Effectiveness of Common Preprocessing Methods of Time Series for Monitoring Crop Distribution in Kenya

Abstract

Accurate crop identification and spatial distribution mapping are important for crop production estimation and famine early warning, especially for food-deficit African agricultural countries. By evaluating existing preprocessing methods for classification using satellite image time series (SITS) in Kenya, this study aimed to provide a low-cost method for cultivated land monitoring in sub-Saharan Africa that lacks financial support. SITS were composed of a set of MODIS Vegetation Indices (MOD13Q1) in 2018, and the classification method included the Support Vector Machine (SVM) and Random Forest (RF) classifier. Eight datasets obtained at three levels of preprocessing from MOD13Q1 were used in the classification: (1) raw SITS of vegetation indices (R-NDVI, R-EVI, and R-NDVI + R-EVI); (2) smoothed SITS of vegetation indices (S-NDVI); and (3) vegetation phenological data (P-NDVI, P-EVI, R-NDVI + P-NDVI, and P-NDVI-1). Both SVM and RF classification results showed that the “R-NDVI + R-EVI” dataset achieved the highest performance, while the three pure phenological datasets produced the lowest accuracy. Correlation analysis between variable importance and rainfall time series demonstrated that the vegetation index SITS during rainfall periods showed higher importance in RF classifiers, thus revealing the potential of saving computational costs. Considering the preprocessing cost of SITS and its negative impact on the classification accuracy, we recommend overlaying the original NDVI with the original EVI time series to map the crop distribution in Kenya.

Spatial changes to climatic suitability and availability of agropastoral farming systems across Kenya (1980–2020)

“…Moreover, Kenya is a key food-producing country (accounting for 65% of its exports) for the East African region and a key player in regional food security (Eichsteller et al, 2022). However, rangelands in Kenya are spreading, increasingly unable to support rain-fed agriculture (Maoncha et al, 2022), suffer from a severe lack of agricultural inputs, and the country ranked 84th out of 107 in terms of food security, with hunger levels defined as serious (Ni et al, 2022). Further, the dominant source of crop production in most Kenyan communities is rain-fed farming and agropastoral activities, which are organized according to ACZs (Kogo et al, 2021).…”