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A growing body of research shows the importance of land use/cover change(LULCC) on modifying the Earth system. Land surface models are used tostimulate land–atmosphere dynamics at the macroscale, but model bias anduncertainty remain that need to be addressed before the importance of LULCCis fully realized. In this study, we propose a method of improving LULCCestimates for land surface modeling exercises. The method is driven byprojectable socio-ecological geospatial predictors available seamlesslyacross sub-Saharan Africa and yielded continuous (annual) estimates of LULCCat 5 km  ×  5 km spatial resolution. The method was developed with2252 sample areaframes of 5 km  ×  5 km consisting of the proportion of several landcover types in Kenya over multiple years. Forty-three socio-ecologicalpredictors were evaluated for model development. Machine learning was usedfor data reduction, and simple (functional) relationships defined bygeneralized additive models were constructed on a subset of thehighest-ranked predictors (<i>p</i> ≤ 10) to estimate LULCC. The predictorsexplained 62 and 65 % of the variance in the proportion of agricultureand natural vegetation, respectively, but were less successful at estimatingmore descriptive land cover types. In each case, population density on anannual basis was the highest-ranked predictor. The approach was compared to acommonly used remote sensing classification procedure, given the wide use ofsuch techniques for macroscale LULCC detection, and outperformed it for eachland cover type. The approach was used to demonstrate significant trends inexpanding (declining) agricultural (natural vegetation) land cover in Kenyafrom 1983 to 2012, with the largest increases (declines) occurring in denselypopulated high agricultural production zones. Future work should address theimprovement (development) of existing (new) geospatial predictors and issuesof model scalability and transferability

Continuous and consistent land use/cover change estimates using socio-ecological data