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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (20 June 2022) | Viewed by 65018

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Special Issue Editors

Prof. Dr. Timothy Dube

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Guest Editor

Institute for Water Studies, Department of Earth Sciences, University of The Western Cape, Robert Sobukwe, Bellville, South Africa
Interests: landscape ecology; biodiversity; land use/land cover; GIS; remote sensing
Special Issues, Collections and Topics in MDPI journals

Dr. Munyaradzi Davis Shekede

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Guest Editor

Department of Geography Geospatial Sciences and Earth Observation (GGEO), University of Zimbabwe, P. Bag MP 167, Mt Pleasant, Harare, Zimbabwe
Interests: Dr Shekede’s research interest are in the applications of earth observation and Geo-information science to understanding earth system dynamics focusing on climate change, water resources and socio-ecological systems.

Dr. Christian Massari

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Guest Editor

National Research Council, Via Madonna Alta 126, Perugia, Italy
Interests: hydrological and land surface water balance modelling; development of land data assimilation systems to ingest remote sensing retrivals; droughts and floods; ecohydrology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Although water resources are critical for sustaining life on Earth, the increase in human population coupled with global environment change, especially climate change and variability have exerted pressure on this finite resource and the environment. Specifically, water scarcity arising from rapid population growth, droughts, erratic rainfall, over-exploitation, as well as increased water pollution from anthropogenic activities such as mining, and urbanization threatens the supply of water of sufficient quality and quantity to meet the global water demand. Approximately a fifth of the global population lives in water scarce areas and this trend is likely to increase in the near future if the current trends, threats and pressures are neither fully understood nor appropriately managed. Fortunately, the increase in the readily available remotely sensed data with improved sensing characteristics (i.e. high spatial, spectral and radiometric resolutions), as well as accompanying advancements in geospatial modelling techniques in recent years provide scientists with indispensable tools for not only monitoring and assessing the state of water resources but also the environment at various spatial scales. In line with the United Nations Sustainable Development Goal (SDG) 6 that seeks to “ensure the availability and sustainable management of water and sanitation for all”, this special issue calls for papers that focus on innovative and state of the art practical applications of cutting-edge remote sensing, and modelling techniques to water resources, and environmental management at various spatial scales. Papers applying remote sensing in assessing and monitoring freshwater (underground and surface) quality, quantity, availability and management, to ensure water security are especially welcome. Themes considered under this special issue include but not limited to the applications of remote sensing and attendant development of geospatial techniques that can be used to: detect and quantify and monitor freshwater water resources and identify potential threats to this resource, and other themes related to water resources and environmental management at various spatial scales.

Prof. Dr. Timothy Dube
Dr. Munyaradzi Davies Shekede
Dr. Christian Massari
Guest Editors

Manuscript Submission Information

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Keywords

Algorithms
Climate change
Droughts
Environmental degradation
Hyperspectral data
Monitoring and assessment
Multispectral remote sensing
Satellite advancements
Sustainable management
Water resources
Water quality assessment
Water scarcity
Water security

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Published Papers (14 papers)

