Monitoring Rice and Sugarcane Crop Growth in the Pearl River Delta using ENVISAT ASAR Data.

摘要

The Pearl River Delta is a typical developing region. It lies in the cloud-prone and rainy area of south China with multi-species of crops cultured in the agriculture areas. With a goal of developing an efficient, timely and accurate crop growth monitoring program in this area, field measurement, satellite SAR remote sensing technique, quantitative analysis of the crop biophysical parameters, and radar backscatter modeling methods have been integrated to study the multi-temporal and multi-polarized SAR data in estimating plant parameters (LAI, fresh biomass) of rice and sugarcane crop, and mapping the agricultural land cover categories of the study area in the PRD.;First, the field survey campaigns have been carried out from March 22, 2007 to December 27, 2007 around 5-15 days in the interval in the study area of Nansha Island. The field work includes the survey of spatial distribution of various land use and crop types and the ground measurements of the crop biophysical parameters (such as the plant height, leave area index, fresh biomass, and plant water content) and the soil parameters (such as the soil water content and surface roughness parameters) of rice field and sugarcane field. And at the same time, the ENVISAT ASAR data were acquired from March 22, 2007 to December 27, 2007 in the interval of 35 days. During the acquisition dates of the ENVISAT ASAR data, the field surveys were also conducted.;Second, field surveys were combined with the ENVISAT ASAR data to map the agricultural area. The analysis of the temporal radar backscatter characteristics of various land cover categories demonstrated that the time series of C-band SAR data is efficient in separating the eight land cover categories (rice paddy, sugarcane, banana, lotus ponds, mangrove wetlands, fish ponds, seawater, and buildings) in the PRD. The decision tree classifier is also approved to work efficiently on satellite SAR images with an overall accuracy of 77% and the Kappa coefficient of 0.74. The acreages of the land cover categories were also derived from the classification result with accuracies from 70% to 90%.;Third, in the study of rice growth monitoring, the trends of the relationships between C-band radar backscattering coefficients and rice parameters (plant height, LAI, fresh biomass, et al.) are proved to be constant with the reports in previous literatures. It was demonstrated that the differences between HH- and VV-polarized backscatter are not so evident (around 0.5 dB) in rice paddy canopies during the crop growth cycle. Moreover, by inducting a semi-empirical soil surface scattering component, a modified Water Cloud Model was developed to simulate the radar backscatter in rice crop canopies in different ground background situations (water surface, and soil surface) and to estimate the rice LAI and above ground fresh Biomass with reasonable accuracy. The rice growth conditions were displayed by LAI map and Biomass map generated from the model estimation, and the accuracies of the LAI and Biomass level classification are 0.77 and 0.71.;Fourth, the sufficient ground measurements and simultaneous C-band HH- and VV-polarized SAR data of sugarcane crop have enriched the knowledge of understanding the temporal radar scatter mechanisms in sugarcane canopies. The C-band VV-polarized radar backscatters are larger than those of HH-polarization during the sugarcane growth cycle, and the difference is around 0.5 dB to 2 dB. The theoretical model MIMICS was adapted in modeling the scattering terms in sugarcane fields to interpret the temporal behavior of radar backscatters. For more robotic operation, the empirical regression models were used in estimation of the sugarcane LAI and fresh biomass, and mapping the sugarcane growth situation. The accuracies of the sugarcane LAI map and Biomass map are 0.74 and 0.70, respectively.;In conclusion, the C-band ENVISAT ASAR data can be efficiently used in the Pearl River Delta to monitor the crop growth, including the crop spatial distribution, crop acreages, and crop growth situation evaluation. The efficient crop growth monitoring program can not only help instruct the flexible farming actions, but also estimate the crop yield production for the decision-making government. (Abstract shortened by UMI.)