Abstract
An accurate inversion of the fraction of absorbed photosynthetically active radiation (FPAR) based on remote sensing data is particularly important for understanding global climate change. At present, there are relatively few studies focusing on the inversion of FPAR using Chinese autonomous satellites. This work intends to investigate the inversion of the FPAR obtained from the FengYun-3C (FY-3C) data of domestic satellites by using the PROSAIL model and the look-up table (LUT) algorithm for different vegetation types from various places in China. After analyzing the applicability of existing models using FY-3C data and MOD09GA data, an inversion strategy for FY-3C data is implemented. This strategy is applied to areas with various types of vegetation, such as grasslands, croplands, shrubs, broadleaf forests, and needleleaf forests, and produces FPAR products, which are cross-validated against the FPAR products from the Moderate Resolution Imaging Spectro Radiometer (MODIS), Geoland Version 1 (GEOV1), and Global Land Surface Satellite (GLASS). Accordingly, the results show that the FPAR retrieved from the FY-3C data has good spatial and temporal consistency and correlation with the three FPAR products. However, this technique does not favor all types of vegetation equally; the FY-FPAR is relatively more suitable for the inversion of grasslands and croplands during the lush period than for others. Therefore, the inversion strategy provides the potential to generate large-area and long-term sequence FPAR products from FY-3C data.
Highlights
Remote sensing has been used to continuously monitor and manage the ecological changes of plants in time and space [1,2]
Based on the parameterization of the PROSAIL model (Table 6), the parameter ranges of grassland, cropland, shrub, broadleaf forest, and needleleaf forest have been taken as the inputs into the PROSAIL model to simulate
The annual FY-fraction of absorbed photosynthetically active radiation (FPAR) for the five research areas of grassland, cropland, shrub, broadleaf forest, and needleleaf forest were inverted successfully based on the inversion strategy from the FY-3C data
Summary
Remote sensing has been used to continuously monitor and manage the ecological changes of plants in time and space [1,2]. Compared with basic measurement methods, remote sensing has the characteristics of real-time, dynamic, and large-scale and can obtain a large area of soil and crop information. Remote sensing is widely used in crop area, growth, yield estimation, and soil moisture monitoring [3,4,5,6,7]. The methods for estimating FPAR based on remote sensing mainly include the empirical statistical model and physical model. The empirical statistical model is widely used due to its simplicity, minimal parameters, and high computational efficiency. This model is less feasible than a more complex physical model, which involves more parameters inferred from the perspective of energy balance
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