Ethiopia: Seasonal Temperature Condition Index

These datasets provide Ethiopia’s Vegetation Condition Index (VCI) values for the past five years, specifically covering the June to September growing season. The VCI is a satellite-derived indicator used to evaluate vegetation health and drought conditions. It distinguishes drought-related stress from other factors affecting vegetation growth. The VCI values range from 0 to 100, where 0% indicates the worst vegetation condition—approaching the historical minimum and signifying severe drought—and 100% indicates optimal conditions, near the historical maximum, reflecting healthy and lush vegetation. The VCI data is obtained from the MODIS satellite. The long-term dataset used for seasonal analysis spans from the year 2000 to the present, focusing on the months of June through September, aligning with Ethiopia’s main growing season. These time series datasets enable the comparison of current vegetation conditions with those of recent growing seasons, supporting drought monitoring and vegetation health assessments.

These datasets provide Ethiopia’s Vegetation Health Index (VHI) values for the past five years, focusing specifically on the growing season from June to September. The Vegetation Health Index (VHI) is a satellite-derived indicator used to monitor the overall health and stress conditions of vegetation, particularly in relation to drought, heat, and moisture availability. It offers an integrated measure by combining data on vegetation greenness and thermal conditions. VHI is a vital remote sensing index for assessing vegetation condition, drought impacts, and agricultural stress. It combines two indices: the Vegetation Condition Index (VCI), which reflects vegetation greenness, biomass, and vigor; and the Temperature Condition Index (TCI), which indicates thermal conditions including heat and water stress. The long-term dataset analyzed for seasonal VHI spans from 2000 to the present, concentrating on the months of June, July, August, and September, corresponding to the Ethiopian growing season. These time series datasets highlight VHI values for the most recent five growing seasons, enabling direct comparison with current year conditions.

NDVI Anomaly is a geospatial metric used to measure how current vegetation conditions deviate from what is considered "normal" or average for a specific location and time period. It helps identify abnormal vegetation growth, whether better or worse than expected, and is especially valuable in agriculture, drought monitoring, food security, and environmental management. The dataset used for this analysis is the MOD13Q1 V6.1 product, which provides a Vegetation Index (VI) value on a per-pixel basis. For this study, we used MODIS data from July for the past three years, along with historical data spanning from 2010 to 2020.

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)

This dataset provides Palmer Drought Severity Index (PDSI) values for Ethiopia from 2020 to 2024, offering insights into drought conditions across the country. The PDSI is a widely used drought index that measures deviations in soil moisture conditions from the long-term average, helping to identify both drought and wet conditions. Unlike traditional PDSI calculations that rely on the Thornthwaite method for estimating potential evapotranspiration (ET), this dataset utilizes a more physically based Penman-Monteith reference ET, which accounts for: * Solar radiation * Temperature * Humidity * Windspeed ## Interpretation of PDSI Values: * PDSI between -0.5 and 0.5 → Near-normal soil moisture conditions * PDSI 4 → Very wet conditions

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