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The rate of surface warming in cities exceeds that in rural areas, but urban greening can partly offset enhanced urban warming that is driven by climate change and urban expansion, according to an analysis of satellite land surface temperature data in 2000 city clusters between 2002 and 2021.

Knowledge of the day-to-day dynamics of surface urban heat island (SUHI) as well as their underlying determinants is crucial to a better design of effective heat mitigation. However, there remains a lack of a globally comprehensive investigation of the responsiveness of SUHI variations to meteorological variables. Based on the MODIS land surface temperature and auxiliary data in 2017, here we investigated 10 000+ cities worldwide to reveal day-to-day SUHI intensity (SUHII) variations (termed as SUHII _dv ) in response to meteorological variables using Google Earth Engine. We found that: (a) meteorological variables related to the thermal admittance, e.g. precipitation, specific humidity (SH) and soil moisture (SM) (represented by daily temperature range in rural area, DTR _r ), reveal a larger regulation on SUHII _dv than those related to the air conditions (e.g. wind speed and near-surface air temperature) over a global scale. (b) Meteorological regulations on SUHII _dv can differ greatly by background climates. The control of SH on SUHII _dv is significantly strengthened in arid zones, while that of wind speed is weakened prominently in equatorial zones. SUHII _dv is more sensitive to SM in cities with higher background temperatures. (c) All meteorological variables, except that related to SM (DTR _r ), show larger impact on SUHII _dv with antecedent precipitation over the global scale. Precipitation is observed to mitigate the SUHII _dv globally, and such effects are even more pronounced in equatorial and arid zones. We consider that our findings should be helpful in enriching the knowledge of SUHI dynamics on multiple timescales.

Northwestern China (NWC) is among the major global hotspots undergoing massive terrestrial water storage (TWS) depletion. Yet driver(s) underlying such region-wide depletion remain controversial, i.e. warming-induced glaciermelting versus anthropogenic activities. Reconciling this controversy is the core initial step to guide policymaking to combat the dual challenges in agriculture production and water scarcity in the vastly dry NWC toward sustainable development. Utilizing diverse observations, we found persistent cropland expansion by >1.2 × 10 ^4 km ^2 since 2003, leading to growth of 59.9% in irrigated area and 19.5% in agricultural water use, despite a steady enhancement in irrigation efficiency. Correspondingly, a substantially faster evapotranspiration (ET) increase occurred in crop expansion areas, whereas precipitation exhibited no long-term trend. Counterfactual analyses suggest that the region-wide TWS depletion is unlikely to have occurred without an increase in crop expansion-driven ET even in the presence of glaciermelting. These findings imply that sustainable water management is critically needed to ensure agriculture and water security in NWC.

Satellite thermal remote sensing provides land surface temperatures (LST) over extensive areas that are vital in various applications, but this technique suffers from its sampling style and the impenetrability of clouds, which frequently generates data gaps. Annual temperature cycle (ATC) models can fill these gaps and estimate continuous daily LST dynamics from a number of thermal observations. However, the standard ATC model (termed ATCS) remains incapable of quantifying the short-term LST variations caused by synoptic conditions. By incorporating in-situ surface air temperatures (SATs) and satellite-derived normalized difference vegetation indexes (NDVIs), here we proposed an enhanced ATC model (ATCE) to describe the daily LST fluctuations. With Aqua/MODIS LST products as validation data, we implemented and tested the ATCE over the Yangtze River Delta region of China. The results demonstrate that, when compared with the ATCS, the overall root mean square errors of the ATCE decrease by 1.0 and 0.8 K for the day and night, respectively. The accuracy improvements vary with land cover types with greater improvements over the forest, grassland, and built-up areas than over cropland and wetland. The assessments at different time scales further confirm that LST fluctuations can be better described by the ATCE. Though with limitations, we consider this new model and its associated parameters hold great potentials in various applications.

Temporally regular and spatially continuous monitoring of surface urban heat island (SUHI) has been extremely difficult until the advent of spaceborne land surface temperature (LST) products. The higher errors of these LST products compared with in-situ measurements, nevertheless, have resulted in a comparatively inaccuracy and may distort the interpretation of SUHI. Although reports have shown that LST quality matters to the SUHI interpretation, a systematic investigation on how the SUHI indicators are responsive to the LST quality across cities within dissimilar bioclimates remains rare. With regard to this issue, our study chose eighty-six major cities across the mainland China and analyzed the SUHI intensity (SUHII) discrepancies (referred to as ΔSUHII) between using and not using quality control (QC) flags from Moderate Resolution Imaging Spectroradiometer data. Our major findings include: (1) the SUHII can be significantly impacted by the MODIS QC flags, and the associated seasonal ΔSUHIIs generally account for 25.5% (29.6%) of the total intensity in the day (night). (2) The ΔSUHIIs differ season-by-season and significant discrepancies also appear among northern and southern cities, with northern ones often possessing a higher annual mean ΔSUHII. (3) The internal ΔSUHIIs within an individual city are also heterogeneous, with the variations exceeding 5.0 K (3.0 K) in northern (southern) cities. (4) The ΔSUHII is significantly negatively related to the SUHII and cloud cover percentage mostly in transitional seasons. Our findings highlight that one needs to be very careful when using the LST-product-based SUHII to interpret the SUHI. [ABSTRACT FROM AUTHOR]