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This article characterized the linear trends and interannual signals of terrestrial water storage (TWS) and meteorological variables including precipitation (P) and evapotranspiration (ET) over the arid Northwestern China (NWC). The relative impaction of P, ET, and human water utilization (HU) on TWS variation among the 10 watersheds of NWC (i.e., 5 watersheds in Xinjiang, 3 watersheds in Hexi Corridor, and 2 watersheds in Qinghai) were then investigated. The result indicated that groundwater storage (GWS) was the main contributor to the TWS variation and matched well with TWS in spatial features or watershed-scale variations. The entire NWC presented growth trends for P (0.05 cm/year) and ET (0.22 cm/year) and decline trends for TWS (−0.19 cm/year) and GWS (−0.20 cm/year). The watersheds in Qinghai province where mainly affected by natural factors showed the increasing TWS/GWS trend. The watersheds in Xinjiang and Hexi Corridor, which had strong impact from human activities generally showed the declining TWS/GWS trends, but Xinjiang showed more intensive declining trend than Hexi Corridor. The analysis of HU indicated that water sustainable management and water-saving technologies had effectively kept down the tendency of TWS/GWS declining in the watersheds in Hexi Corridor, however, they were not sufficient to address the water shortage caused by farmland expansion, slight P growth, and high ET growth in Xinjiang. Groundwater use, as the main source to compensate for the increase in HU (especially agricultural water use), exacerbated TWS/GWS loss in Xinjiang. This article provides valuable information for the water management over the arid NWC. [ABSTRACT FROM AUTHOR]

The French Critical Zone research infrastructure, OZCAR-RI, gathers 20 observatories sampling various compartments of the critical zone, each having developed their own data management and distribution systems. A common information system (Theia/OZCAR IS) was built to make their in situ observation FAIR (findable, accessible, interoperable, reusable). The IS architecture was designed after consultation of the users, data producers and IT teams involved in data management. A common data model based on various metadata standards was defined to create information fluxes between observatories' ISs and the Theia/OZCAR IS. Controlled vocabularies were defined to develop a data discovery web portal offering a faceted search with various criteria, including variables names and categories that were harmonized in a thesaurus published on the web. This paper describes the IS architecture, the pivot data model and open-source solutions used to implement data discovery, and future steps to implement data downloading and interoperability services. [ABSTRACT FROM AUTHOR]

Urban growth-related changes in land use and land cover have segmented urban areas into zones of distinct surface and air temperatures (i.e., Local Climate Zones—LCZ). While studies have revealed inter-LCZ temperature variations, understanding controls of variations in Land Surface Temperature (LST) within LCZs has largely remained uninvestigated. In view of the need for LCZ-specific heat mitigation strategies, this study investigated factors driving LST variations within LCZs. To achieve this, an LCZ map for Harare was developed and correlated with LST, both derived using Landsat 8 data. The contribution index (CI) was then used to determine the relative contribution of LCZs to cooling and warming of the city. The contribution of the Normalized Difference Vegetation Index (NDVI), Normalized Difference Bareness Index (NDBaI), Normalized Difference Built-up Index (NDBI), Modified Normalized Difference Water Index (MNDWI), Urban Index (UI), and Aspect and Elevation as quantitative measures of surface controls of LST were investigated between and within LCZs. LST generally increased with built-up density and reduced with increases in surface water and vegetation. The study showed that the cooling effect of water bodies was reduced in contribution to their insignificant proportion of the study area. At the city scale, NDVI, MNDWI, NDBI, and UI had the strongest influence on LST (correlation coefficient > 0.5). At the intra-LCZ scale, the contribution of these surface properties remained significant, though to varied extents. The study concluded that surface wetness is a significant cooling determinant in densely built-up LCZs, while in other LCZs, it combines with vegetation abundance and health to mitigate elevated surface temperature. Aspect and elevation had low but significant correlations with LST in most LCZs. The study recommends that intra-LCZ controls of LST must be considered in heat mitigation efforts. [ABSTRACT FROM AUTHOR]

