This dataset contains maps of the location and temporal distribution of surface water from 1984 to 2021 and provides statistics on the extent and change of those water surfaces. For more information see the associated journal article: High-resolution mapping of global surface water and its long-term changes (Nature, 2016) and the online Data Users Guide. These data were generated using 4,716,475 scenes from Landsat 5, 7, and 8 acquired between 16 March 1984 and 31 December 2021. Each pixel was individually classified into water / non-water using an expert system and the results were collated into a monthly history for the entire time period and two epochs (1984-1999, 2000-2021) for change detection. This mapping layers product consists of 1 image containing 7 bands. It maps different facets of the spatial and temporal distribution of surface water over the last 38 years. Areas where water has never been detected are masked.

A Google Earth Engine App developed to delineate water bodies around the globe from 1984 until present and to provide 16 day estimates of surface area of water bodies as well as shapefiles to the user. The app uses a novel framework to filters only those images that cloud is on top of the water body and allows users to choose from a list of spectral water indices to map water bodies. The app also allows users to select the choice of threshold (i.e., a fixed zero threshold or dynamic threshold to separate water form non-water background).

Recently, the satellite images have been used in remote sensing allowing observations with high temporal and spatial distribution. The use of water indices has proved to be an effective methodology in the monitoring of surface water resources. However, precise or automatic methodologies using satellite imagery to determine reservoir volumes are lacking. To fulfil that gap, this methodology proposes 3 stages: use Google Earth Engine (GEE) to select images; automatically calculate flooded surface areas applying water indices; determine the volume stored in reservoirs over those years based on the relation between the flooded area and the stored volume. The method was applied in four reservoirs and contemplate Landsat 4 and 5 ETM and Landsat 8 OLI. For the calculation of the flooded area the NDWI Indexes (McFeeters, 1996; Gao, 1996), and the MNDWI index (Xu, 2006) were applied and tested. The estimation of stored volume of water was made based on the area indices and a cross-check between real stored volume and calculated volume was made. Finally, an analysis on the selection of the best fit water indices was made. The results of every case studies herein displayed showed a quantifiable proficiency and reliability for quite a varied natural conditions. As a conclusion, this methodology could be seen as a tool for water resources management in developing countries, and not only, to measure automatically trends of stored volumes and its relation with the precipitation, and could eventually be extended to other types of surface water bodies, as lakes and coastal lagoons.

MERIT Hydro is a new global flow direction map at 3 arc-second resolution (~90 m at the equator) derived from the version 1.0.3 of the MERIT DEM elevation data and water body datasets (G1WBM, GSWO and OpenStreetMap). MERIT Hydro contains the output of a new algorithm that extracts river networks near-automatically by separating actual inland basins from noise caused by the errors in input elevation data. After a minimum amount of hand-editing, the constructed hydrography map shows good agreement with existing quality-controlled river network datasets in terms of flow accumulation area and river basin shape (see Figure 9a in the paper). The location of river streamlines was realistically aligned with existing satellite-based global river channel data (see Figure 10a in the paper). Relative error in the drainage area was smaller than 0.05 for 90% of GRDC gauges, confirming the accuracy of the delineated global river networks. Discrepancies in flow accumulation area were found mostly in arid river basins containing depressions that are occasionally connected at high water levels and thus resulting in uncertain watershed boundaries. MERIT Hydro improves on existing global hydrography datasets in terms of spatial coverage (between 90N and 60S) and representation of small streams, mainly due to increased availability of high-quality baseline geospatial datasets. You can use this web app to visualize MERIT Hydro data. The app's source code is available. Known Problems: River width (Update from GWD-LR v1): Width algorithm was updated to consider sub-pixel water fraction. Now, 30 m water map is used for width calculation at 90 m resolution. Currently river width is calculated for each channel separately in braided/anabranching sections. Merged river width should be calculated. Water body map: There are some inconsistencies between the DEM land sea mask and wate body data (such as new islands along the coast). The quality of OpenStreeetMap water body layer is not uniform in all areas. Channel bifurcations: Channel bifurcation is not well represented in the current version. Each pixel is assumed to have only one downstream direction. Secondary (or multiple) downstream direction should be considered, to represent complex river networks in the delta regions, floodplains, and braided rivers. Underground rivers/tunnels: Major underground rivers/tunnels should be implemented to improve large-scale water balance. River/lake separation: Rivers and lakes need to be separated better for some applications. Below-sea-level areas: The areas below sea level in coastal regions are not well represented in adjusted elevation data. Flow direction over glaciers: Flow direction over glaciers is not well represented, because the elevation of glacier centerline is higher than glacier edge. Supplementary data: It would be better to add location of GRDC gauging stations, water falls, reservoirs, etc. Data Sources: U-Tokyo MERIT DEM U-Tokyo G1WBM water body data OpenStreetMap water body layer EC-JRC Global Surface Water Occurrence U-Maryland Landsat forest cover data

