No abstract

Water resource planning and management necessitates understanding soil moisture changes with depth in the root zone at the farm scale. For measuring soil moisture, remote sensing methods have been relatively successful. Soil moisture is estimated from image data, using in situ moisture and an empirical scattering model via regression fit analysis. However, in situ sensor data are prone to misinterpretations, requiring verification. Herein, we aimed at investigating the application of soil moisture from the water balance model towards verification of in situ soil moisture sensor data before in situ data was assessed for its relationship with remote sensing data. In situ soil moisture sensor data was obtained at 10 and 30 cm, and CROPWAT8.0 furnished root zone soil moisture data. The correlation between the in situ soil moisture at 10 and 30 cm was 0.78; the correlation between the soil moisture from CROPWAT8.0 and the in situ soil moisture were 0.64 and 0.62 at 10 and 30 cm, respectively. The R2 between Sentinel-1 backscatter coefficients and in situ moisture were 0.74 and 0.68 at each depth, respectively. Therefore, the water balance model could verify sensor results before assessing in situ soil moisture data for relationship with remote sensing data.

In this study, satellite-based measures of surface energy balance and the mapping evapotranspiration at high resolution with internalized calibration (METRIC) from Landsat imagery were used to estimate the spatiotemporal distribution of actual evapotranspiration (ETa) in northern Thailand, constituting a procedure that has rarely been performed in southeast Asia. Subsequently, we compared the ETa obtained from METRIC with that calculated using the FAO-56 dual-crop coefficient method via the SIMDualKc software and found a strong correlation. An assessment of the accuracy of all the sample plots revealed the R2, Root-Mean-Square Error (RMSE), and mean absolute error (MAE) values to be 0.830, 0.730, and 0.575 mm d−1, respectively. Differences in the cumulative ETa values derived from SIMDualKc and METRIC ranged in magnitude from 0.93–3.57% for rice and 3.08–7.99% for longan. The ETa values for forestland and waterbodies were higher than those for agricultural areas and areas with other forms of land use. The spatiotemporal distribution of the seasonal ETa during the dry season was consistent with the climate, vegetation, and anthropogenic activity. Thus, our results indicate that METRIC is a reliable tool for estimating ETa for water resource management under different environmental conditions and improving water use efficiency over large areas.