Quantitative Estimation of Net Primary Productivity by an Improved tCASA Model Using Landsat Time Series Data: A Case Study of Central Plains, China

Net primary productivity (NPP) can effectively represent the growth status of plants. The accurate estimation and spatiotemporal variation analysis of NPP is of great significance for understanding global climate change and carbon cycle. The Carnegie–Ames–Stanford Approach (CASA) model has been extensively utilized for estimating NPP by integrating remote sensing data, climate data, and land cover type data to achieve precise estimations of NPP. However, the traditional CASA model fails to account for the fact that crop types can change over time, leading to inaccurate estimations of NPP in the region. To address this issue, an adaptive temperature stress coefficient for the CASA model (tCASA) is proposed to estimate the NPP of the Central Plains Urban Agglomeration (CPUA) using Landsat time-series data spanning from 2002 to 2022. Based on the Theil-Sen Median Estimators, the Mann–Kendall test, and partial correlation analysis, this article delves into the impact of climate and human activities on NPP. The results indicate the following: the improved tCASA model exhibits a stronger linear correlation with the test dataset, achieving a reduction of 11.11 gC/m<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> in the root mean square error. The NPP in CPUA demonstrates an increasing trend of 3.0683 gC/m<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>/year. Across the entire study area, 62.879% of the regions displayed an improvement in NPP, whereas 26.143% showed signs of degradation. Temperature and solar radiation demonstrate a strong positive correlation with NPP, with 73.421% and 72.839% of the region, respectively, positively linked. In contrast, precipitation shows a weaker correlation with NPP, with only 36.848% of the region indicating a positive relationship between precipitation and NPP.