This Web Feature Layer contains data that will help you determine access to safe water at a regional scale with a global extent. The data for this map was compiled in 2018 and at this time some regional water access information was unknown.Access to safe water is a good example of global inequality.

Global Inequality: where something is not fairly shared out to everyone.

In many areas of the world, we take it for granted that the tap will always provide safe and clean water for drinking, cooking and for washing with. However, more than one billion people worldwide have no choice but to use potentially harmful sources of water for bathing, cooking and even drinking. Every day this has the result of causing the death of more than 6,000 children.

In the developing world more than one billion people have inadequate access to water.

It has been estimated that 12% of the world’s population uses 85% of its water.Student workbook associated with this WebMap

Context

In CDP competition's starter notebook, one of the KPI mentioned is shadow water price. The research paper used World Resources Institutes' data on water stress to estimate the shadow price.

Content

This is a geo file, a world map showing water stress by regions.

Acknowledgements

https://www.wri.org/resources/charts-graphs/water-stress-country

Inspiration

Shortage of water is one of the big consequences of climate change. This data reveals at regional level where the risky areas are and how severe is the problem.

The Watersheds of the World is a comprehensive digital atlas of the world's river basins. The database is provided online and on CD-ROM by the Water Resources eAtlas, a collaborative product of WRI, IUCN, IWMI, and the Ramsar Convention on Wetlands. The Water Resources eAtlas embodies an ongoing effort to link, integrate, and communicate information on water resources management. The Watersheds of the World database is the first contribution to the eAtlas.

The online version and CD-ROM of the Watersheds of the World provide maps and statistical data of land cover, land use, population density, and biodiversity for 154 basins and sub-basins around the world. The database lists indicators and variables for each of these basins and, where appropriate, provides links and references to relevant information. It further contains 20 global indicator maps at the basin level that portray issues affecting water resources and freshwater biodiversity.

Colored buttons function as a menu to select individual basins by continent. Each continental menu provides access to interactive maps and lists of basins per continent through which you can access individual basin profiles.

There is also a button for global indicator maps which links to the following:

Primary Watersheds Map Freshwater Fish Species Richness by Basin Endemic Freshwater Fish Species by Basin Endemic Bird Areas by Basin Wetland Area by Basin Cropland Area by Basin Grassland, Savanna and Shrubland Area by Basin Forest Cover by Basin Remaining Original Forest Cover by Basin Dryland Area by Basin Urban and Industrial Area by Basin Protected Area by Basin Average Population Density by Basin Degree of River Fragmentation and Flow Regulation by Basin Annual Renewable Water Supply per Person by Basin for 1995 and Projections for 2025 Environmental Water Scarcity Index by Basin Large Dams under Construction by Basin Ramsar Sites by Basin Virtual Water Flows Selected Basins with IUCN and IWMI Projects

All basin profiles and global maps can also be downloaded as PDFs.

Freshwater scarcity is a growing concern, placing considerable importance on the accuracy of indicators used to characterize and map water scarcity worldwide. We improve upon past efforts by using estimates of blue water footprints (consumptive use of ground- and surface water flows) rather than water withdrawals, accounting for the flows needed to sustain critical ecological functions and by considering monthly rather than annual values. We analyzed 405 river basins for the period 1996–2005. In 201 basins with 2.67 billion inhabitants there was severe water scarcity during at least one month of the year. The ecological and economic consequences of increasing degrees of water scarcity – as evidenced by the Rio Grande (Rio Bravo), Indus, and Murray-Darling River Basins – can include complete desiccation during dry seasons, decimation of aquatic biodiversity, and substantial economic disruption.

Blue Raster worked with the World Resources Institute (WRI) to build the Agricultural Exposure to Water Stress interactive map which highlights the intersection between 20 commodity crops, from coffee to cocoa to oranges, with different levels of baseline water stress.WRI describes water stress as “the ratio of total water withdrawals to the available renewable supply in an area. In highly water-stressed regions, 40 percent or more of the supply is used annually. When that ratio gets up to 80 percent, it’s considered extremely stressed.”

Water is essential to the progress of human societies. It is required for a healthy environment and a thriving economy. Food production, electricity generation, and manufacturing, among other things, all depend on it. However, many decision-makers lack the technical expertise to fully understand hydrological information.

In response to growing concerns from the private sector and other actors about water availability, water quality, climate change, and increasing demand, WRI applied the composite index approach as a robust communication tool to translate hydrological data into intuitive indicators of water-related risks.

