Estévez José, Berger Katja, Vicent Jorge, Rivera-Caicedo Juan Pablo, Wocher Matthias, Verrelst Jochem
Image Processing Laboratory (IPL), Parc Científic, Universitat de València, 46980 Paterna, Spain.
Department of Geography, Ludwig-Maximilians-Universität München (LMU), Luisenstr. 37, 80333 Munich, Germany.
Remote Sens (Basel). 2021 Apr 20;13(8):1589. doi: 10.3390/rs13081589.
In support of cropland monitoring, operational Copernicus Sentinel-2 (S2) data became available globally and can be explored for the retrieval of important crop traits. Based on a hybrid workflow, retrieval models for six essential biochemical and biophysical crop traits were developed for both S2 bottom-of-atmosphere (BOA) L2A and S2 top-of-atmosphere (TOA) L1C data. A variational heteroscedastic Gaussian process regression (VHGPR) algorithm was trained with simulations generated by the combined leaf-canopy reflectance model PROSAILat the BOA scale and further combined with the Second Simulation of a Satellite Signal in the Solar Spectrum (6SV) atmosphere model at the TOA scale. Established VHGPR models were then applied to S2 L1C and L2A reflectance data for mapping: leaf chlorophyll content ( ), leaf water content ( ), fractional vegetation coverage (FVC), leaf area index (LAI), and upscaled leaf biochemical compounds, i.e., LAI * (laiCab) and LAI * (laiCw). Estimated variables were validated using in situ reference data collected during the Munich-North-Isar field campaigns within growing seasons of maize and winter wheat in the years 2017 and 2018. For leaf biochemicals, retrieval from BOA reflectance slightly outperformed results from TOA reflectance, e.g., obtaining a root mean squared error (RMSE) of 6.5 μg/cm (BOA) vs. 8 μg/cm (TOA) in the case of . For the majority of canopy-level variables, instead, estimation accuracy was higher when using TOA reflectance data, e.g., with an RMSE of 139 g/m (BOA) vs. 113 g/m (TOA) for laiCw. Derived maps were further compared against reference products obtained from the ESA Sentinel Application Platform (SNAP) Biophysical Processor. Altogether, the consistency between L1C and L2A retrievals confirmed that crop traits can potentially be estimated directly from TOA reflectance data. Successful mapping of canopy-level crop traits including information about prediction confidence suggests that the models can be transferred over spatial and temporal scales and, therefore, can contribute to decision-making processes for cropland management.
为支持农田监测，哥白尼哨兵 - 2（S2）业务数据已在全球范围内可用，可用于探索重要作物特征的反演。基于混合工作流程，针对S2大气底部（BOA）L2A数据和S2大气顶部（TOA）L1C数据，开发了六种基本生化和生物物理作物特征的反演模型。使用由组合叶冠层反射率模型PROSAIL在BOA尺度生成的模拟数据对变分异方差高斯过程回归（VHGPR）算法进行训练，并在TOA尺度进一步与太阳光谱卫星信号的第二次模拟（6SV）大气模型相结合。然后将已建立的VHGPR模型应用于S2 L1C和L2A反射率数据进行制图：叶片叶绿素含量（ ）、叶片含水量（ ）、植被覆盖度（FVC）、叶面积指数（LAI）以及上采样的叶片生化化合物，即LAI * （laiCab）和LAI * （laiCw）。使用在2017年和2018年玉米和冬小麦生长季节的慕尼黑 - 北 - 伊萨尔田间试验期间收集的原位参考数据对估计变量进行验证。对于叶片生化物质，从BOA反射率进行反演的结果略优于TOA反射率的结果，例如，在 的情况下，获得的均方根误差（RMSE）为6.5 μg/cm（BOA），而TOA为8 μg/cm。相反，对于大多数冠层水平变量，使用TOA反射率数据时估计精度更高，例如，laiCw的RMSE为139 g/m（BOA），而TOA为113 g/m。将导出的地图与从欧洲航天局哨兵应用平台（SNAP）生物物理处理器获得的参考产品进行进一步比较。总体而言，L1C和L2A反演之间的一致性证实了作物特征有可能直接从TOA反射率数据中估计出来。包括预测置信度信息在内的冠层水平作物特征的成功制图表明，这些模型可以在空间和时间尺度上进行转移，因此可以为农田管理的决策过程做出贡献。
