AUTHOR=Ruan Yongjian , Ruan Baozhen , Xin Qinchuan , Liao Xi , Jing Fengrui , Zhang Xinchang 

TITLE=phenoC++: An open-source tool for retrieving vegetation phenology from satellite remote sensing data

JOURNAL=Frontiers in Environmental Science

VOLUME=Volume 11 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2023.1097249

DOI=10.3389/fenvs.2023.1097249

ISSN=2296-665X

ABSTRACT=Satellite-retrieved vegetation phenology has great potential for application in characterizing seasonal and annual land surface dynamics. However, obtaining regional-scale vegetation phenology from satellite remote sensing data often requires extensive data processing and computation, which makes accurate and rapid retrieval of regional-scale phenology a challenge. To retrieve vegetation phenology from satellite remote sensing data, we developed an open-source tool called phenoC++, which uses parallel technology in C++. phenoC++ includes six common algorithms (i.e., amplitude threshold (AT), first-order derivative (FOD), second-order derivative (SOD), third-order derivative (TOD), relative change rate (RCR), and curvature change rate (CCR)). We implemented the proposed phenoC++, and evaluated its performance at a site scale with PhenoCam-observed phenology metrics. The result shows that the SOS derived from MODIS images by phenoC++ six methods (i.e., AT, FOD, SOD, RCR, TOD, and CCR) obtaining obtained r values of 0.75, 0.76, 0.75, 0.76, 0.64 and 0.67, and RMSE values of 21.36, 20.41, 22.38, 19.11, 33.56 and 32.14, respectively. The satellite-retrieved EOS by phenoC++ six methods obtained r values of 0.58, 0.59, 0.57, 0.56, 0.36 and 0.40, and RMSE values of 52.43, 46.68, 55.13, 49.46, 71.13 and 69.34, respectively. Using PhenoCam-observed phenology as a baseline, the SOS retrieved by phenoC++ was superior to MCD12Q2, while the EOS retrieved by phenoC++ was slightly inferior to that of MCD12Q2. Moreover, compared with MCD12Q2 at a regional scale, the phenoC++-retrieved vegetation phenology yields more effective pixels. The innovative features of phenoC++ are as follows, 1) it integrated six algorithms for retrieving SOS and EOS; 2) it is fast in processing data at a large scale, and the input startup parameters are simple; 3) it outputs phenology metrics in GeoTiff format image, which more convenient to use with other geospatial data. The phenoC++ could aid in investigating and addressing the phenology problems of the ecological environment at large scales.