This dataset updates the Aqueduct™ water risk framework, in which we combine 13 water risk indicators—including quantity, quality, and reputational risks—into a composite overall water risk score.

This database and the Aqueduct tools enable comparison of water-related risks across large geographies to identify regions or assets deserving of closer attention. Aqueduct 3.0 introduces an updated water risk framework and new and improved indicators. It also features different hydrological sub-basins. We introduce indicators based on a new hydrological model that now features (1) integrated water supply and demand, (2) surface water and groundwater modelling, (3) higher spatial resolution, and (4) a monthly time series that enables the provision of monthly scores for selected indicators.

Key elements of Aqueduct, such as overall water risk, cannot be directly measured and therefore are not validated. Aqueduct remains primarily a prioritization tool and should be augmented by local and regional deep dives.

User Guide Includes column descriptors and other metadata regarding the dataset https://github.com/wri/aqueduct30_data_download/blob/master/metadata.md

Source https://www.wri.org/resources/data-sets/aqueduct-global-maps-30-data

About Aqueduct Aqueduct’s tools map water risks such as floods, droughts, and stress, using open-source, peer-reviewed data. Beyond the tools, the Aqueduct team works one-on-one with companies, governments, and research partners to help advance best practices in water resources management and enable sustainable growth in a water-constrained world.

Over the past six years, the Aqueduct tools have reached hundreds of thousands of users across the globe and informed decision-makers in and beyond the water sector. Aqueduct data and insights have been featured in major media outlets including, the Economist, the Guardian, Bloomberg Businessweek, the New York Times and Vox’s Netflix show Explained.

This iteration of Aqueduct represents our most robust look at water risks to date, including more granular data, higher resolution, new indicators, improved tool function and access to underlying hydrological models.

The map is compiled for the SOLAW Report: "Sources of water for agriculture". Data are available from AQUASTAT - programme of the Land and Water Division of the Food and Agriculture Organization of the United Nations. Perhaps the most widespread indicator of water scarcity at country level that can be found in literature is per capita availability of average renewable water resources, using threshold values of 500, 1 000 and 1 700 m3/person per year (Falkenmark and Widstrand, 1992; UN-Water, 2006b). Under this system countries or regions are considered to be facing absolute water scarcity if water availability is < 500 m3 per capita per year, chronic water shortage if water availability is between 500 and 1 000 m3, regular water stress between 1 000 and 1 700 m3, and occasional stress or local stress can occur also at levels above 1 700 m3. This relatively simple approach to measuring water scarcity was primarily based on estimates of the number of people who can reasonably live with a certain unit of water resources (Falkenmark, 1984). This indicator is widely used because it can be easily calculated for every country in the world and for every year, based on long-term average annual water resources data (FAO, 2010a) and available population data (UN, 2009).

There are multiple well-recognized and peer-reviewed global datasets that can be used to assess water availability and water pollution. Each of these datasets are based on different inputs, modeling approaches, and assumptions. Therefore, in SBTN Step 1: Assess and Step 2: Interpret & Prioritize, companies are required to consult different global datasets for a robust and comprehensive State of Nature (SoN) assessment for water availability and water pollution.

To streamline this process, WWF, the World Resources Institute (WRI), and SBTN worked together to develop two ready-to-use unified layers of SoN – one for water availability and one for water pollution – in line with the Technical Guidance for Steps 1: Assess and Step 2: Interpret & Prioritize. The result is a single file (shapefile) containing the maximum value both for water availability and for water pollution, as well as the datasets’ raw values (as references). This data is publicly available for download from this repository.

These unified layers will make it easier for companies to implement a robust approach, and they will lead to more aligned and comparable results between companies. A temporary App is available at https://arcg.is/0z9mOD0 to help companies assess the SoN for water availability and water pollution around their operations and supply chain locations. In the future, these layers will become available both in the WRI’s Aqueduct and in the WWF Risk Filter Suite.

For the SoN for water availability, the following datasets were considered:

• Baseline water stress (Hofste et al. 2019), data available here

• Water depletion (Brauman et al. 2016), data available here

• Blue water scarcity (Mekonnen & Hoekstra 2016), data upon request to the authors

For the SoN for water pollution, the following datasets were considered:

• Coastal Eutrophication Potential (Hofste et al. 2019), data available here

• Nitrate-Nitrite Concentration (Damania et al. 2019), data available here

• Periphyton Growth Potential (McDowell et al. 2020), data available here

In general, the same processing steps were performed for all datasets:

1. Compute the area-weighted median of each dataset at a common spatial resolution, i.e. HydroSHEDS HydroBasins Level 6 in this case.

