Fractional vegetation cover estimation algorithm for Chinese GF-1 wide field view data - Jia, Kun; Liang, Shunlin; Gu, Xingfa; Baret, F.; Wei, Xiangqin; Wang, Xiaoxia; Yao, Yunjun; Yang, Linqing; Li, Yuwei (Remote sensing of environment, 2016)
Wide field view (WFV) sensor on board the Chinese GF-1, the first satellite of the China High-resolution Earth Observation System, is acquiring multi-spectral data with decametric spatial resolution, high temporal resolution and wide coverage, which are valuable data sources for environment monitoring. The objective of this study is to develop a general and reliable fractional vegetation cover (FVC) estimation algorithm for GF-1 WFV data under various land surface conditions. The algorithm is expected to estimate FVC from GF-1 WFV reflectance data with spatial resolution of 16m and temporal resolution of four dates. The proposed algorithm is based on training back propagation neural networks (NNs) using PROSPECT+SAIL radiative transfer model simulations for GF-1 WFV canopy reflectance and corresponding FVC values. Green, red and near-infrared bands' reflectances of GF-1 WFV data are the input variables of the NNs, as well as the corresponding FVC is the output variable, and finally 842,400 simulated samples covering various land surface conditions are used for training the NNs. A case study in Weichang County of China, having abundant land cover types, was conducted to validate the performance of the proposed FVC estimation algorithm for GF-1 WFV data. The validation results showed that the proposed algorithm worked effectively and generated reasonable FVC estimates with R2=0.790 and root mean square error of 0.073 based on the field survey data. The proposed algorithm can be operated without prior knowledge on the land cover and has the potential for routine production of high quality FVC products using GF-1 WFV surface reflectance data. •High quality fractional vegetation cover estimation algorithm for GF-1 data•Fractional vegetation cover estimation using physical model and neural networks•Ground validation indicated the satisfactory FVC estimates.