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Editorial

Jump to: Research, Other

5 pages, 199 KiB

Open AccessEditorial

Remote Sensing for Water Resources and Environmental Management

by Timothy Dube, Munyaradzi D. Shekede and Christian Massari

Remote Sens. 2023, 15(1), 18; https://doi.org/10.3390/rs15010018 - 21 Dec 2022

Cited by 22 | Viewed by 5332

Abstract

In line with the United Nations Sustainable Development Goal (SDG) 6, the main goal of the Special Issue on “Remote sensing for water resources and environmental management” was to solicit papers from a diverse range of scientists around the world on the use of cutting-edge remote sensing technologies to assess and monitor freshwater quality, quantity, availability, and management to ensure water security. Special consideration was given to scientific manuscripts that covered, but were not limited to, the development of geospatial techniques and remote sensing applications for detecting, quantifying, and monitoring freshwater water resources, identifying potential threats to water resources and agriculture, as well as other themes related to water resources and environmental management at various spatial scales. The Special Issue attracted over thirteen peer-reviewed scientific articles, with the majority of manuscripts originating from China. Most of the studies made use of satellite datasets, ranging from coarse spatial resolution data, such as the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO), to medium spatial resolution data, such as the Landsat series, ERA5, Modern-Era Retrospective Analysis for Research and Application Land version 2 reanalysis product (MERRA2), CLSM and NOAH ET, and MODIS (Moderate Resolution Imaging Spectroradiometer). Google Earth Engine (GEE) data, together with big data processing techniques, such as the remote sensing-based energy balance model (ALEXI/DisALEXI approach) and the STARFM data fusion technique, were used for analyzing geospatial datasets. Overall, this Special Issue demonstrated significant knowledge gaps in various big data image processing techniques and improved computing processes in assessing and monitoring water resources and the environment at various spatial and temporal scales. Full article

(This article belongs to the Special Issue Remote Sensing for Water Resources and Environmental Management)

Research

Jump to: Editorial, Other

14 pages, 5890 KiB

Open AccessArticle

Integrating In Situ and Current Generation Satellite Data for Temporal and Spatial Analysis of Harmful Algal Blooms in the Hartbeespoort Dam, Crocodile River Basin, South Africa

by Khalid Ali, Tamiru Abiye and Elhadi Adam

Remote Sens. 2022, 14(17), 4277; https://doi.org/10.3390/rs14174277 - 30 Aug 2022

Cited by 7 | Viewed by 3323

Abstract

The Hartbeespoort Dam is a discharge point of a catchment that is characterized by urbanization, mining, industrial, and agricultural activities. These activities coupled with fluxes of heavily polluted wastewater from informal settlements, wastewater treatment works, as well as runoff from golf courses have led to the development of recurring harmful algal blooms (HABs). The predominant cyanobacteria scum that is largely covering the Dam water is toxic to fish and poses serious public health risks. Phosphorus is the limiting nutrient in terrestrial aquatic systems and excess concentration in the waters usually results in eutrophication. The productivity level in Hartbeespoort Dam is also a function of total phosphorous (TP) level, showing a positive correlation with chlorophyll-a, an index for phytoplankton which are predominantly HABs in this Dam. Analysis of long-term in situ water quality data (1980–2020) show that TP is not the only driver, changes in surface water temperatures also affect the productivity level, especially, when TP levels are below a threshold of approximately 0.4 mg/L. Chlorophyll-a was retrieved from current generation high resolution satellite (Landsat and Sentinel) at 5-year interval. Standard band ratio-based ocean color model applied to satellite data produced an accuracy of R² = 0.86 and RMSE of 5.56 µg/L. Time series analysis of in situ and satellite data show similar trends including capturing the effect of biocontrol on productivity levels between the late 1980s and the early 1990s, after which productivity increased with an increased flux of TP. Since 2015, the average annual surface temperature in the Dam has decreased leading to the decline in productivity level despite increasing levels of TP. The spatial dynamics of the HABs is a function of the discharges levels of the various rivers draining into the Dam as well as its geometry. Relatively higher concentrations are observed near river discharges and in areas of restricted water circulation. Full article

(This article belongs to the Special Issue Remote Sensing for Water Resources and Environmental Management)

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15 pages, 1983 KiB

Open AccessArticle

Comparison of UAS and Sentinel-2 Multispectral Imagery for Water Quality Monitoring: A Case Study for Acid Mine Drainage Affected Areas (SW Spain)

by Melisa A. Isgró, M. Dolores Basallote, Isabel Caballero and Luis Barbero

Remote Sens. 2022, 14(16), 4053; https://doi.org/10.3390/rs14164053 - 19 Aug 2022