2. Classify datasets to a common range as reclassifying raw values to 1-5 values, where 0 (zero) was used for cells or features with no data. See the documentation for more details.

3. Identify the maximum value between the classified datasets, separately, for Water Availability and for Water Pollution.

For transparency and reproducibility, the code is publicly available at https://github.com/rafaexx/sbtn-SoN-water

Monthly and annual evapotranspiration (ET) maps of Berlin, Germany at a 10-m resolution are provided. This dataset is related to the manuscript "City-wide, high-resolution mapping of evapotranspiration to guide climate-resilient planning" (under review).

The monthly and annual ET sums are provided as rasters (.tif files). The monthly ET sums are given in the files named "ETmonthly_2019_(month).tif" The annual ET sum for 2019 is named "ETannual_2019.tif". The coordinate reference system (CRS) is "+proj=longlat +datum=WGS84 +no_defs."

For access to daily ET maps in 2019, please contact Stenka Vulova (stenka.vulova@tu-berlin.de).

The abstract of the manuscript is given for background information on the dataset:

"The impacts of global change, including extreme heat and water scarcity, are threatening an ever-growing urban world population. Evapotranspiration (ET) mitigates the urban heat island, reducing the effect of heat waves. It can also be used as a proxy for vegetation water use, making it a crucial tool to plan resilient green cities. To optimize the trade-off between urban greening and water security, reliable and up-to-date maps of ET for cities are urgently needed. Despite its importance, few studies have mapped urban ET accurately for an entire city in high spatial and temporal resolution. We mapped the ET of Berlin, Germany in high spatial (10-m) and temporal (hourly) resolution for the year of 2019. A novel machine learning (ML) approach combining Sentinel-2 time series, open geodata, and flux footprint modeling was applied. Two eddy flux towers with contrasting surrounding land cover provided the training and testing data. Flux footprint modeling allowed us to incorporate comprehensive land cover types in training the ML models. Open remote sensing and geodata used as model inputs included Normalized Difference Vegetation Index (NDVI) from Sentinel-2, building height, impervious surface fraction, vegetation fraction, and vegetation height. NDVI was used to indicate vegetation phenology and health, as plant transpiration contributes to the majority of terrestrial ET. Hourly reference ET (RET) was calculated and used as input to capture the temporal dynamics of the meteorological conditions. Predictions were carried out using Random Forest (RF) regression. Weighted averages extracted from hourly ET maps using flux footprints were compared to measured ET from the two flux towers. Validation showed that the approach is reliable for mapping urban ET, with a mean R2 of 0.76 and 0.56 and a mean RMSE of 0.0289 mm and 0.0171 mm at the more vegetated site and the city-center site, respectively. Lastly, the variation of ET between Local Climate Zones (LCZs) was analyzed to support urban planning. This study demonstrated the capacity to map urban ET at an unprecedented high spatial and temporal resolution with a novel methodology, which can be used to support the sustainable management of green infrastructure and water resources in an urbanizing world facing climate change."

Aqueduct 4.0 is the latest iteration of WRI's water risk framework designed to translate complex hydrological data into intuitive indicators of water related risk. This dataset has curated 13 water risk indicators for quantity, quality and reputational concerns into a comprehensive framework. For 5 of the 13 indicators, a global hydrological model called PCR-GLOBWB 2 has been used to generate novel datasets on sub-basic water supply. The PCR-GLOBWB 2 model is also used to project future sub-basin water conditions using CMIP6 climate forcings. The projections center around three periods (2030, 2050, and 2080) under three future scenarios (business-as-usual SSP 3 RCP 7.0, optimistic SSP 1 RCP 2.6, and pessimistic SSP 5 RCP 8.5). The water risk indicators have been aggregated by category (quantity, quality, reputational, and overall) into composite risk scores using sector-specific weighting schemes. In addition, select sub-basin scores have been aggregated into country and provincial administrative boundaries using a weighted average approach, where sub-basins with more demand have a higher influence over the final administrative score. The WRI Aqueduct baseline monthly dataset provides monthly data on key water risk indicators which includes indicators such as baseline water stress, baseline water depletion and interannual variability.This monthly data allows for a more detailed analysis of water risk dynamics throughout the year, which is crucial for understanding seasonal water scarcity, planning for water management interventions, and adapting to changing water availability patterns. This technical note explains in detail the framework, methodology, and data used in developing Aqueduct Floods.

