SSM) estimation at a large scale but lack root zone SM (RZSM) information. Establishing the deduction method of RZSM from [...] Read more.
Modern smart agriculture initiative presents more requests for soil moisture (SM) monitoring over large agricultural areas. Remote sensing techniques facilitate high-resolution surface SM (SSM) estimation at a large scale but lack root zone SM (RZSM) information. Establishing the deduction method of RZSM from the SSM has long been the focus of most attention. Data assimilation methods are promising techniques for RZSM estimation, developing numerous assimilated reanalysis datasets, e.g., ERA5 and the latest Soil Moisture Active and Passive (SMAP) L4 SM product. However, data latency and large computation during data collecting and processing often inhibits further applications. This work proposes a rapid estimation scheme for estimating RZSM with short latency and small computations, based on the Exponential Filter (EF) method. The EF model with single parameter T was firstly calibrated and validated using the SSM and RZSM of ERA5 reanalysis dataset, obtaining the optimum parameter T map for each grid. Then, the fast-updating SMAP L3 SSM product together with the scale-matched optimum T were adopted as inputs into the EF model to retrieve RZSM estimation of each grid. Specifically, such estimation scheme was tested over the central and eastern agricultural areas of China, using a dense monitoring network of 796 SM observation sites, which contains various land uses, as well as meteorological and hydrological conditions. The calibrated optimum parameter T presented an increasing trend with good physical explanations. Furthermore, all the estimated RZSMs were found to have good performances on capturing the temporal-spatial variations of RZSM and well reflecting seasonal RZSM changes. Overall, such an estimation scheme was proven to be a desirable alternative for estimating RZSM over large agricultural areas. Full article
(This article belongs to the Special Issue Remote Sensing for Water Resources Assessment in Agriculture)
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20 pages, 4168 KiB  
Article
Comparing PlanetScope to Landsat-8 and Sentinel-2 for Sensing Water Quality in Reservoirs in Agricultural Watersheds
by Abubakarr S. Mansaray, Andrew R. Dzialowski, Meghan E. Martin, Kevin L. Wagner, Hamed Gholizadeh and Scott H. Stoodley
Remote Sens. 2021, 13(9), 1847; https://doi.org/10.3390/rs13091847 - 9 May 2021
Cited by 37 | Viewed by 6217
Abstract
Agricultural runoff transports sediments and nutrients that deteriorate water quality erratically, posing a challenge to ground-based monitoring. Satellites provide data at spatial-temporal scales that can be used for water quality monitoring. PlanetScope nanosatellites have spatial (3 m) and temporal (daily) resolutions that may [...] Read more.
Agricultural runoff transports sediments and nutrients that deteriorate water quality erratically, posing a challenge to ground-based monitoring. Satellites provide data at spatial-temporal scales that can be used for water quality monitoring. PlanetScope nanosatellites have spatial (3 m) and temporal (daily) resolutions that may help improve water quality monitoring compared to coarser-resolution satellites. This work compared PlanetScope to Landsat-8 and Sentinel-2 in their ability to detect key water quality parameters. Spectral bands of each satellite were regressed against chlorophyll a, turbidity, and Secchi depth data from 13 reservoirs in Oklahoma over three years (2017–2020). We developed significant regression models for each satellite. Landsat-8 and Sentinel-2 explained more variation in chlorophyll a than PlanetScope, likely because they have more spectral bands. PlanetScope and Sentinel-2 explained relatively similar amounts of variations in turbidity and Secchi Disk data, while Landsat-8 explained less variation in these parameters. Since PlanetScope is a commercial satellite, its application may be limited to cases where the application of coarser-resolution satellites is not feasible. We identified scenarios where PS may be more beneficial than Landsat-8 and Sentinel-2. These include measuring water quality parameters that vary daily, in small ponds and narrow coves of reservoirs, and at reservoir edges. Full article
(This article belongs to the Special Issue Remote Sensing for Water Resources Assessment in Agriculture)
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19 pages, 3104 KiB  
Article
Using RGISTools to Estimate Water Levels in Reservoirs and Lakes
by Ana F. Militino, Manuel Montesino-SanMartin, Unai Pérez-Goya and M. Dolores Ugarte
Remote Sens. 2020, 12(12), 1934; https://doi.org/10.3390/rs12121934 - 15 Jun 2020
Cited by 7 | Viewed by 3780
Abstract
The combination of freely accessible satellite imagery from multiple programs improves the spatio-temporal coverage of remote sensing data, but it exhibits barriers regarding the variety of web services, file formats, and data standards. Ris an open-source software environment with state-of-the-art statistical packages for [...] Read more.
