Abstract
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.
Highlights
Monitoring vegetation phenology is vital for understanding the influence of vegetation dynamics on a changing climate [1]
The end-to-end framework consists of the following key steps: (1) optimizing the hybrid Gaussian Process Regression (GPR) retrieval models of crop traits to facilitate their implementation on Google Earth Engine (GEE); (2) time series processing of the traits and gap-filling through GPR fitting, and (3) calculation of common phenological metrics
This study presents an integral workflow for the retrieval of crop traits and their associated land surface phenology metrics in the GEE cloud platform
Summary
Monitoring vegetation phenology is vital for understanding the influence of vegetation dynamics on a changing climate [1]. Since climate change is one of the major pressures on agricultural production, assessing the phenology of cultivated lands is becoming increasingly relevant. Phenological data help to trace plant development, monitor agricultural production processes, estimate crop yield [2], and ensure food and nutritional security for a growing world population [3,4,5,6]. Plant phenology is assessed at the ground level and involves visual observations of phenological events, which is labour and time consuming [10]. Spaceborne observations are employed to monitor the spatiotemporal development of plants at the landscape level, which is known as ‘land surface phenology’ (LSP) [11]
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