摘要
Remote sensing and crop growth models have enhanced our ability to understand soil water balance in irrigated agriculture. However, limited efforts have been made to adopt data assimilation methodologies in these linked models that use stochastic parameter estimation with genetic algorithm (GA) to improve irrigation scheduling. In this study, an innovative irrigation scheduling technique, based on soil moisture and crop water productivity, was evaluated with data from Sirsa Irrigation Circle of Haryana State, India. This was done by integrating SEBAL (Surface Energy Balance Algorithm for Land)-based evapotranspiration (ET) rates with the SWAP (Soil-Water-Atmosphere-Plant), a process-based crop growth model, using a GA. Remotely sensed ET and ground measurements from an experiment field were combined to estimate SWAP model parameters such as sowing and harvesting dates, irrigation scheduling, and groundwater levels to estimate soil moisture. Modeling results showed that estimated sowing, harvesting, and irrigation application dates were within ±10 days of observations and produced good estimates of ET and soil moisture fluxes. The SWAP-GA model driven by the remotely sensed ET moderately improved surface soil moisture estimates suggesting that it has the potential to serve as an operational tool for irrigation scheduling purposes.
Remote sensing and crop growth models have enhanced our ability to understand soil water balance in irrigated agriculture. However, limited efforts have been made to adopt data assimilation methodologies in these linked models that use stochastic parameter estimation with genetic algorithm (GA) to improve irrigation scheduling. In this study, an innovative irrigation scheduling technique, based on soil moisture and crop water productivity, was evaluated with data from Sirsa Irrigation Circle of Haryana State, India. This was done by integrating SEBAL (Surface Energy Balance Algorithm for Land)-based evapotranspiration (ET) rates with the SWAP (Soil-Water-Atmosphere-Plant), a process-based crop growth model, using a GA. Remotely sensed ET and ground measurements from an experiment field were combined to estimate SWAP model parameters such as sowing and harvesting dates, irrigation scheduling, and groundwater levels to estimate soil moisture. Modeling results showed that estimated sowing, harvesting, and irrigation application dates were within ±10 days of observations and produced good estimates of ET and soil moisture fluxes. The SWAP-GA model driven by the remotely sensed ET moderately improved surface soil moisture estimates suggesting that it has the potential to serve as an operational tool for irrigation scheduling purposes.
