Remote Sensing in Water Resources Management Models

Topic Information

Dear Colleagues,

Almost 74% of the Earth's surface is covered with water. However, only 0.02% of all the water on Earth is in streams, lakes, rivers, and reservoirs as freshwater available for direct human consumption. The remaining freshwater is found underground (0.6%), in the atmosphere (0.001%), and in the icecaps (2.2%). Freshwater is a scarce resource worldwide due to land use and climate changes. Hence, the need for spatiotemporal data on freshwater, for water resource management, is increasing. However, the acquisition of spatial and temporal data on freshwater resources has been a major challenge facing ecological and hydrological researchers and policymakers. With the advent of remote sensing technology in the near past, data collection has fundamentally improved with the introduction of satellite sensors with higher spatial and temporal resolution on space-borne platforms. Most of these datasets are freely available on the Internet. This has been further advanced by the development of open-source remote sensing services, spatially distributed hydrological models, and software for data processing, analysis, and visualization. The performance of remote sensing data and hydrological models to capture the effect of ongoing development and management decisions, however, has to be evaluated. Spatiotemporal analysis of freshwater dynamics under land use and climate changes using spatially explicit hydrological models and remote sensing data can provide information for water resource management, the effect of ongoing developments, management decisions, and policy implications. Therefore, water resource modeling is essential for sustainable water resource management. The Topic “Remote Sensing in Water Resources Management Models” invites high-quality papers focused on the design and development of methods, strategies, and new technologies for water resource management and development impact assessment using hydrological models and remote sensing technologies under land use and climate changes. Potential topics include, but are not limited to:

• Land-use change and water resource management;
• Climate change and water scarcity;
• Remote sensing and water resource management;
• Hydrological modeling and remote sensing;
• Water resource management and sustainable development;
• Population growth and water resource scarcity;
• Spatiotemporal dynamics of water resource management;
• New technologies for water resource management;
• Methods for water resource modeling.

Dr. Jinsong Deng
Prof. Dr. Yang Hong
Prof. Dr. Salah Elsayed
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Environments environments	3.5	5.7	2014	25.7 Days	CHF 1800
Forests forests	2.4	4.4	2010	16.9 Days	CHF 2600
Remote Sensing remotesensing	4.2	8.3	2009	24.7 Days	CHF 2700
Water water	3.0	5.8	2009	16.5 Days	CHF 2600

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Published Papers (14 papers)

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15 pages, 7262 KiB  

Open AccessArticle

Hydrological Dynamics of the Pantanal, a Large Tropical Floodplain in Brazil, Revealed by Analysis of Sentinel-2 Satellite Imagery

by Edelin Jean Milien, Gustavo Manzon Nunes, Girard Pierre, Stephen K. Hamilton and Catia Núnes Da Cunha

Water 2023, 15(12), 2180; https://doi.org/10.3390/w15122180 - 9 Jun 2023

Cited by 3 | Viewed by 3846

Abstract

Extensive tropical floodplain wetlands, such as the Brazilian Pantanal, are complex ecosystems composed of mosaics of permanently and seasonally flooded habitats and are increasingly threatened by land use and climate change. Spatial and interannual variability in the seasonal flood pulse is a fundamental [...] Read more.

Extensive tropical floodplain wetlands, such as the Brazilian Pantanal, are complex ecosystems composed of mosaics of permanently and seasonally flooded habitats and are increasingly threatened by land use and climate change. Spatial and interannual variability in the seasonal flood pulse is a fundamental ecological driver in these ecosystems. This study analyzes optical imagery from the Sentinel-2 satellite to determine the extent and seasonal patterns of inundation over five years in the northern Pantanal, a Ramsar site renowned for its wildlife. The study site is bordered by the Cuiabá and São Lourenço rivers, each with distinct flow regimes. Inundation patterns were revealed with a combination of water indices, supervised classification of land cover, and a digital elevation model. Total extent of flooding was underestimated by the optical imagery, but open water bodies were readily delineated with the land cover classification. Full article

(This article belongs to the Topic Remote Sensing in Water Resources Management Models)

