There is a growing concern over change in vegetation dynamics and drought patterns with the increasing climate variability and warming trends in Africa, particularly in the semiarid regions of East Africa. Here, several geospatial techniques and datasets were used to analyze the spatio-temporal vegetation dynamics in response to climate (precipitation and temperature) and drought in Eritrea from 2000 to 2017. A pixel-based trend analysis was performed, and a Pearson correlation coefficient was computed between vegetation indices and climate variables. In addition, vegetation condition index (VCI) and standard precipitation index (SPI) classifications were used to assess drought patterns in the country. The results demonstrated that there was a decreasing NDVI (Normalized Difference Vegetation Index) slope at both annual and seasonal time scales. In the study area, 57.1% of the pixels showed a decreasing annual NDVI trend, while the significance was higher in South-Western Eritrea. In most of the agro-ecological zones, the shrublands and croplands showed decreasing NDVI trends. About 87.16% of the study area had a positive correlation between growing season NDVI and precipitation (39.34%, p < 0.05). The Gash Barka region of the country showed the strongest and most significant correlations between NDVI and precipitation values. The specific drought assessments based on VCI and SPI summarized that Eritrea had been exposed to recurrent droughts of moderate to extreme conditions during the last 18 years. Based on the correlation analysis and drought patterns, this study confirms that low precipitation was mainly attributed to the slowly declining vegetation trends and increased drought conditions in the semi-arid region. Therefore, immediate action is needed to minimize the negative impact of climate variability and increasing aridity in vegetation and ecosystem services.

Global agricultural lands are becoming saline because of human activities that have affected crop production and food security worldwide. In this study, the spatiotemporal variability of soil electrical conductivity (EC) in Central Asia was evaluated based on high-resolution multi-year predicted soil EC data, Moderate Resolution Imaging Spectroradiometer (MODIS) land cover product, precipitation, reference evapotranspiration, population count, and soil moisture datasets. We primarily detected pixel-based soil EC trends over the past three decades and correlated soil EC with potential deriving factors. The results showed an overall increase in salt-affected areas between 1990 and 2018 for different land cover types. The soil EC trend increased by 6.86% (p < 0.05) over Central Asia during 1990–2018. The open shrub lands dominated by woody perennials experienced the highest increasing soil salinity trend, particularly in Uzbekistan and Turkmenistan local areas, while there was a decreasing soil EC trend in the cropland areas, such as in Bukhara and Khorezm (Uzbekistan). The main factors that affect the variability of soil salinity were strongly associated with population pressure and evapotranspiration. This study provides comprehensive soil EC variations and trends from the local to regional scales. Agriculture and land resource managers must tackle the rising land degradation concerns caused by the changing climate in arid lands and utilise geoinformatics.

The global carbon cycle research requires precise and sufficient observations of the column-averaged dry-air mole fraction of CO ${}_2$ (XCO ${}_2$ ) in addition to conventional surface mole fraction observations. In addition, assessing the consistency of multi-satellite data are crucial for joint utilization to better infer information about CO ${}_2$ sources and sinks. In this work, we evaluate the consistency of long-term XCO ${}_2$ retrievals from the Greenhouse Gases Observing Satellite (GOSAT), Orbiting Carbon Observatory 2 (OCO-2) in comparison with Total Carbon Column Observing Network (TCCON) and the 3D model of CO ${}_2$ mole fractions data from CarbonTracker 2017 (CT2017). We create a consistent joint dataset and compare it with the long-term model data to assess their abilities to characterize the carbon cycle climate. The results show that, although slight increasing differences are found between the GOSAT and TCCON XCO ${}_2$ in the northern temperate latitudes, the GOSAT and OCO-2 XCO ${}_2$ retrievals agree well in general, with a mean bias ± standard deviation of differences of 0.21 ± 1.3 ppm. The differences are almost within ±2 ppm and are independent of time, indicating that they are well calibrated. The differences between OCO-2 and CT2017 XCO ${}_2$ are much larger than those between GOSAT and CT XCO ${}_2$ , which can be attributed to the significantly different spatial representatives of OCO-2 and the CT-transport model 5 (TM5). The time series of the combined OCO-2/GOSAT dataset and the modeled XCO ${}_2$ agree well, and both can characterize significantly increasing atmospheric CO ${}_2$ under the impact of a large El Niño during 2015 and 2016. The trend calculated from the dataset using the seasonal Kendall (S-K) method indicates that atmospheric CO ${}_2$ is increasing by 2–2.6 ppm per year.

