Abstract. Soils across sub-Saharan Africa are exposed to extensive degradation, reducing their ability to produce crops and support livestock. While there has been a significant research effort focussing on soil degradation in sub-Saharan croplands, less research effort had been directed towards grasslands. Here, we tested the effectiveness of remote sensing to classify the soil degradation status of smallholder grazing lands. Focussing on grasslands used by smallholders in the districts of Nyando and Kuresoi in Western Kenya, we first used remote sensing (RS) to classify grasslands as either equilibrium, transition or degraded, and then tested how this classification related to measured soil parameters indicative of soil degradation. We then used this classification, which was based on a temporal analysis of Normalised Differential Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Normalised Differential Water Index (NDWI) between 2013 and 2018, to identify 90 field sites across the two districts, which we then sampled and analysed for a range of physical, chemical and biological soil properties. Only soil microbial biomass carbon (C) showed consistent alignment with the RS classification, although there was some overlap with other soil parameters at one or other of the sites. To group the sites using the soil parameters, which we split by district and into stable and transient soil variables, K-means clustering was undertaken. Two clusters were produced. One of the clusters included sites with higher levels of C, nitrogen (N), phosphorus (P) and pH, that aligned well with the RS classification at Kuresoi, with seven out of ten equilibrium sites being assigned to this cluster. The other cluster, in Nyando, had high soil C and P, but low pH and relatively low soil bulk density, and corresponded to 12 out of the 16 equilibrium sites. Overall, our results suggest that while the use of RS methods for classifying degraded grasslands and the soils supporting them does have significant advantages in terms of time and costs over field survey, supplementing these methods with a limited set of soil parameters related to nutrient cycling, such as microbial biomass C, soil P, percent C and N, and soil pH, could enhance our ability to to identify degraded soils and target restoration efforts.
Quinton et al. (Wed,) studied this question.