Lowland Rice Mapping in Sédhiou Region (Senegal) Using Sentinel 1 and Sentinel 2 Data and Random Forest

• Published in: Remote sensing (Basel, Switzerland) Vol. 12; no. 20; p. 3403
• Main Authors: Fiorillo, Edoardo, Di Giuseppe, Edmondo, Fontanelli, Giacomo, Maselli, Fabio
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2020
• Subjects: Algorithms; Backscattering; Casamance; Cereal crops; Classification; Classifiers; Developing countries; Food security; Forest management; Growing season; Investigations; Land cover; LDCs; Normalized difference vegetative index; Phenology; Precipitation; Radar imaging; random forest; Remote sensing; Rice; Rice fields; rice mapping; Satellites; Senegal; Sentinel 1 data; Sentinel 2 data; Spatial distribution; Statistical methods; Time series; Vegetation; Vegetation index; Senegal; Southeast Asia; Senegal River
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs12203403

In developing countries, information on the area and spatial distribution of paddy rice fields is an essential requirement for ensuring food security and facilitating targeted actions of both technical assistance and restoration of degraded production areas. In this study, Sentinel 1 (S1) and Sentinel 2 (S2) imagery was used to map lowland rice crop areas in the Sédhiou region (Senegal) for the 2017, 2018, and 2019 growing seasons using the Random Forest (RF) algorithm. Ground sample datasets were annually collected (416, 455, and 400 samples) for training and testing yearly RF classification. A procedure was preliminarily applied to process S2 scenes and yield a normalized difference vegetation index (NDVI) time series less affected by clouds. A total of 93 predictors were calculated from S2 NDVI time series and S1 vertical transmit–horizontal receive (VH) and vertical transmit–vertical receive (VV) backscatters. Guided regularized random forest (GRRF) was used to deal with the arising multicollinearity and identify the most important predictors. The RF classifier was then applied to the selected predictors. The algorithm predicted the five land cover types present in the test areas, with a maximum accuracy of 87% and kappa coefficient of 0.8 in 2019. The broad land cover maps identified around 12,500 (2017), 13,800 (2018), and 12,800 (2019) ha of lowland rice crops. The study highlighted a partial difficulty of the classifier to distinguish rice from natural herbaceous vegetation (NHV) due to similar temporal patterns and high intra-class variability. Moreover, the results of this investigation indicated that S2-derived predictors provided more valuable information compared to VV and VH backscatter-derived predictors, but a combination of radar and optical imagery always outperformed a classification based on single-sensor inputs. An example is finally provided that illustrates how the maps obtained can be combined with ground observations through a ratio estimator in order to yield a statistically sound prediction of rice area all over the study region.