Evaluation of NPP using three models compared with MODIS-NPP data over China

Abstract

Estimating net primary productivity (NPP) is significant in global climate change research and carbon cycle. However, there are many uncertainties in different NPP modeling results and the process of NPP is challenging to model on the absence of data. In this study, we used meteorological data as input to simulate vegetation NPP through climate-based model, synthetic model and CASA model. Then, the results from three models were compared with MODIS NPP and observed data over China from 2000 to 2015. The statistics evaluation metrics (Relative Bias (RB), Pearson linear Correlation Coefficient (CC), Root Mean Square Error (RMSE), and Nash-Sutcliffe efficiency coefficient (NSE)) between simulated NPP and MODIS NPP were calculated. The results implied that the CASA-model performed better than the other two models in terms of RB, RMSE, NSE and CC whether on the national or the regional scale. It has a higher CC with 0.51 and a smaller RMSE with 111.96 g C·m-2·yr-1 in the whole country. The synthetic model and CASA-model has the same advantages at some regions, and there are lower RMSE in Southern China (86.35 g C·m-2·yr-1), Xinjiang (85.53 g C·m-2·yr-1) and Qinghai-Tibet Plateau (93.22 g C·m-2·yr-1). The climate-based model has widespread overestimation and large systematic errors, along with worse performances (NSEmax = 0.45) and other metric indexes unsatisfactory, especially Qinghai-Tibet Plateau with relatively lower accuracy because of the unavailable observation data. Overall, the CASA-model is much more ideal for estimating NPP all over China in the absence of data. This study provides a comprehensive intercomparison of different NPP-simulated models and can provide powerful help for researchers to select the appropriate NPP evaluation model.

Yield estimation of summer maize based on multi‐source remote‐sensing data

“…Yang et al, 2022). Within the Chinese region, compared with statistical models (Thornthwaite-Memorial) and process models, CASA showed the highest agreement with MODIS NPP and observations (Sun et al, 2021). The CASA can predict crop yield at different scales (Fang et al, 2021); its parameters are easy to obtain, and most of the parameters required in the model can be extracted from remote-sensing data, which largely overcomes the drawback of the lack of information from ground stations.…”

“…The CASA is a light‐use efficiency model that integrates environmental factors and the characteristics of vegetation, mainly using remote sensing (RS) and geographic information systems (GIS) as technical means, which is driven by remote‐sensing data and meteorological data such as temperature, precipitation, solar radiation, vegetation type, and soil type (B. Yang et al., 2022). Within the Chinese region, compared with statistical models (Thornthwaite–Memorial) and process models, CASA showed the highest agreement with MODIS NPP and observations (Sun et al., 2021). The CASA can predict crop yield at different scales (Fang et al., 2021); its parameters are easy to obtain, and most of the parameters required in the model can be extracted from remote‐sensing data, which largely overcomes the drawback of the lack of information from ground stations.…”

A New Framework of 17 Hydrological Ecosystem Services (HESS17) for Supporting River Basin Planning and Environmental Monitoring

“…NPP can be subsequently used to separate carbon assimilates into (i) above ground; (ii) below ground; (iii) soil organic matter; (iv) dead wood and litter. A review of NPP models from remote sensing is provided by Sun [109]. Figure 3 illustrates an example of carbon capture calculated from NPP and humification process using remote sensing data.…”

A dataset of lake-catchment characteristics for the Tibetan Plateau

“…Soil erodibility was also included in the dataset, and the Soil Erodibility Dataset of the Pan-Third Pole in 2020 (Yang and He, 2019) was used. The data on soil water content were derived from the Global High-Resolution Soil-Water Balance dataset (Trabucco and Zomer, 2010), which defines the fraction of soil water content available for evapotranspiration processes (as a percentage of the maximum soil water content) and is therefore a measure of soil water stress. We calculated catchment-level soil water content at monthly and annual scales.…”

“…The GPP and NPP data came from the MODIS products (MOD17A2H.006 and MOD17A3HGF.006). The R 2 between monthly MODIS GPP and eddy covariance measurements was reported to be 0.64 on average, and the RMSE was 2.55 g C m −2 d −1 in alpine grassland, which is the most widely distributed biome on the TP (Zhu et al, 2018); the R 2 between MODIS NPP and in situ observations in 23 stations across China was reported to be 0.81, and the RMSE was 73.44 g C m −2 (Sun et al, 2021). The RMSE of fractional snow cover data from Jiang et al (2022) was 0.14 taking the results from highresolution Landsat images as reference.…”