5 January 2022 Tempo-differentially selected growth rate model development and improved extraction of remotely sensed phenology in the Qinghai–Tibet Plateau
Dong He, Xiaobing Zhou, Xianglin Huang, Wenmin Zhang, Qingjiu Tian, Nianxu Xu, Yanbiao Xi, Jia Tian, Faisal Mumtaz
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Abstract

We show an improvement in extraction of remotely sensed phenology in the Qinghai–Tibet Plateau (QTP). The improvement includes multiple preprocessing, newly proposed absolute growth rate and relative growth rate models based on botany and phenology, and selection of the appropriate growth rate model at different growing stages. We refer to this model as the tempo-differentially selected growth rate model (TDSGM). The newly developed TDSGM is a comprehensive and accurate remote sensing phenological extraction model without manual intervention, which is better than the current mainstream methods. Results show that: (1) influence of elevation and longitude on the QTP phenology has been more effective than that of latitude and vegetation types; (2) length of the growing season (LOS) was in a shortening trend in the long term, even it showed a slight extension in recent years in the short term; (3) the shortest LOS was identified in the area with an altitude of 3000 to 5500 m, which accounts for 90% of the total area of QTP. High instability of LOS was concentrated in meadows at 4000 to 5000 m; and (4) most previous studies on remotely sensed phenology showed that LOS was more related to the start of the growing season. However, we show that the LOSs of four out of seven QTP vegetation types were closely related to the end of the growth season, consistent with 1455 ground observations.

© 2022 Society of Photo-Optical Instrumentation Engineers (SPIE) 1931-3195/2022/$28.00 © 2022 SPIE
Dong He, Xiaobing Zhou, Xianglin Huang, Wenmin Zhang, Qingjiu Tian, Nianxu Xu, Yanbiao Xi, Jia Tian, and Faisal Mumtaz "Tempo-differentially selected growth rate model development and improved extraction of remotely sensed phenology in the Qinghai–Tibet Plateau," Journal of Applied Remote Sensing 16(1), 018501 (5 January 2022). https://doi.org/10.1117/1.JRS.16.018501
Received: 27 August 2021; Accepted: 20 December 2021; Published: 5 January 2022
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KEYWORDS
Vegetation

Data modeling

Remote sensing

Environmental sensing

Data centers

Climate change

Climatology

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