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Remote Sensing
Volume 18
Issue 1
10.3390/rs18010130
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Fusing Enhanced Flux Measurements and Multi-Source Satellite Observations to Improve GPP Estimation for the Qinghai–Tibet Plateau Based on AutoML Techniques

by
Mengyao Zhao
1,
Ying Yang
1,
Guoyong Weng
2,
Wei He
3,4,5,
Hua Yang
6,*,
Ngoc Tu Nguyen
7,
Jianqiong Wang
5,
Shuai Liu
1,
Jiayi Chen
1,
Xinhui Lei
3,4,
Teng Ma
3,4,
Ziyi Huang
3,4 and
Peipei Xu
1
1
School of Geography and Tourism, Anhui Normal University, Wuhu 241002, China
2
Taizhou Huangyan Urban and Rural Water Supply Co., Ltd., Taizhou 318000, China
3
Zhejiang Carbon Neutral Innovation Institute, Zhejiang University of Technology, Hangzhou 310014, China
4
Zhejiang Key Laboratory of Low-Carbon Control Technology for Industrial Pollution, School of Environment, Zhejiang University of Technology, Hangzhou 310014, China
5
Qinghai Provincial Key Laboratory of Greenhouse Gases and Carbon Neutrality, China Atmospheric Background Reference Observatory, Qinghai Provincial Meteorological Bureau, Xining 810004, China
6
State Key Laboratory of Remote Sensing and Digital Earth, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
7
State Key Laboratory of Water Disaster Prevention, College of Hydrology and Water Resources, Hohai University, Nanjing 210024, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2026, 18(1), 130; https://doi.org/10.3390/rs18010130 (registering DOI)
Submission received: 11 November 2025 / Revised: 23 December 2025 / Accepted: 29 December 2025 / Published: 30 December 2025
(This article belongs to the Section Ecological Remote Sensing)
Versions Notes

Abstract

The Qinghai–Tibet Plateau (QTP) plays a crucial role in the terrestrial carbon cycle, but the gross primary productivity (GPP) estimates for the region remain highly uncertain due to limited flux observations and modeling challenges. Here, we integrated 65.2 site years of eddy covariance data from 19 flux sites with multi-source remote sensing observations to develop a data driven GPP model for the QTP. Eleven machine learning algorithms from two automated machine learning (AutoML) platforms, H2O AutoML and FLAML, were evaluated to construct an ensemble model named AutoML. The model showed strong performance at site-level across alpine meadow, steppe, wetland, and shrub ecosystems, achieving R2 up to 0.95 and RMSE as low as 0.42 g C m−2 d−1. By validating extracted site-level GPP values from the upscaling GPP datasets against with flux observations, AutoML-GPP demonstrates overall superior or equivalent performance over global GPP products (FLUXCOM X-base, GOSIF, and FluxSat). Regional upscaling estimated a mean annual total GPP of 374.20 Tg C yr−1 from 2002 to 2018, with a slight upward trend of 0.08 Tg C yr−1. Spatially, higher GPP occurred mainly in the eastern QTP, with anomalies linked to climate extremes in 2008, 2010, and 2015. AutoML-GPP effectively captures climate-induced interannual anomalies in the QTP’s GPP, coinciding with GOSIF-GPP and FluxSat GPP, and outperforming the recent released well-known global upscaling flux dataset FLUXCOM X-base. This study provides improved GPP estimation for the QTP, offering new insights into carbon cycling and climate–vegetation interactions.
Keywords: gross primary productivity; automated machine learning; Qinghai–Tibet Plateau; eddy covariance measurements; multi-source remote sensing gross primary productivity; automated machine learning; Qinghai–Tibet Plateau; eddy covariance measurements; multi-source remote sensing

Share and Cite

MDPI and ACS Style

Zhao, M.; Yang, Y.; Weng, G.; He, W.; Yang, H.; Nguyen, N.T.; Wang, J.; Liu, S.; Chen, J.; Lei, X.; et al. Fusing Enhanced Flux Measurements and Multi-Source Satellite Observations to Improve GPP Estimation for the Qinghai–Tibet Plateau Based on AutoML Techniques. Remote Sens. 2026, 18, 130. https://doi.org/10.3390/rs18010130

AMA Style

Zhao M, Yang Y, Weng G, He W, Yang H, Nguyen NT, Wang J, Liu S, Chen J, Lei X, et al. Fusing Enhanced Flux Measurements and Multi-Source Satellite Observations to Improve GPP Estimation for the Qinghai–Tibet Plateau Based on AutoML Techniques. Remote Sensing. 2026; 18(1):130. https://doi.org/10.3390/rs18010130

Chicago/Turabian Style

Zhao, Mengyao, Ying Yang, Guoyong Weng, Wei He, Hua Yang, Ngoc Tu Nguyen, Jianqiong Wang, Shuai Liu, Jiayi Chen, Xinhui Lei, and et al. 2026. "Fusing Enhanced Flux Measurements and Multi-Source Satellite Observations to Improve GPP Estimation for the Qinghai–Tibet Plateau Based on AutoML Techniques" Remote Sensing 18, no. 1: 130. https://doi.org/10.3390/rs18010130

APA Style

Zhao, M., Yang, Y., Weng, G., He, W., Yang, H., Nguyen, N. T., Wang, J., Liu, S., Chen, J., Lei, X., Ma, T., Huang, Z., & Xu, P. (2026). Fusing Enhanced Flux Measurements and Multi-Source Satellite Observations to Improve GPP Estimation for the Qinghai–Tibet Plateau Based on AutoML Techniques. Remote Sensing, 18(1), 130. https://doi.org/10.3390/rs18010130

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