Yield Estimation of Paddy Rice Based on Satellite Imagery: Comparison of Global and Local Regression Models

Abstract

Precisely estimating the yield of paddy rice is crucial for national food security and development evaluation. Rice yield estimation based on satellite imagery is usually performed with global regression models; however, estimation errors may occur because the spatial variation is not considered. Therefore, this study proposed an approach estimating paddy rice yield based on global and local regression models. In our study area, the overall per-field data might not available because it took lots of time and manpower as well as resources. Therefore, we gathered and accumulated 26 to 63 ground survey sample fields, accounting for about 0.05% of the total cultivated areas, as the training samples for our regression models. To demonstrate whether the spatial autocorrelation or spatial heterogeneity exists and dominates the estimation, global models including the ordinary least squares (OLS), support vector regression (SVR), and the local model geographically weighted regression (GWR) were used to build the yield estimation models. We obtained the representative independent variables, including 4 original bands, 11 vegetation indices, and 32 texture indices, from SPOT-7 multispectral satellite imagery. To determine the optimal variable combination, feature selection based on the Pearson correlation was used for all of the regression models. The case study in Central Taiwan rendered that the error rate was between 0.06% and 13.22%. Through feature selection, the GWR model’s performance was more relatively stable than the OLS model and nonlinear SVR model for yield estimation. Where the GWR model considers the spatial autocorrelation and spatial heterogeneity of the relationships between the yield and the independent variables, the OLS and nonlinear SVR models lack this feature; this led to the rice yield estimation of GWR in this study be more stable than those of the other two models.

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“…A study in [38] concluded that soil quality is important in predicting the productivity of crops, which would lead to provide complementary information and improve the accuracy of yield prediction. Remote sensing will help to cover a vast scale with non-invasive and efficient techniques to detect the spatial variability in plant status with high temporal resolution [39]. There is a large implementation of remote sensing approaches based on infrared VOLUME 4, 2016 This work is licensed under a Creative Commons Attribution 4.0 License.…”

Special Issue on Selected Papers from the “International Symposium on Remote Sensing 2018”

“…Shiu and Chuang [17], in their paper entitled "Yield Estimation of Paddy Rice Based on Satellite Imagery: Comparison of Global and Local Regression Models", proposed an efficient approach to estimate paddy rice yield using (1) the global regression models including the ordinary least squares (OLS) and support vector regression (SVR) and (2) the local model of geographically weighted regression (GWR).…”