Cited by 14 | Viewed by 3491

Abstract

Uncrewed Aerial Systems (UAS) and satellites are used for monitoring and assessing the quality of surface waters. Combining both sensors in a joint tool may scale local water quality retrieval models to regional and global scales by translating UAS-based models to satellite imagery. The main objective of this study is to examine whether Sentinel-2 (S2) data can complement UAS data, specifically from the MicaSense RedEdge MX-Dual sensor, for inland water quality monitoring in mining environments affected by acid mine drainage (AMD). For this purpose, a comparison between UAS reflectance maps and atmospherically corrected S2 imagery was performed. S2 data were processed with Case 2 Regional Coast Colour (C2RCC) and Case 2 Regional Coast Colour for Complex waters (C2X) atmospheric correction (AC) processors. The correlation between the UAS data and the atmospherically corrected S2 data was evaluated on a band-by-band and a pixel-by-pixel basis, and the compatibility of the spectral data was analyzed through statistical methods. The results showed C2RCC and C2X performed better for acidic greenish-blue and non-acidic greenish-brown water bodies concerning the UAS data than for acidic dark reddish-brown waters. However, significant differences in reflectance between the UAS sensor and both S2 AC processors have been detected. The poor agreement between sensors should be considered when combining data from both instruments since these could have further consequences in developing multi-scale models. Full article

(This article belongs to the Special Issue Remote Sensing for Water Resources and Environmental Management)

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17 pages, 3472 KiB

Open AccessArticle

Application of a PLS-Augmented ANN Model for Retrieving Chlorophyll-a from Hyperspectral Data in Case 2 Waters of the Western Basin of Lake Erie

by Khalid A. Ali and Wesley J. Moses

Remote Sens. 2022, 14(15), 3729; https://doi.org/10.3390/rs14153729 - 3 Aug 2022

Cited by 9 | Viewed by 2502

Abstract

We present results that demonstrate the utility of machine learning techniques that are based on partial least squares (PLS) and artificial neural networks (ANNs) for estimating low-moderate chlorophyll-a (chl-a) concentrations in the western basin of Lake Erie (WBLE). Previous ocean color studies have resulted in a large number of algorithms that are based on spectral indices to estimate water quality parameters (WQPs) such as chl-a concentration from remote sensing reflectance. However, these spectral index algorithms are based on reflectance features at specific wavelengths and do not take advantage of the wealth of spectral information that is contained in hyperspectral data, and are often not easily adaptable to waters with conditions that are different from those in the datasets that were used to originally calibrate the indices. Recently, there have been efforts to use machine learning techniques that are based on ANNs and PLS regression to exploit the spectral richness contained in hyperspectral data and retrieve WQPs. In this study, we have combined an ANN model with output from PLS regression to retrieve chl-a concentration from hyperspectral data in the WBLE. We compared the results from the PLS-ANN method to those that were obtained from a band-ratio algorithm that is based on reflectances in the blue and green spectral regions, a band ratio algorithm that is based on reflectances in the red and near-infrared (NIR) spectral regions, and a PLS-only approach. For a dataset that was collected in 2012, with chl-a concentrations ranging from 0.48 to 21.2 µg/L, the PLS-ANN method yielded a root mean square error (RMSE) of 1.22 µg/L, whereas the blue-green ratio algorithm yielded an RMSE of 1.75 µg/L, the NIR-red ratio algorithm yielded an RMSE of 1.95 µg/L, and the PLS-only approach yielded an RMSE of 1.95 µg/L. The PLS-ANN method takes advantage of the PLS regression to identify specific wavelengths that contain most information about the variation in chl-a concentration, minimize spectral collinearity and redundancy in the data, and simplify the neural network’s input structure. The better performance of the PLS-ANN method can also be attributed to the neural network’s ability to account for nonlinearity in the relationship between chl-a concentration and spectral reflectance. The results indicate that the PLS-ANN method can be reliably used to estimate and monitor low-moderate chl-a concentrations in optically complex waters. Full article

(This article belongs to the Special Issue Remote Sensing for Water Resources and Environmental Management)