Climate variability and drought are increasing in the Horn of Africa. Evaluating land use/land cover (LULC) changes and their impacts on water availability and variation are vital for regional land use planning and water resources management. LULC changes during 2000–2015 were estimated using high‐resolution Landsat images and the Google Earth Engine cloud platform, and land use dynamic index (K). The impact of LULC change on water yield was evaluated using the Integrated Valuation of Ecosystem Services and Tradeoff (InVEST) model. The results at a regional scale show that there were rapid decreases in the area of forests and barren lands (‐K) while there was a drastic increase in the built‐up area (+K values). The transition was found to occur from forested land to low and very low biomass areas with 51.13% and 16.7%, respectively. There were similar LULC changes in the Mereb‐Gash River Basin. The mean annual water yield increased for all the catchments during 2000–2015 and with the peak in 2005. The highest annual sum water yield decreased in the forested lands from 43.18 million m³ in 2000 to 4.1 million m³ in 2015. There was a strong positive correlation between areal changes (%) and the annual water yield variations (%) for all the LULC types except for water body, and the correlation was significantly positive for forest (p < 0.01). The study demonstrates that the decrease in forested areas and expansion in the built‐up areas had large impacts on water yield. The impacts may increase pressure on ecosystem services, exacerbate water scarcity, and food insecurity.

Understanding the response of vegetation and ecosystem resilience to climate variability and drought conditions is essential for ecosystem planning and management. In this study, we assessed the vegetation changes and ecosystem resilience in the Horn of Africa (HOA) since 2000 and detected their drivers based mainly on analysis of the Moderate Resolution Imaging Spectroradiometer (MODIS) products. We found that the annual and seasonal trends of NDVI (Normalized Difference Vegetation Index) generally increased during the last two decades over the Horn of Africa particularly in western parts of Ethiopia and Kenya. The weakest annual and seasonal NDVI trends were observed over the grassland cover and tropical arid agroecological zones. The NDVI variation negatively correlated with Land Surface Temperature (LST) and positively correlated with precipitation at a significant level (p < 0.05) account for 683,197 km² and 533,385 km² area, respectively. The ecosystem Water Use Efficiency (eWUE) showed overall increasing trends with larger values for the grassland biome. The precipitation had the most significant effect on eWUE variation compared to LST and annual SPEI (Standardized Evapotranspiration Index). There were about 54.9% of HOA resilient to drought disturbance, whereas 32.6% was completely not-resilient. The ecosystems in the humid agroecological zones, the cropland, and wetland were slightly not-resilient to severe drought conditions in the region. This study provides useful information for policy makers regarding ecosystem and dryland management in the context of climate change at both national and regional levels.

Complete water accounting (WA) and crop water productivity (CWP) analysis is crucial for evaluating water use efficiency (WUE). This study aims to evaluate the contributions of hydro-meteorological factors to the changes of WA and CWP and subsequent WUE based on the data from 2009–2020 in the Nile River Basin (NRB), East Africa (EA). The Mann-Kendall (MK) statistical test and Sen’s slope estimator were applied to detect the trends of climatic factors, and the AquaCrop model was used to simulate the crop yields in response to water balance and consumption based on crop physiological, soil water, and salt budget concepts. For the years 2012 and 2019, the mean of climatic water deficit P − ETa was 71.03 km³ and 37.03 km³, respectively, which was expected to rise to ~494.57 km³ by 2050. The results indicated that the basin water budget was unbalanced due to the coupled impact of year-to-year hot and dry conditions and increase in water abstraction, an indication of water deficit or stress. CWP and WUE increased during the study period with different changing patterns. CWP was also found to correlate to the yield of major crops (p-value > 0.05). It was concluded that climatic factors influenced the crop yield, CWP, and WUE in the study area. Thus, the improvement of CWP and WUE should rely on advanced water-saving innovations. The findings of this study could help water managers to improve water productivity by focusing on water account potentials and creating regional advantages by deploying water in combination with surplus flow from upstream to downstream consumption.