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In terrestrial ecosystems, vegetation is sensitive to climate change and human activities. Its spatial-temporal changes also affect the ecological and social environment. In this paper, we considered the Beijing–Tianjin–Hebei region to study the spatio-temporal vegetation patterns. The detailed analysis of a moderate-resolution imaging spectroradiometer (MODIS) data were carried out through the Google Earth Engine (GEE) platform. Our results show a slow and tortuous upward trend in the average leaf area index (LAI) in the study region for the periods 2001–2020. Specifically, Beijing had the highest LAI value, with an average of 1.64 over twenty years, followed by Hebei (1.30) and Tianjin (1.04). Among different vegetation types, forests had the highest normalized difference vegetation index (NDVI) with the range of 0.62–0.78, followed by shrubland (0.58–0.75), grassland (0.34–0.66), and cropland (0.38–0.54) over the years. Spatially, compared to the whole study area, index value in the northwestern part of the Beijing–Tianjin–Hebei region increased greatly in many areas, such as northwest Beijing, Chengde, and Zhangjiakou, indicating a significant ecological optimization. Meanwhile, there was ecological degradation in the middle and southeast regions, from Tangshan southeastward to Handan, crossing Tianjin, Langfang, the east part of Baoding, Shijiazhuang, and the west part of Cangzhou. Air temperature and precipitation were positively and significantly correlated with net primary production (NPP) and precipitation stood out as a key driver. Additionally, an intensification of the urbanization rate will negatively impact the vegetation NPP, with the shrubland and forest being affected most relative to the cropland. [ABSTRACT FROM AUTHOR]

High-resolution leaf area index (LAI) maps from remote sensing data largely depend on empirical models, which link field LAI measurements to the vegetation index. The existing empirical methods often require the field measurements to be sufficient for constructing a reliable model. However, in many regions of the world, there are limited field measurements available. This paper presents a prior knowledge-based (PKB) method to derivate LAI with limited field measurements, in an effort to improve the accuracy of empirical model. Based on the assumption that the experimental sites with the same vegetation type can be represented by similar models, a priori knowledge for crops was extracted from the published models in various cropland sites. The knowledge, composed of an initial guess of each model parameter with the associated uncertainty, was then combined with the local field measurements to determine a semi-empirical model using the Bayesian inversion method. The proposed method was evaluated at a cropland site in the Huailai region of Hebei Province, China. Compared with the regression method, the proposed PKB method can effectively improve the accuracy of empirical model and LAI estimation, when the field measurements were limited. The results demonstrate that a priori knowledge extracted from the universal sites can provide important auxiliary information to improve the representativeness of the empirical model in a given study area. [ABSTRACT FROM AUTHOR]