A Google Earth Engine implementation of the Floodwater Depth Estimation Tool (FwDET) This is a Google Earth Engine implementation of the Floodwater Depth Estimation Tool (FwDET) developed by the Surface Dynamics and Modeling Lab at the University of Alabama that calculates flood depth using a flood extent layer and a digital elevation model. This research is made possible by the CyberSeed Program at the University of Alabama. Project name: WaterServ: A Cyberinfrastructure for Analysis, Visualization and Sharing of Hydrological Data. Please see the associated publications: 1. Peter, B.G., Cohen, S., Lucey, R., Munasinghe, D., Raney, A. and Brakenridge, G.R., 2020. Google Earth Engine Implementation of the Floodwater Depth Estimation Tool (FwDET-GEE) for rapid and large scale flood analysis. IEEE Geoscience and Remote Sensing Letters, 19, pp.1-5. https://ieeexplore.ieee.org/abstract/document/9242297 2. Cohen, S., Peter, B.G., Haag, A., Munasinghe, D., Moragoda, N., Narayanan, A. and May, S., 2022. Sensitivity of remote sensing floodwater depth calculation to boundary filtering and digital elevation model selections. Remote Sensing, 14(21), p.5313. https://github.com/csdms-contrib/fwdet 3. Cohen, S., A. Raney, D. Munasinghe, J.D. Loftis J, A. Molthan, J. Bell, L. Rogers, J. Galantowicz, G.R. Brakenridge7, A.J. Kettner, Y. Huang, Y. Tsang, (2019). The Floodwater Depth Estimation Tool (FwDET v2.0) for Improved Remote Sensing Analysis of Coastal Flooding. Natural Hazards and Earth System Sciences, 19, 2053–2065. https://doi.org/10.5194/nhess-19-2053-2019 4. Cohen, S., G. R. Brakenridge, A. Kettner, B. Bates, J. Nelson, R. McDonald, Y. Huang, D. Munasinghe, and J. Zhang (2018), Estimating Floodwater Depths from Flood Inundation Maps and Topography, Journal of the American Water Resources Association, 54 (4), 847–858. https://doi.org/10.1111/1752-1688.12609 Sample products and data availability: https://sdml.ua.edu/models/fwdet/ https://sdml.ua.edu/michigan-flood-may-2020/ https://cartoscience.users.earthengine.app/view/fwdet-gee-mi https://alabama.app.box.com/s/31p8pdh6ngwqnbcgzlhyk2gkbsd2elq0 GEE implementation output: fwdet_gee_brazos.tif ArcMap implementation output (see Cohen et al. 2019): fwdet_v2_brazos.tif iRIC validation layer (see Nelson et al. 2010): iric_brazos_hydraulic_model_validation.tif Brazos River inundation polygon access in GEE: var brazos = ee.FeatureCollection('users/cartoscience/FwDET-GEE-Public/Brazos_River_Inundation_2016') Nelson, J.M., Shimizu, Y., Takebayashi, H. and McDonald, R.R., 2010. The international river interface cooperative: public domain software for river modeling. In 2nd Joint Federal Interagency Conference, Las Vegas, June (Vol. 27). Google Earth Engine Code /* ---------------------------------------------------------------------------------------------------------------------- # FwDET-GEE calculates floodwater depth from a floodwater extent layer and a DEM Authors: Brad G. Peter, Sagy Cohen, Ronan Lucey, Dinuke Munasinghe, Austin Raney Emails: bpeter@ua.edu, sagy.cohen@ua.edu, ronan.m.lucey@nasa.gov, dsmunasinghe@crimson.ua.edu, aaraney@crimson.ua.edu Organizations: BP, SC, DM, AR - University of Alabama; RL - University of Alabama in Huntsville Last Modified: 10/08/2020 To cite this code use: Peter, Brad; Cohen, Sagy; Lucey, Ronan; Munasinghe, Dinuke; Raney, Austin, 2020, "A Google Earth Engine implementation of the Floodwater Depth Estimation Tool (FwDET-GEE)", https://doi.org/10.7910/DVN/JQ4BCN, Harvard Dataverse, V2 ------------------------------------------------------------------------------------------------------------------------- This is a Google Earth Engine implementation of the Floodwater Depth Estimation Tool (FwDETv2.0) [1] developed by the Surface Dynamics and Modeling Lab at the University of Alabama that calculates flood depth using a flood extent layer and a digital elevation model. This research is made possible by the CyberSeed Program at the University of Alabama. Project name: WaterServ: A Cyberinfrastructure for Analysis, Visualization and Sharing of Hydrological Data. GitHub Repository (ArcMap and QGIS implementations): https://github.com/csdms-contrib/fwdet ------------------------------------------------------------------------------------------------------------------------- How to run this code with your flood extent GEE asset: User of this script will need to update path to flood extent (line 32 or 33) and select from the processing options. Available DEM options (1) are USGS/NED (U.S.) and USGS/SRTMGL1_003 (global). Other options include (2) running the elevation outlier filtering algorithm, (3) adding water body data to the inundation extent, (4) add a water body data layer uploaded by the user rather than using the JRC global surface water data, (5) masking out regular water body data, (6) masking out 0 m depths, (7) choosing whether or not to export, (8) exporting additional data layers, and (9) setting an export file name....

