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Derivation of biophysical parameters from fused remote sensing data

机译：从融合的遥感数据推导生物物理参数

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Resent launches of optical space-borne remote sensing systems with high resolution, high temporal repetition rate, and constant viewing angles (e.g.: Venμs, Sentinel-2) will fortify the potential of remote sensing applications in the context of agricultural monitoring (e.g.: the derivation of biophysical parameters). The practicality of optical and/or thermal remote sensing data is often limited by tile and cloud coverage. Spatial-Temporal-Adaptive Fusion Model (e.g.: STARFM) can be used to combine data from different remote sensing sensors to overcome these limitations. In order to investigate the reliability of synthesized remote sensing data in agricultural monitoring, we evaluated the quality and integrity of predicted FPAR and LAI data on maize. In this context, we used synthesized daily LANDSAT-like data products and a RandomForest to fill possible spatial and/or temporal data gaps and to predict FPAR and LAI for the entire growing period in 2015. The evaluation of the biophysical time series was concluded using a weekly to bi-weekly ground measurements in different phenological stages of the maize plant. The quality assessment of the entire growth period revealed the high potential of synthetic remote sensing data for agricultural monitoring. The quality of the results range between R
机译：近期发射具有高分辨率，高时间重复率和恒定视角的光学星载遥感系统（例如：Venμs，Sentinel-2），将加强农业监测（例如：生物物理参数的推导）。光学和/或热遥感数据的实用性通常受限于图块和云的覆盖范围。可以使用时空自适应融合模型（例如STARFM）组合来自不同遥感传感器的数据，以克服这些限制。为了调查合成遥感数据在农业监测中的可靠性，我们评估了玉米中FPAR和LAI预测数据的质量和完整性。在这种情况下，我们使用合成的类似于LANDSAT的每日数据产品和RandomForest来填补可能的空间和/或时间数据空白，并预测2015年整个生长期的FPAR和LAI。对生物物理时间序列的评估使用玉米植物在不同物候期的每周至每两周一次地面测量。在整个生长期的质量评估表明，合成遥感数据可用于农业监测。结果的质量介于R之间

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