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23 pages, 3501 KiB

Open AccessArticle

Improving Soil Moisture Estimation via Assimilation of Remote Sensing Product into the DSSAT Crop Model and Its Effect on Agricultural Drought Monitoring

by Hongkui Zhou, Guangpo Geng, Jianhua Yang, Hao Hu, Li Sheng and Weidong Lou

Remote Sens. 2022, 14(13), 3187; https://doi.org/10.3390/rs14133187 - 2 Jul 2022

Cited by 22 | Viewed by 3660

Abstract

Accurate knowledge of soil moisture is crucial for agricultural drought monitoring. Data assimilation has proven to be a promising technique for improving soil moisture estimation, and various studies have been conducted on soil moisture data assimilation based on land surface models. However, crop growth models, which are ideal tools for agricultural simulation applications, are rarely used for soil moisture assimilation. Moreover, the role of data assimilation in agricultural drought monitoring is seldom investigated. In the present work, we assimilated the European Space Agency (ESA) Climate Change Initiative (CCI) soil moisture product into the Decision Support System for Agro-technology Transfer (DSSAT) model to estimate surface and root-zone soil moisture, and we evaluated the effect of data assimilation on agricultural drought monitoring. The results demonstrate that the soil moisture estimates were significantly improved after data assimilation. Root-zone soil moisture had a better agreement with in situ observation. Compared with the drought index based on soil moisture modeled without remotely-sensed observations, the drought index based on assimilated data could improve at least one drought level in agricultural drought monitoring and performed better when compared with winter wheat yield. In conclusion, crop growth model-based data assimilation effectively improves the soil moisture estimation and further strengthens soil moisture-based drought indices for agricultural drought monitoring. Full article

(This article belongs to the Special Issue Remote Sensing for Water Resources and Environmental Management)

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19 pages, 6173 KiB

Open AccessArticle

Impacts of Climate Change, Glacier Mass Loss and Human Activities on Spatiotemporal Variations in Terrestrial Water Storage of the Qaidam Basin, China

by Xuewen Yang, Ninglian Wang, An’an Chen, Zhijie Li, Qian Liang and Yujie Zhang

Remote Sens. 2022, 14(9), 2186; https://doi.org/10.3390/rs14092186 - 3 May 2022

Cited by 11 | Viewed by 2773

Abstract

Monitoring the variations in terrestrial water storage (TWS) is crucial for understanding the regional hydrological processes, which helps to allocate and manage basin-scale water resources efficiently. In this study, the impacts of climate change, glacier mass loss, and human activities on the variations in TWS of the Qaidam Basin over the period of 2002−2020 were investigated by using Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) data, and other hydrological and meteorological data. The results indicate that TWS anomalies (TWSA) derived from five GRACE solutions experienced significant increasing trends over the study period, with the change rates ranging from 4.85 to 6.90 mm/year (1.37 to 1.95 km³/year). The GRACE TWSA averaged from different GRACE solutions exhibited an increase at a rate of 5.83 ± 0.12 mm/year (1.65 ± 0.03 km³/year). Trends in individual components of TWS indicate that the increase in soil moisture (7.65 mm/year) contributed the most to the variations in TWS. Through comprehensive analysis, it was found that the temporal variations in TWS of the Qaidam Basin were dominated by the variations in precipitation, and the spatial variations in TWS of the Qaidam Basin were mostly driven by the increase in glacier meltwater due to climate warming, particularly in the Narin Gol Basin. In addition, the water consumption associated with human activities had relatively fewer impacts. Full article

(This article belongs to the Special Issue Remote Sensing for Water Resources and Environmental Management)

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21 pages, 6650 KiB

Open AccessArticle

Assessment of Three Long-Term Satellite-Based Precipitation Estimates against Ground Observations for Drought Characterization in Northwestern China

by Hao Guo, Min Li, Vincent Nzabarinda, Anming Bao, Xiangchen Meng, Li Zhu and Philippe De Maeyer