In CDP competition's starter notebook, one of the KPI mentioned is shadow water price. The research paper used World Resources Institutes' data on water stress to estimate the shadow price.

Content This is a shape file, a world map showing projected water risk by regions.

Acknowledgements https://www.wri.org/resources/data-sets/aqueduct-global-maps-30-data

Inspiration Shortage of water is one of the big consequences of climate change. This data reveals at regional level where the risky areas are and how severe is the problem.

Blue Raster and the World Resources Institute (WRI) created the Aqueduct Country and River Basin Rankings map, which shows water stress scores for 180 nations, the world's 100 largest river basins by area, and the 100 most populous river basins. WRI found that "18 river basins face extremely high levels of baseline water stress, meaning that more than 80 percent of the water naturally available to agricultural, domestic, and industrial users is withdrawn annually—leaving businesses, farms, and communities vulnerable to scarcity." Read more about the project and WRI's efforts towards sustainable water management at: http://www.blueraster.com/aqueduct-mapping-water-risk-around-the-globe/

Data sources used to generate the covariates in the analysis.

Estimated impact and efficiency of alternative risk mitigation scenarios.

About this AppThe Drought Aware app provides information about areas in the U.S. affected by drought across different time intervals and over multiple drought intensities. The app shows summaries about the affected population and the potential impacts to crops, agricultural labor, rivers, and reservoirs.Use this AppDisplay drought maps for different weeks by clicking on the time-series chart (top bar) or by scrolling through time using the sector chart (top-left). Hover on each drought intensity level in the sector chart to highlight the areas on the map and display the area percentage. Click on the map to display a panel with summary information for the selected area. The panel includes three categories (1) population, (2) water, and (3) agriculture. App CategoriesThe Drought Aware app summarizes information in three categories:Population: displays the estimated people and households affected by drought at each intensity level, describes some of the vulnerable populations, and lists the related drought risk indexes. The data is available at County and State levels. Water: depicts the major local rivers, the average inter-annual river flow, and the relevant local reservoirs. The data is available at the Subregion Hydrologic Units (HUC4)Agriculture: shows the potential economic impact by major crop, the affected labor, and the agricultural exposure to droughts. The data is available at County and State levels. Drought Definitions Abnormally Dry (D0) Going into drought there is short-term dryness slowing planting, growth of crops or pastures. Coming out of drought there are some lingering water deficits; pastures or crops not fully recovered. Moderate Drought (D1) Some damage to crops and pastures. Streams, reservoirs, or wells low, some water shortages developing or imminent. Voluntary water-use restrictions requested. Severe Drought (D2) Crop or pasture losses likely. Water shortages are common. Water restrictions imposed. Extreme Drought (D3) Major crop/pasture losses. Widespread water shortages or restrictions. Exceptional Drought (D4) Exceptional and widespread crop/pasture losses. Shortages of water in reservoirs, streams, and wells create water emergencies.Data SourcesThe data layers used in this app can be found in ArcGIS Living Atlas of the World:U.S. Drought Monitor American Community Survey (ACS)USDA Census of AgricultureFEMA National Risk IndexNational Water Model (NWM)National Hydrography Dataset (NHD)National Inventory of Dams (NID)National Boundary Dataset (WBD)UpdateThe data behind the app is updated every week once a U.S. Drought Monitor map is released. The update process is automated using a live feed routine. This layer is provided for informational purposes and is not monitored 24/7 for accuracy and currency.RevisionsOct 16, 2024: Official release of the Drought Aware app.

The Millennium Ecosystem Assessment: MA Scenarios provides data and information on population, income, cereal production and consumption, meat production and consumption, land cover, water stress, water availability, acidification and nitrogen deposition. These scenarios provide useful insight into the complex factors that drive ecosystem change, estimating the magnitude of regional pressures on ecosystems and critical uncertainties that could undermine sustainable development. They also provide an understanding of the importance of institutions and values as the long-range outlook for the world's ecosystems depends on the course taken by global and regional development in the coming decades. The integration of changing ecosystem conditions into the global scenarios was taken as both effects and causes. To preserve access to the original set of socioeconomic and natural resource scenarios used by the Millennium Ecosystem Assessment (MA) and other related research.

