Crops monitoring and yield estimation using sentinel products in semi-arid smallholder irrigation schemes

ABSTRACT

The use of earth observation data for crop mapping and monitoring in West Africa has concentrated on rainfed systems due to its pre-dominance in the sub-region. However, irrigated systems, though of limited extent, provide critical livelihood support to many. Accurate statistics on irrigated crops are, thus, needed for effective management and decision making. This study explored the use of Sentinel 1 (S-1) and Sentinel 2 (S-2) data to map the extent and yield of irrigated crops in an informal irrigation scheme in Burkina Faso. Random Forest classification and regression were used together with an extensive field data comprising 842 polygons. Four irrigated crops (tomoto, onion, green bean and other) were classified while the yield of tomatoes was modelled through regression analysis. Apart from spectral bands, derivatives (e.g. biophysical parameters and vegetation indices) from S-2 were used. Different data configuration of S-1, S-2 and their derivatives were tested to ascertain optimal temporal windows for accurate irrigated crop mapping and yield estimation. Results of the crop classification revealed a greater overall accuracy (76.3%) for S-2 compared to S-1 (69.4%), with S-2 biophysical parameters (especially the fraction of absorbed photosynthetic active radiation i.e fAPAR) being prominent. For yield prediction, however, S-1 VV polarization came up as the most prominent predictor in the regression analysis ($R_{\mathrm{a}\mathrm{d}\mathrm{j}}^2$= 0.63), while the addition of S-2 fAPAR marginally improved the fit ($R_{\mathrm{a}\mathrm{d}\mathrm{j}}^2$= 0.64). Tomato yield in the study area was found to range from 1 to 16 kg m⁻², although about 83% of the area have yields of less than 10 kg m⁻². Our study revealed that early season images (acquired in December) perform better in classifying irrigated crop compared to mid or late season. On the other hand, the use of early to mid-season (December to February) images for yield modelling produced reasonable prediction accuracy. This indicates the possibility of using S-1 and S-2 data to predict crop yield prior to harvest season for efficient planning and food security attainment.

Acknowledgements

We acknowledge financial support from the German Federal Ministry of Education and Research (BMBF) through the graduate research program of the West African Science Service Centre on Climate Change and Adapted Land Use (WASCAL). This research was developed in the framework of TIGER Bridge project – Water for Agriculture sponsored by the European Space Agency.

Disclosure Statement

No potential conflict of interest was reported by the authors.