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22 pages, 7231 KiB  

Open AccessArticle

Impact of Climate Change Parameters on Groundwater Level: Implications for Two Subsidence Regions in Iran Using Geodetic Observations and Artificial Neural Networks (ANN)

by Saeid Haji-Aghajany, Yazdan Amerian and Alireza Amiri-Simkooei

Remote Sens. 2023, 15(6), 1555; https://doi.org/10.3390/rs15061555 - 12 Mar 2023

Cited by 9 | Viewed by 2772

Abstract

This study aims to investigate how changes in meteorological indicators affect groundwater resources, and hence to predict groundwater levels using these indicators, particularly in regions experiencing drought and subsidence. Precipitation, temperature, evapotranspiration and precipitable water vapor (PWV) are important meteorological parameters to predict [...] Read more.

This study aims to investigate how changes in meteorological indicators affect groundwater resources, and hence to predict groundwater levels using these indicators, particularly in regions experiencing drought and subsidence. Precipitation, temperature, evapotranspiration and precipitable water vapor (PWV) are important meteorological parameters to predict groundwater levels. Two subsidence areas with different weather conditions were selected to conduct a comprehensive study on the effect of temperature and precipitation on groundwater level changes. The correct locations of the two subsidence areas were determined by analyzing Interferometric Synthetic Aperture Radar (InSAR) images of Sentinel-1A using the small baseline subset algorithm. The interferograms were processed to correct tropospheric effects using the advanced integration method. Specifying the exact locations of the two areas, the meteorological parameters were downscaled using the Statistical DownScaling Model (SDSM), synoptic observations, meteorological data, and the General Circulation Model (GCM). An Artificial Neural Network (ANN) was then employed to predict the groundwater level changes as a function of meteorological data, including Global Positioning System (GPS)-based PWV and the evapotranspiration index. The trained ANN, along with the downscaled meteorological indicators, was used to predict groundwater level changes over two time periods. In the first period, the prediction was performed over the current years to investigate the performance of the method using the available data, whereas in the second period, the prediction was performed for the coming years, up until 2030. The results confirmed the high performance of the prediction algorithm, and the importance of including PWV and evapotranspiration in groundwater level predictions. The Pearson correlation coefficient was used to check the relationship between groundwater level changes and meteorological variables. The statistical significance of these coefficients was tested at the significance level $\alpha =0.05$. In more than 80% of the cases, the correlation coefficients were statistically significant, reaching more than 0.70 in some of the months. It is also observed that an increase in the depth of groundwater level has an obvious relationship with an increase in temperature and a decrease in rainfall. Full article

(This article belongs to the Topic Remote Sensing in Water Resources Management Models)

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15 pages, 3460 KiB  

Open AccessArticle

Dynamic Coupling Model of Water Environment of Urban Water Network in Pearl River Delta Driven by Typhoon Rain Events

by Weiping Shen, Yuhao Jin, Peitong Cong and Gengying Li

Water 2023, 15(6), 1084; https://doi.org/10.3390/w15061084 - 11 Mar 2023

Cited by 2 | Viewed by 1718

Abstract

Typhoon rain dominates meteorology-rainfall-runoff-environmental factor changes at the regional scale and regulates water resources in the river network area by means of multi-field coupled meteorological, hydrological, and geographic models, shaping complex water resources and water environment scenarios in the Pearl River Delta. Because [...] Read more.

