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Estimation of grassland net primary productivity in permafrost of Qinghai-Tibet Plateau based on machine learning.

LI Chuan-hua1,2*, SUN Hao1, WANG Yu-tao1, CAO Hong-juan1, YIN Huan-huan1, ZHOU Min1, ZHU Tong-bin1   

  1. (1College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China; 2Cryosphere Research Station on the QinghaiTibetan Plateau, State Key Laboratory of Cryospheric Science, Northwest Institute of EcoEnvironment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China).
  • Published:2020-05-10

Abstract: There is still a great uncertainty in the estimation of net primary productivity (NPP). In this study, machine learning algorithm (RF and RBF-ANN) was used to estimate the NPP of grassland in permafrost of Qinghai-Tibet Plateau from 2002 to 2018. We analyzed the temporal and spatial pattern, variation characteristics, and response of grassland NPP to climate factors in the permafrost of Qinghai-Tibet Plateau. The results showed that: (1) The estimation results of machine learning are reliable and simple. (2) In the permafrost of Qinghai-Tibet Plateau, NPP showed a decreasing trend from southeast to northwest. The total NPP was 175.39 Tg C·a-1, and the average NPP per unit area was 164.10 g C·m-2·a-1, showing a fluctuating trend. (3) The area with increased NPP accounted for 20.49% of the total area. The amplitudes of NPPincrease differed with grassland types, with an order of alpine wet meadow > alpine meadow > alpine steppe > alpine desert steppe. (4) Temperature was the dominant factor driving grassland NPP change in permafrost area of the Qinghai-Tibet Plateau. The influence of precipitation gradually weakened along the southeast to northwest.

Key words: yield reduction risk, WOFOST crop model, winter wheat.