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Remote Sensing for Improved Understanding of Land Surface, Hydrology, and...
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Remote Sensing for Improved Understanding of Land Surface, Hydrology, and Water Quality

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 28731

Special Issue Editors

Dr. Adnan Rajib

E-Mail Website
Chief Guest Editor
Department of Environmental Engineering, Texas A&M University, Kingsville, TX, USA
Interests: ecosystem modeling; climate risks; earth observations; environmental informatics
Special Issues, Collections and Topics in MDPI journals
Dr. Apoorva Shastry

E-Mail Website
Co-Guest Editor
Research Scientist, Universities Space Research Association, Mountain View, CA, USA
Interests: remote sensing of hydrology; river hydraulics; flood modeling; floodplain topography; data assimilation

Special Issue Information

Dear Colleagues,

Traditional modeling and analysis tools designed to represent land surface, hydrology, and water quality are not fully accurate. As a result, many of our existing land and water management practices yield imprecise results in response to climate and anthropogenic changes. With the evolution of remote sensing techniques and computational resources, spatially explicit and temporally continuous use of remotely sensed data has emerged as an efficient solution to this problem.

The goal of this special issue is to aggregate contributions (original research and review articles) that use remote sensing data for improved representation of the land surface, hydrology, and water quality. High-quality articles involving any of these approaches, e.g., GIS analyses, hydrologic modeling, and machine learning will get priority.

Potential topics include, but are not limited to, the following:

  • Advanced techniques, machine learning algorithms, Google Earth Engine applications, and any associated data integration workflows;
  • High-resolution mapping and monitoring of surface water storage systems, including reservoirs, wetlands, river corridors, and other landscape water storage features;
  • Improved water balance simulation via assimilation of remotely sensed precipitation, evapotranspiration, leaf area index, soil moisture, snow water equivalent, and streamflow
  • Flood and drought hazard forecasting, mapping, and management;
  • Water use, crop yield assessment, and water quality management in agricultural landscapes;
  • Next-generation remote sensing techniques (e.g., Unmanned Aerial Vehicles) for improved representation of landscape features.

Dr. Adnan Rajib

Dr. Apoorva Shastry
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

  • Hydrology
  • Water quality
  • Earth observations
  • Data assimilation
  • Machine learning
  • Google Earth Engine
  • GIS

