SEBAL

The experiment explored AET seasonal dynamics of a sugarcane field at the Navsari Agricultural University (NAU) in Navsari using the high-resolution remote sensing to address the deficiency of ground-based micrometeorological instruments, e.g. lysimeters or eddy covariance towers. Surface Energy Balance Algorithm on Land (SEBAL) was run in a Python-GRASS GIS setting that combined Landsat 8 satellite data on land with local weather data. The outputs of SEBAL were checked by comparing the estimates with the cloud-based METRIC-EEFlux algorithm. Phenological measurements using NDVI time-series data monitored the sugarcane growth cycle with the greatest vegetative vigor in June and senescence during the months of November to December. Spatial analysis showed that seasonal AET calculated using SEBAL was between 1459 and 1496 mm. An effective rainfall of 676 mm was taken into consideration to estimate the total irrigation water requirements (IWR) of 783 to 820 mm. Conversely METRIC-EEFlux always gave higher values of AET, with the values falling between 1627 and 1698 mm. The pixel-by-pixel comparison of the two models each day showed a moderate correlation (R2 = 0.63) with a Root Mean square error (RMSE) of 1.40mm/day. SEBAL had a mean error (MBE) of bias of -1.17 mm/day, which shows that it was generally underestimated compared to METRIC-EEFlux. The results notwithstanding these differences in the algorithms, SEBAL is a powerful, well-adjusted tool to measure crop water needs and support sound water management decisions in data deficient areas.

Land surface heat fluxes encompass net radiation flux (Rn), soil heat flux (G), sensible heat flux (H), and latent heat flux (LE), all of which play a crucial role in understanding energy transfer within earth–atmosphere interactions. This study utilized Landsat 8 data to estimate land surface heat fluxes over the Navsari district of South Gujarat, India using the SEBAL (Surface Energy Balance Algorithm for Land) model. Rn followed a seasonal trend of summer > autumn > spring > winter, with median values ranging from 607.7 W/m2 in summer to 459.9 W/m² in winter. G exhibited a similar pattern, while H varied as summer > winter > spring > autumn. LE showed the opposite trend, peaking in autumn (427.3 W/m2) and decreasing through spring, winter, and summer. Notably, the LE remained higher than the H across all seasons. Rn was primarily allocated to LE across most LULC types, except in water bodies, where it was nearly evenly distributed between LE and G. In the absence of ground-based instruments, SEBAL outputs were validated using EEFlux METRIC, a cloud-based evapotranspiration (ET) estimation tool. The validation showed strong agreement for land surface temperature (LST) (R2 = 0.976, RMSE = 5.63 K) and moderate agreement for ET (R2 = 0.632, RMSE = 1.40 mm/day), albedo (R2 = 0.532, RMSE = 0.06), and crop coefficient Kc (R2 = 0.452, RMSE = 0.18). The SEBAL model was also applied to estimate seasonal ET and determine the total water requirement for sugarcane.