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.

With the rapid progress of urbanization, the loss of cultivated land has attracted great attention worldwide, and economic compensation is one of the incentives commonly used by the governments to enhance farmers’ enthusiasm in protecting cultivated land. In recent years, although various economic compensation modes have been implemented by the Chinese government, such modes are still experimental and exploratory. Thus, designing and implementing a national economic compensation mode is urgent to effectively protect the quantity and quality of cultivated land. This study focuses on the mode of cultivated land protection fund (CLPF) in Chengdu, which is the earliest mode of the implementation of economic compensation in China in 2008. First, we analyzed the farmers’ satisfaction with the CLPF through a face-to-face interview with 296 farmers in Chengdu. Then, we used the path analysis method to identify the influencing factors of farmers’ satisfaction from the characteristics of farmers and the policy. Results show that the CLPF was generally supported by farmers. Nevertheless, room for improvement still exists. Particularly, farmers’ satisfaction was low in the design of the government’s supervision management of the CLPF. Farmers’ satisfaction with compensation standard, funding use requirement, and the government’s supervision management were remarkably affected by factors, including farmers’ educational level, cultivated land area, total annual agricultural income, farmers’ knowledge of the CLPF, farmers’ recognition of the value of the CLPF, and farmers’ perception of the changes in household economics. Particularly, the direct influence of farmers’ perception of the changes in household economics was the most important. Finally, we proposed the recommendations for constructing a national economic compensation mode for cultivated land protection. Our results have certain guiding significance for promoting the sustainable development of cultivated land protection policies by means of economic incentives in China and other countries.

The temporal and spatial evolution of landscape pattern is the most intuitive form of land use transition. Analyzing the change of landscape pattern and its driving factors is of great significance to land use management and water quality protection in the basin. Based on the land use data obtained from the remote sensing image interpretation of the Yellow River Basin (Henan section) in 1990, 2000, 2010, and 2020, the landscape pattern evolution characteristics of the Yellow River Basin (Henan section) were quantitatively studied using the methods of multi-angle land use transfer matrix, land use information atlas, and landscape pattern index, and the influencing factors of landscape pattern evolution of the Yellow River Basin (Henan section) were revealed using the geographic detectors (a new statistical method to measure the explanatory power of independent variables to dependent variables mainly by analyzing the overall differences among various types of geographical spaces). The results show that: (1) From 1990 to 2020, the mutual transformation of land use types in the Yellow River Basin (Henan section) was frequent, and the transformation tracks were diversified. Among them, the outflow behavior of land use types is mainly manifested in the transformation from cultivated land to construction land, and the inflow behavior of land use types is mainly manifested in the transformation from grassland and water to cultivated land. (2) In the information map of land use change in the Yellow River Basin (Henan section) from 1990 to 2020, the stable type had the widest distribution range, accounting for 94.60% of the total area of the study area, with two main change patterns: “cultivated land-cultivated land-cultivated land-cultivated land” and “woodland-woodland-woodland-woodland”, which indicates that the landscape pattern of the basin dominated by cultivated land and woodland has not changed fundamentally. The four land use change structure types, repeated change, early change, intermediate change and continuous change, account for a relatively small proportion and are concentrated in the vicinity of the Yellow River. (3) At the landscape level, the watershed generally shows the trend of decreasing landscape fragmentation, increasing landscape heterogeneity and constantly balancing landscape patch types. At the level of patch type, the landscape dominance of cultivated land decreases, while that of construction land increases. The occupation of construction land is the main reason for the fragmentation and homogenization of cultivated land. (4) From the perspective of landscape scale and patch type scale, through the geographical exploration of various natural factors and socio-economic factors that potentially affect the landscape pattern evolution, it is found that the spatial differences of natural factors such as slope, elevation, temperature, and precipitation can better reflect the spatial heterogeneity of the landscape pattern in the Yellow River Basin (Henan section) than those of socio-economic factors such as GDP and population density, and the interaction of any two driving factors has a greater influence on the spatial distribution characteristics of landscape pattern than any single factor, indicating that the formation of spatial heterogeneity in the Yellow River Basin (Henan section) is the result of the interaction of various influencing factors. The results of this study can provide ideas for exploring the trend and influencing mechanism of landscape pattern change in the basin, and have important reference significance for ecological environment management, ecosystem protection, and land use planning in the Yellow River Basin (Henan section).

Super-pixel algorithms based on convolutional neural networks with fuzzy C-means clustering are widely used for high-spatial-resolution remote sensing images segmentation. However, this model requires the number of clusters to be set manually, resulting in a low automation degree due to the complexity of the iterative clustering process. To address this problem, a segmentation method based on a self-learning super-pixel network (SLSP-Net) and modified automatic fuzzy clustering (MAFC) is proposed. SLSP-Net performs feature extraction, non-iterative clustering, and gradient reconstruction. A lightweight feature embedder is adopted for feature extraction, thus expanding the receiving range and generating multi-scale features. Automatic matching is used for non-iterative clustering, and the overfitting of the network model is overcome by adaptively adjusting the gradient weight parameters, providing a better irregular super-pixel neighborhood structure. An optimized density peak algorithm is adopted for MAFC. Based on the obtained super-pixel image, this maximizes the robust decision-making interval, which enhances the automation of regional clustering. Finally, prior entropy fuzzy C-means clustering is applied to optimize the robust decision-making and obtain the final segmentation result. Experimental results show that the proposed model offers reduced experimental complexity and achieves good performance, realizing not only automatic image segmentation, but also good segmentation results.

When segmenting high-spatial-resolution remote sensing images using convolution neural networks, it is difficult to balance the number of segmented regions against the contour accuracy. This article proposes a segmentation method that combines the minimum area adaptive watershed transform based on morphological reconstruction with the modified deep edge detection network (MDEDNet). First, the minimum area adaptive watershed algorithm is adopted to reconstruct the gradient features of the image and perform watershed segmentation, giving the initial segmentation results. A ground object contour optimization method and MDEDNet are then used to further improve the accuracy of the segmentation, resulting in the final segmented image. Experiments on image sets of varying complexity indicate that the algorithm proposed in this article can achieve accurate image segmentation with good segmentation results and fast operation efficiency.