Estimation of Crop Yield From Combined Optical and SAR Imagery Using Gaussian Kernel Regression

Abstract

The synthetic aperture radar (SAR) interferometric coherence can complement optical data for the estimation of crop growth parameters, but it has not been yet investigated for predicting crop yield. Many studies have used machine learning methods, such as neural networks, random forest, and Gaussian process regression, to estimate crop yield from remotely sensed data. However, their performance depends on the amount of available ground truth data. This study proposed Gaussian kernel regression for rice yield estimation from optical and SAR imagery using a limited amount of ground truth data. The main objective was to investigate the synergetic use of Sentinel-2 vegetation indices and Sentinel-1 interferometric coherence data through Gaussian kernel regression for estimating rice grain yield. The prediction accuracy was assessed using in situ measured yield data collected in 2019 and 2020 over Xinghua county in Jiangsu Province, China. In all cases, Gaussian kernel regression outperformed the probabilistic Gaussian regression and Bayesian linear inference. With the independently used optical and SAR data, a better prediction accuracy was achieved with the optical red edge difference vegetation index (RDVI1) (r 2 = 0.65, RMSE = 0.61 t/ha) than with the interferometric coherence (r 2 = 0.52 and RMSE = 0.79 t/ha).The highest prediction accuracy can be achieved by combining RDVI1 with interferometric coherence at the heading stage (r 2 = 0.81 and RMSE = 0.55 t/ha). The results suggest the value of synergy between satellite interferometric coherence and optical indices for crop yield mapping with Gaussian kernel regression.

Challenges and opportunities in remote sensing-based crop monitoring: a review

“…The interferometric coherence and VH:VV ratio are sensitive to the canopy development of wheat [ 93 ]. The coherence SAR information coupled with the red-edge vegetation index also performs well for rice yield prediction at the heading stage [ 94 ]. The integration of weather variables, remote sensing-based VIs and other metrics, such as the SIF values from the Global Ozone Monitoring Experiment version 2 (GOME-2), allowed more accurate crop yield prediction [ 95 ].…”

Yield Response of Different Rice Ecotypes to Meteorological, Agro‐Chemical, and Soil Physiographic Factors for Interpretable Precision Agriculture Using Extreme Gradient Boosting and Support Vector Regression

“…Alebele et al proposed a Gaussian kernel for the estimation of rice yield by using SAR and optical imagery where ground truth data are used in a limited amount [22]. e primary goal was to study the synergistic use of Sentinel-2 statistical parameters and Sentinel-1 interferometric coherence data for predicting rice grain production using Gaussian kernel regression for the prediction of accuracy was assessed using in situ measured yield data collected in 2019 and 2020 over Xinghua county in Jiangsu Province, China.…”

AMIO-Net: An Attention-Based Multiscale Input–Output Network for Building Change Detection in High-Resolution Remote Sensing Images

“…Over the years, with development of remote sensing technology, we can get a large number of remote sensing images. These images can be used for crop production forecast [4], mapping human activity [5], and surface observation [6], etc. And deep learning methods have been proved superior to traditional methods in feature extraction [7] [8].…”