The layers included in the code were from the study conducted by the research group of CNR-IBE (Institute of BioEconomy of the National Research Council of Italy) and ISPRA (Italian National Institute for Environmental Protection and Research), published by the Sustainability journal (https://doi.org/10.3390/su14148412).

Link to the Google Earth Engine (GEE) code (link: https://code.earthengine.google.com/715aa44e13b3640b5f6370165edd3002)

You can analyze and visualize the following spatial layers by accessing the GEE link:

Daytime summer land surface temperature (raster data, horizontal resolution 30 m, from Landsat-8 remote sensing data, years 2015-2019)

Surface thermal hot-spot (raster data, horizontal resolution 30 m) was obtained by using a statistical-spatial method based on the Getis-Ord Gi* approach through the ArcGIS Pro tool.

Surface albedo (raster data, horizontal resolution 10 m, Sentinel-2A remote sensing data, year 2017)

Impervious area (raster data, horizontal resolution 10 m, ISPRA data, year 2017)

Tree cover (raster data, horizontal resolution 10 m, ISPRA data, year 2018)

Grassland area (raster data, horizontal resolution 10 m, ISPRA data, year 2017)

Water bodies (raster data, horizontal resolution 2 m, Geoscopio Platform of Tuscany, year 2016)

Sky View Factor (raster data, horizontal resolution 1 m, lidar data from the OpenData platform of Florence, year 2016)

Buildings' units of Florence (shapefile from the OpenData platform of Florence) include data on the residential real estate value from the Real Estate Market Observatory (OMI) of the National Revenue Agency of Italy (source: https://www1.agenziaentrate.gov.it/servizi/Consultazione/ricerca.htm, accessed on 14 July 2022). Data on the characterization of the buffer area (50 m) surrounding the buildings are included in this shapefile [the names of table attributes are reported in the square brackets]: averaged values of the daytime summer land surface temperature [LST_media], thermal hot-spot pattern [Thermal_cl], mean values of sky view factor [SVF_medio], surface albedo [alb_medio], and average percentage areas of imperviousness [ImperArea%], tree cover [TreeArea%], grassland [GrassArea%] and water bodies [WaterArea%].

Here attached the .txt file of the GEE code.

E-mail

Giulia Guerri, CNR-IBE, giulia.guerri@ibe.cnr.it

Marco Morabito, CNR-IBE, marco.morabito@cnr.it

Alfonso Crisci, CNR-IBE, alfonso.crisci@ibe.cnr.it

This project aims to use remote sensing data from the Landsata database from Google Earth Engine to evaluate the spatial extent changes in the Bear Lake located between the US states of Utah and Idaho. This work is part of a term project submitted to Dr Alfonso Torres-Rua as a requirment to pass the Remote Sensing of Land Surfaces class (CEE6003). More information about the course is provided below. This project uses the geemap Python package (https://github.com/giswqs/geemap) for dealing with the google earth engine datasets. The content of this notebook can be used to:

learn how to retrive the Landsat 8 remote sensed data. The same functions and methodology can also be used to get the data of other Landsat satallites and other satallites such as Sentinel-2, Sentinel-3 and many others. However, slight changes might be required when dealing with other satallites then Landsat. Learn how to create time lapse images that visulaize changes in some parameters over time. Learn how to use supervised classification to track the changes in the spatial extent of water bodies such as Bear Lake that is located between the US states of Utah and Idaho. Learn how to use different functions and tools that are part of the geemap Python package. More information about the geemap Pyhton package can be found at https://github.com/giswqs/geemap and https://github.com/diviningwater/RS_of_Land_Surfaces_laboratory Course information:

