Monitoring land degradation (LD) to improve the measurement of the sustainable development goal (SDG) 15.3.1 indicator (“proportion of land that is degraded over a total land area”) is key to ensure a more sustainable future. Current frameworks rely on default medium-resolution remote sensing datasets available to assess LD and cannot identify subtle changes at the sub-national scale. This study is the first to adapt local datasets in interplay with high-resolution imagery to monitor the extent of LD in the semiarid Kiteto and Kongwa (KK) districts of Tanzania from 2000–2019. It incorporates freely available datasets such as Landsat time series and customized land cover and uses open-source software and cloud-computing. Further, we compared our results of the LD assessment based on the adopted high-resolution data and methodology (AM) with the default medium-resolution data and methodology (DM) suggested by the United Nations Convention to Combat Desertification. According to AM, 16% of the area in KK districts was degraded during 2000–2015, whereas DM revealed total LD on 70% of the area. Furthermore, based on the AM, overall, 27% of the land was degraded from 2000–2019. To achieve LD neutrality until 2030, spatial planning should focus on hotspot areas and implement sustainable land management practices based on these fine resolution results.

Indices assessing country-level climate and disaster risk at the global scale have experienced a steep rise in popularity both in science and international climate policy. A number of widely cited products have been developed and published over the recent years, argued to contribute critical knowledge for prioritizing action and funding. However, it remains unclear how their results compare, and how consistent their findings are on country-level risk, exposure, vulnerability and lack of coping, as well as adaptive capacity. This paper analyses and compares the design, data, and results of four of the leading global climate and disaster risk indices: The World Risk Index, the INFORM Risk Index, ND-GAIN Index, and the Climate Risk Index. Our analysis clearly shows that there is considerable degree of cross-index variation regarding countries’ risk levels and comparative ranks. At the same time, there is above-average agreement for high-risk countries. In terms of risk sub-components, there is surprisingly little agreement in the results on hazard exposure, while strong inter-index correlations can be observed when ranking countries according to their socio-economic vulnerability and lack of coping as well as adaptive capacity. Vulnerability and capacity hotspots can hence be identified more robustly than risk and exposure hotspots. Our findings speak both to the potential as well as limitations of index-based approaches. They show that a solid understanding of index-based assessment tools, and their conceptual and methodological underpinnings, is necessary to navigate them properly and interpret as well as use their results in triangulation.