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Understanding the net ecosystem productivity (NEP) is essential for understanding ecosystem functioning and the global carbon cycle. Utilizing meteorological and The Advanced Very High Resolution Radiometer (AVHRR) remote sensing data, this study employed the Carnegie–Ames–Stanford Approach (CASA) and the Geostatistical Model of Soil Respiration (GSMSR) to map a monthly vegetation NEP in China from 1982 to 2020. Then, we examined the spatiotemporal trends of NEP and identified the drivers of NEP changes using the Geodetector model. The mean NEP over the 39-year period amounted to 265.38 gC·m−2. Additionally, the average annual carbon sequestration amounted to 1.89 PgC, indicating a large carbon sink effect. From 1982 to 2020, there was a general fluctuating increasing trend observed in the annual mean NEP, exhibiting an overall average growth rate of 4.69 gC·m−2·a−1. The analysis revealed that the majority of the vegetation region in China, accounting for 93.45% of the entirety, exhibited increasing trends in NEP. According to the Geodetector analysis, precipitation change rate, solar radiation change rate, and altitude were the key driving factors in NEP change rate. Furthermore, the interaction between the precipitation change rate and altitude demonstrated the most significant effect. [ABSTRACT FROM AUTHOR]

The accuracy of vegetation indices (VIs) in estimating forest stand age is significantly inadequate due to insufficient consideration of the differences in the physiological functions of forest ecosystems, which limits the accuracy of carbon sink simulation. In this study, remote sensing inversion and mapping of forest stand age were carried out on the Loess Plateau under consideration of the remote sensing mechanism of VIs and the physiological function and canopy structure of the forest using multiple linear regression (MLR) and random forest (RF) models. The main conclusions are as follows: (1) The canopy reflectance of different forest stands has a significant change pattern, and the older the forest stands, the lower the NIR reflectance. The relationship between forest stands and red edge is the most significant, and r is 0.53, and the relationship between Simple Ratio Index (SR), near-infrared reflectance of vegetation (NIRv), normalized difference vegetation index (NDVI), Global Vegetation Index and forest stands is more nonlinear than linear. (2) Principal component analysis (PCA) of canopy spectral information shows that SR, NDVI and red edge (B5) could explain 98% of all spectral information. SR, NDVI and red edge (B5) were used to construct a multiple linear regression model and random forest (RF) algorithm model, and RF has high estimation accuracy (R2 = 0.63). (3) The accuracy of the model was evaluated using reference data, and it was found that the accuracy of the RF model (R2 = 0.63) was higher than that of the linear regression model (R2 = 0.61), but both models underestimated the forest stand age when the forest stand age was greater than 50a, which may be caused by the saturation of the reflectance of the old forest canopy. The RF model was used to generate the dataset of forest stand information in the Loess Plateau, and it was found that the forest is dominated by young forests (<20a), accounting for 38.26% of the forest area, and the average age of forests in the Loess Plateau is 56.1a. This study not only improves the method of forest stand age estimation, but also provides data support for vegetation construction in the Loess Plateau. [ABSTRACT FROM AUTHOR]