Typhoon rain dominates meteorology-rainfall-runoff-environmental factor changes at the regional scale and regulates water resources in the river network area by means of multi-field coupled meteorological, hydrological, and geographic models, shaping complex water resources and water environment scenarios in the Pearl River Delta. Because of limitations in the monitoring capacity of the typhoon process, quantifying the ephemeral processes and spatial heterogeneity information of typhoon rain events is difficult, which makes the degree of research on typhoon rainfall-runoff transformation processes low and the progress in regional water resources and water environment evaluations based on typhoon events slow. In this study, typhoon rain event data, namely, remote-sensing spectra, measured water quality parameters, and meteorological factors, in the Pearl River Delta during 2022 were first collected. Next, a dynamic coupling model between typhoon rain events and the water network environment was established to simulate and predict the water environment conditions of the Zhongshan City water network controlled by the regulation of typhoon rain events. By inputting the quantitative data of the typhoon rain events, the water environment conditions of the river network in Zhongshan City after the typhoon rain events were simulated and output. The results showed that the distribution of dissolved oxygen concentrations and ammonia nitrogen concentrations were consistent: the concentration was highest in the central urban area, which is more urbanised than other areas, and it was lowest in the area far from the urban centre. Moreover, under the influence of Typhoon Ma-on, the water environment of the Zhongshan City water network changed over time: dissolved oxygen concentrations decreased and then increased, and ammonia nitrogen concentrations increased and then decreased. The water quality prediction model proposed in this study helps to improve the understanding of the dynamic impact of typhoon rain on the water quality of an urban water network in the Pearl River Delta and is conducive to improving the formulation of water environment control strategies during typhoon transit. Full article

(This article belongs to the Topic Remote Sensing in Water Resources Management Models)

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20 pages, 4486 KiB  

Open AccessArticle

Analysis of Seasonal Water Characteristics and Water Quality Responses to the Land Use/Land Cover Pattern: A Case Study in Tianjin, China

by Linshan Zhang, Lifu Zhang, Donghui Zhang, Yi Cen, Sa Wang, Yan Zhang and Liaoran Gao

Water 2023, 15(5), 867; https://doi.org/10.3390/w15050867 - 23 Feb 2023

Cited by 5 | Viewed by 2816

Abstract

As the second largest city in northern China, Tianjin has a unique geographical and social status. Following its rapid economic development, Tianjin is experiencing high levels of surface water pollution. The land use/land cover (LULC) pattern has a considerable impact on hydrological cycling [...] Read more.

As the second largest city in northern China, Tianjin has a unique geographical and social status. Following its rapid economic development, Tianjin is experiencing high levels of surface water pollution. The land use/land cover (LULC) pattern has a considerable impact on hydrological cycling and pollutant transmission, and thus on regional water quality. A full understanding of the water quality response to the LULC pattern is critical for water resource management and improvement of the natural environment in Tianjin. In this study, surface water monitoring station data and LULC data from 2021 to 2022 were used to investigate the surface water quality in Tianjin. A cluster analysis was conducted to compare water quality among monitoring stations, a factor analysis was conducted to identify potential pollution sources, and an entropy weight calculation was used to analyze the impact of the land use pattern on water quality. The mean total nitrogen (TN) concentration exceeded the class Ⅴ water quality standard throughout the year, and the correlation coefficient of the relationship between dissolved oxygen (DO) and pH exceeded 0.5 throughout the year, with other water quality parameters showing seasonal changes. On the basis of their good water quality, the water quality monitoring stations near large water source areas were distinguished from those near areas with other LULC patterns via the cluster analysis. The factor analysis results indicated that the surface water in Tianjin suffered from nutrient and organic pollution, with high loadings of ammonia nitrogen (NH₃N), TN, and total phosphorus (TP). Water pollution was more serious in areas near built-up land, especially in the central urban area. The entropy weight calculation results revealed that water, built-up land, and cultivated/built-up land had the greatest impact on NH₃N, while cultivated land had the greatest impact on electrical conductivity (EC). This study discusses the seasonal changes of surface water and impact of land use/land cover pattern on water quality at a macro scale, and highlighted the need to improve surface water quality in Tianjin. The results provide guidance for the sustainable utilization and management of local water resources. Full article

(This article belongs to the Topic Remote Sensing in Water Resources Management Models)

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19 pages, 4624 KiB  

Open AccessArticle

Data Integration for Investigating Drivers of Water Quality Variability in the Banja Reservoir Watershed

by Erica Matta, Mariano Bresciani, Giulio Tellina, Karin Schenk, Philipp Bauer, Fabian Von Trentini, Nils Ruther and Alena Bartosova

Water 2023, 15(3), 607; https://doi.org/10.3390/w15030607 - 3 Feb 2023

Cited by 4 | Viewed by 2184

Abstract

It is increasingly important to know the water quality of a reservoir, given the prospect of an environment poor in water reserves, which are based on intense and short-lived precipitation events. In this work, vegetation indices (NDVI, EVI) and bio-physical parameters of the [...] Read more.

