Natural infrastructure is essential in reducing thermal discomfort caused by the urban heat island (UHI) effect. Optimizing and planning green and blue spaces can help establish nature-based urban heat mitigation strategies that benefit sustainable urban development. Most current studies on urban heat mitigation have focused on the single heat reduction effect of green space or blue space, while there has been a lack of research on the combined cooling effects of blue and green spaces. Moreover, existing heat mitigation models and methods cannot directly guide the optimization of blue–green spatial patterns at the urban scale. This has led to an unclear relationship between heat mitigation effects and blue–green spatial patterns. Based on land use data, meteorological data, and biophysical information as inputs, this paper utilized the InVEST urban cooling model (UCM) and scenario analysis method to simulate urban heat mitigation patterns by setting up different blue–green space configuration scenarios. The relative contribution of blue–green space changes to the variation of heat mitigation benefits was quantitatively estimated using the difference comparison method, and the relationship between heat reduction effects and urban blue–green spatial patterns was elucidated using spatial analysis methods. The results show that the InVEST UCM captured some of the variability in the surface thermal response of Wuhan and can be applied to the modeling of urban heat mitigation patterns. Furthermore, they show that consideration of the cooling effect of water evaporation can improve the simulation accuracy to some extent. In Wuhan, there were regional differences in heat mitigation patterns and the heat mitigation effect was significantly higher in the suburbs than in the city. Additionally, urban parks, lakes, and mountains with surface or block distribution had noticeable cooling benefits. Finally, the scenario simulation results demonstrate that green space was more efficient at mitigating heat, while blue space was more critical for the geographical partitioning of the UHI. These findings can provide a reference for the planning and optimal management of urban blue and green spaces, as well as for the design of heat reduction policies.

The terrestrial water storage anomaly (TWSA) observed by the Gravity Recovery and Climate Experiment (GRACE) satellite and its successor GRACE Follow-On (GRACE-FO) provides a new means for monitoring floods. However, due to the coarse temporal resolution of GRACE/GRACE-FO, the understanding of flood occurrence mechanisms and the monitoring of short-term floods are limited. This study utilizes a statistical model to reconstruct daily TWS by combining monthly GRACE observations with daily temperature and precipitation data. The reconstructed daily TWSA is utilized to monitor the catastrophic flood event that occurred in the middle and lower reaches of the Yangtze River basin in 2020. Furthermore, the study compares the reconstructed daily TWSA with the vertical displacements of eight Global Navigation Satellite System (GNSS) stations at grid scale. A modified wetness index (MWI) and a normalized daily flood potential index (NDFPI) are introduced and compared with in situ daily streamflow to assess their potential for flood monitoring and early warning. The results show that terrestrial water storage (TWS) in the study area increases from early June, reaching a peak on 19 July, and then receding till September. The reconstructed TWSA better captures the changes in water storage on a daily scale compared to monthly GRACE data. The MWI and NDFPI based on the reconstructed daily TWSA both exceed the 90th percentile 7 days earlier than the in situ streamflow, demonstrating their potential for daily flood monitoring. Collectively, these findings suggest that the reconstructed TWSA can serve as an effective tool for flood monitoring and early warning.

The satellite gravity mission GRACE(-FO) has not yet reached its designed baseline accuracy. Previous studies demonstrated that the deficiency in the sensor system or the related signal processing might be responsible, which in turn motivates us to keep revising the sensor data processing, typically the spacecraft’s attitude. Many efforts in the past have been made to enhance the attitude modeling for GRACE, for instance, the latest release reprocesses the attitude by fusing the angular acceleration with the star camera/tracker (SC) measurements, which helps to reduce the error in Level-2 temporal gravity fields. Therefore, in addition to GRACE, revising GRACE-FO attitude determination might make sense as well. This study starts with the most original raw GRACE-FO Level-1A data including those from three SCs and one IMU (Inertial Measurement Unit) sensors, and manage to generate a new publicly available Level-1B attitude product called HUGG-01 covering from June 2018 to December 2020, using our independently-developed software. The detailed treatment of individual payload is present in this study, and an indirect Kalman filter method is introduced to fuse the multiple sensors to acquire a relatively stable and precise attitude estimation. Unlike the direct SC combination method with a predefined weight as recommended in previous work, we propose an involvement of each SC measurement in the Kalman filter to enable a dynamic weight adjustment. Intensive experiments are further carried out to assess the HUGG-01, which demonstrate that the error level of HUGG-01 is entirely within the design requirement, i.e., the resulting KBR pointing variations are well controlled within 1 mrad (pitch), 5 mrad (roll) and 1 mrad (yaw). Moreover, comparisons with the official JPL-V04 attitude product demonstrate an equivalent performance in the low-to-middle spectrum, with even a slightly lower noise level (in the high spectrum) than JPL-V04. Further analysis on KBR range-rate residuals and gravity recovery on January 2019 indicates that, i.e., RMS of the difference (HUGG-01 minus JPL-V04) for the range rate is less than $3.234\times {10}^{-8}$ m/s, and the amplitude of geoid height difference is approximately $0.5$ cm. Both differences are below the sensitivity of the state-of-the-art satellite gravity mission, demonstrating a good agreement between HUGG-01 and JPL-V04.