Remote Sens. 2022, 14(4), 828; https://doi.org/10.3390/rs14040828 - 10 Feb 2022

Cited by 30 | Viewed by 5540

Abstract

Long-term satellite-based precipitation estimates (LSPE) play a significant role in climatological studies like drought monitoring. In this study, three popular LSPEs (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR), Rainfall Estimates from Rain Gauge and Satellite Observations (CHIRPS) and Multi-Source Weighted-Ensemble Precipitation (MSWEP)) were evaluated on a monthly scale using ground-based stations for capturing drought event characteristics over northwestern China from 1983 to 2013. To reflect dry or wet evolution, the Standardized Precipitation Index (SPI) was adopted, and the Run theory was used to identify drought events and their characteristics. The conventional statistical indices (relative bias (RB), correlation coefficient (CC), and root mean square error (RMSE)), as well as categorical indices (probability of detection (POD), false alarm ratio (FAR), and missing ratio (MISS)) are used to evaluate the capability of LSPEs in estimating precipitation and drought characteristics. We found that: (1) three LSPEs showed generally satisfactory performance in estimating precipitation and characterizing drought events. Although LSPEs have acceptable performance in identifying drought events with POD greater than 60%, they still have a high false alarm ratio (>27%) and a high missing ratio (>33%); (2) three LSPEs tended to overestimate drought severity, mainly because of an overestimation of drought duration; (3) the ability of CHIRPS to replicate the temporal evolution of precipitation and SPI values is limited; (4) in severe drought events, PERSIANN-CDR tends to overestimate precipitation, and drought severity, as well as drought area; (5) among the three LSPEs, MSWEP outperformed the other two in identifying drought events (POD > 66%) and characterizing drought features. Finally, we recommend MSWEP for drought monitoring studies due to its high accuracy in estimating drought characteristics over northwestern China. In drought monitoring applications, the overestimation of PERSIANN-CDR for drought peak value and area, as well as CHIRPS’s inferiority in capturing drought temporal evolution, must be considered. Full article

(This article belongs to the Special Issue Remote Sensing for Water Resources and Environmental Management)

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28 pages, 7779 KiB

Open AccessEditor’s ChoiceArticle

Interannual Variability of Water Level in Two Largest Lakes of Europe

by Andrey G. Kostianoy, Sergey A. Lebedev, Evgeniia A. Kostianaia and Yaan A. Prokofiev

Remote Sens. 2022, 14(3), 659; https://doi.org/10.3390/rs14030659 - 29 Jan 2022

Cited by 8 | Viewed by 3551

Abstract

Regional climate change affects the state of inland water bodies and their water balance, which is determined by a number of hydrometeorological and hydrogeological factors. An integral characteristic of changes in the water balance is the behavior of the level of lakes and reservoirs, which not only largely determines the physical and ecological state of water bodies, but also significantly affects the coastal infrastructure and socio-economic development of the region. This paper investigates the interannual variability of the level of the Ladoga and Onega lakes, the largest lakes in Europe located in the northwest of Russia, according to satellite altimetry data for 1993–2020. For this purpose, we used three specialized altimetry databases: DAHITI, G-REALM, and HYDROWEB. Water level data from these altimetry databases were compared with in-situ records at water level gauge stations. Information on air temperature (1945–2019) and precipitation (1966–2019) acquired at three meteostations located at Ladoga and Onega lakes was used to investigate interannual trends in the regional climate change. Finally, we discuss the potential impact of the lake level rise and regional climate warming on the infrastructure and operability of railways in this region. Full article

(This article belongs to the Special Issue Remote Sensing for Water Resources and Environmental Management)

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26 pages, 7183 KiB

Open AccessArticle

Satellite Remote Sensing of Water Quality Variation in a Semi-Enclosed Bay (Yueqing Bay) under Strong Anthropogenic Impact

by Bozhong Zhu, Yan Bai, Zhao Zhang, Xianqiang He, Zhihong Wang, Shugang Zhang and Qian Dai

