Area-to-point regression Kriging approach fusion of Landsat 8 OLI and Sentinel 2 data for assessment of soil macronutrients at Anaimalai, Coimbatore

Vaithiyanathan, Dhayalan; Sudalaimuthu, Karuppasamy

doi:10.1007/s10661-022-10571-1

Cited by 4 publications

(1 citation statement)

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“…The advantage of this hybrid approach over the regression-based approach is that the spatial pattern of the coarse resolution data can be reproduced by considering the residual correction, even when the regression model does not have high explanatory power [2]. Area-to-point regression kriging (ATPRK) [21] is a representative model for this approach and has been widely applied to the spatial downscaling of remote sensing images and products [22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis of Regression-Based Trend Estimates to Input Errors in Spatial Downscaling of Coarse Resolution Remote Sensing Data

Kwak,

Hong,

Park

2023

Applied Sciences

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This paper compared the predictive performance of different regression models for trend component estimation in the spatial downscaling of coarse resolution satellite data using area-to-point regression kriging in the context of the sensitivity to input data errors. Three regression models, linear regression, random forest, and support vector regression, were applied to trend component estimation. An experiment on downscaling synthetic Landsat data with different noise levels demonstrated that a regression model with higher explanatory power and residual correction led to the highest predictive performance only when the input coarse resolution data were assumed to be error-free. Through an experiment on spatial downscaling of coarse resolution monthly Advanced Microwave Scanning Radiometer-2 soil moisture products with significant errors, we found that the higher explanatory power of regression models did not always lead to better predictive performance. The residual correction and normalization of trend components also degraded the predictive performance. Using trend components as a final downscaling result showed the best performance in both experiments as the input errors increased. As the predictive performance of spatial downscaling results is susceptible to input errors, the findings of this study should be considered to evaluate downscaling results and develop advanced spatial downscaling methods.

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