It is increasingly important to know the water quality of a reservoir, given the prospect of an environment poor in water reserves, which are based on intense and short-lived precipitation events. In this work, vegetation indices (NDVI, EVI) and bio-physical parameters of the vegetation (LAI, FC), meteorological variables, and hydrological data are considered as possible drivers of the spatial and temporal variability of water quality (WQ) of the Banja reservoir (Albania). Sentinel-2 and Landsat 8/9 images are analyzed to derive WQ parameters and vegetation properties, while the HYPE model provides hydrological variables. Timeseries of the considered variables are examined using graphical and statistical methods and correlations among the variables are computed for a five-year period (2016–2022). The added-value of integrating earth observation derived data is demonstrated in the analysis of specific time periods or precipitation events. Significant positive correlations are found between water turbidity and hydrological parameters such as river discharge or runoff (0.55 and 0.40, respectively), while negative correlations are found between water turbidity and vegetation descriptors (−0.48 to −0.56). The possibility of having easy-to-use tools (e.g., web portal) for the analysis of multi-source data in an interactive way, facilitates the planning of hydroelectric plants management operations. Full article

(This article belongs to the Topic Remote Sensing in Water Resources Management Models)

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25 pages, 5771 KiB  

Open AccessArticle

Impacts of Spatiotemporal Gaps in Satellite Soil Moisture Data on Hydrological Data Assimilation

by Khaled Mohammed, Robert Leconte and Mélanie Trudel

Water 2023, 15(2), 321; https://doi.org/10.3390/w15020321 - 12 Jan 2023

Cited by 1 | Viewed by 1844

Abstract

Soil moisture modeling is necessary for many hydrometeorological and agricultural applications. One of the ways in which the modeling of soil moisture (SM) can be improved is by assimilating SM observations to update the model states. Remotely sensed SM observations are prone to [...] Read more.

Soil moisture modeling is necessary for many hydrometeorological and agricultural applications. One of the ways in which the modeling of soil moisture (SM) can be improved is by assimilating SM observations to update the model states. Remotely sensed SM observations are prone to being riddled with data discontinuities, namely in the horizontal and vertical spatial, and temporal, dimensions. In this study, a set of synthetic experiments were designed to assess how much impact each of these individual components of spatiotemporal gaps can have on the modeling performance of SM, as well as streamflow. The results show that not having root-zone SM estimates from satellite derived observations is most impactful in terms of the modeling performance. Having temporal gaps and horizontal spatial gaps in the satellite SM data also impacts the modeling performance, but to a lesser degree. Real-data experiments with the remotely sensed Soil Moisture Active Passive (SMAP) product generally brought improvements to the SM modeling performance in the upper soil layers, but to a lesser degree in the bottom soil layer. The updating of the model SM states with observations also resulted in some improvements in the streamflow modeling performance during the synthetic experiments, but not during the real-data experiments. Full article

(This article belongs to the Topic Remote Sensing in Water Resources Management Models)

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19 pages, 4414 KiB  

Open AccessArticle

Satellite-Based Determination of the Water Footprint of Carrots and Onions Grown in the Arid Climate of Saudi Arabia

by Khalid A. Al-Gaadi, Rangaswamy Madugundu, ElKamil Tola, Salah El-Hendawy and Samy Marey

Remote Sens. 2022, 14(23), 5962; https://doi.org/10.3390/rs14235962 - 25 Nov 2022

Cited by 7 | Viewed by 2513

Abstract

Increasing demand for food, climate change, and other human interventions are leading to significant increases in water consumption by the agricultural sector. This requires rationalizing the water used for the production of agricultural crops through improved irrigation management practices. Therefore, this study aimed [...] Read more.