The USAF Weather (AFW) supports a number of military and U.S. government agencies by providing authoritative weather analysis and forecast products for any location globally, including soil moisture analyses. The long history of supporting soil moisture products and partnering with other U.S. government agencies led to the partnering between the U.S. Air Force (USAF) and NASA Goddard Space Flight Center, resulting in a merger of those organizations' modeling systems, collaborative development of the Land Information System (LIS), and operational fielding of the system within the USAF 557th Weather Wing [557 WW; formerly, Headquarters Air Force Weather Agency (HQ AFWA)]. In 2009, the USAF implemented the NASA LIS and later made it the primary software system to generate global soil hydrology and energy budget products. The implementation of LIS delivered a significant upgrade over the existing Land Data Assimilation System (LDAS) the USAF operated, the Agriculture Meteorology (AGRMET) system. Implementation enabled the rapid integration of new LDAS technology into USAF operations, and led to a long-term NASA–USAF partnership resulting in continued development, integration, and implementation of new LIS capabilities. This paper documents both the history of the USAF Weather organization capabilities enabling the generation of soil moisture and other land surface analysis products, and describes the USAF–NASA partnership leading to the development of the merged LIS-AGRMET system. The article also presents a successful example of a mutually beneficial partnership that has enabled cutting-edge land analysis capabilities at the USAF, while transitioning NASA software and satellite data into USAF operations. [ABSTRACT FROM AUTHOR]

The land surface model is extensively used to simulate turbulence fluxes and hydrological and momentum variables at the land–atmosphere interface. In this study, the Community Land Model, version 5 (CLM5), driven by the 0.1° × 0.1° Chinese Meteorological Forcing Dataset (CMFD) and the field-surveyed soil parameters, is used to simulate land surface processes during 1979–2018. Various high-quality land surface datasets are adopted to assess the model simulations. In general, the CLM5 well captures the monthly variations of 0–10-cm soil moisture in subregions, particularly in the Tibetan Plateau, with an anomaly correlation coefficient between 0.56 and 0.88. However, the simulated soil moisture shows overall wet biases in the whole country, resulting from several reasons. The model simulation is skillful in replicating both the magnitude and spatial pattern when they are compared with the MODIS snow cover dataset. Compared with in situ measured soil temperature in multiple soil layers within 320-cm soil depth from 1980 to 2018, the simulations accurately capture spatial patterns, vertical profiles, and long-term warming trends. For land surface energy components, the simulations have a highly temporal correlation with the observation of Chinese Flux Observation and Research Network (ChinaFLUX) cropland and grassland sites, except for four forest sites, where biases exist in both atmospheric forcing variables and surface vegetation phenology in the model default input dataset. In summary, this study reveals the overall capability of CLM5 in reproducing land surface energy fluxes and hydrological variables over conterminous China, and the validation results may also provide some references for future model improvement and application. Significance Statement: The offline Community Land Model, version 5 (CLM5), driven by a 0.1° × 0.1° (∼10 km) horizontal resolution atmospheric forcing dataset and a set of field-surveyed soil parameters, are used to simulate the land surface hydrological and heat fluxes in continental China for 1980–2018. The simulated hydrological variables and energy fluxes are validated with various sources of high-quality observation-based datasets. From our systematic evaluations, the current CLM5 high–resolution simulation accurately captures the spatial patterns and temporal variations in most of the water and energy balance components, although biases exist in some simulated variables. Overall, this study reveals the capability of the offline CLM5 simulation in conterminous China and provides the reference for future model improvement and application. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The simulation performance of the land surface model Community Land Model 5.0 (CLM5.0) is tested by using the eddy correlation system and micrometeorological tower observational data at the Maqu observation field in 2016. The results show that the CLM5.0 model has a good simulation effect on the soil temperature and can well reflect the seasonal variation characteristics of soil temperature. In addition, the model can well simulate the seasonal variation in shallow soil moisture (5 cm and 10 cm). However, the deviations between the simulated and observed values are large. CLM5.0 cannot simulate the water holding capacity of shallow soil well, resulting in its underestimation. The simulation performance of the upward longwave radiation and net radiation is substantially better than that of the upward shortwave radiation due to the change in surface albedo caused by precipitation from March to July. CLM5.0 can also well reproduce the variation trend of the latent heat flux and 5 cm soil heat flux (heat exchange of soil at 5 cm). When the leaf area index (LAI) and Medlyn slope values are changed to 10%, 20%, and 50% of the default values, the simulation effects of soil moisture at depths of 5 cm and 10 cm are significantly improved, and the best simulation effects are achieved at 0.5 times the default values. This result indicates that lower LAI and Medlyn slope values are more suitable for simulating shallow soil moisture. [ABSTRACT FROM AUTHOR]

