Predicting winter wheat condition, grain yield and protein content using multi‐temporal EnviSat‐ASAR and Landsat TM satellite images
Abstract:Since optical and microwave sensors respond to very different target characteristics, their role in crop monitoring can be viewed as complementary. In particular, the all-weather capability of Synthetic Aperture Radar (SAR) sensors can ensure that data gaps that often exist during monitoring with optical sensors are filled. There were three Landsat Thematic Mapper (TM) satellite images and three Envisat Advanced Synthetic Aperture Radar (ASAR) satellite images acquired from reviving stage to milking stage of winter wheat. These data were successfully used to monitor crop condition and forecast grain yield and protein content. Results from this study indicated that both multi-temporal Envisat ASAR and Landsat TM imagery could provide accurate information about crop conditions. First, bivariate correlation results based on the linear regression of crop variables against backscatter suggested that the sensitivity of ASAR C-HH backscatter image to crop or soil condition variation depends on growth stage and time of image acquisition. At the reviving stage, crop variables, such as biomass, Leaf Area Index (LAI) and plant water content (PWC), were significantly positively correlated with C-HH backscatter ( r = 0.65, 0.67 and 0.70, respectively), and soil water content at 5 cm, 10 cm and 20 cm depths were correlated significantly with C-VV backscatter ( r = 0.44, 0.49 and 0.46, respectively). At booting stage, only a significant and negative correlation was observed between biomass and C-HH backscatter ( r = -0.44), and a saturation of the SAR signal to canopy LAI could explain the poor correlation between crop variables and C-HH backscatter. Furthermore, C-HH backscatter was correlated significantly with soil water content at booting and milking stage. Compared with ASAR backscatter data, the multi-spectral Landsat TM images were more sensitive to crop variables. Secondly, a significant and negative correlation between grain yield and ASAR C-HH & C-VV backscatter at winter wheat booting stage was observed ( r = -0.73 and -0.55, respectively) and a yield prediction model with a correlation coefficient of 0.91 was built based on the Normalized Difference Water Index (NDWI) data from Landsat TM on 17 April and ASAR C-HH backscatter on 27 April. Finally, grain protein content was found to be correlated significantly with ASAR C-HH backscatter at milking stage ( r = -0.61) and with Structure Insensitive Pigment Index (SIPI) data from Landsat TM at grain-filling stage ( r = 0.53), and a grain protein content prediction model with a correlation coefficient of 0.75 was built based on the C-HH backscatter and SIPI data.
Document Type: Research Article
Affiliations: National Engineering Research Center for Information Technology in Agriculture, PO Box 2449#26, Beijing, 100089, People's Republic of China
Publication date: February 20, 2006