Increasing demand for food, climate change, and other human interventions are leading to significant increases in water consumption by the agricultural sector. This requires rationalizing the water used for the production of agricultural crops through improved irrigation management practices. Therefore, this study aimed to estimate the water footprint (WF) of onion (Allium cepa L.) and carrot (Daucus carota) crops using the CROPWAT model and the SSEB (Simplified Surface Energy Balance) algorithm. Experiments were carried out at two center-pivot irrigated fields belonging to Tawdeehiya Commercial Farms in the southeastern region of the Riyadh governorate, Saudi Arabia. Individual bands and vegetation indices (VIs) were retrieved from Sentinel-2 satellite data, including the normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), optimized soil adjusted vegetation index (OSAVI), renormalized difference vegetation index (RDVI), and enhanced vegetation index (EVI), and the land surface temperatures (LST) extracted from Landsat-8 data were used to estimate crop productivity (CP), crop water use (CWU) (i.e., evapotranspiration—ET_a), and crop WF. Crop growth/phenology stages and georeferenced biophysical parameters were recorded during the growth period, and crop yield samples were collected randomly from predetermined sampling locations. It was found that the NIR band was appropriate for predicting onion yield (R² = 0.68; p > F = 0.02) and carrot yield (R² = 0.77; p > F = 0.02). The results also showed the feasibility of using the RDVI and EVI to estimate the yields of onion and carrot crops, with bias values of 15% and –17%, respectively. The CWU has also been successfully estimated using the SSEB algorithm, with an overall accuracy of 89%. The SSEB-estimated CWU was relatively high compared to the applied amounts by 10.6% (onions) and 12.6% (carrots). Finally, the crop WF was successfully estimated at 312 m³ t⁻¹ and 230 m³ t⁻¹ for carrots and onions, respectively, with an overall accuracy of 71.11%. The outcomes of this study can serve as a reference for crop irrigation management practices in the study region and areas with similar environmental conditions. Full article

(This article belongs to the Topic Remote Sensing in Water Resources Management Models)

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19 pages, 1531 KiB  

Open AccessReview

Water Quality Observations from Space: A Review of Critical Issues and Challenges

by Cameron Murray, Albert Larson, Joseph Goodwill, Yeqiao Wang, Dawn Cardace and Ali S. Akanda

Environments 2022, 9(10), 125; https://doi.org/10.3390/environments9100125 - 4 Oct 2022

Cited by 9 | Viewed by 6748

Abstract

Water is the basis of all life on this planet. Yet, approximately one in seven people in the world do not have access to safe water. Water can become unsafe due to contamination by various organic and inorganic compounds due to various natural [...] Read more.

Water is the basis of all life on this planet. Yet, approximately one in seven people in the world do not have access to safe water. Water can become unsafe due to contamination by various organic and inorganic compounds due to various natural and anthropogenic processes. Identifying and monitoring water quality changes in space and time remains a challenge, especially when contamination events occur over large geographic areas. This study investigates recent advances in remote sensing that allow us to detect and monitor the unique spectral characteristics of water quality events over large areas. Based on an extensive literature review, we focus on three critical water quality problems as part of this study: algal blooms, acid mine drainage, and suspended solids. We review the advances made in applications of remote sensing in each of these issues, identify the knowledge gaps and limitations of current studies, analyze the existing approaches in the context of global environmental changes, and discuss potential ways to combine multi-sensor methods and different wavelengths to develop improved approaches. Synthesizing the findings of these studies in the context of the three specific tracks will help stakeholders to utilize, share, and embed satellite-derived earth observations for monitoring and tracking the ever-evolving water quality in the earth’s limited freshwater reserves. Full article

(This article belongs to the Topic Remote Sensing in Water Resources Management Models)

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24 pages, 11916 KiB  

Open AccessArticle

Terrestrial Water Storage Dynamics: Different Roles of Climate Variability, Vegetation Change, and Human Activities across Climate Zones in China

by Shiyu Deng, Mingfang Zhang, Yiping Hou, Hongyun Wang, Enxu Yu and Yali Xu

Forests 2022, 13(10), 1541; https://doi.org/10.3390/f13101541 - 21 Sep 2022

Cited by 1 | Viewed by 2037

Abstract

Understanding terrestrial water storage (TWS) dynamics and associated drivers (e.g., climate variability, vegetation change, and human activities) across climate zones is essential for designing water resources management strategies in a changing environment. This study estimated TWS anomalies (TWSAs) based on the corrected Gravity [...] Read more.