The task of quantifying spatial and temporal variations in terrestrial water, energy, and vegetation conditions is challenging due to the significant complexity and heterogeneity of these conditions, all of which are impacted by climate change and anthropogenic activities. To address this challenge, Earth Observations (EOs) of the land and their utilization within data assimilation (DA) systems are vital. Satellite EOs are particularly relevant, as they offer quasi‐global coverage, are non‐intrusive, and provide uniformity, rapid measurements, and continuity. The past three decades have seen unprecedented growth in the number and variety of land remote sensing technologies launched by space agencies and commercial companies around the world. There have also been significant developments in land modeling and DA systems to provide tools that can exploit these measurements. Despite these advances, several important gaps remain in current land DA research and applications. This paper discusses these gaps, particularly in the context of using DA to improve model states for short‐term numerical weather and sub‐seasonal to seasonal predictions. We outline an agenda for land DA priorities so that the next generation of land DA systems will be better poised to take advantage of the significant current and anticipated shifts and advancements in remote sensing, modeling, computational technologies, and hardware resources. Plain Language Summary: Satellite remote sensing measurements have enabled the monitoring of the Earth's land surface with unprecedented scale and frequency. These measurements allow us to monitor the changes on the land surface and understand the contribution of human activities toward them. The information from such observations is combined with the modeled estimates through data assimilation (DA) algorithms. This article discusses the progress made in the development of land DA systems and the major gaps that remain. The paper also outlines priorities that we need to consider in the development of next generation land DA systems so that the potential of land remote sensing measurements can be fully realized. Key Points: Land data assimilation has shown significant promise for short‐term forecasting applicationsSignificant gaps remain in the current land data assimilation systems related to observation utilization and modelsCoordinated development with the modeling and observational community and adoption of technological enhancements are needed in the future [ABSTRACT FROM AUTHOR]

The Canadian Large Ensembles Adjusted Dataset version 1 (CanLEADv1) contains 50‐member ensembles of bias‐adjusted near‐surface global and regional climate model variables on a 0.5° grid over North America for historical and future scenarios (1950–2100). Canadian Earth System Model Large Ensembles (CanESM2 LE) and Canadian Regional Climate Model Large Ensemble (CanRCM4 LE) datasets are bias‐corrected using a multivariate quantile‐mapping algorithm for statistical consistency – in terms of marginal distributions and multivariate dependence structure – with two observationally constrained historical meteorological forcing datasets. For each observational dataset, bias‐adjusted variables are provided for two sets of 50‐member initial‐condition CanESM2 ensembles (historical plus RCP8.5 scenarios, 1950–2005 and 2006–2100, respectively; and historicalNAT scenario, 1950–2020, which excludes anthropogenic forcings), and one 50‐member CanRCM4 ensemble (historical plus RCP8.5). The archive includes daily minimum temperature, maximum temperature, precipitation, relative humidity, surface pressure, wind speed, incoming shortwave radiation and incoming longwave radiation. Intended uses include hydrological and land surface impact modelling, as well as related event attribution studies. [ABSTRACT FROM AUTHOR]

The coastal zone is an area that includes the sea coast and adjacent parts of the land and sea, where the mutual interaction of these environments is clearly marked. Hence, the modelling of the land and seabed parts of the coastal zone is crucial and necessary in order to determine the dynamic changes taking place in this area. The accurate determination of the terrain in the coastal zone is now possible thanks to the use of Unmanned Aerial Vehicles (UAVs) and Unmanned Surface Vehicles (USVs). The aim of this article is to present land and seabed surface modelling in the coastal zone using UAV/USV-based data integration. Bathymetric and photogrammetric measurements were carried out on the waterbody adjacent to a public beach in Gdynia (Poland) in 2022 using the DJI Phantom 4 Real Time Kinematic (RTK) UAV and the AutoDron USV. As a result of geospatial data integration, topo-bathymetric models in the coastal zone were developed using the following terrain-modelling methods: Inverse Distance to a Power (IDP), kriging, Modified Shepard’s Method (MSM) and Natural Neighbour Interpolation (NNI). Then, the accuracies of the selected models obtained using the different interpolation methods, taking into account the division into land and seabed parts, were analysed. Research has shown that the most accurate method for modelling both the land and seabed surfaces of the coastal zone is the kriging (linear model) method. The differences between the interpolated and measurement values of the R95 measurement are 0.032 m for the land part and 0.034 m for the seabed part. It should also be noted that the data interpolated by the kriging (linear model) method showed a very good fit to the measurement data recorded by the UAVs and USVs.

