Combining spectral and textural information in UAV hyperspectral images to estimate rice grain yield

The speedy development of UAV (Unmanned Aerial Vehicle) has provided more data choices for crop yield estimation. In most cases, spectral information derived from multispectral or hyperspectral images is used alone for yield estimation, while variables that contain spatial information such as textural measures were not considered. As UAV can acquire images with spatial resolution at centimeter-level, which contain rich spatial information of observed objects, the test of textural measures in improving yield estimation becomes possible. In this study, UAV-based hyperspectral images of rice in the east of China were acquired in 2017, 2018 and 2019 three consecutive year during rice growing seasons. Eight gray level co-occurrence matrix (GLCM) based texture measures were derived, and all two band combinations of these textural measures as well as spectral reflectance in three types (normalized type, differential type and simple ratio type) were calculated to developing textural and spectral index. Grain yield estimation models were established using spectral indices alone and spectral indices combining textural measures, respectively. The results show that models based on spectral indices combining textural measures all performed better than these using spectral indices alone, with coefficient of determination R2 of the best model greater than 0.8, RMSE (Root Mean Square Error) of 0.421 Mg ha−1 and MAPE (Mean Absolute Percentage Error) of 4.66% for calibration results, and 0.521 Mg ha−1 and 6.63% for validation results. Meanwhile, spectral indices combining textural measures can adjust the problems of overestimation and underestimation in models developed with spectral indices alone. This kind of improvement is especially obvious when grain yield is below 5.0 Mg ha−1 or above 8.0 Mg ha−1. Besides, the estimation accuracy of the model based on spectral index combining textural measure at the booting stage was equivalent to the model based on spectral indices at the booting and heading stages, indicating that textural measures may be helpful in monitoring grain yield at an earlier stage.