Understanding terrestrial water storage (TWS) dynamics and associated drivers (e.g., climate variability, vegetation change, and human activities) across climate zones is essential for designing water resources management strategies in a changing environment. This study estimated TWS anomalies (TWSAs) based on the corrected Gravity Recovery and Climate Experiment (GRACE) gravity satellite data and derived driving factors for 214 watersheds across six climate zones in China. We evaluated the long-term trends and stationarities of TWSAs from 2004 to 2014 using the Mann–Kendall trend test and Augmented Dickey-Fuller stationarity test, respectively, and identified the key driving factors for TWSAs using the partial correlation analysis. The results indicated that increased TWSAs were observed in watersheds in tropical and subtropical climate zones, while decreased TWSAs were found in alpine and warm temperate watersheds. For tropical watersheds, increases in TWS were caused by increasing water conservation capacity as a result of large-scale plantations and the implementation of natural forest protection programs. For subtropical watersheds, TWS increments were driven by increasing precipitation and forestation. The decreasing tendency in TWS in warm temperate watersheds was related to intensive human activities. In the cold temperate zone, increased precipitation and soil moisture resulting from accelerated and advanced melting of frozen soils outweigh the above-ground evapotranspiration losses, which consequently led to the upward tendency in TWS in some watersheds (e.g., Xiaoxing’anling mountains). In the alpine climate zone, significant declines in TWS were caused by declined precipitation and soil moisture and increased evapotranspiration and glacier retreats due to global warming, as well as increased agriculture activities. These findings can provide critical scientific evidence and guidance for policymakers to design adaptive strategies and plans for watershed-scale water resources and forest management in different climate zones. Full article

(This article belongs to the Topic Remote Sensing in Water Resources Management Models)

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18 pages, 3486 KiB  

Open AccessArticle

Latitudinal and Altitudinal Gradients of Riverine Landscapes in Andean Rivers

by Evelyn Habit, Alejandra Zurita, Gustavo Díaz, Aliro Manosalva, Pedro Arriagada, Oscar Link and Konrad Górski

Water 2022, 14(17), 2614; https://doi.org/10.3390/w14172614 - 25 Aug 2022

Cited by 5 | Viewed by 2712

Abstract

Exact knowledge of the physical structures of different river sections that govern their ecological structure and function is essential for the efficient conservation and management of riverine ecosystems. Eleven Andean river basins (Maipo, Rapel, Mataquito, Maule, Itata, Biobío, Toltén, Valdivia, Bueno and Puelo) [...] Read more.

Exact knowledge of the physical structures of different river sections that govern their ecological structure and function is essential for the efficient conservation and management of riverine ecosystems. Eleven Andean river basins (Maipo, Rapel, Mataquito, Maule, Itata, Biobío, Toltén, Valdivia, Bueno and Puelo) comprise large scale latitudinal and altitudinal gradients and accommodate 71% of the Chilean population that strongly depend on their ecosystem services. Here, based on 16 hydrogeomorphic variables (on basin, valley and channel scales), we assessed the riverine landscapes (Functional Process Zones; FPZs) of these river basins using a top-down multivariate statistical approach. Two steep valley and downstream slope FPZs, three sinuous FPZs and two braided FPZs emerged in 8906 river sections. The proportion of the occurrence of FPZs was characterised by a clear latitudinal pattern which is strongly related to the proportions of each river basin within the large morphostructural units of Chile. As such, the proportion of each river basin within the Andes Cordillera, Central Valley and Coastal Cordillera is a strong driver of the fluvial geomorphology and, thus, of the FPZs’ arrangement in each river network. FPZ classification captured geomorphic diversity that coincided with the latitudinal and altitudinal gradients of Chilean Andean river basins strongly related to the hydrological characteristics of the assessed river basins and large scale spatial distribution of fish fauna endemism. As such, the identified large geomorphic units (FPZs) that are strongly tied up with hydrology and ecology hierarchies of riverine landscape provide robust operational tools that can be instrumental for river ecosystem monitoring and management at a basin scale. Full article