Humans and ecosystems are deeply connected to, and through, the hydrological cycle. However, impacts of hydrological change on social and ecological systems are infrequently evaluated together at the global scale. Here, we focus on the potential for social and ecological impacts from freshwater stress and storage loss. We find basins with existing freshwater stress are drying (losing storage) disproportionately, exacerbating the challenges facing the water stressed versus non-stressed basins of the world. We map the global gradient in social-ecological vulnerability to freshwater stress and storage loss and identify hotspot basins for prioritization (n = 168). These most-vulnerable basins encompass over 1.5 billion people, 17% of global food crop production, 13% of global gross domestic product, and hundreds of significant wetlands. There are thus substantial social and ecological benefits to reducing vulnerability in hotspot basins, which can be achieved through hydro-diplomacy, social adaptive capacity building, and integrated water resources management practices.

[From The Landmap Project: Introduction, "http://www.landmap.ac.uk/background/intro.html"]

 A joint project to provide orthorectified satellite image mosaics of Landsat,
 SPOT and ERS radar data and a high resolution Digital Elevation Model for the
 whole of the UK. These data will be in a form which can easily be merged with
 other data, such as road networks, so that any user can quickly produce a
 precise map of their area of interest.

 Predominately aimed at the UK academic and educational sectors these data and
 software are held online at the Manchester University super computer facility
 where users can either process the data remotely or download it to their local
 network.

 Please follow the links to the left for more information about the project or
 how to obtain data or access to the radar processing system at MIMAS. Please
 also refer to the MIMAS spatial-side website,
 "http://www.mimas.ac.uk/spatial/", for related remote sensing materials.

What does the data show?

The Drought Severity Index is not threshold based. Instead, it is calculated with 12-month rainfall deficits provided as a percentage of the mean annual climatological total rainfall (1981–2000) for that location. It measures the severity of a drought, not the frequency.

12-month accumulations have been selected as this is likely to indicate hydrological drought. Hydrological drought occurs due to water scarcity over a much longer duration (longer than 12 months). It heavily depletes water resources on a large scale as opposed to meteorological or agricultural drought, which generally occur on shorter timescales of 3-12 months. However this categorisation is not fixed, because rainfall deficits accumulated over 12-months could lead to different types of drought and drought impacts, depending on the level of vulnerability to reduced rainfall in a region.

The DSI 12 month accumulations are calculated for two baseline (historical) periods 1981-2000 (corresponding to 0.51°C warming) and 2001-2020 (corresponding to 0.87°C warming) and for global warming levels of 1.5°C, 2.0°C, 2.5°C, 3.0°C, 4.0°C above the pre-industrial (1850-1900) period.

What are the possible societal impacts?

The DSI 12-month accumulations measure the drought severity. Higher values indicate more severe drought. The DSI is based on 12-month rainfall deficits. The impacts of the differing length of rainfall deficits vary regionally due to variation in vulnerability. Depending on the level of vulnerability to reduced rainfall, rainfall deficits accumulated over 12 months could lead to meteorological, agricultural and hydrological drought.

What is a global warming level?

The DSI 12-month accumulations are calculated from the UKCP18 regional climate projections using the high emissions scenario (RCP 8.5) where greenhouse gas emissions continue to grow. Instead of considering future climate change during specific time periods (e.g. decades) for this scenario, the dataset is calculated at various levels of global warming relative to the pre-industrial (1850-1900) period. The world has already warmed by around 1.1°C (between 1850–1900 and 2011–2020), whilst this dataset allows for the exploration of greater levels of warming.

The global warming levels available in this dataset are 1.5°C, 2°C, 2.5°C, 3°C and 4°C. The data at each warming level was calculated using a 21 year period. These 21 year periods are calculated by taking 10 years either side of the first year at which the global warming level is reached. This time will be different for different model ensemble members. To calculate the value for the DSI 12-month accumulations, an average is taken across the 21 year period.

We cannot provide a precise likelihood for particular emission scenarios being followed in the real world future. However, we do note that RCP8.5 corresponds to emissions considerably above those expected with current international policy agreements. The results are also expressed for several global warming levels because we do not yet know which level will be reached in the real climate as it will depend on future greenhouse emission choices and the sensitivity of the climate system, which is uncertain. Estimates based on the assumption of current international agreements on greenhouse gas emissions suggest a median warming level in the region of 2.4-2.8°C, but it could either be higher or lower than this level.

