Multi-Sensor Data Fusion to Estimate Soil Moisture and Evapotranspiration for Irrigation Scheduling.

Remote sensing is an effective tool to capture land surface information over large geographic extents. Estimating soil moisture content (SMC) and evapotranspiration (ET) using various sensors provide a basis for irrigation scheduling. Preliminary results showed high spatiotemporal data help estimate soil moisture and ET, which improves irrigation scheduling in a timely manner. However, high-resolution remotely sensed data is rarely used to estimate SM and ET values for prompt decision making. Estimating soil moisture content and Evapotranspiration by remote sensing approach will give a rational method for measuring the SM and ET in a more dynamic way for irrigation scheduling. In this study, we integrated data from MODIS and Landsat satellites to estimate the SMC and ET over a region employing data fusion techniques in a more applicable way. We also get data from soil moisture sensor in the different commercially managed agricultural field which is installed to get the moisture information at a different depth to compare with the acquired satellite data. It showed a reliable relationship with the in situ measurements and satellite derived data after preliminary analysis of the images. In other words, providing a robust and comparatively more reliable method for estimating SMC and ET over a region that integrates satellite data can facilitate to take prompt decision for irrigation scheduling and introduce a novel approach to use Unmanned Aerial System (UAS) for getting more reasonable information on this facet of SMC and ET estimation.