(This article belongs to the Topic Remote Sensing in Water Resources Management Models)

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21 pages, 3299 KiB  

Open AccessArticle

Estimation of Corn Latent Heat Flux from High Resolution Thermal Imagery

by Yan Zhu, Elaina M. Ludwig and Keith A. Cherkauer

Remote Sens. 2022, 14(11), 2682; https://doi.org/10.3390/rs14112682 - 3 Jun 2022

Viewed by 2399

Abstract

Crop evapotranspiration (ET), which is directly related to latent heat flux, is also a key indicator in determining the water status of crops. In order to estimate the latent heat flux, two-source energy balance (TSEB) models have been developed for thermal imagery from [...] Read more.

Crop evapotranspiration (ET), which is directly related to latent heat flux, is also a key indicator in determining the water status of crops. In order to estimate the latent heat flux, two-source energy balance (TSEB) models have been developed for thermal imagery from satellite platforms. However, because of the coarse resolution of thermal sensors on the satellite, distinguishing soil and vegetation is difficult which complicates the calculation process and introduces errors in latent heat estimates. In this research, high-resolution thermal datasets (0.05 m) and corresponding RGB datasets (0.03 m) were used for calculating crop latent heat flux using an adapted TSEB model. The RGB datasets were used for supervised classification of soil and vegetation, and the classification results were then used to filter the thermal mosaics to separate vegetation and soil temperatures. The vegetation temperature is used for calculating latent heat flux and the results are validated against the ground reference measurements of latent heat using a handheld porometer. The objective of this research is to introduce a workflow including an adapted TSEB model which is customized for high resolution thermal images from unmanned aircraft systems (UAS) to estimate the latent heat flux of row crops in agricultural fields. Nine dates of data collection in 2018 and 2020 have been evaluated and the root mean square error (RMSE) varies between 16 to 106 W/m² depending on the days after planting (DAP) and the time of measurement for each day. The results indicate that the workflow introduced here is able to provide estimates of instantaneous latent heat flux (evapotranspiration) measurements for row crops in agricultural fields which will enable people to make reliable decisions related to irrigation scheduling. Full article

(This article belongs to the Topic Remote Sensing in Water Resources Management Models)

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26 pages, 5061 KiB  

Open AccessArticle

A Satellite-Based Tool for Mapping Evaporation in Inland Water Bodies: Formulation, Application, and Operational Aspects

by Erica Matta, Marina Amadori, Gary Free, Claudia Giardino and Mariano Bresciani

Remote Sens. 2022, 14(11), 2636; https://doi.org/10.3390/rs14112636 - 31 May 2022

Cited by 3 | Viewed by 3308

Abstract

With the increase of evaporation projected for water bodies worldwide, there is a growing need for flexible and low data-demanding tools enabling the monitoring and management of water resources. This study presents a simple satellite-based tool named LakeVap specifically designed for mapping evaporation [...] Read more.

With the increase of evaporation projected for water bodies worldwide, there is a growing need for flexible and low data-demanding tools enabling the monitoring and management of water resources. This study presents a simple satellite-based tool named LakeVap specifically designed for mapping evaporation from lakes and reservoirs. LakeVap requires a small amount of potentially available data with a global coverage. The tool follows a Dalton-type approach and produces instantaneous (i.e., hourly) and daily evaporation maps from satellite-derived Lake Surface Water Temperature (LSWT) maps and single-point/gridded meteorological data. The model is tested on Lake Garda, Italy, by using a long time series of LSWT (ESA CCI-Lakes) and different sources of meteorological forcing. The accuracy of LakeVap evaporation outputs is checked by comparison with those from a hydro-thermodynamic model (Delft3D) specifically set up and validated for the case study. Results are consistent and sensitive to the representativeness of the meteorological forcing. In the test site, wind speed is found to be the most spatially variable parameter, and it is significantly underestimated by the ERA5 meteorological dataset (up to 100%). The potential application of LakeVap to other case studies and in operational contexts is discussed. Full article

