Land cover maps with high accuracy are essential for environmental protection and climate change research. The 30-meter-resolution maps, with their better resolution and longer historical records, are extensively utilized to assess changes in land cover and their effects on carbon storage, land–atmosphere energy balance, and water cycle processes. However, current data products use different classification methods, resulting in significant classification inconsistency and triggering serious disagreements among related studies. Here, we compared four mainstream land cover products in China, namely GLC_FCS30, CLCD, Globeland30, and CNLUCC. The result shows that only 50.34% of the classification results were consistent across the four datasets. The differences between pairs of datasets ranged from 21.10% to 37.53%. Importantly, most inconsistency occurs in transitional zones among land cover types sensitive to climate change and human activities. Based on the accuracy evaluation, CLCD is the most accurate land cover product, with an overall accuracy reaching 86.98 ± 0.76%, followed by CNLUCC (81.38 ± 0.87%) and GLC_FCS30 (77.83 ± 0.80%). Globeland30 had the lowest accuracy (75.24 ± 0.91%), primarily due to misclassification between croplands and forests. Misclassification diagnoses revealed that vegetation-related spectral confusion among land cover types contributed significantly to misclassifications, followed by slope, cloud cover, and landscape fragmentation, which affected satellite observation angles, data availability, and mixed pixels. Automated classification methods using the random forest algorithm can perform better than those that depend on traditional human–machine interactive interpretation or object-based approaches. However, their classification accuracy depends more on selecting training samples and feature variables.

Biodiversity, vital for ecosystem stability and human well‐being, faces threats from land use and climate change. Accurately predicting these effects is crucial for effective conservation. High emission development scenario is commonly viewed as the most detrimental to biodiversity. However, recent researches suggest a more complex relationship between development paths and biodiversity outcomes. Our study addresses this by using an emergy‐based approach to estimate current provincial‐level biodiversity potential and project future species richness losses (amphibians, mammals, and birds) across climate zones and provincial divisions under various SSP‐RCP scenarios for 2030, 2050, 2070, and 2090. The results revealed significant regional variations in China's biodiversity potential, with the highest in southwestern provinces. Future land use trends indicate increased construction land and barren alongside a decline in grasslands, leading to considerable habitat loss and fragmentation under various scenarios, stressing conservation needs. Future biodiversity loss follows the Hu line and climate zones, with significant decreases in the south and humid regions. Land‐use changes could reduce species richness by 1–6 per 10 km grid cell. High‐emission scenario SSP585 do not necessarily have the most detrimental effects on biodiversity and different scenarios require targeted focus on specific climatic zones and provinces. These findings underscore that different scenarios require targeted conservation efforts in specific regions sensitive to biodiversity loss. Our study provides a scientific foundation for these targeted efforts, ensuring that regions are prioritized under various future scenarios. This approach aids in developing effective conservation strategies amidst the complex interplay of land use dynamics and climate change.

Hydrological connectivity is an essential indicator of wetland pattern and functional stability. The reduction of connectivity usually means the degradation of wetland ecological function, internal energy flow, and nutrient cycle disturbance. Taking Hanshiqiao wetland as a case, we used Morphological Spatial Pattern Analysis (MSPA) with the Connectivity Index (IIC, Integral Index of Connectivity; PC, Probability of Connectivity) to analyze the change in hydrological connectivity of Hanshiqiao Wetland from both spatial and temporal aspects. The results showed that the hydrological connectivity of Hanshiqiao Wetland significantly improved with the implementation of the wetland restoration project. According to the changes in MSPA function types, the spatial morphological evolution of Hanshiqiao Wetland can be divided into two stages: the recovery stage and the stable stage. In the restoration stage, the area of the core wetland gradually increased, and many croplands and islet wetlands were transformed into the core wetland. The area of the core wetland recovered from 33 hm² in 2005 to 119 hm² in 2020. However, during the stable period, the landscape pattern of Hanshiqiao Wetland did not change significantly, and the hydrological connectivity of the wetland was mainly affected by water resource supply. In general, during the restoration period of Hanshiqiao Wetland, the changes in core wetlands played a leading role in the hydrological connectivity of Hanshiqiao Wetland. In the stable period, the main factors affecting the hydrological connectivity of Hanshiqiao Wetland are upstream recharge water, land-use change, and climate change. However, with climate warming and population surge, upstream water supply gradually decreases, and Hanshiqiao Wetland faces an increasingly severe water resource crisis. Therefore, to maintain the hydrological connectivity of Hanshiqiao Wetland, it is necessary to increase the artificial ecological water supply. The combined MSPA model and grey relational analysis method can better reveal the evolution characteristics and driving mechanism of wetland hydrological connectivity, which can provide a methodological reference for other wetland-related research.

Evaluations of the impacts of increasing urbanization ratios on air temperatures are less quantitative and accurate due to the lack of high‐resolution land cover data sets. Here, based on the latest released high‐resolution (30‐m) land cover data from 1985 to 2017, annual urbanization ratios around each station in Beijing were calculated. Warming rates and temperature extremes were compared between nonrural stations (17 stations with continuously increasing urbanization ratios) and purely rural stations (3 stations with continuous urbanization ratios of zero). Although urbanized areas only occupied 22.2% of the total area in Beijing, urbanization ratios at nonrural stations already reached an average of 62.4% (10 km²)⁻¹in 2017, and their average rate reached 15.1% (10 km²)⁻¹ decade⁻¹. From 1985 to 2017, each 10% increase in the urbanization ratio with 10 km²had led to additional annual warming of 0.14 ± 0.11, 0.17 ± 0.08, and 0.30 ± 0.17°C at nonrural stations for daily maximum (T_max), mean (T_mean), and minimum (T_min) air temperatures, respectively. Meanwhile, each 10% increase in the urbanization ratio with 10 km²had led to additional changes of 11.6 ± 5.1, 12.7 ± 5.8, and −11.1 ± 5.2 days at nonrural stations for hot days, hot nights, and chilly nights, respectively. Additionally, the inner‐annual urbanization contributions to the warming increased significantly at rates of 10.7% decade⁻¹and 14.1% decade⁻¹forT_meanandT_min, and reached 49.0% and 52.6% in 2017, respectively. The inner‐annual urbanization contributions to the increasing hot nights and the decreasing chilly nights changed at rates of 16.7% decade⁻¹and 15.6% decade⁻¹, and reached 57.8% and 53.1% in 2017, respectively.