Data for: Crop specific algorithms trained over ground measurements provide the best performance for GAI and fAPAR estimates from Landsat-8 observations

This dataset encompasses a large number (> 700) of in-situ observations over elemantary sampling units (about 20m2) of LAI (effective and actual), fAPAR and fraction of vegetation cover (fCover) collected over a network of agricultural sites during the ImagineS project (http://fp7-imagines.eu/) in the period 2013-2016. The ground dataset was collected with digital hemispherical photography (DHP), LAI2200, and AccuPAR devices following well stablished protocols in agreement with CEOS LPV good practices. The ground data is complemented with concomitant Landsat-8/OLI observations, the sun zenith angle at the acquisition and the NDVI. This results in a unique database to calibrate and validate algorithms for retrieval of biophysical variables over crops.本数据集涵盖2013至2016年间，ImagineS项目（http://fp7-imagines.eu/）在农业站点网络中采集的逾700组原位观测（in-situ observations）数据，观测覆盖约20平方米的基本采样单元（elementary sampling units），观测指标包括叶面积指数（LAI，含有效叶面积指数与实际叶面积指数）、光合有效辐射吸收比率（fAPAR）以及植被覆盖度（fCover）。该地面数据集采用数字半球摄影术（DHP）、LAI2200仪器与AccuPAR设备，遵循符合CEOS LPV（国际地球观测卫星委员会陆地产品验证工作组）良好实践规范的既定标准流程完成采集。地面数据集还配套了同期获取的陆地卫星8号/陆地成像仪（Landsat-8/OLI）观测数据、成像时刻的太阳天顶角以及归一化差分植被指数（NDVI）。本数据集可用于校准与验证作物生物物理参数反演算法，是一款独一无二的专用数据库。