A threshold method for robust and fast estimation of land-surface phenology using Google Earth Engine
Descals, Adrià (Centre de Recerca Ecològica i d'Aplicacions Forestals)
Verger, Aleixandre (Centre de Recerca Ecològica i d'Aplicacions Forestals)
Yin, Gaofei (Centre de Recerca Ecològica i d'Aplicacions Forestals)
Peñuelas, Josep (Centre de Recerca Ecològica i d'Aplicacions Forestals)

Data:	2021
Resum:	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.
Ajuts:	European Commission 835541 European Commission 610028 Ministerio de Ciencia e Innovación CGL2016-79835-P Agencia Estatal de Investigación BES-2017-080197 Agència de Gestió d'Ajuts Universitaris i de Recerca 2017/SGR-1005
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Time series analysis ; Vegetation mapping ; Smoothing methods ; Estimation ; Remote sensing ; Measurement ; Indexes
Publicat a:	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 14 (2021) , p. 601-606, ISSN 2151-1535

DOI: 10.1109/JSTARS.2020.3039554

6 p, 1.5 MB

El registre apareix a les col·leccions:
Documents de recerca > Documents dels grups de recerca de la UAB > Centres i grups de recerca (producció científica) > Ciències > CREAF (Centre de Recerca Ecològica i d'Aplicacions Forestals) > Imbalance-P
Articles > Articles de recerca
Articles > Articles publicats