What are the naming conventions and how do I explore the data?

This data contains a field for each global warming level and two baselines. They are named ‘DSI12’ (Drought Severity Index for 12 month accumulations), the warming level or baseline, and 'upper' 'median' or 'lower' as per the description below. E.g. 'DSI12 2.5 median' is the median value for the 2.5°C projection. Decimal points are included in field aliases but not field names e.g. 'DSI12 2.5 median' is 'DSI12_25_median'.

To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578

Please note, if viewing in ArcGIS Map Viewer, the map will default to ‘DSI12 2.0°C median’ values.

What do the ‘median’, ‘upper’, and ‘lower’ values mean?

Climate models are numerical representations of the climate system. To capture uncertainty in projections for the future, an ensemble, or group, of climate models are run. Each ensemble member has slightly different starting conditions or model set-ups. Considering all of the model outcomes gives users a range of plausible conditions which could occur in the future.

For this dataset, the model projections consist of 12 separate ensemble members. To select which ensemble members to use, DSI 12 month accumulations were calculated for each ensemble member and they were then ranked in order from lowest to highest for each location.

The ‘lower’ fields are the second lowest ranked ensemble member. The ‘upper’ fields are the second highest ranked ensemble member. The ‘median’ field is the central value of the ensemble.

This gives a median value, and a spread of the ensemble members indicating the range of possible outcomes in the projections. This spread of outputs can be used to infer the uncertainty in the projections. The larger the difference between the lower and upper fields, the greater the uncertainty.

‘Lower’, ‘median’ and ‘upper’ are also given for the baseline periods as these values also come from the model that was used to produce the projections. This allows a fair comparison between the model projections and recent past.

Useful links

This dataset was calculated following the methodology in the ‘Future Changes to high impact weather in the UK’ report. Further information on the UK Climate Projections (UKCP). Further information on understanding climate data within the Met Office Climate Data Portal

Dataset Abstract:
Sugarcane is an important source of food, biofuel, and farmer income in many countries. At the same time, sugarcane is implicated in many social and environmental challenges, including water scarcity and nutrient pollution. Currently, few of the top sugar-producing countries generate reliable maps of where sugarcane is cultivated. To fill this gap, we introduce a dataset of detailed sugarcane maps for the top 13 producing countries in the world, comprising nearly 90% of global production. Maps were generated for the 2019-2022 period by combining data from the Global Ecosystem Dynamics Investigation (GEDI) and Sentinel-2 (S2). GEDI data were used to provide training data on where tall and short crops were growing each month, while S2 features were used to map tall crops for all cropland pixels each month. Sugarcane was then identified by leveraging the fact that sugar is typically the only tall crop growing for a substantial fraction of time during the study period. Comparisons with field data, pre-existing maps, and official government statistics all indicated high precision and recall of our maps. Agreement with field data at the pixel level exceeded 80% in most countries, and sub-national sugarcane areas from our maps were consistent with government statistics. Exceptions appeared mainly due to problems in underlying cropland masks, or to under-reporting of sugarcane area by governments.
The final maps should be useful in studying the various impacts of sugarcane cultivation and producing maps of related outcomes such as sugarcane yields.

USAGE: Users must mask the provided sugarcane map with the most appropriate crop mask from the ones provided. If none of the provided crop masks are suitable, users can use an external crop mask instead.

Validation results for the sugarcane maps are detailed in Section 4.3 of the paper. For Indonesia and Guatemala, no field-level data or raster datasets were available for validation of our sugarcane maps.

Dataset:
5 bands
b1: Number of tall months
b2: Sugarcane Map: 0 = non-sugarcane, 1 = sugarcane
b3: ESA crop mask: 0 = non-cropland, 1 = cropland
b4: ESRI crop mask: 0 = non-cropland, 1 = cropland
b5: GLAD crop mask: 0 = non-cropland, 1 = cropland

The dataset can be accessed on Google Earth Engine (GEE) at
https://code.earthengine.google.com/?asset=projects/lobell-lab/gedi_sugarcane/maps/imgColl_10m_ESAESRIGLAD

Example GEE script for visualizing and masking the sugarcane maps by country available at:
https://code.earthengine.google.com/545a87ce9bc29f2b5ad180955d974f8c?asset=projects%2fl Bell-lab%2Fgedi_sugarcane%2 Maps%2FimgColl_10m_ESAESRIGLAD