Name: Remote Sensing of Land Surfaces class (CEE6003) Instructor: Alfonso Torres-Rua (alfonso.torres@usu.edu) School: Utah State University Semester: Spring semester 2023

A high-resolution circa-2020 map of urban lakes ( ≥0.001 km2) in China. The 10-m-resoultion Sentinel-2 imagery as well as a simple but robust water extraction method were used to generate waterbodies in China on Google Earth Engine. After initially filtering out urban water bodies based on the spatial relationships with urban area boundary, we combined high-resolution historical imagery to manually remove non-lake waters such as rivers and paddy fields, and to edit and supplement the missing urban lakes separately. The accuracy of our dataset was evaluated in terms of both area and count of lakes by comparing with manually vectorizing results in randomly sampled urban units. The results showed that our dataset is highly accurate and trustworthy, with the averaged accuracy of 81.85% in area and 93.35% in count.

The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. This DEM is derived from an edited DSM named WorldDEM&trade, i.e. flattening of water bodies and consistent flow of rivers has been included. Editing of shore- and coastlines, special features such as airports and implausible terrain structures has also been applied. The WorldDEM product is based on the radar satellite data acquired during the TanDEM-X Mission, which is funded by a Public Private Partnership between the German State, represented by the German Aerospace Centre (DLR) and Airbus Defence and Space. More details are available in the dataset documentation. Earth Engine asset has been ingested from the DGED files. Note: See the code example for the recommended way of computing slope. Unlike most DEMs in Earth Engine, this is an image collection due to multiple resolutions of source files that make it impossible to mosaic them into a single asset, so the slope computations need a reprojection.

This dataset contains 30m 5-yearly land cover maps in Qilian Mountain Area from 1990 to 2020. The dataset is produced mainly using 30m Landsat series data of long time series, and has the following characteristics: 1) high precision for using geographical division and hierarchical classification decision tree strategy, cooperating with visual interpretation; 2) good consistency for using change detection method based on benchmark map; 3) high production efficiency for main operations are carried out on the Google Earth Engine (GEE) cloud platform. This dataset includes 9 categories, including cropland, forest, grassland, shrubland, wetland, water bodies, impervious surface, bareland, and snow&ice, with an overall accuracy of over 90%.

This data set includes land cover classification products of 30 meters in Qilian mountain area from 1985 to 2019. Firstly, the product uses Landsat-8/OLI to construct the 2015 time series data. According to the different NDVI time series curves of various ground features, the knowledge of different features is summarized, the rules are set to extract different features, and the land cover classification map in 2015 is obtained. The classification system refers to IGBP classification system and from_ LC classification system can be divided into 10 categories: cultivated land, woodland, grassland, shrub, wetland, water body, impervious surface, bare land, glacier and snow. According to the accuracy evaluation of Google Earth HD images and field survey data, the overall accuracy of land cover classification products in 2015 was as high as 92.19%. Based on the land cover classification products in 2015, based on the Landsat series data and strong geodetic data processing ability of Google Earth engine platform, the land cover classification products from 1985 to 2019 are produced by using the idea and method of change detection. By comparing the classification products, it is concluded that the land cover classification products based on Google Earth engine platform have good consistency with the classification products based on time series method. In short, the land cover data set in the core area of Qilian Mountain has high overall accuracy, and the method based on Google Earth engine platform sample training can expand the existing classification products in time and space, and can reflect more land cover type change information in a long time series.

