Assessing Evapotranspiration and Water Use Efficiency of Cassava Using Landsat-8 and SEBS: A Case Study of the Thai Tapioca Development Institute, Thailand

Suchada Tongngern; Jianzhong  Lu; Kitsanai  Charoenjit; Phattraporn  Soytong; Kexin  Soh

Authors

Suchada Tongngern Faculty of Geoinformatics, Burapha University, Thailand. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University,China
Jianzhong Lu State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University,China
Kitsanai Charoenjit Faculty of Geoinformatics, Burapha University, Thailand
Phattraporn Soytong Faculty of Geoinformatics, Burapha University, Thailand
Kexin Soh State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University,China

Keywords:

water use efficiency, evapotranspiration, SEBS, landsat 8, GPP

Abstract

Background and Objectives: Water use efficiency (WUE) is a critical determinant of sustainable agricultural water management, quantifying the relationship between crop productivity and water consumption through evapotranspiration (ET). ET governs the exchange of water and energy among the soil, vegetation, and atmosphere, making its accurate estimation essential for optimizing irrigation strategies and managing water resources at both field and regional scales. Remote sensing has emerged as a viable approach for spatially and temporally monitoring ET; however, satellite-derived spectral measurements cannot directly quantify ET and must be integrated with surface energy balance models. This study applied the Surface Energy Balance System (SEBS) model to estimate ET in cassava (Manihot esculenta Crantz) cultivation at the Thai Tapioca Development Institute, Tambon Huai Bong, Dan Khun Thot District, Nakhon Ratchasima Province, Thailand, and to assess satellite-based WUE using the GPP/ET approach, while acknowledging the conceptual distinction from field agronomic WUE.

Methodology: The SEBS model was implemented using three primary input datasets: (1) satellite-derived land surface parameters including surface albedo (derived via the Liang (2001) algorithm), land surface temperature, NDVI, and fractional vegetation cover extracted from Landsat 8 OLI/TIRS imagery (path 129, row 49, Collection 2 Level 2), supplemented by Gross Primary Productivity (GPP) from MODIS (MOD17A2H V6, 500 m, 8-day composite) and DEM data; (2) meteorological variables including air pressure, air temperature, relative humidity, and wind speed sourced from GLDAS (Noah model, 0.25 degree, 3-hourly); and (3) downward solar and longwave radiation fluxes. A total of 48 Landsat 8 scenes acquired between 2018 and 2021 were processed using ENVI 5.3 and ILWIS 3.8.5. Monthly ET was derived as a residual of the surface energy balance, with instantaneous fluxes scaled to daily totals using the evaporative fraction approach. MODIS GPP data (500 m) were resampled to 30 m using bilinear interpolation to harmonize spatial resolution with Landsat 8 ET, and 8-day GPP composites were temporally matched to the nearest Landsat 8 overpass date. SEBS-estimated ET was validated against in-situ measurements from the GISTDA weather station; field ET was derived from station-recorded net radiation and meteorological variables via the surface energy balance equation. Satellite-based WUE was calculated as GPP/ET (Yu et al., 2008), with cloud screening applied using QA band thresholds (cloud cover < 20%). In-field agronomic WUE was independently calculated as cassava yield/ET using published yield data (Malipan & Sittinam, 2011) for reference comparison.

Main Results: SEBS-derived monthly ET ranged from 0 to 4.891 mm day^-1, while field-measured ET ranged from 0.032 to 2.035 mm day^-1. The minimum RMSE of 0.37 mm day^-1 and MAE of 0.31 mm day^-1in 2020; the 0 mm minimum ET values reflect cloud-masked months (July 2018 and October 2020, with >80% cloud cover) rather than true hydrological conditions. Satellite-based WUE (GPP/ET) ranged from 0.0035 to 0.2834 kg C m^-3, while independently computed field agronomic WUE (yield/ET) ranged from 0.3325 to 22.083 kg m^-3. The large difference between these two metrics is expected and ecologically meaningful: satellite GPP represents total carbon fixed by photosynthesis, while agronomic yield represents only the harvested starchy root fraction, which constitutes approximately 30-40% of total biomass with further losses due to autotrophic respiration. A direct comparison of these two WUE frameworks is therefore not valid.

Conclusions: The SEBS model, when integrated with multi-source remote sensing datasets, provides a reliable framework for estimating ET in tropical cassava systems. The pronounced difference between satellite-based WUE (GPP/ET) and agronomic WUE (yield/ET) reflects fundamental conceptual differences between these metrics rather than model error alone. Future research should incorporate cloud-penetrating sensors, cassava harvest index data for GPP-to-yield conversion, and field biomass measurements to enable direct agronomic WUE validation and to support sustainable irrigation management in monsoon-dominated environments.

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