Preprints
https://doi.org/10.5194/gmd-2021-258
https://doi.org/10.5194/gmd-2021-258
Submitted as: model description paper
10 Mar 2022
Submitted as: model description paper | 10 Mar 2022
Status: this preprint is currently under review for the journal GMD.

Improved CASA model based on satellite remote sensing data: Simulating net primary productivity of Qinghai Lake Basin alpine grassland

Chengyong Wu1,3, Kelong Chen2,3, Chongyi E2,3, Xiaoni You1, Dongcai He1, Liangbai Hu1, Baokang Liu1, Runke Wang1, Yaya Shi1, Chengxiu Li1, and Fumei Liu2 Chengyong Wu et al.
  • 1School of Resources and Environmental Engineering, Tianshui Normal University, Tianshui, 741001, China
  • 2MOE Key Laboratory of Tibetan Plateau Land Surface Processes and Ecological Conservation / Qinghai Province Key Laboratory of Physical Geography and Environmental Processes, Xining, 810008, China
  • 3Academy of Plateau Science and Sustainability, Xining, 810008, China

Abstract. The Carnegie-Ames-Stanford Approach (CASA) model is widely used to estimate vegetation net primary productivity (NPP) at regional scale. However, the CASA is still driven by multi-source data, e.g. satellite remote sensing (RS) data, and ground observations that are time-consuming to obtain. RS data, can conveniently provide real-time surface information at the regional scale, thus replacing ground observation data to drive CASA model. We attempted to improve the CASA model in this study using DEM data derived from radar RS and RS products data generated from Moderate Resolution Imaging Spectroradiometer satellite sensor. We applied it to simulate the NPP of alpine grasslands in Qinghai Lake Basin, which is located in the northeastern Qinghai-Tibetan Plateau, China. The accuracy of the RS data driven CASA, with mean absolute percent error (MAPE) of 23.32 % and root mean square error (RMSE) of 26.26 g C•m-2•month-1, was higher than that of the multi-source data driven CASA, with MAPE of 49.08 % and RMSE of 65.21 g C•m-2•month-1. The NPP simulated by RS data driven CASA in July 2020 shows an average value of 110.17 ± 26.25 g C•m-2•month-1, which is similar to published results and comparable with the measured NPP. The results of this work indicate that simulating alpine grassland NPP with satellite RS data rather than ground observations is feasible. We may provide a workable reference for rapidly simulating grassland, farmland, forest, and other vegetation NPP to satisfy the requirements of precision agriculture, precision livestock farming, accounting carbon stocks, and other applications.

Chengyong Wu et al.

Status: open (until 25 May 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CEC1: 'Comment on gmd-2021-258', Juan Antonio Añel, 21 Apr 2022 reply
    • AC1: 'The modified model code and the relevant data of gmd-2021-258', Chengyong Wu, 01 May 2022 reply
  • RC1: 'Comment on gmd-2021-258', Anonymous Referee #1, 25 Apr 2022 reply
    • AC2: 'Reply on RC1', Chengyong Wu, 04 May 2022 reply

Chengyong Wu et al.

Chengyong Wu et al.

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Short summary
The traditional Carnegie-Ames-Stanford Approach (CASA) model driven by multi-source data such as meteorology, soil, and RS has notable disadvantages. We drove the CASA entirely by RS data and conducted a case study of alpine grassland. The simulated result is similar to published and measured Net primary productivity (NPP). It may provide a reference for rapidly simulating vegetation NPP to satisfy the requirements of precision agriculture and animal husbandry, accounting carbon stocks, etc.