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

16 pages, 5349 KiB  
Article
Characterization of the 2014 Indus River Flood Using Hydraulic Simulations and Satellite Images
by Aqil Tariq, Hong Shu, Alban Kuriqi, Saima Siddiqui, Alexandre S. Gagnon, Linlin Lu, Nguyen Thi Thuy Linh and Quoc Bao Pham
Remote Sens. 2021, 13(11), 2053; https://doi.org/10.3390/rs13112053 - 23 May 2021
Cited by 44 | Viewed by 6715
Abstract
Rivers play an essential role to humans and ecosystems, but they also burst their banks during floods, often causing extensive damage to crop, property, and loss of lives. This paper characterizes the 2014 flood of the Indus River in Pakistan using the US [...] Read more.
Rivers play an essential role to humans and ecosystems, but they also burst their banks during floods, often causing extensive damage to crop, property, and loss of lives. This paper characterizes the 2014 flood of the Indus River in Pakistan using the US Army Corps of Engineers Hydrologic Engineering Centre River Analysis System (HEC-RAS) model, integrated into a geographic information system (GIS) and satellite images from Landsat-8. The model is used to estimate the spatial extent of the flood and assess the damage that it caused by examining changes to the different land-use/land-cover (LULC) types of the river basin. Extreme flows for different return periods were estimated using a flood frequency analysis using a log-Pearson III distribution, which the Kolmogorov–Smirnov (KS) test identified as the best distribution to characterize the flow regime of the Indus River at Taunsa Barrage. The output of the flood frequency analysis was then incorporated into the HEC-RAS model to determine the spatial extent of the 2014 flood, with the accuracy of this modelling approach assessed using images from the Moderate Resolution Imaging Spectroradiometer (MODIS). The results show that a supervised classification of the Landsat images was able to identify the LULC types of the study region with a high degree of accuracy, and that the most affected LULC was crop/agricultural land, of which 50% was affected by the 2014 flood. Finally, the hydraulic simulation of extent of the 2014 flood was found to visually compare very well with the MODIS image, and the surface area of floods of different return periods was calculated. This paper provides further evidence of the benefit of using a hydrological model and satellite images for flood mapping and for flood damage assessment to inform the development of risk mitigation strategies. Full article
(This article belongs to the Special Issue Remote Sensing for Improved Understanding of Land Surface, Hydrology, and Water Quality)
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20 pages, 4619 KiB  
Article
Extensive Evaluation of a Continental-Scale High-Resolution Hydrological Model Using Remote Sensing and Ground-Based Observations
by Bowen Zhu, Xianhong Xie, Chuiyu Lu, Tianjie Lei, Yibing Wang, Kun Jia and Yunjun Yao
Remote Sens. 2021, 13(7), 1247; https://doi.org/10.3390/rs13071247 - 25 Mar 2021
Cited by 12 | Viewed by 2604
Abstract
Extreme hydrologic events are getting more frequent under a changing climate, and a reliable hydrological modeling framework is important to understand their mechanism. However, existing hydrological modeling frameworks are mostly constrained to a relatively coarse resolution, unrealistic input information, and insufficient evaluations, especially [...] Read more.
Extreme hydrologic events are getting more frequent under a changing climate, and a reliable hydrological modeling framework is important to understand their mechanism. However, existing hydrological modeling frameworks are mostly constrained to a relatively coarse resolution, unrealistic input information, and insufficient evaluations, especially for the large domain, and they are, therefore, unable to address and reconstruct many of the water-related issues (e.g., flooding and drought). In this study, a 0.0625-degree (~6 km) resolution variable infiltration capacity (VIC) model developed for China from 1970 to 2016 was extensively evaluated against remote sensing and ground-based observations. A unique feature in this modeling framework is the incorporation of new remotely sensed vegetation and soil parameter dataset. To our knowledge, this constitutes the first application of VIC with such a long-term and fine resolution over a large domain, and more importantly, with a holistic system-evaluation leveraging the best available earth data. The evaluations using in-situ observations of streamflow, evapotranspiration (ET), and soil moisture (SM) indicate a great improvement. The simulations are also consistent with satellite remote sensing products of ET and SM, because the mean differences between the VIC ET and the remote sensing ET range from −2 to 2 mm/day, and the differences for SM of the top thin layer range from −2 to 3 mm. Therefore, this continental-scale hydrological modeling framework is reliable and accurate, which can be used for various applications including extreme hydrological event detections. Full article
(This article belongs to the Special Issue Remote Sensing for Improved Understanding of Land Surface, Hydrology, and Water Quality)
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30 pages, 10422 KiB  
Article
GYMEE: A Global Field-Scale Crop Yield and ET Mapper in Google Earth Engine Based on Landsat, Weather, and Soil Data
by Hadi Jaafar and Roya Mourad
