Mapping Water-logging Damage on Winter Wheat at Parcel Level Using High Spatial Resolution Satellite Data

Damage to agricultural crops due to water-logging is becoming more frequent in this era of global climate change. In this study, we investigated the effect of water-logging on winter wheat growth and yield at a parcel scale using high spatial resolution satellite data. For this purpose, a dynamic mapping of winter wheat LAI and biomass during growth under water-logging conditions was implemented. In addition, yield mapping and yield loss estimation were also carried out to arrive at the potential impacts of water-logging on winter wheat. Two varieties were investigated at tillering, jointing to booting and flowering. Field conditions were representative of water-logging treatment (contrast check (CK), waterlogging (WL) and flooding (FL)). A total of eight high spatial resolution optical satellite images were obtained. Seven vegetation indices (VIs) were utilized as independent variables for LAI, biomass and yield estimation. Optimal models were selected according to coefficient of determination (R²), correlation coefficient (R) and root mean square error (RMSE) for subsequent mapping of LAI, biomass and yield. Results show that the power model, having EVI as the independent variable, had the highest accuracy for biomass estimation (R² of 0.78 and RMSE of 0.076 kg/m²) while the exponential model with NDVI is the optimal model for LAI estimation (with 0.74 in R² and 0.64 in RMSE). GNDVI is the optimal VI for yield mapping, with 0.563 in R_cv (cross validated R) and 843.76 kg/hm² in RMSE_cv (cross validated RMSE). As for the assessment of water-logging damage on winter wheat growth, FL showed a more serious impact than that of WL, based on results of the dynamic mapping of biomass and LAI at the same development stages. Water-logging from jointing to booting showed a more serious damage impact with respect to biomass and yield than that at the tillering and flowering stages. Our results have demonstrated the potential of multi-source and high spatial resolution remotely-sensed optical data for quantifying the impact of water-logging on winter wheat growth and grain yield at parcel scales, a research direction that has rarely been reported in literature. This study thus provides a fundamental basis for crop yield loss evaluation and water-logging monitoring that would benefit even small-holder and subsistence farmers.