Remote Sens. 2022, 14(3), 550; https://doi.org/10.3390/rs14030550 - 24 Jan 2022

Cited by 18 | Viewed by 5067

Abstract

The semi-enclosed bays impacted by heavy anthropogenic activities have weak water exchange and purification capacities. Most of the sea bays have suffered severe eutrophication, water quality deterioration, ecosystem degradation and other problems. Although many countries and local governments have carried out corresponding environmental protection actions, the evaluation of their effectiveness still requires monitoring technology and data support for long-term water environment change. In this study, we take Yueqing Bay, the fourth largest bay in China, as a case to study the satellite-based water quality monitoring and variation analysis. We established a nutrient retrieval model for Yueqing Bay to produce a long-term series of nutrient concentration products in Yueqing Bay from 2013 to 2020, based on Landsat remote sensing images and long-term observation data, combined with support vector machine learning and water temperature and satellite spectra as input parameters, and then we analyzed its spatiotemporal variations and driving factors. In general, nutrient concentrations in the western part of the bay were higher than those in the eastern part. Levels of dissolved inorganic nitrogen (DIN) were lower in summer than in spring and winter, and reactive phosphate (PO₄-P) levels were lower in summer and higher in autumn. In terms of natural factors, physical effects (e.g., seasonal variations in flow field) and biological effects (e.g., seasonal differences in the intensity of plankton photosynthesis) were the main causes of seasonal differences in nutrient concentration in Yueqing Bay. Nutrient concentration generally increased from 2013 to 2015 but decreased slightly after 2015. Over the past decade, the economy and industry of Yueqing Bay basin have developed rapidly. Wastewater resulting from anthropogenic production and consumption was transported via streams into Yueqing Bay, leading to the continuous increase in nutrient concentrations (the variation rates:

a_{D I N} > 0, a_{P O_{4} - P} > 0

), which directly or indirectly caused high nutrient concentrations in some areas of the bay (e.g., Southwest Shoal at the mouth of Yueqing Bay). After 2015, the various ecological remediation policies adopted by cities around Yueqing Bay have mitigated, to some extent, the increasing nutrient concentration trends (the variation rates:

a_{D I N} < 0, a_{P O_{4} - P} < 0

), but not significantly (P > 0.1). The environmental restoration of Yueqing Bay also requires continuous and long-term ecological protection and restoration work to be effective. This research can provide a reference for ecological environment monitoring and remote sensing data application for similar semi-enclosed bays, and support the sustainable development of the bay. Full article

(This article belongs to the Special Issue Remote Sensing for Water Resources and Environmental Management)

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24 pages, 7511 KiB

Open AccessArticle

Assessing Satellite, Land Surface Model and Reanalysis Evapotranspiration Products in the Absence of In-Situ in Central Asia

by Friday Uchenna Ochege, Haiyang Shi, Chaofan Li, Xiaofei Ma, Emeka Edwin Igboeli and Geping Luo

Remote Sens. 2021, 13(24), 5148; https://doi.org/10.3390/rs13245148 - 18 Dec 2021

Cited by 17 | Viewed by 3659

Abstract

Shortfalls in regular evapotranspiration (ET) monitoring and evaluation pose a huge challenge to agricultural water resource distribution in arid Central Asia (CA). In this study, a first detailed regional assessment of GLEAM, ERA5, MERRA2, CLSM and NOAH ET products in CA was performed by systematically implementing the triple collocation (TC) method, in which about 36,936 grid cells for each ET data (within a six-triplet design) were collocated, at 0.25° and with monthly resolutions during 2003–2020. The reliability of the strategy adopted was confirmed in four arid biomes using standard evaluation metrics (R, RMSE and BIAS), and by spatiotemporal cross-validation of the six ET triplets across CA. Results show that the systematic TC method produced more robust ET product assessment metrics with reduced RMSEs compared to the initial ET product validation using in-situ, which showed weak-positive correlation and high negative bias-range (i.e., −21.02 ≤ BIAS < 16 mm) in the four arid biomes of CA. The spatial cross-validation by TC showed that the magnitude of ET random errors significantly varies, and confirms the systematic biases with site-scale measurements. The highest ET uncertainties by CLSM (27.43%), NOAH (29.16%), MERRA2 (38.28%), ERA5 (36.75), and GLEAM (41%) were more evident in the shrubland, cropland, grassland, cropland again, and desert biomes, respectively. Moreover, error magnitudes in high altitudes (Tianshan Mountain range) are generally lower than in plain-desert areas. All ET products spatially captured ET dynamics over CA, but none simultaneously outperformed the other. These findings are invaluable in the utilization of the assessed ET products in supporting regional water resource management, particularly in CA. Full article

(This article belongs to the Special Issue Remote Sensing for Water Resources and Environmental Management)

