The purpose of this study is to comprehensively quantify the spatial-temporal patterns of surface urban heat island (SUHI) by investigating the relationship between Land surface temperature (LST) and the land-cover types and associated landscape components in the case of Beijing, China. The spatial and temporal adaptive reflectance fusion model (STARFM) developed by Gao et al. was employed to create the high spatial resolution LST time series, using LST data from the MODIS/Terra and LANDSAT 8 over the period from May to November in 2013. This paper also investigated the application of the Chinese new high spatial resolution satellite GaoFen-1 in urban thermal environments studies which were insufficiently studied previously. The impacts of four landscape metrics (LSMs) on urban LST were investigated on the basis of two scenes of GaoFen images acquired in 2013 summer (June 19 and August 10). Results showed that SUHI effect was prevalent in Beijing from May to October. The intensity of SUHI magnitude was found accentuated mainly in the summer months (July and August), indicating that the trend of surface UHI effect is inconsistent with that of canopy-layer UHI effect. No obvious linear relationships were observed between subplot LST and impervious surfaces LSMs. However, four impervious surfaces LSMs were correlated well with the temporal dynamics of LST. We also found the configurational patterns of green space could put substantial influences as strong as corresponding compositional patterns and the lower vegetation coverage in downtown could better account for the urban LST.

The urban heat island (UHI) effect resulting from rapid urbanization generally has a negative impact on urban residents. Shijiazhuang, the capital of Hebei Province in China, was selected to assess surface thermal patterns and its correlation with Land Cover Types (LCTs). This study was conducted using Landsat TM images on the mesoscale level and airborne hyperspectral thermal images on the microscale level. Land surface temperature (LST) was retrieved from four scenes of Landsat TM data in the summer days to analyze the thermal spatial patterns and intensity of surface UHI (SUHI). Surface thermal characteristics were further examined by relating LST to percentage of imperious surface area (ISA%) and four remote sensing indices (RSIs), the Normalized Difference Vegetation Index (NDVI), Universal Pattern Decomposition method (VIUPD), Normalized Difference Built-up Index (NDBI) and Biophysical Composition Index (BCI). On the other hand, fives scenes of airborne TASI (Thermal Airborne Spectrographic Imager sensor) images were utilized to describe more detailed urban thermal characteristics of the downtown of Shijiazhuang city. Our results show that an obvious surface heat island effect existed in the study area during summer days, with a SUHI intensity of 2–4 °C. The analyses reveal that ISA% can provide an additional metric for the study of SUHI, yet its association with LST is not straightforward and this should a focus in future work. It was also found that two physically based indices, VIUPD and BCI, have the potential to account for the variation in urban LST. The results concerning on TASI indicate that diversity of impervious surfaces (rooftops, concrete, and mixed asphalt) contribute most to the SUHI, among all of the land cover features. Moreover, the effect of impervious surfaces on LST is complicated, and the composition and arrangement of land cover features may play an important role in determining the magnitude and intensity of SUHI. Overall, the analysis of urban thermal signatures at two spatial scales complement each other and the use of airborne imagery data with higher spatial resolution is helpful in revealing more details for understanding urban thermal environments.

The response of terrestrial water storages to dryland vegetation growth remains poorly understood. Using multiple proxies from satellite observations and model outputs, we show an overall increase (decrease) in vegetation growth (terrestrial water storages) across drylands globally during 1982–2016. Terrestrial water storages in greening drylands correlate negatively with vegetation growth, particularly for cropland-dominated regions, and such response is pronounced when the growth rate of vegetation productivity is high. Reduction in terrestrial water storage is dominated by precipitation and evapotranspiration variability rather by than runoff. We predict reduction in terrestrial water storage of 41–84% by 2100, accompanying expansion of drylands by 4.1–10.6%. Our findings, which indicate sustained adverse response of terrestrial water storage to vegetation growth in drylands, highlight the need for concerted planning for balanced ecological restoration, agricultural management, and water resource utilization that will affect 5.17 billion people, 64% of whom live in developing countries.

We demonstrate an indirect, rather than direct, role of quasi-resonant amplification of planetary waves in a summer weather extreme. We find that there was an interplay between a persistent, amplified large-scale atmospheric circulation state and soil moisture feedbacks as a precursor for the June 2021 Pacific Northwest “Heat Dome” event. An extended resonant planetary wave configuration prior to the event created an antecedent soil moisture deficit that amplified lower atmospheric warming through strong nonlinear soil moisture feedbacks, favoring this unprecedented heat event.

Spatiotemporally continuous soil moisture (SM) data are increasingly in demand for ecological and hydrological research. Satellite remote sensing has potential for mapping SM, but the continuity of satellite-derived SM is hampered by data gaps resulting from inadequate satellite coverage, snow cover, frozen soil, radio-frequency interference, and so on. Therefore, we propose a new gap-filling approach to reconstruct daily SM time series using the European Space Agency Climate Change Initiative (ESA CCI). The developed approach integrates satellite observations, model-driven knowledge, and a machine learning algorithm that leverages both spatial and temporal domains. Taking SM in China as an example, the reconstructed SM showed high accuracy when validated against multiple sets of in situ measurements, with a root mean square error (RMSE) and a mean absolute error (MAE) of 0.09–0.14 and 0.07–0.13 cm³ cm⁻³, respectively. Further evaluation with a 10-fold cross-validation revealed median values of the coefficient of determination (R²), RMSE, and MAE of 0.56, 0.025, and 0.019 cm³ cm⁻³, respectively. The reconstructive performance was noticeably reduced both when excluding one explanatory variable and keeping the other variables unchanged and when removing the spatiotemporal domain strategy or the residual calibration procedure. In comparison with gap-filled SM data based on a satellite-derived diurnal temperature range (DTR), the gap-filled SM data from bias-corrected model-derived DTRs exhibited relatively lower accuracy but higher spatial coverage. Application of our gap-filling approach to long-term SM datasets (2005–2015) produced a promising result (R²=0.72). A more accurate trend was achieved relative to that of the original CCI SM when assessed with in situ measurements (i.e., 0.49 versus 0.28, respectively, in terms of R²). Our findings indicate the feasibility of integrating satellite observations, model-driven knowledge, and spatiotemporal machine learning to fill gaps in short- and long-term SM time series, thereby providing a potential avenue for applications to similar studies.