The Soil andWater Assessment Tool (SWAT) model combined with a temperature index and elevation band algorithm was applied to the Hunza watershed, where snow and glacier-melt are the major contributor to river flow. This study’s uniqueness is its use of a snow melt algorithm (temperature index with elevation bands) combined with the SWAT, applied to evaluate the performance of the SWAT model in the highly snow and glacier covered watershed of the Upper Indus Basin in response to climate change on future streamflow volume at the outlet of the Hunza watershed, and its contribution to the Indus River System in both space and time, despite its limitation; it is not designed to cover the watershed of heterogeneous mountains. The model was calibrated for the years 1998–2004 and validated for the years 2008–2010. The model performance is evaluated using the four recommended statistical coefficients with uncertainty analysis (p-factor and r-factor). Simulations generated good calibration and validation results for the daily flow gauge. The model efficiency was evaluated, and a strong relationship was observed between the simulated and observed flows. The model results give a coefficient of determination (R2) of 0.82 and a Nash–Sutcliffe Efficiency index (NS) of 0.80 for the daily flow with values of p-factor (79%) and r-factor (76%). The SWAT model was also used to evaluate climate change impact on hydrological regimes, the target watershed with three GCMs (General Circulation Model) of the IPCC fifth report for 2030–2059 and 2070–2099, using 1980–2010 as the control period. Overall, temperature (1.39 C to 6.58 C) and precipitation (31%) indicated increased variability at the end of the century with increasing river flow (5%–10%); in particular, the analysis showed smaller absolute changes in the hydrology of the study area towards the end of the century. The results revealed that the calibrated model was more sensitive towards temperature and precipitation, snow-melt parameters and Curve Number (CN2). The SWAT results also provided reliable information for the daily runoff from the sub-basin watersheds responding to changing climatic conditions. SWAT can thus be used to devise effective strategies for future sustainable water management in the region, while combating vulnerabilities against floods and water storage in downstream water reservoirs such as the Diamer-Basha dam.

Land cover information is vital for research and applications concerning natural resources and environmental modeling. Accuracy assessment is an important dimension in use and production of land cover information. GlobeLand30 is a relatively new global land cover information product with a fine spatial resolution of 30 m and is potentially useful for many applications. This paper describes the methods for and results from the first country-wide and statistically based accuracy assessment of GlobeLand30 2010 land cover dataset over China. For this, a total of 8400 validation sample pixels were collected based on a sampling design featuring two levels of stratification (ten geographical regions, each with nine or eight land-cover classes). Validation sample data with reference class labels were acquired from visual interpretation based on Google Earth high-resolution satellite images. Error matrices for individual regions and entire China were estimated properly based on the sampling design adopted, with the former aggregated to get the latter through suitable weighting. Results were obtained, with agreement at a sample pixel defined both as a match between the map (class) label and either the primary or alternate reference label therein and, more strictly, as a match between the map label and the primary reference label only. Based on the former definition of agreement, the overall accuracy of GlobeLand30 2010 land cover for China was assessed to be 84.2%. User’s accuracy and producer’s accuracy were both greater than 80% for cultivated land, forest, permanent snow and ice, and bareland, with user’s accuracy for water bodies estimated 94.2% (82.1% for wetland, 79.8% for artificial surface) and producer’s accuracy for grassland estimated 89.0%. These indicate that GlobeLand30 2010 depicts land cover circa 2010 in China quite accurately, although estimates of accuracy indicators based on the latter definition of agreement were lower as expected with an estimated national overall accuracy of 81.0%. Regional and class variations in accuracy were revealed and examined in the light of their associations with land cover distributions and patterns. Implications for use and production of GlobeLand30 land cover information were discussed, so were commonality and lack of it between GlobeLand30 and other fine-resolution land cover products.

