Spatio-Temporal Estimation of Rice Height Using Time Series Sentinel-1 Images

Huijin Yang,Heping Li,Wei Wang,Jianhui Zhao,Ning Li,Bin Pan

doi:10.3390/rs14030546

Abstract

Rice height, as the fundamental biophysical attribute, is a controlling factor in crop phenology estimation and yield estimation. The aim of this study was to use time series Sentinel-1A images to estimate the spatio-temporal distribution of rice height. In this study, a particle filter (PF) was applied for the real-time estimation of rice height compared with a simplified water cloud model (SWCM) on the basis of rice mapping and transplanting date. It was found that the VH backscatter (σvho) can potentially be applied to accurately estimate rice height compared with VV backscatter (σvvo), the σvho/σvv0 ratio, and the Radar Vegetation Index (RVI, 4* σvho/(σvho+σvvo)). The results show that the rice height estimation by PF generated a better result with a root-mean-square error (RMSE) equal to 7.36 cm and a determination factor (R2) of 0.95 compared with SWCM (RMSE = 12.59 cm and R2 = 0.86). Moreover, rice height in the south and east of the study area was higher than in the north and west. The reason for this is that the south and east are near to the South China Sea, and there are higher temperatures and earlier transplanting. Altogether, our results demonstrate the potential of PF and σvho to study the spatio-temporal distribution of crop height estimation. As a result, the PF method can contribute greatly to improvements in crop monitoring.

Highlights

Published: 24 January 2022Rice, as one of the most important crops, has a great impact on food security, and on water resource management and climate change
The RVI and the σvh o /σvv 0 ratio were used for crop growth monitoring [49,50,51], our results indicated that the RVI and the σvh o /σvv 0 ratio are not suitable for rice height estimation compared with VH backscatter
The spatio-temporal distributions of rice above-ground height estimation were determined by particle filter (PF) and simplified water cloud model (SWCM) using Sentinel-1A time-series data on the basis of rice map and transplanting date

Summary

Introduction

As one of the most important crops, has a great impact on food security, and on water resource management and climate change. Global rice consumption has increased in general since the 1960s [1], and rice fields consume a large amount of water and release methane gas and carbon dioxide gas [2]. Rice height, as the fundamental biophysical attribute, is a controlling factor in crop phenology estimation, yield estimation and rice scattering models. Remote sensing technology can be applied for the retrieval of rice height at a regional or even a global scale, which requires less manpower and is less expensive than conventional technology. Sentinel-1 images with a fine spatio-temporal scale are used for rice height estimation in our study

Objectives

Methods

Results

Discussion

Conclusion