Fractional vegetation cover estimation algorithm for Chinese GF-1 wide field view data
Fractional vegetation cover estimation algorithm for Chinese GF-1 wide field view data
复制标题
中国高分一号大视场数据植被覆盖度分数估计算法
DOI：
10.1016/j.rse.2016.02.019
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发表时间：
2016-05-01
影响因子：
13.5
通讯作者：
Li, Yuwei
中科院分区：
文献类型：
--
作者：
Jia, Kun;Liang, Shunlin;Li, Yuwei
中国GF-1卫星是中国高分辨率对地观测系统的第一颗卫星，其上的宽视场传感器正在获取具有十米空间分辨率、高时间分辨率和大覆盖范围的多光谱数据，这些数据是环境监测的宝贵数据源。本研究的目的是开发一个通用的和可靠的植被覆盖度（FVC）估计算法的GF-1 WFV数据在各种陆面条件下。该算法预计可根据空间分辨率为16 m、时间分辨率为4个日期的GF-1 WFV反射率数据估计FVC。该算法是基于训练后向传播神经网络（NN）使用前景+ SAIL辐射传输模型模拟GF-1 WFV冠层反射率和相应的FVC值。以GF-1 WFV数据的绿色、红色和近红外波段的反射率为输入变量，相应的FVC为输出变量，最后使用842,400个覆盖不同地表条件的模拟样本对神经网络进行训练。以具有丰富土地覆被类型的围场县为例，验证了基于GF-1 WFV数据的FVC估计算法的性能。验证结果表明，该算法有效地工作，并产生合理的FVC估计的R-2 = 0.790和均方根误差为0.073的基础上的实地调查数据。所提出的算法可以在没有先验知识的土地覆盖和常规生产的高品质的FVC产品使用GF-1 WFV表面反射率数据的潜力。(C)2016爱思唯尔公司保留所有权利。
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 16 m 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 R-2 = 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. (C) 2016 Elsevier Inc All rights reserved.