The timely and accurate acquisition of winter wheat acreage is crucial for food security. This study investigated the feasibility of extracting the spatial distribution map of winter wheat in Henan Province by using synthetic aperture radar (SAR, Sentinel-1A) and optical (Sentinel-2) images. Firstly, the SAR images were aggregated based on the growth period of winter wheat, and the optical images were aggregated based on the moderate resolution imaging spectroradiometer normalized difference vegetation index (MODIS-NDVI) curve. Then, five spectral features, two polarization features, and four texture features were selected as feature variables. Finally, the Google Earth Engine (GEE) cloud platform was employed to extract winter wheat acreage through the random forest (RF) algorithm. The results show that: (1) aggregated images based on the growth period of winter wheat and sensor characteristics can improve the mapping accuracy and efficiency; (2) the extraction accuracy of using only SAR images was improved with the accumulation of growth period. The extraction accuracy of using the SAR images in the full growth period reached 80.1%; and (3) the identification effect of integrated images was relatively good, which makes up for the shortcomings of SAR and optical images and improves the extraction accuracy of winter wheat.

It is increasingly recognized that classification accuracy should be characterized locally at the level of individual pixels to depict its spatial variability to better inform users and producers of land-cover information than by conventional error-matrix-based methods. Local or per-pixel accuracy is usually estimated through empirical modelling, such as logistic regression, which often proceeds in a class-aggregated or a class-stratified way, with the latter being generally more accurate due to its accommodation for between-class inhomogeneity in accuracy-context relations. As an extension to class-stratified modelling, class-heterogeneity-stratified modelling, in which logistic models are built separately for contextually heterogeneous vs. homogeneous sub-strata in individual strata of map classes, is proposed in this paper for proper handling of within-class inhomogeneity in accuracy-context relations to increase accuracy of estimation. Unlike in existing literature where sampling is usually approached separately, the double-stratification method is also adopted in sampling design so that more sample data are likely allocated to heterogeneous sub-strata (which are more prone to misclassifications than homogeneous ones). This class-heterogeneity-stratified method furnished for sampling and modelling jointly thus constitutes an integrative framework for accuracy estimation and information refinement. As the first step in building up such a framework, this paper investigates the proposed double-stratification method’s performance and sensitivity to sample size regarding local accuracy estimation in comparison with those of existing methods through a case study concerning Globeland30 2010 land cover over Wuhan, China. A detailed review of existing methods for analyses, estimation, and use of local accuracy was provided, helping to put the proposed research in a broader context. Candidate explanatory variables for logistic regression included sample pixels’ map classes, positions, and contextual features that were computed in different-sized moving windows. Relative performances of these methods were evaluated based on an independent reference sample, with all methods found reliable. It was confirmed that the proposed method is in general the most accurate, as observed with varying sample sizes. The proposed method’s competitive performance is thus proved, reinforcing its potential for information refinement. Extensions to and uncertainty aspects of the proposed method were discussed, with further research proposed.

Addressing the imbalance between supply and demand for urban parks necessitates an assessment of their service accessibility and spatial equity. This study integrates multi-source geographic data, uses multiple data sources to generate a population distribution with high spatial resolution, and constructs park service areas with multiple time thresholds based on travel preference surveys. The network analysis method is used to evaluate the supply–demand ratio and spatial equity by using location entropy, Lorenz curves, and the Gini coefficient to identify the optimal location. The results reveal a significant difference in the supply–demand ratio of parks. Within the 5 min time threshold, only 14.68% of the pixels in the park supply area meet the needs of residents, while the proportions for the 15 min and 30 min time service area expands to 71.74% and 86.34%, respectively. The distribution of parks exhibits apparent spatial inequity. Equity is highest for the 15 min service area (Gini coefficient = 0.25), followed by the 30 min area (Gini coefficient = 0.27) and 5 min areas (Gini coefficient = 0.37). Among the 80 streets in the study area, the per capita green space location entropy of 11 streets is zero. A targeted site selection analysis for areas with park supply deficiencies led to the proposed addition of 11 new parks. After this optimization, the proportion of regions achieving supply–demand balance or better reached 80.38%, significantly alleviating the supply–demand conflict. This study reveals the characteristics of park supply–demand imbalance and spatial equity under different travel modes and time thresholds, providing a scientific basis for the precise planning and equity enhancement of parks in high-density cities.