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24 pages, 29937 KiB

Open AccessArticle

Characterizing Leaf Nutrients of Wetland Plants and Agricultural Crops with Nonparametric Approach Using Sentinel-2 Imagery Data

by Mandla Dlamini, George Chirima, Mbulisi Sibanda, Elhadi Adam and Timothy Dube

Remote Sens. 2021, 13(21), 4249; https://doi.org/10.3390/rs13214249 - 22 Oct 2021

Cited by 9 | Viewed by 2854

Abstract

In arid environments of the world, particularly in sub-Saharan Africa and Asia, floodplain wetlands are a valuable agricultural resource. However, the water reticulation role by wetlands and crop production can negatively impact wetland plants. Knowledge on the foliar biochemical elements of wetland plants enhances understanding of the impacts of agricultural practices in wetlands. This study thus used Sentinel-2 multispectral data to predict seasonal variations in the concentrations of nine foliar biochemical elements in plant leaves of key floodplain wetland vegetation types and crops in the uMfolozi floodplain system (UFS). Nutrient concentrations in different floodplain plant species were estimated using Sentinel-2 multispectral data derived vegetation indices in concert with the random forest regression. The results showed a mean R² of 0.87 and 0.86 for the dry winter and wet summer seasons, respectively. However, copper, sulphur, and magnesium were poorly correlated (R² ≤ 0.5) with vegetation indices during the summer season. The average % relative root mean square errors (RMSE’s) for seasonal nutrient estimation accuracies for crops and wetland vegetation were 15.2 % and 26.8%, respectively. There was a significant difference in nutrient concentrations between the two plant types, (R² = 0.94 (crops), R² = 0.84 (vegetation). The red-edge position 1 (REP1) and the normalised difference vegetation index (NDVI) were the best nutrient predictors. These results demonstrate the usefulness of Sentinel-2 imagery and random forests regression in predicting seasonal, nutrient concentrations as well as the accumulation of chemicals in wetland vegetation and crops. Full article

(This article belongs to the Special Issue Remote Sensing for Water Resources and Environmental Management)

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24 pages, 6414 KiB

Open AccessArticle

Estimating Evapotranspiration of Mediterranean Oak Savanna at Multiple Temporal and Spatial Resolutions. Implications for Water Resources Management

by Elisabet Carpintero, Martha C. Anderson, Ana Andreu, Christopher Hain, Feng Gao, William P. Kustas and María P. González-Dugo

Remote Sens. 2021, 13(18), 3701; https://doi.org/10.3390/rs13183701 - 16 Sep 2021

Cited by 6 | Viewed by 3179

Abstract

Mediterranean oak savanna is composed of a mixture of scattered oak trees, crops, pasture, and shrubs. It is the most widespread agroforestry landscape in Europe, and its conservation faces multiple threats including water scarcity, which has been exacerbated by global warming and greater climate variability. Evapotranspiration (ET) can be used as a proxy of the vegetation water status and response to water shortage conditions, providing relevant information about the ecosystem stability and its hydrological dynamics. This study evaluates a framework to estimate ET at multiple spatial and temporal scales and applies it to the monitoring of the oak savanna vegetation water consumption for the years 2013–2015. We used a remote sensing-based energy balance model (ALEXI/DisALEXI approach), and the STARFM data fusion technique to provide daily ET estimates at 30 m resolution. The results showed that modeled energy balance components compared well to ground measurements collected by an eddy covariance system, with root mean square error (RMSE) values ranging between 0.60 and 2.18 MJ m⁻² d⁻¹, depending on the sensor dataset (MODIS or Landsat) and the flux. The daily 30 m ET series generated by STARFM presented an RMSE value of 0.67 mm d⁻¹, which yielded a slight improvement compared to using MODIS resolution or more simple interpolation approaches with Landsat. However, the major advantage of the high spatio-temporal resolution was found in the analysis of ET dynamics over different vegetation patches that shape the landscape structure and create different microclimates. Fine-scale ET maps (30 m, daily) provide key information difficult to detect at a coarser spatial resolution over heterogeneous landscapes and may assist management decisions at the field and farm scale. Full article

