A comparative study on satellite- and model-based crop phenology in West Africa

Abstract

Crop phenology is essential for evaluating crop production in the food insecure regions of West Africa. The aim of the paper is to study whether satellite observation of plant phenology are consistent with ground knowledge of crop cycles as expressed in agro-simulations. We used phenological variables from a MODIS Land Cover Dynamics (MCD12Q2) product and examined whether they reproduced the spatio-temporal variability of crop phenological stages in Southern Mali. Furthermore, a validated cereal crop growth model for this region, SARRA-H (System for Regional Analysis of Agro-Climatic Risks), provided precise agronomic information. Remotely-sensed green-up, maturity, senescence and dormancy MODIS dates were extracted for areas previously identified as crops and were compared with simulated leaf area indices (LAI) temporal profiles generated using the SARRA-H crop model, which considered the main cropping practices. We studied both spatial (eight sites throughout South Mali during 2007) and temporal (two sites from 2002 to 2008) differences between simulated crop cycles and determined how the differences were indicated in satellite-derived phenometrics. The spatial comparison of the phenological indicator observations and simulations showed mainly that (i) the satellite-derived start-of-season (SOS) was detected approximately 30 days before the model-derived SOS; and (ii) the satellite-derived end-of-season (EOS) was typically detected 40 days after the model-derived EOS. Studying the inter-annual difference, we verified that the mean bias was globally consistent for different climatic conditions. Therefore, the land cover dynamics derived from the MODIS time series can reproduce the spatial and temporal variability of different start-of-season and end-of-season crop species. In particular, we recommend simultaneously using start-of-season phenometrics with crop models for yield forecasting to complement commonly used climate data and provide a better estimate of vegetation phenological changes that integrate rainfall variability, land cover diversity, and the main farmer practices.