Land degradation is a threat to ecosystems and to already vulnerable people who live in dryland environments. The United Nations Convention to Combat Desertification (UNCCD) has been tasked with addressing the global challenges posed by land degradation. Although the complexities around land degradation have more recently been acknowledged, spatial modelling of this phenomenon remains a global challenge. In this study, measures of productivity derived from Landsat 8 and Sentinel-2A data have been integrated with local climate, land use, and socio-economic data to provide a more meaningful regional conceptual framework for land degradation. We create a novel method to map land condition utilising a set of 26 biological, topographic, climatic and land use covariates. The model results can be viewed in isolation as a measure of vegetation cover but are more useful when interpreted within the context of the environmental covariates. This can be accomplished through the Succulent Karoo Land Condition Inspector Google Earth Engine application. The data are intended to assist conservation planners in providing spatial data related to the current condition of the land in the biome, as well as to highlight areas where biodiversity is potentially at greater threat and where restoration initiatives or climate change adaptation are most needed.

土地退化对生态系统和生活在旱地环境中的脆弱人群构成威胁。《联合国防治荒漠化公约》（UNCCD）的任务是应对土地退化带来的全球挑战。尽管最近人们认识到土地退化的复杂性，但这种现象的空间建模仍然是一个全球性挑战。在这项研究中，将来自 Landsat 8 和 Sentinel-2A 数据的生产力测量与当地气候、土地利用和社会经济数据相结合，为土地退化提供了更有意义的区域概念框架。我们创建了一种利用 26 个生物、地形、气候和土地利用协变量来绘制土地状况图的新颖方法。模型结果可以单独视为植被覆盖的衡量标准，但在环境协变量的背景下解释时更有用。这可以通过 Succulent Karoo Land Condition Inspector Google Earth Engine 应用程序来完成。这些数据旨在协助保护规划者提供与生物群落土地当前状况相关的空间数据，并突出显示生物多样性可能受到更大威胁以及最需要恢复举措或气候变化适应的地区。