Remote sensing derived variables for modelling above ground biomass

The dataset displays remote sensing modelling variables extracted from active and passive remote sensing technology acquired in the summer and winter season of 2017. The extracted variables included Sentinel-1A derived variables which contained 16 GLCMs, VH and VV backscatter channels, and VH/VV band ratio. Sentinel-2 MSI predictor variables for wetland vegetation AGB estimation in both summer and winter included ten reflectance bands and eight vegetation indices (VIs). The VIs used included traditional VIs such as the Normalized Difference Vegetation Index (NDVI), Simple Ratio (SR), Green Red Vegetation Index (GRVI), Green Normalized Difference Vegetation Index (GNDVI). Other used VIs were derived from the red-edge regions (NDVIre5: Normalized Difference Vegetation Index Red-edge 1; NDVIre6: Normalized Difference Vegetation Index Red-edge 2; NDVIre7: Normalized Difference Vegetation Index Red-edge 3; SRre5: Simple Ratio Red-edge 1). All these variables were used for development of predictive models of above ground biomass of wetland vegetation.

本数据集收录了2017年夏冬两季通过主动遥感与被动遥感技术获取的遥感建模变量。
提取得到的变量包括由哨兵1号A星（Sentinel-1A）生成的特征集，涵盖16个灰度共生矩阵（Gray Level Co-occurrence Matrix, GLCM）、VH与VV后向散射通道，以及VH/VV波段比值。
用于夏冬两季湿地植被地上生物量（Above Ground Biomass, AGB）估算的哨兵2号多光谱仪器（Sentinel-2 Multi-Spectral Instrument, MSI）预测变量包含10个反射率波段与8种植被指数（Vegetation Indices, VIs）。
所使用的植被指数包括传统植被指数，如归一化差分植被指数（Normalized Difference Vegetation Index, NDVI）、简单比值指数（Simple Ratio, SR）、绿红植被指数（Green Red Vegetation Index, GRVI）以及绿归一化差分植被指数（Green Normalized Difference Vegetation Index, GNDVI）。
其余植被指数则提取自红边波段区域，具体包括NDVIre5（红边1号归一化差分植被指数，Normalized Difference Vegetation Index Red-edge 1）、NDVIre6（红边2号归一化差分植被指数，Normalized Difference Vegetation Index Red-edge 2）、NDVIre7（红边3号归一化差分植被指数，Normalized Difference Vegetation Index Red-edge 3）以及SRre5（红边1号简单比值指数，Simple Ratio Red-edge 1）。
上述所有变量均被用于构建湿地植被地上生物量的预测模型。