|Kamble, Baburao - University Of Nebraska|
|Irmak, Ayse - University Of Nebraska|
|Hubbard, Kenneth - University Of Nebraska|
Submitted to: Hydrological Sciences Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/28/2013
Publication Date: 9/1/2013
Citation: Kamble, B., Irmak, A., Gowda, P., Kiyoshi, H. 2013. Irrigation scheduling using genetic algorithm based satellite data assimilation approach. Advances in Remote Sensing. 2:258-268. http://dx.doi.org/10.4236/ars.2013.23028.
Interpretive Summary: Fast depleting water resources is demanding for improved water use efficiency in irrigated agriculture. This can be partly achieved by improving the existing irrigation scheduling techniques. Linking remote sensing based evapotranspiration (ET) and crop growth models have enhanced our ability to understand soil water balance in the root zone. In this study, an attempt was made to estimate crop water demand to schedule irrigation by linking a remote sensing based ET model with a soil-water-plant-atmosphere model. Results indicate that the linked models have the potential to be used as an operational tool to predict irrigation requirements.
Technical Abstract: Linked 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 the estimated sowing, harvesting, and irrigation application dates were within plus or minus five days of observations and produced good estimates of water cycle states and fluxes. The SWAP-GA model driven by the remotely sensed ET moderately improved surface soil moisture estimates, suggesting that it has the potential to be used as an operational tool to schedule irrigation or predict irrigation requirements.