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Article

Improved Spring Vegetation Phenology Calculation Method Using a Coupled Model and Anomalous Point Detection

by 1,2, 1,2,*, 1,2 and 1,2
1
State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100875, China
2
Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(12), 1432; https://doi.org/10.3390/rs11121432
Received: 13 April 2019 / Revised: 5 June 2019 / Accepted: 12 June 2019 / Published: 17 June 2019
(This article belongs to the Special Issue Land Surface Phenology )
High temporal resolution remote sensing satellite data can be used to collect vegetation phenology observations over regional and global scales. Logistic and polynomial functions are the most widely used methods for fitting time series normalized difference vegetation index (NDVI) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS). Furthermore, the maximum in the curvature of the fitted curve is usually considered as the spring green-up date. However, the existing green-up date calculation methods have low accuracy for sparse vegetation. This paper proposes an improved green-up date calculation method using a coupled model and anomalous point detection (CMAPD). This model is based on a combination of logistic and polynomial functions, which is used to fit time series vegetation index. Anomalous values were identified using the nearest neighbor algorithm, and these values were corrected by the combination of growing degree-days (GDD) and land use type. Then, the trends and spatial patterns of green-up date was analyzed in the Sanjiangyuan area. The results show that the coupled model fit the time series data better than a single logistic or polynomial function. Besides, the anomalous point detection method properly controlled the green-up date within the local threshold, and could reflect green-up date more accurately. In addition, a weak statistically significant advance trend for average vegetation green-up date was observed from 2000 to 2016. However, in 10.4% of the study area, the the green-up date has significant advanced. Regression analysis showed that the green-up date is correlated to elevation: the green-up date is clearly later at higher elevations. View Full-Text
Keywords: MODIS NDVI; curve fitting; growing degree-days; green-up date; CMAPD; elevation gradient MODIS NDVI; curve fitting; growing degree-days; green-up date; CMAPD; elevation gradient
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MDPI and ACS Style

Luo, Q.; Song, J.; Yang, L.; Wang, J. Improved Spring Vegetation Phenology Calculation Method Using a Coupled Model and Anomalous Point Detection. Remote Sens. 2019, 11, 1432. https://doi.org/10.3390/rs11121432

AMA Style

Luo Q, Song J, Yang L, Wang J. Improved Spring Vegetation Phenology Calculation Method Using a Coupled Model and Anomalous Point Detection. Remote Sensing. 2019; 11(12):1432. https://doi.org/10.3390/rs11121432

Chicago/Turabian Style

Luo, Qian; Song, Jinling; Yang, Lei; Wang, Jindi. 2019. "Improved Spring Vegetation Phenology Calculation Method Using a Coupled Model and Anomalous Point Detection" Remote Sens. 11, no. 12: 1432. https://doi.org/10.3390/rs11121432

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