Snow cover products are primarily derived from the Moderate-resolution Imaging Spectrometer (MODIS) and Advanced Very-High-Resolution Radiometer (AVHRR) datasets. MODIS achieves both snow/non-snow discrimination and snow cover fractional retrieval, while early AVHRR-based snow cover products only focused on snow/non-snow discrimination. The AVHRR Climate Data Record (AVHRR-CDR) provides a nearly 40-year global dataset that has the potential to fill the gap in long-term snow cover fractional monitoring. Our study selects the Qinghai–Tibet Plateau as the experimental area, utilizing AVHRR-CDR surface reflectance data (AVH09C1) and calibrating with the MODIS snow product MOD10A1. The snow cover percentage retrieval from the AVHRR dataset is performed using Surface Reflectance at 0.64 μm (SR1) and Surface Reflectance at 0.86 μm (SR2), along with a simulated Normalized Difference Snow Index (NDSI) model. Also, in order to detect the effects of land-cover type and topography on snow inversion, we tested the accuracy of the algorithm with and without these influences, respectively (vanilla algorithm and improved algorithm). The accuracy of the AVHRR snow cover percentage data product is evaluated using MOD10A1, ground snow-depth measurements and ERA5. The results indicate that the logic model based on NDSI has the best fitting effect, with R-square and RMSE values of 0.83 and 0.10, respectively. Meanwhile, the accuracy was improved after taking into account the effects of land-cover type and topography. The model is validated using MOD10A1 snow-covered areas, showing snow cover area differences of less than 4% across 6 temporal phases. The improved algorithm results in better consistency with MOD10A1 than with the vanilla algorithm. Moreover, the RMSE reaches greater levels when the elevation is below 2000 m or above 6000 m and is lower when the slope is between 16° and 20°. Using ground snow-depth measurements as ground truth, the multi-year recall rates are mostly above 0.7, with an average recall rate of 0.81. The results also show a high degree of consistency with ERA5. The validation results demonstrate that the AVHRR snow cover percentage remote sensing product proposed in this study exhibits high accuracy in the Tibetan Plateau region, also demonstrating that land-cover type and topographic factors are important to the algorithm. Our study lays the foundation for a global snow cover percentage product based on AVHRR-CDR and furthermore lays a basic work for generating a long-term AVHRR-MODIS fractional snow cover dataset.

The Central Place Theory (CPT) proposed the basic concepts of central places and their service areas. Urban parks provide a wide variety of ecosystem services to residents. Most studies on central places focus on urban commercial facilities; however, it remains unclear whether parks exhibit patterns of central places, what features their service areas, and hierarchical structures. Based on mobile signaling data, we identified the service areas, dominant influence areas, and hierarchical structures of Beijing and Changsha. We also analyzed the factors influencing the hierarchical structure of park services, as well as the number of visitors and dominant service areas at each level of parks. We found that visits by residents to parks in Beijing and Changsha exhibit a clear hierarchical structure. Parks that occupy the top level attract a large number of residents and demonstrate strong service capacity and extensive coverage. We also found that park area and infrastructure attributes are significantly correlated with the hierarchical outcomes in Changsha but entirely different results in Beijing. Box plot analysis of visitor numbers and service areas at each level reveals that the influencing factors for these two aspects differ. Overall, both cities’ parks exhibit centrality and hierarchical structures in providing services to residents; however, there is a considerable difference in the factors influencing visitor numbers and dominant service areas for the two cities. These conclusions provide important theoretical support for government officials to better understand the characteristics of park services and offer practical guidance for optimizing urban park planning, enhancing service efficiency, and formulating policies that promote equitable access to green spaces.

Context The value realization of the ecosystem services (ES) with directional ecosystem service flows (ESF) is sensitive to the way ES supply and demand are spatially coupled, of which the nature of the propagation dynamics of ESFs and the mechanisms of effective demand generation remain to be clarified.Objectives The paper focuses on a typical directionally flowed urban ES, the park-recreational service, and proposes a formal model for analyzing its ES value realization mechanism, which discerns the ES value contribution of distance-decaying ES supply from the that of the demand-side factors. A cellular big-data supported empirical study conducted in Ningbo, China is conducted as a case illustrating the application of the model.Methods Statistical physical analysis on people’s park visitation behavior is performed to uncover the distance decay patterns of recreational ESF propagation. Further, with the conceptualization of the ES-consuming propensity, which captures the demand-side factors influencing effective ES demand generation including population demographics, socio-economic status, and urban place semantics, we construct spatial regression models that explain these factors’ contributions in ES value realization.Results The statistical physical analysis reveals both universality and park type-specific idiosyncrasies in the ESF distance decay patterns, corroborating the findings in previous studies. The spatial regression models yield up to 80% explanatory power, and results show that the ES-consuming propensity accounts for one quarter of the variation of realized ES value, with endogenous factors dominating effective ES demand generation while exogenous factors less influential.

Snow cover products are primarily derived from the Moderate-resolution Imaging Spectrometer (MODIS) and Advanced Very High-Resolution Radiometer (AVHRR) datasets. MODIS achieves both snow/non-snow discrimination and snow cover fractional retrieval, while early AVHRR-based snow cover products only focused on snow/non-snow discrimination. The AVHRR Climate Data Record (AVHRR -CDR) provides a nearly 40-year global dataset that has a potential to fill a gap in long term snow cover fractional monitoring. Our study selects the Qinghai-Tibet Plateau as the experimental area, utilizing AVHRR-CDR surface reflectance data (AVH09C1) and calibrating with MODIS snow product MOD10A1. The snow cover percentage retrieval from the AVHRR dataset is performed using Surface reflectance at 0.64μm (SR1) and Surface reflectance at 0.86μm (SR2), along with a simulated Normalized Difference Snow Index (NDSI) model. Also, in order to detect the effects of land cover type and topography on snow inversion, we tested the accuracy of the algorithm with and without these influences, respectively (vanilla algorithm & improved algorithm). The accuracy of the AVHRR snow cover percentage data product is evaluated using MOD10A1、 ground snow-depth measurements and ERA5. Results indicate that the logic model based on NDSI has the best fitting effect, with R-square and RMSE values of 0.83 and 0.10, respectively. Meanwhile, the accuracy was improved after taking into account the effects of land cover type and topography. The model is validated using MOD10A1 snow-covered areas, showing snow cover area differences of less than 4% across 6 temporal phases. Improved algorithm results in better consistency with MOD10A1 than vanilla algorithm. Moreover, the RMSE reaches greater levels when the elevation is below 2,000 meters or above 6,000 meters, and is lower when the slope is between 16° and 20°. Using ground snow-depth measurements as ground truth, the multi-year recall rates are mostly above 0.7, with an average recall rate of 0.81. Results also show a high degree of consistency with ERA5. The validation results demonstrate that the AVHRR snow cover percentage remote sensing product proposed in this study exhibits high accuracy in the Tibetan Plateau region, also demonstrating that land cover type and topographic factors are important to the algorithm. Our study lays the foundation for the global snow cover percentage product based on AVHRR-CDR，furthermore lays a basic work for generating a long-term AVHRR-MODIS fractional snow cover dataset.