| 20 Aug 2025
Soil degradation assessment across tropical grassland of Western Kenya
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.
General comment
This manuscript tackles an important and timely question: whether multi-year satellite data on phenology (based on NDVI/EVI/NDWI processed with TIMESAT) can classify soil degradation states in smallholder grasslands and meaningfully relate to on-the-ground soil condition. The study spans two Kenyan landscapes and couples a remote-sensing classification (2013–2018) with field sampling at 90 sites (Oct–Nov 2019) for a suite of physical, chemical, and microbial variables (0–10 cm). The overall conclusion—that only microbial biomass C (and to a lesser extent bulk density) consistently aligns with the remote sensing classes—has practical implications for monitoring and restoration. However, several aspects of the methodology need clarification or strengthening before the evidence can fully support the claims.
Major concerns.
The paper mixes sensors (Landsat TM/OLI and Sentinel-2) and resamples to 10 m, but the harmonization/preprocessing steps are not fully described.
The specific land cover ESA product used for masking is not named or discussed in terms of accuracy/limitations for these mosaics.
Terminology should be standardized (e.g., “Normalized Difference Vegetation Index,” and clarify that your NDWI formulation uses NIR–SWIR, i.e., Gao-type, to avoid confusion with the original NDWI.)
Degradation states are defined from average distributions of TIMESAT metrics and then selected by visual consistency with Google Earth, without an independent accuracy assessment. At minimum, the manuscript should report a quantitative agreement/uncertainty analysis for the remote sensing maps.
With only ~35 scenes over 2013–2018 (≈ 6 per year) and no explicit treatment of cloud cover impacts on phenology fits, TIMESAT-derived timing metrics are likely uncertain. Moreover, remote sensing labels summarize 2013–2018 whereas field sampling is in 2019 a gap that can be consequential in smallholder systems. These choices plausibly weaken soil–RS correspondence.
Several sections are overly detailed (lab methods) while key methodological choices (RS preprocessing, TIMESAT parameters) are terse.
Please also state whether a research permit/ethical clearance was obtained.
L78-80: Reporting a single stocking rate (1–2 cattle ha⁻¹) without nuance is misleading; please contextualize it by describing the different production systems.
L82-83; The discussion of soil degradation is overly simplified. Even if not the central objective, the manuscript should briefly address the complexity of degradation processes and site-specific drivers at the study locations
l172: Use ‘difference’ rather than ‘differential’ here.”
L310: This is an isolated citation.