The combination of freely accessible satellite imagery from multiple programs improves the spatio-temporal coverage of remote sensing data, but it exhibits barriers regarding the variety of web services, file formats, and data standards. Ris an open-source software environment with state-of-the-art statistical packages for the analysis of optical imagery. However, it lacks the tools for providing unified access to multi-program archives to customize and process the time series of images. This manuscript introduces RGISTools, a new software that solves these issues, and provides a working example on water mapping, which is a socially and environmentally relevant research field. The case study uses a digital elevation model and a rarely assessed combination of Landsat-8 and Sentinel-2 imagery to determine the water level of a reservoir in Northern Spain. The case study demonstrates how to acquire and process time series of surface reflectance data in an efficient manner. Our method achieves reasonably accurate results, with a root mean squared error of 0.90 m. Future improvements of the package involve the expansion of the workflow to cover the processing of radar images. This should counteract the limitation of the cloud coverage with multi-spectral images. Full article
(This article belongs to the Special Issue Remote Sensing for Water Resources Assessment in Agriculture)
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Review

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22 pages, 4996 KiB  
Review
The Role of Remote Sensing Data and Methods in a Modern Approach to Fertilization in Precision Agriculture
by Dorijan Radočaj, Mladen Jurišić and Mateo Gašparović
Remote Sens. 2022, 14(3), 778; https://doi.org/10.3390/rs14030778 - 7 Feb 2022
Cited by 30 | Viewed by 5860
Abstract
The precision fertilization system is the basis for upgrading conventional intensive agricultural production, while achieving both high and quality yields and minimizing the negative impacts on the environment. This research aims to present the application of both conventional and modern prediction methods in [...] Read more.
The precision fertilization system is the basis for upgrading conventional intensive agricultural production, while achieving both high and quality yields and minimizing the negative impacts on the environment. This research aims to present the application of both conventional and modern prediction methods in precision fertilization by integrating agronomic components with the spatial component of interpolation and machine learning. While conventional methods were a cornerstone of soil prediction in the past decades, new challenges to process larger and more complex data have reduced their viability in the present. Their disadvantages of lower prediction accuracy, lack of robustness regarding the properties of input soil sample values and requirements for extensive cost- and time-expensive soil sampling were addressed. Specific conventional (ordinary kriging, inverse distance weighted) and modern machine learning methods (random forest, support vector machine, artificial neural networks, decision trees) were evaluated according to their popularity in relevant studies indexed in the Web of Science Core Collection over the past decade. As a shift towards increased prediction accuracy and computational efficiency, an overview of state-of-the-art remote sensing methods for improving precise fertilization was completed, with the accent on open-data and global satellite missions. State-of-the-art remote sensing techniques allowed hybrid interpolation to predict the sampled data supported by remote sensing data such as high-resolution multispectral, thermal and radar satellite or unmanned aerial vehicle (UAV)-based imagery in the analyzed studies. The representative overview of conventional and modern approaches to precision fertilization was performed based on 121 samples with phosphorous pentoxide (P2O5) and potassium oxide (K2O) in a common agricultural parcel in Croatia. It visually and quantitatively confirmed the superior prediction accuracy and retained local heterogeneity of the modern approach. The research concludes that remote sensing data and methods have a significant role in improving fertilization in precision agriculture today and will be increasingly important in the future. Full article
(This article belongs to the Special Issue Remote Sensing for Water Resources Assessment in Agriculture)
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Other

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15 pages, 4615 KiB  
Technical Note
Hyperspectral Imaging from a Multipurpose Floating Platform to Estimate Chlorophyll-a Concentrations in Irrigation Pond Water
by Geonwoo Kim, Insuck Baek, Matthew D. Stocker, Jaclyn E. Smith, Andrew L. Van Tassell, Jianwei Qin, Diane E. Chan, Yakov Pachepsky and Moon S. Kim
Remote Sens. 2020, 12(13), 2070; https://doi.org/10.3390/rs12132070 - 27 Jun 2020
Cited by 16 | Viewed by 3805
Abstract
This study provides detailed information about the use of a hyperspectral imaging system mounted on a motor-driven multipurpose floating platform (MFP) for water quality sensing and water sampling, including the spatial and spectral calibration for the camera, image acquisition and correction procedures. To [...] Read more.
This study provides detailed information about the use of a hyperspectral imaging system mounted on a motor-driven multipurpose floating platform (MFP) for water quality sensing and water sampling, including the spatial and spectral calibration for the camera, image acquisition and correction procedures. To evaluate chlorophyll-a concentrations in an irrigation pond, visible/near-infrared hyperspectral images of the water were acquired as the MFP traveled to ten water sampling locations along the length of the pond, and dimensionality reduction with correlation analysis was performed to relate the image data to the measured chlorophyll-a data. About 80,000 sample images were acquired by the line-scan method. Image processing was used to remove sun-glint areas present in the raw hyperspectral images before further analysis was conducted by principal component analysis (PCA) to extract three key wavelengths (662 nm, 702 nm, and 752 nm) for detecting chlorophyll-a in irrigation water. Spectral intensities at the key wavelengths were used as inputs to two near-infrared (NIR)-red models. The determination coefficients (R2) of the two models were found to be about 0.83 and 0.81. The results show that hyperspectral imagery from low heights can provide valuable information about water quality in a fresh water source. Full article
(This article belongs to the Special Issue Remote Sensing for Water Resources Assessment in Agriculture)
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