Für die globale Wassermaske werden die SWBD-Daten (SRTM Water Body Data) in Kombination mit MODIS-Daten mit einer Auflösung von 250 m verwendet, um eine vollständige globale Karte der Oberflächengewässer mit einer räumlichen Auflösung von 250 m zu erstellen, die etwa zwischen 2000 und 2002 erstellt wurde. Dieser Datensatz ist für die Verarbeitung von Rasterdaten und zum Maskieren von Wasser in der …

The Alaska Lake and Pond Occurrence Dataset (ALPOD) is a spatially explicit map of lakes and ponds across Alaska and their seasonal fluctuations. The core product is an open water occurrence raster that: (a) separates lakes and ponds from other components of the landscape (e.g., rivers and wetlands); (b) is built from Sentinel-2 imagery and has 10-m resolution; and (c) records the percentages of time that each pixel was open water and attached to a lake or pond during the 2016-2021 ice-free seasons at near-daily temporal resolution. The number of water bodies depends on the chosen occurrence threshold, but a conservative estimate is that ALPOD maps over 800,000 lakes and ponds larger than 0.001 km2. The lake occurrence rasters are tiled by UTM zone and latitude. ALPOD also includes a vector product defined using a 25% occurrence threshold. ALPOD was created using a U-Net lake identification model and manual inspection to produce a maximum possible lake extent mask. This mask serves as the region of interest for an adaptive NDWI threshold water classification algorithm written in Google Earth Engine (GEE), which was used to classify open water within the maximum lake mask in every available cloud- and ice-free Sentinel-2 image during the study period. ALPOD is suitable for investigations of individual water bodies as well as lake and pond patterns across Alaska. The data are provided in GeoTIFF and shapefile formats.

This dataset provides estimates of carbon dioxide (CO2) and methane (CH4) diffusive fluxes from waterbodies, and watershed landcover data for the central-interior of the Yukon-Kuskokwim Delta (YK delta), Alaska. Dissolved concentrations of methane and carbon dioxide were predicted using an integrated terrestrial-aquatic approach to scale observations based on landscape and waterbody remote sensing drivers. The observations include ~300 samples of surface water dissolved gases collected in July 2016-2019 from the central region of the YK Delta, Alaska. A machine learning model was used to generate estimated fluxes. Model inputs include Sentinel-2 MSI with derived normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), an Arctic digital elevation model (DEM) with derived slope and flow accumulation, Sentinel-1 C-band July and December VV and VH composites, and a landcover map. Waterbody size, shape, and reflectance were determined using object-based image analysis in Google Earth Engine. Landscape-level input data were averaged in non-nested sub-basins calculated using the System for Automated Geoscientific Analyses (SAGA) "channel network" algorithm at three threshold sizes. Cross validation was used to tune and select variables for gradient boosting models. The trained gradient boosting models were then used to predict dissolved methane and carbon dioxide in all waterbodies (~17,000) in the region. These aquatic concentrations were converted to fluxes using an average gas transfer velocity from observations (0.33 m/d). The data are provided in GeoTIFF and shapefile formats.

Global Water Mask wykorzystuje dane SWBD (SRTM Water Body Data) w połączeniu z danymi MODIS 250m, aby utworzyć pełną globalną mapę powierzchni wody o rozdzielczości przestrzennej 250 m w okresie 2000–2002 r. Ten zbiór danych jest przeznaczony do przetwarzania danych rastrowych i maskowania wody w końcowym …

This dataset contains land cover products in Qilian Mountain Area in 2018. The dataset was produced by two steps. Firstly, land cover product in 2015 is produced using time series Landsat-8/OLI data. In view of the different NDVI time series curves of various land features with time variation, the knowledge of different land features is summarized, the extraction rules of different land features are set, and the land cover classification map in 2015 is obtained. The classification system refers to IGBP and FROM_LC classification system. It is divided into 10 categories: cultivated land, woodland, grassland, shrub, wetland, water body, impermeable surface, bare land, glacier and snow cover. According to the accuracy evaluation of Google Earth high-definition image and field survey data, the overall accuracy of land cover classification products in 2015 is as high as 92.19%. Secondly, taking the land cover classification products in 2015 as the base map, a large number of samples are selected according to the proportion of different types. Based on the Landsat series data and powerful data processing ability of Google Earth Engine platform, the random forest classifier is selected to train the band information and NDVI, MNDWI, NDBI and other indices by using the idea of in-depth learning. The land cover of 2018 is produced. It is concluded that the land cover classified products based on Google Earth Engine platform have good consistency with those based on time series method. In conclusion, the land cover data set in the core area of Qilian Mountains has high overall accuracy, and the method based on sample training of Google Earth Engine platform can expand the existing classification products in time and space, and the frequency of every five years can reflect more land cover type change information in long time series.

