Eritrea Cropland Mask

The dataset contains global crop at 0.0089286 deg resolution (about 1 squared kilometer).   Each pixel represents the area fraction of the specific cover (i.e. percentage of the pixel with rangelands)

This dataset contains the Ethiopian dataset on the Irrigated Lands , from the Waterdata , Global Irrigated Area for Mapping for the nominaL year of 2000 using satellite images at the resolution of about 5000 metres

This dataset provides a spatial mask of Ethiopia rangelands, identifying areas dominated by grasslands, shrublands, and sparse vegetation that are primarily used for grazing and pastoral livelihoods. The rangeland mask for Ethiopia delineates regions where pastoralism and agro-pastoralism are prevalent, helping distinguish these land systems from croplands, forests, or urban areas. The dataset is derived from a combination of remote sensing data and land cover classifications, ensuring consistency across ecological zones-

Crop production is among the most extensive human activities on the planet – with critical importance for global food security, land use, environmental burden, and climate. Yet despite the key role that croplands play in global land use and Earth systems, there remains little understanding of how spatial patterns of global crop cultivation have recently evolved and which crops have contributed most to these changes. Here we construct a new data library of subnational crop-specific irrigated and rainfed harvested area statistics and combine it with global gridded land cover products to develop a global gridded (5-arcminute) irrigated and rainfed cropped area (MIRCA-OS) dataset for the years 2000 to 2015 for 23 crop classes. These global data products support critical insights into the spatially detailed patterns of irrigated and rainfed cropland change since the start of the century and provide an improved foundation for a wide array of global assessments spanning agriculture, water resource management, land use change, climate impact, and sustainable development

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Aridity index (AI) is an effective estimator of drought status, and spatiotemporally continuous long-term AI dataset is critical for drought assessment and applications.This dataset offers valuable support for research on dryland ecosystems, agriculture, and climate change, offering critical insights to address global environmental and sustainability challenges. Resolution ;0.05 × 0.05   in the resolution  ( referenced year 2022)