(This article belongs to the Topic Remote Sensing in Water Resources Management Models)

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18 pages, 3950 KiB  

Open AccessArticle

The Development of A Rigorous Model for Bathymetric Mapping from Multispectral Satellite-Images

by Jiasheng Xu, Guoqing Zhou, Sikai Su, Qiaobo Cao and Zhou Tian

Remote Sens. 2022, 14(10), 2495; https://doi.org/10.3390/rs14102495 - 23 May 2022

Cited by 68 | Viewed by 4321

Abstract

Models for bathymetry retrieval from multispectral images have not considered the errors caused by tidal fluctuation. A rigorous bathymetric model that considers the variation in tide height time series, including the tide height calculation and instantaneous tide height correction at the epoch of [...] Read more.

Models for bathymetry retrieval from multispectral images have not considered the errors caused by tidal fluctuation. A rigorous bathymetric model that considers the variation in tide height time series, including the tide height calculation and instantaneous tide height correction at the epoch of satellite flight into the bathymetric retrieval model, is proposed in this paper. The model was applied on Weizhou Island, located in Guangxi Province, China, and its accuracy verificated with four check lines and seven checkpoints. A scene from the Landsat 8 satellite image was used as experimental data. The reference (“true”) water depth data collected by a RESON SeaBat 7125 multibeam instrument was used for comparison analysis. When satellite-derived bathymetry is compared, it is found that maximum absolute error, mean absolute error, and RMSE have decreased 54, 45, and 30% relative to that of the traditional model in the entire test field. The accuracy of the water depths retrieved by our model increased 30 and 56% when validated using four check lines and seven checkpoints, respectively. Therefore, it can be concluded that the model proposed in this paper can effectively improve the accuracy of bathymetry retrieved from Landsat 8 images. Full article

(This article belongs to the Topic Remote Sensing in Water Resources Management Models)

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13 pages, 2884 KiB  

Open AccessArticle

On the Evaluation of Both Spatial and Temporal Performance of Distributed Hydrological Models Using Remote Sensing Products

by Tam V. Nguyen, Bhumika Uniyal, Dang An Tran and Thi Bich Thuc Pham

Remote Sens. 2022, 14(9), 1959; https://doi.org/10.3390/rs14091959 - 19 Apr 2022

Cited by 3 | Viewed by 2436

Abstract

Evaluating the spatial and temporal model performance of distributed hydrological models is necessary to ensure that the simulated spatial and temporal patterns are meaningful. In recent years, spatial and temporal remote sensing data have been increasingly used for model performance evaluation. Previous studies, [...] Read more.

Evaluating the spatial and temporal model performance of distributed hydrological models is necessary to ensure that the simulated spatial and temporal patterns are meaningful. In recent years, spatial and temporal remote sensing data have been increasingly used for model performance evaluation. Previous studies, however, have focused on either the temporal or spatial model performance evaluation. In addition, temporal (or spatial) model performance evaluation is often conducted in a spatially (or temporally) lumped approach. Here, we evaluated (1) the temporal model performance evaluation in a spatially distributed approach (spatiotemporal) and (2) the spatial model performance in a temporally distributed approach (temporospatial). We further demonstrated that both spatiotemporal and temporospatial model performance evaluations are necessary since they provide different aspects of the model performance. For this, a case study was developed using the Soil and Water Assessment Tool (SWAT) for the Upper Baitarani catchment in India, and the spatiotemporal and temporospatial model performance was evaluated against three different remotely based actual evapotranspiration (ETa) products (MOD16 A2, SSEBop, and TerraClimate). The results showed that an increase in the spatiotemporal model performance would not necessarily lead to an increase in the temporospatial model performance and vice versa, depending on the evaluation statistics. Overall, this study has highlighted the necessity of a joint spatiotemporal and temporospatial model performance evaluation to understand/improve spatial and temporal model behavior/performance. Full article

(This article belongs to the Topic Remote Sensing in Water Resources Management Models)

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