Climate change is causing an intensification in tundra fires across the Arctic, including the unprecedented 2015 fires in the Yukon-Kuskokwim (YK) Delta. The YK Delta contains extensive surface waters (∼33% cover) and significant quantities of organic carbon, much of which is stored in vulnerable permafrost. Inland aquatic ecosystems act as hot-spots for landscape CO2 and CH4 emissions and likely represent a significant component of the Arctic carbon balance, yet aquatic fluxes of CO2 and CH4 are also some of the most uncertain. We measured dissolved CH4 and CO2 concentrations (n = 364), in surface waters from different types of waterbodies during summers from 2016 to 2019. We used Sentinel-2 multispectral imagery to classify landcover types and area burned in contributing watersheds. We develop a model using machine learning to assess how waterbody properties (size, shape, and landscape properties), environmental conditions (O2, temperature), and surface water chemistry (dissolved orga..., This landcover classification was created for the purposes of determining watershed landcover as potential drivers of downstream waterbody CH4 and CO2 concentrations. The region of interest is a watershed in the central portion of the Yukon-Kuskokwim Delta, Alaska, where field observations were based. The landcover map has been clipped to the watershed extent, and included as a shapefile. We created a 10-m resolution landcover map for the region of interest to determine the presence and abundance of various terrestrial, wetland, surface waterbodies, and disturbed areas in sample watersheds. We used an unsupervised k-means algorithm (Google Earth Engine, “wekaKMeans†) with the surface reflectance raw bands, derived bands (NDWI, NDVI), slope, and elevation as inputs for the classification. The Alaska Interagency Coordination Center historical wildfire database was used for wildfire delineations. Wildfires in the region of interest included fire scars from the 1970s, 1990s, and early 2000s..., The data type for the landcover map is integer, with 0 = no data (includes areas outside the research watershed and a small area where and old fire scar reburned in 2015 that could not be mapped), 1= lichen dominant tundra on peat plateaus, some graminoids and prostrate dwarf shrubs 2= degrading permafrost on peat plateaus (either sparse but productive wetland graminoid, exposed mud, or shallow water, depending on antecedent rainfall) 3= vegetated wetland (peat fens, often with small channels and adjacent sphagnum bog or mosses underlying graminoids), lower in elevation relative to peat plateaus 4= shrub tundra, often the 'banks' or edges of peat plateaus 5= tundra at the edges of degrading permafrost on peat plateaus, often wetter and more liekly to be dominated by sphagnum species and sphagnum fuscum 6= vegetated wetland (fens, but darker green, possibly tall shrubs. This was lumped with category 3 for most analyses), lower in elevation relative to peat plateaus. 7= waterbody edges; i...

Le masque d'eau mondial utilise les données SWBD (SRTM Water Body Data) en combinaison avec les données MODIS 250 m pour créer une carte mondiale complète des eaux de surface à une résolution spatiale de 250 m, vers 2000-2002. Cet ensemble de données est destiné au traitement des données raster et au masquage de l'eau dans les produits de données raster finaux.

Lakes, an important components of terrestrial ecosystems, vary widely in size, depth, and ecological characteristics. Small lakes, which are more common than larger ones, play vital roles in local ecosystems. however, these water bodies, particularly those that are terminated, exhibit high sensitivity to climate and environmental changes risking lives of hundred species. In such lakes, any alteration in water inflow significantly impacts the lake’s level and size, thereby affecting its physical parameters like temperature, pH, and turbidity. To address these impacts, remote sensing methods can be used to find the changes in any lake water level and water quality. This project aims to study the aspects using Utah Lake as a case study, utilizing remote sensing imagery to understand and illustrate the dynamics of lake size and water quality. LANDSAT images are extracted from Google Earth Engine and analyzed to positively define the proportional correlation between the lake level and the turbidity. Wet and dry years are selected for this analysis based on literature. The outcomes of this analysis will aid in identifying critical changes and inform decision-makers on protecting different species and habitants and planning and managing the lake's water resources to ensure both societal and ecological benefits.

This data set is a 30m land use / cover classification product in the Sahel region of Africa every five years from 1990 to 2020. The product is based on a collaborative framework of land cover classification integrating machine learning and multiple data fusion, and integrates supervised land cover classification with existing thematic land cover maps by using Google Earth engine (GEE) cloud computing platform. The classification system adopts FROM_ GLC classification system includes 8 categories: cultivated land, forest, grassland, shrub, wetland, water body, impervious surface and bare land. The data set has been verified by a large number of seasonal samples in the Sahel region. The overall accuracy of the data set is about 75%, and the accuracy of change area detection is more than 70%. It is also very similar to FAO and the existing land cover map. The data set can provide data support for the sustainable use of land resources and environmental protection in the Sahel region of Africa.