Special Issue "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: 31 December 2021.

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 papers will be 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 2400 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 (3 papers)

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Research

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Article
Comparing PlanetScope to Landsat-8 and Sentinel-2 for Sensing Water Quality in Reservoirs in Agricultural Watersheds
Remote Sens. 2021, 13(9), 1847; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13091847 - 09 May 2021
Cited by 4 | Viewed by 1251
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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Article
Using RGISTools to Estimate Water Levels in Reservoirs and Lakes
Remote Sens. 2020, 12(12), 1934; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12121934 - 15 Jun 2020
Cited by 3 | Viewed by 1126
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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Technical Note
Hyperspectral Imaging from a Multipurpose Floating Platform to Estimate Chlorophyll-a Concentrations in Irrigation Pond Water
Remote Sens. 2020, 12(13), 2070; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12132070 - 27 Jun 2020
Cited by 5 | Viewed by 1204
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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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Applications of Remote Sensing in Precision Agriculture: A review
Authors: Rajendra P. Sishodia; Ram L. Ray
Affiliation: Cooperative Agricultural Research Center, College of Agriculture and Human Sciences, Prairie View A & M University, Prairie View, TX-77446, United States
Abstract: Agriculture, an engine of economic growth for many nations, provides for the most basic needs of humankind, food and fiber. Introduction of new farming techniques during the past century (e.g. Green Revolution) have helped agriculture to keep pace with growing demands for food and other agricultural products. However, further increases in food demand with growing population and rising income levels are likely to put additional strain on natural resources. With growing recognition of the negative impacts of agriculture on the environment, new techniques and approaches should be able to meet the future food demands while maintaining or reducing the environmental footprint of agriculture. It is also important that these technologies are economically attractive to farmers for their adoption/integration in mainstream agriculture. Emerging technologies such as Geospatial Technologies, Internet of Things, Big Data Analysis, and Artificial Intelligence could be utilized to make informed management decisions aimed to increase crop production. Precision Agriculture (PA) entails the application of a suite of such technologies to optimize agricultural inputs to increase agricultural production and reduce inputs losses. Use of remote sensing technologies for PA has increased rapidly during past decades. Unprecedented availability of high resolution satellite images have promoted the use of remote sensing in many PA applications including crop monitoring, irrigation management, nutrient application, disease and pests management and yield prediction. In this paper, we review the studies published during 2015-2020 to shed a light on the most recent applications of remote sensing in PA. Variable fertilizer rate application technologies based on remote sensing such as Green Seeker and Crop Circle have already been incorporated in commercial agriculture. Use of Unmanned Aerial Vehicles (UAVs) have increased tremendously during the last decade due to their cost-effectiveness and flexibility in obtaining on demand high resolution (cm-scale) images needed for PA applications. At the same time, the availability of large amount of high resolution satellite data have prompted many researchers to explore advanced data storage and processing techniques such as cloud computing and machine learning. Given the complexity of image processing and the amount of technical knowledge and expertise needed, it is critical to explore and develop simple yet reliable workflow for real time application in PA. Development of accurate yet easy to use, farmer-friendly systems is likely to result in wider adoption of remote sensing technologies in commercial PA applications.

Title: Possibility to evaluate the short-term effects of sewage sludge disposal based on Sentinel-2 vegetation monitoring
Authors: Kovács, F; Ladányi, Zs.
Affiliation: University of Szeged
Abstract: The agricultural use of sewage sludge is one of the means of refilling the soil nutrients, an effective solution for sustainable environmental management. In order to monitor and verify the short-term effects of sludge disposal – in parallel with other soil observations – we planned a multi-year, high-resolution data collection for monitoring the effects of disposal in some arable land parcells on 14 pieces of 50x50 m2 quadrates in southeastern Hungary. Using the resolutions of free Sentinel-2, pre-processed satellite imagery, data acquisition was applied at the highest temporal and spatial resolution supplemented with LANDSAT-8 recordings, evaluating the vegetation period from 2016 to 2019 (almost 100 images). We evaluated the photosynthetic activity of the summer semester and the changes in biomass production in space and time based on vegetation index (EVI, NDVI). The difference in the vegetation cycle of the plants on the arable land and the difference in the land use (LU/LC) are clearly visible in the values of EVI and NDVI. Based on the indices, yields are either stagnant or declining over the past 4 years in both areas. The statistical and spatial index differences between the affected and non-affected areas of sludge disposal are generally not significant in the short term. It can be seen differences in the case of the sunflower and maize biomass products. These can be assessed as the effect of sludge disposal within 1–3 years, but continuous monitoring is required to verify this. In the case of colza and winter wheat – based on the available data – we did not find a similar effects, and in some cases the pre-placement EVI/NDVI values were higher. VI heterogeneity within parcels is also well patterned in space by quadrates. The spatial heterogeneity characteristic of the qudrates was not changed by the sewage sludge disposal in the examined period. Geometric resolution and multi-time recording can be used to map a former alluvial form, which is an important factor in understanding parcels yield changes. Using two different indices together is useful. In addition to the general advantages of EVI, the accuracy of the evaluation of the rich vegetation period and its applicability in the comparative analysis can be emphasized. NDVI may be more sensitive in the dynamics of smaller or sparse vegetation, or sometimes in the observation of variability characteristic of rich vegetation. It is interestin, that the difference between NDVI and EVI is smaller in areas treated with sewage sludge. LANDSAT data are generally closely related to the Sentinel-based index values and confirm the results of the evaluation, but they cannot supplement the study on their own due to the significant differences on the parcels at certain times. In addition to the change of agricultural management, in the detection of significant differences, it is justified to include the follow years data in further analysis, which also helps to narrow down the data deficient periods; continue of monitoring for 2020 is in progress.

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