Monitoring crop leaf area index time variation from higher resolution remotely sensed data

35th International Symposium on Remote Sensing of Environment

地球科学;生态环境科学

Jiao, Sihong^1

Beijing Polytechnic College, Beijing 100042, China^1

关键词: Back-ground knowledge;    Canopy reflectance model;    Determination coefficients;    Dynamic process modeling;    Ecological environments;    Global climate changes;    Moderate resolution imaging spectroradiometer;    Time series variations;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/17/1/012049/pdf
DOI  :  10.1088/1755-1315/17/1/012049

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

The leaf area index (LAI) is significant for research on global climate change and ecological environment. China HJ-1 satellite has a revisit cycle of four days, providing CCD data (HJ-1 CCD) with a resolution of 30 m. However, the HJ-1 CCD is incapable of obtaining observations at multiple angles. This is problematic because single angle observations provide insufficient data for determining the LAI. This article proposes a new method for determining LAI using HJ-1 CCD data. The proposed method uses background knowledge of dynamic land surface processes that are extracted from MODerate resolution Imaging Spectroradiometer (MODIS) LAI 1-km resolution data. To process the uncertainties that arise from using two data sources with different spatial resolutions, the proposed method is implemented in a dynamitic Bayesian network scheme by integrating a LAI dynamic process model and a canopy reflectance model with remotely sensed data. Validation results showed that the determination coefficient between estimated and measured LAI was 0.791, and the RMSE was 0.61. This method can enhance the accuracy of the retrieval results while retaining the time series variation characteristics of the vegetation LAI. The results suggest that this algorithm can be widely applied to determining high-resolution leaf area indices using data from China HJ-1 satellite even if information from single angle observations are insufficient for quantitative application.