Abstract
Cloud-based platforms are changing the way of analyzing remotely sensed data by providing high computational power and rapid access to massive volumes of data. Several types of studies use cloud-based platforms for global-scale analyses, but the number of land-surface phenology (LSP) studies that use cloud-based platforms is low. We analyzed the performance of the state-of-the-art LSP algorithms and propose a new threshold-based method that we implemented in Google Earth Engine (GEE). This new LSP method, called maximum separation (MS) method, applies a moving window that estimates the ratio of observations that exceed a given threshold before and after the central day. The start and end of the growing season are the days of the year when the difference between the ratios before and after the central day are minimal and maximal. The MODIS phenology metrics estimated with the MS method showed similar performances as traditional threshold methods when compared with ground estimations derived from the PhenoCam dataset, a network of digital cameras that provides near-surface remotely sensed observations of vegetation phenology. The main advantage of the MS method is that it can be directly applied to daily nonsmoothed time series without any additional preprocessing steps. The implementation of the proposed method in GEE allowed the processing of global phenological maps derived from MODIS. The distribution of code in GEE allows the reproducibility of results and the rapid processing of LSP metrics by the scientific community.
Highlights
T HE study of land-surface phenology (LSP) entails the estimation of metrics from remotely sensed vegetation seasonality [1]
The influence of the temperature and other climatic factors on the vegetation dynamics is still under discussion [4], [5], and reliable estimates of phenological metrics at a global scale are required to understand the links between the climatic factors and vegetation phenology
TH2 showed the best results for the SoS, while the maximum separation (MS) showed better results for the EoS, overall differences between LSP methods were marginal and none of the MODIS variables excelled in the comparison with PhenoCam
Summary
T HE study of land-surface phenology (LSP) entails the estimation of metrics from remotely sensed vegetation seasonality [1]. Its study is important for reliably estimating vegetation dynamics, commonly the start and end of the growing season (SoS and EoS), which correspond to the timing of Manuscript received August 21, 2020; revised October 23, 2020; accepted November 11, 2020. Phenology has recently gained importance because of its linkage with global warming. Several studies have found that the length of the growing season has increased in recent decades due to the global warming [2], [3]. The influence of the temperature and other climatic factors on the vegetation dynamics is still under discussion [4], [5], and reliable estimates of phenological metrics at a global scale are required to understand the links between the climatic factors and vegetation phenology
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