Mapping numerically classified soil taxa in Kilombero Valley, Tanzania using machine learning

Inadequacy of spatial soil information is one of the limiting factors to making evidence-based decisions to improve food security and land management in the developing countries. Various digital soil mapping (DSM) techniques have been applied in many parts of the world to improve availability and usability of soil data, but less has been done in Africa, particularly in Tanzania and at the scale necessary to make farm management decisions. The Kilombero Valley has been identified for intensified rice production. However the valley lacks detailed and up-to-date soil information for decision-making. The overall objective of this study was to develop a predictive soil map of a portion of Kilombero Valley using DSM techniques. Two widely used decision tree algorithms and three sources of Digital Elevation Models (DEMs) were evaluated for their predictive ability. Firstly, a numerical classification was performed on the collected soil profile data to arrive at soil taxa. Secondly, the derived taxa were spatially predicted and mapped following SCORPAN framework using Random Forest (RF) and J48 machine learning algorithms. Datasets to train the model were derived from legacy soil map, RapidEye satellite image and three DEMs: 1 arc SRTM, 30 m ASTER, and 12 m WorldDEM. Separate predictive models were built using each DEM source. Mapping showed that RF was less sensitive to the training set sampling intensity. Results also showed that predictions of soil taxa using 1 arc SRTM and 12 m WordDEM were identical. We suggest the use of RF algorithm and the freely available SRTM DEM combination for mapping the soils for the whole Kilombero Valley. This combination can be tested and applied in other areas which have relatively flat terrain like the Kilombero Valley.