(This article belongs to the Special Issue Remote Sensing for Water Resources and Environmental Management)

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20 pages, 2415 KiB

Open AccessArticle

Analysis of the Future Land Use Land Cover Changes in the Gaborone Dam Catchment Using CA-Markov Model: Implications on Water Resources

by Botlhe Matlhodi, Piet K. Kenabatho, Bhagabat P. Parida and Joyce G. Maphanyane

Remote Sens. 2021, 13(13), 2427; https://doi.org/10.3390/rs13132427 - 22 Jun 2021

Cited by 65 | Viewed by 6582

Abstract

Land use/land cover (LULC) changes have been observed in the Gaborone dam catchment since the 1980s. A comprehensive analysis of future LULC changes is therefore necessary for the purposes of future land use and water resource planning and management. Recent advances in geospatial modelling techniques and the availability of remotely sensed data have become central to the monitoring and assessment of both past and future environmental changes. This study employed the cellular automata and Markov chain (CA-Markov) model combinations to simulate future LULC changes in the Gaborone dam catchment. Classified Landsat images from 1984, 1995, 2005 and 2015 were used to simulate the likely LULCs in 2015 and 2035. Model validation compared the simulated and observed LULCs of 2015 and showed a high level of agreement with Kappa variation estimates of K_no (0.82), K_loc (0.82) and K_standard (0.76). Simulation results indicated a projected increase of 26.09%, 65.65% and 55.78% in cropland, built-up and bare land categories between 2015 and 2035, respectively. Reductions of 16.03%, 28.76% and 21.89% in areal coverage are expected for shrubland, tree savanna and water body categories, respectively. An increase in built-up and cropland areas is anticipated in order to meet the population’s demand for residential, industry and food production, which should be taken into consideration in future plans for the sustainability of the catchment. In addition, this may lead to water quality and quantity (both surface and groundwater) deterioration in the catchment. Moreover, water body reductions may contribute to water shortages and exacerbate droughts in an already water-stressed catchment. The loss of vegetal cover and an increase in built-up areas may result in increased runoff incidents, leading to flash floods. The output of the study provides useful information for land use planners and water resource managers to make better decisions in improving future land use policies and formulating catchment management strategies within the framework of sustainable land use planning and water resource management. Full article

(This article belongs to the Special Issue Remote Sensing for Water Resources and Environmental Management)

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Application of the Random Forest Classifier to Map Irrigated Areas Using Google Earth Engine

by James Magidi, Luxon Nhamo, Sylvester Mpandeli and Tafadzwanashe Mabhaudhi

Remote Sens. 2021, 13(5), 876; https://doi.org/10.3390/rs13050876 - 26 Feb 2021

Cited by 104 | Viewed by 10144

Abstract

Improvements in irrigated areas’ classification accuracy are critical to enhance agricultural water management and inform policy and decision-making on irrigation expansion and land use planning. This is particularly relevant in water-scarce regions where there are plans to increase the land under irrigation to enhance food security, yet the actual spatial extent of current irrigation areas is unknown. This study applied a non-parametric machine learning algorithm, the random forest, to process and classify irrigated areas using images acquired by the Landsat and Sentinel satellites, for Mpumalanga Province in Africa. The classification process was automated on a big-data management platform, the Google Earth Engine (GEE), and the R-programming was used for post-processing. The normalised difference vegetation index (NDVI) was subsequently used to distinguish between irrigated and rainfed areas during 2018/19 and 2019/20 winter growing seasons. High NDVI values on cultivated land during the dry season are an indication of irrigation. The classification of cultivated areas was for 2020, but 2019 irrigated areas were also classified to assess the impact of the Covid-19 pandemic on agriculture. The comparison in irrigated areas between 2019 and 2020 facilitated an assessment of changes in irrigated areas in smallholder farming areas. The approach enhanced the classification accuracy of irrigated areas using ground-based training samples and very high-resolution images (VHRI) and fusion with existing datasets and the use of expert and local knowledge of the study area. The overall classification accuracy was 88%. Full article

(This article belongs to the Special Issue Remote Sensing for Water Resources and Environmental Management)

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