Evaluation of various machine learning-based bias correction approaches for NASA POWER air temperatures: a case study of Nigeria

Abstract

Remotely sensed air temperature data from NASA POWER are widely used in regions with scarce climatic observations, particularly for agricultural applications such as calculating crop water requirements. This study employed a suite of machine learning (ML) algorithms to correct biases in NASA POWER air temperature outputs, including multiple support vector regression (SVR) variants—Linear SVR, Quadratic SVR, Cubic SVR, Fine Gaussian SVR, Medium Gaussian SVR, Coarse Gaussian SVR—and ensemble decision tree models: bagged trees (BGT) and boosted trees (BT). The objective of this study was to assess the ability of different ML algorithms to reduce biases in NASA POWER air temperature data, with the broader goal of identifying the most suitable ML method for air temperature bias correction in Nigeria. For this analysis, we used daily air temperature records from seven meteorological stations across diverse regions of Nigeria. The performance of NASA POWER minimum and maximum air temperature datasets was evaluated using standard error metrics. Subsequent application of ML algorithms significantly improved data accuracy: the normalized root mean square error (NRMSE) of the corrected outputs was mostly below 10%, indicating excellent predictive performance when ML was integrated. Among the SVR variants tested, Fine Gaussian SVR consistently yielded the best prediction results. This finding suggests that Fine Gaussian SVR is a robust tool for enhancing the reliability of air temperature data—critical for improving the accuracy of crop water requirement calculations in regions where in-situ air temperature observations are limited.