The Han River Basin is a main agricultural production area and a water source for the middle route of the South-to-North Water Diversion Project in China. Over the past 20 years, human exploitation and ecological construction have disturbed the sustainability of land productivity in the Han River Basin. Theil–Sen trend analysis, Mann–Kendall statistical test, and Hurst index methods were applied to examine spatial–temporal trends and sustainability characteristics of land net primary productivity (NPP) change in the Han River Basin from 2001 to 2019 using MOD17A3 NPP product, natural, and socio-economic data obtained from Google Earth Engine (GEE). The findings demonstrated that the interannual variation of land NPP exhibited a fluctuating upward trend, with a more pronounced growth rate from 2001 to 2010 than from 2011 to 2019. The spatial heterogeneity of land NPP was evident, with high values in the west and low values in the east. Of the basin area, 57.82% presented a significant increase in land NPP, while only 0.96% showed a significant decrease. In the future, land NPP in the Han River Basin will present sustained growth. The results were also compared with Trends.Earth’s calculations for the SDG 15.3.1 sub-indicator of land productivity. In addition, the spatial heterogeneity of factors influencing land NPP change was explored using a multiscale geographically weighted regression (MGWR) model. Precipitation and population count were the dominant factors in most regions. Besides, precipitation, population count, and human modification all exhibited inhibitory effects on the increase in land NPP except for elevation. The research can provide a scientific basis for tracking land degradation neutrality (LDN) progress and achieving sustainable socio-ecological development of the Han River Basin.

Accurately monitoring spatio-temporal changes in lake water levels is important for studying the impacts of climate change on freshwater resources, and for predicting natural hazards. In this study, we applied multi-mission radar satellite altimetry data from the Laurentian Great Lakes, North America to optimally reconstruct multi-decadal lake-wide spatio-temporal changes of water level. We used the results to study physical processes such as teleconnections of El Niño and southern oscillation (ENSO) episodes over approximately the past three-and-a-half decades (1985–2018). First, we assessed three reconstruction methods, namely the standard empirical orthogonal function (EOF), complex EOF (CEOF), and complex independent component analysis (CICA), to model the lake-wide changes of water level. The performance of these techniques was evaluated using in-situ gauge data, after correcting the Glacial Isostatic Adjustment (GIA) process using a contemporary GIA forward model. While altimeter-measured water level was much less affected by GIA, the averaged gauge-measured water level was found to have increased up to 14 cm over the three decades. Our results indicate that the CICA-reconstructed 35-year lake level was more accurate than the other two techniques. The correlation coefficients between the CICA reconstruction and the in situ water-level data were 0.96, 0.99, 0.97, 0.97, and 0.95, for Lake Superior, Lake Michigan, Lake Huron, Lake Erie, and Lake Ontario, respectively; ~7% higher than the original altimetry data. The root mean squares of errors (RMSE) were 6.07 cm, 4.89 cm, 9.27 cm, 7.71 cm, and 9.88 cm, respectively, for each of the lakes, and ~44% less than differencing with the original altimetry data. Furthermore, the CICA results indicated that the water-level changes in the Great Lakes were significantly correlated with ENSO, with correlation coefficients of 0.5–0.8. The lake levels were ~25 cm higher (~30 cm lower) than normal during EI Niño (La Niña) events.