Salinero-Delgado Matías, Estévez José, Pipia Luca, Belda Santiago, Berger Katja, Gómez Vanessa Paredes, Verrelst Jochem
Image Processing Laboratory (IPL), University of Valencia, C/Catedrático José Beltrán 2, Paterna, 46980 Valencia, Spain.
Institut Cartogràfic i Geològic de Catalunya (ICGC), Parc de Montjüic, 08038 Barcelona, Spain.
Remote Sens (Basel). 2021 Dec 29;14(1):146. doi: 10.3390/rs14010146.
Monitoring cropland phenology from optical satellite data remains a challenging task due to the influence of clouds and atmospheric artifacts. Therefore, measures need to be taken to overcome these challenges and gain better knowledge of crop dynamics. The arrival of cloud computing platforms such as Google Earth Engine (GEE) has enabled us to propose a Sentinel-2 (S2) phenology end-to-end processing chain. To achieve this, the following pipeline was implemented: (1) the building of hybrid Gaussian Process Regression (GPR) retrieval models of crop traits optimized with active learning, (2) implementation of these models on GEE (3) generation of spatiotemporally continuous maps and time series of these crop traits with the use of gap-filling through GPR fitting, and finally, (4) calculation of land surface phenology (LSP) metrics such as the start of season (SOS) or end of season (EOS). Overall, from good to high performance was achieved, in particular for the estimation of canopy-level traits such as leaf area index (LAI) and canopy chlorophyll content, with normalized root mean square errors (NRMSE) of 9% and 10%, respectively. By means of the GPR gap-filling time series of S2, entire tiles were reconstructed, and resulting maps were demonstrated over an agricultural area in Castile and Leon, Spain, where crop calendar data were available to assess the validity of LSP metrics derived from crop traits. In addition, phenology derived from the normalized difference vegetation index (NDVI) was used as reference. NDVI not only proved to be a robust indicator for the calculation of LSP metrics, but also served to demonstrate the good phenology quality of the quantitative trait products. Thanks to the GEE framework, the proposed workflow can be realized anywhere in the world and for any time window, thus representing a shift in the satellite data processing paradigm. We anticipate that the produced LSP metrics can provide meaningful insights into crop seasonal patterns in a changing environment that demands adaptive agricultural production.
由于云层和大气伪影的影响，利用光学卫星数据监测农田物候仍然是一项具有挑战性的任务。因此，需要采取措施来克服这些挑战，并更好地了解作物动态。谷歌地球引擎（GEE）等云计算平台的出现，使我们能够提出一种哨兵2号（S2）物候端到端处理链。为实现这一目标，实施了以下流程：（1）构建通过主动学习优化的作物性状混合高斯过程回归（GPR）反演模型；（2）在GEE上实现这些模型；（3）通过GPR拟合进行数据填补，生成这些作物性状的时空连续地图和时间序列；最后，（4）计算诸如季节开始（SOS）或季节结束（EOS）等陆地表面物候（LSP）指标。总体而言，实现了从良好到高性能的结果，特别是对于冠层水平性状如叶面积指数（LAI）和冠层叶绿素含量的估计，归一化均方根误差（NRMSE）分别为9%和10%。借助S2的GPR数据填补时间序列，重建了整个图块，并在西班牙卡斯蒂利亚-莱昂的一个农业区域展示了生成的地图，该区域有作物历数据可用于评估从作物性状得出的LSP指标的有效性。此外，将来自归一化植被指数（NDVI）的物候用作参考。NDVI不仅被证明是计算LSP指标的可靠指标，还用于证明定量性状产品的良好物候质量。得益于GEE框架，所提出的工作流程可以在世界任何地方和任何时间窗口实现，从而代表了卫星数据处理范式的转变。我们预计，生成的LSP指标能够为不断变化的环境中需要适应性农业生产的作物季节模式提供有意义的见解。