Remote Sens. 2021, 13(4), 773; https://doi.org/10.3390/rs13040773 - 20 Feb 2021
Cited by 24 | Viewed by 9021
Abstract
In this study, we used Landsat Earth observations and gridded weather data along with global soil datasets available in Google Earth Engine (GEE) to estimate crop yield at 30 m resolution. We implemented a remote sensing and evapotranspiration-based light use efficiency model globally [...] Read more.
In this study, we used Landsat Earth observations and gridded weather data along with global soil datasets available in Google Earth Engine (GEE) to estimate crop yield at 30 m resolution. We implemented a remote sensing and evapotranspiration-based light use efficiency model globally and integrated abiotic environmental stressors (temperature, soil moisture, and vapor deficit stressors). The operational model (Global Yield Mapper in Earth Engine (GYMEE)) was validated against actual yield data for three agricultural schemes with different climatic, soil, and management conditions located in Lebanon, Brazil, and Spain. Field-level crop yield data on wheat, potato, and corn for 2015–2020 were used for assessment. The performance of GYMEE was statistically evaluated through root-mean-square error (RMSE), mean absolute error (MAE), mean bias error (MBE), relative error (RE), and index of agreement (d). The results showed that the absolute difference between the modeled and predicted field-level yield was within ±16% for the analyzed crops in both Brazil and Lebanon study sites and within ±15% in the Spain site (except for two fields). GYMEE performed best for wheat crop in Lebanon with a low RMSE (0.6 t/ha), MAE (0.5 t/ha), MBE (−0.06 t/ha), and RE (0.83%). A very good agreement was observed for all analyzed crop yields, with an index of agreement (d) averaging at 0.8 in all studied sites. GYMEE shows potential in providing yield estimates for potato, wheat, and corn yields at a relative error of ±6%. We also quantified and spatialized the soil moisture stress constraint and its impact on reducing biomass production. A showcasing of moisture stress impact on two emphasized fields from the Lebanon site revealed that a 12% difference in soil moisture stress can decrease yield by 17%. A comparison between the 2017 and 2018 seasons for the potato culture of Lebanon showed that the 2017 season with lower abiotic stresses had higher light use efficiency, above-ground biomass, and yield by 5%, 10%, and 9%, respectively. The results show that the model is of high value for assessing global food production. Full article
(This article belongs to the Special Issue Remote Sensing for Improved Understanding of Land Surface, Hydrology, and Water Quality)
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17 pages, 3940 KiB  
Article
Watershed Modeling with Remotely Sensed Big Data: MODIS Leaf Area Index Improves Hydrology and Water Quality Predictions
by Adnan Rajib, I Luk Kim, Heather E. Golden, Charles R. Lane, Sujay V. Kumar, Zhiqiang Yu and Saranya Jeyalakshmi
Remote Sens. 2020, 12(13), 2148; https://doi.org/10.3390/rs12132148 - 4 Jul 2020
Cited by 36 | Viewed by 8971
Abstract
Traditional watershed modeling often overlooks the role of vegetation dynamics. There is also little quantitative evidence to suggest that increased physical realism of vegetation dynamics in process-based models improves hydrology and water quality predictions simultaneously. In this study, we applied a modified Soil [...] Read more.
Traditional watershed modeling often overlooks the role of vegetation dynamics. There is also little quantitative evidence to suggest that increased physical realism of vegetation dynamics in process-based models improves hydrology and water quality predictions simultaneously. In this study, we applied a modified Soil and Water Assessment Tool (SWAT) to quantify the extent of improvements that the assimilation of remotely sensed Leaf Area Index (LAI) would convey to streamflow, soil moisture, and nitrate load simulations across a 16,860 km2 agricultural watershed in the midwestern United States. We modified the SWAT source code to automatically override the model’s built-in semiempirical LAI with spatially distributed and temporally continuous estimates from Moderate Resolution Imaging Spectroradiometer (MODIS). Compared to a “basic” traditional model with limited spatial information, our LAI assimilation model (i) significantly improved daily streamflow simulations during medium-to-low flow conditions, (ii) provided realistic spatial distributions of growing season soil moisture, and (iii) substantially reproduced the long-term observed variability of daily nitrate loads. Further analysis revealed that the overestimation or underestimation of LAI imparted a proportional cascading effect on how the model partitions hydrologic fluxes and nutrient pools. As such, assimilation of MODIS LAI data corrected the model’s LAI overestimation tendency, which led to a proportionally increased rootzone soil moisture and decreased plant nitrogen uptake. With these new findings, our study fills the existing knowledge gap regarding vegetation dynamics in watershed modeling and confirms that assimilation of MODIS LAI data in watershed models can effectively improve both hydrology and water quality predictions. Full article
(This article belongs to the Special Issue Remote Sensing for Improved Understanding of Land Surface, Hydrology, and Water Quality)
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