An Application of Improved MODIS-Based Potential Evapotranspiration Estimates in a Humid Tropic Brantas Watershed—Implications for Agricultural Water Management

Abstract

The reliance on native MODIS-16 PET potential evapotranspiration (PET) in scarce-data-driven areas is growing in support among ecohydrological studies, yet information about its performance is limited or unknown as validation studies are mostly concentrated in developed countries. This study aimed to assess its performance at the monthly level using four ground measurements in a tropical watershed system with complex topography, applying a machine learning artificial neural network (ANN) to improve the estimates, and using the ANN-adjusted MODIS-16 PET to characterize the spatio-temporal patterns of PET in the Brantas watershed, as well as to understand the monthly patterns of water deficiency in areas under eight different vegetation covers. The results showed that the native MODIS-16 PET experienced overestimation with an RMSE of 37–66 mm/month and NRSME of up to 33%. The performance decreased in drier periods. The ANN-based adjustment using only one variable showed improved estimates with a reduction of RSME to only 14 mm and lower than 10% NRMSE. Sari-temporal patterns of PET in the Brantas watershed showed that the PET characteristics were not uniform. The southern part of the Brantas watershed has areas with relatively lower PET that are, thus, more prone to water deficiency. Complex topography and climate gradients within the watershed apparently became the multi-controllers of PET variations. The difference in vegetation cover also influenced the magnitudes of water deficiency.

Leveraging Remotely Sensed and Climatic Data for Improved Crop Yield Prediction in the Chi Basin, Thailand

“…However, climate change has a considerable influence on the agriculture sector, and it could lead to an increase in temperatures by 1.4 to 5.8 degrees Celsius in 2100 [4]. This will increase crop water requirements due to increased evapotranspiration, which will mainly affect crop production [63]. This study shows acceptable accuracy for crop yield prediction that can be used by policymakers for management at the country and province scales.…”

New Trends and Challenges in Precision and Digital Agriculture

“…More and more scientific work in the field of precision agriculture is focusing on the use of satellite data in the development of ML models. Data from Sentinel 1 and Sentinel 2 satellites [34,64], and the MODIS optical-mechanical scanner deployed on Terra and Aqua satellites [16,[65][66][67] are commonly used. These tools allow the collection of spectral and multispectral data, which are aids for calculating vegetation indices (VIs).…”

Using the water balance approach to understand pool dynamics along non-perennial rivers in the semi-arid areas of South Africa

“…MODIS 16 evapotranspiration data are widely and commonly used, and Jovanovic et al (2015) found that MODIS 16 evapotranspiration and potential evapotranspiration estimates over South Africa's landscape were satisfactory; however they cautioned that the spatial resolution can limit its potential for small-scale use. Some studies have argued that MODIS 16-derived PET is suited for small-scale application (Astuti et al, 2022). Bugan et al (2020) utilised the MODIS 16 dataset in a hydrological model at a catchment level and concluded that the dataset has the potential to be used in data-scarce regions.…”