Sediment properties such as water content (WC) and grain size (GS) are essential to characterize the environmental conditions of tidal flats. This article aimed to develop appropriate models to estimate the WC and GS of surface sediments for an intertidal flat on the Red river delta (Vietnam) using Sentinel 2A (S2A) images. The spectral reflectance, WC, and GS of 96 sub-samples from 12 sediment samples collected on December 17, 2017 were measured to clarify their relationships. The WC was highly correlated with the reflectance ratio of two shortwave-infrared bands, R(2190)/R(1610) (R2 = 0.93). The median GS (D50) at 0%, 15%, and 20% of WC was significantly correlated with the reflectance ratio of the near-infrared band (842 nm) versus the visible-green band (560 nm) (R2 > 0.78). Next, D50 was estimated from a multivariate regression model using this band ratio, the visible-red band (665 nm), and WC. The accuracy of the models was verified by comparisons with WC and D50 from 20 samples collected on March 12th 2019 (RMSE of both WC and D50 < 15%). Then, the WC and sediment type distributions were mapped by applying these models to two S2A scenes. The maps showed high WC (>30%) in very fine sediments (silts), which is consistent with other intertidal flats with similar sediment types. This article was limited to fine sediment samples. Therefore, our next step is to incorporate coarse sediments into the models to provide more universal mapping of WC and sediment types.

Abstract The task of quantifying spatial and temporal variations in terrestrial water, energy, and vegetation conditions is challenging due to the significant complexity and heterogeneity of these conditions, all of which are impacted by climate change and anthropogenic activities. To address this challenge, Earth Observations (EOs) of the land and their utilization within data assimilation (DA) systems are vital. Satellite EOs are particularly relevant, as they offer quasi‐global coverage, are non‐intrusive, and provide uniformity, rapid measurements, and continuity. The past three decades have seen unprecedented growth in the number and variety of land remote sensing technologies launched by space agencies and commercial companies around the world. There have also been significant developments in land modeling and DA systems to provide tools that can exploit these measurements. Despite these advances, several important gaps remain in current land DA research and applications. This paper discusses these gaps, particularly in the context of using DA to improve model states for short‐term numerical weather and sub‐seasonal to seasonal predictions. We outline an agenda for land DA priorities so that the next generation of land DA systems will be better poised to take advantage of the significant current and anticipated shifts and advancements in remote sensing, modeling, computational technologies, and hardware resources.

Flash droughts, characterized by their unusually rapid intensification, have garnered increasing attention within the weather, climate, agriculture, and ecological communities in recent years due to their large environmental and socioeconomic impacts. Because flash droughts intensify quickly, they require different early warning capabilities and management approaches than are typically used for slower-developing "conventional" droughts. In this essay, we describe an integrated research-and-applications agenda that emphasizes the need to reconceptualize our understanding of flash drought within existing drought early warning systems by focusing on opportunities to improve monitoring and prediction. We illustrate the need for engagement among physical scientists, social scientists, operational monitoring and forecast centers, practitioners, and policy-makers to inform how they view, monitor, predict, plan for, and respond to flash drought. We discuss five related topics that together constitute the pillars of a robust flash drought early warning system, including the development of 1) a physically based identification framework, 2) comprehensive drought monitoring capabilities, and 3) improved prediction over various time scales that together 4) aid impact assessments and 5) guide decision-making and policy. We provide specific recommendations to illustrate how this fivefold approach could be used to enhance decision-making capabilities of practitioners, develop new areas of research, and provide guidance to policy-makers attempting to account for flash drought in drought preparedness and response plans. [ABSTRACT FROM AUTHOR]

Satellite-derived observations of land surface temperature (LST) are being utilized in a growing number of land surface studies; however, these observations are generally obtained from optical sensors that exclude cloudy observations of the land surface. The impact of using only clear-sky observations of land surfaces on monthly and annual estimates of daytime LST over two U.S. Climate Reference Network (USCRN) sites was evaluated over five years with daily in situ LST observations available for all-sky (clear and cloudy) conditions. The in situ LST observations were obtained for the nominal daytime observations associated with the MODIS sensors on board the Terra and Aqua satellites and were identified as all-sky or clear-sky conditions by utilizing cloud information provided with the MODIS LST product. Both monthly/annual mean and monthly/annual maximum values of daytime LST were significantly different when only clear-sky values were utilized, in comparison with all-sky values. Monthly averaged differences between the mean clear- and all-sky daytime LST (dLST) values ranged from −0.1° ± 1.5°C for January to 5.6° ± 1.8°C for May. Annually averaged dLST values, over the five years of the study, were 2.58°C, and differences between the maximum values of clear- and all-sky daytime LST values were −1.03°C. Although significant differences between mean annual clear-sky and all-sky daytime LST values were more frequent than differences observed for the annual maximum daytime LST values, the results suggest that the exclusive use of either mean or maximum clear-sky daytime LST values is not advisable for applications in which the use of daytime all-sky LST values would be more applicable. [ABSTRACT FROM AUTHOR]

A lightning event striking the sea could induce several damages to the near infrastructures located on the coast. The possible damages are connected to the induced voltages caused by the electromagnetic fields generated by the lightning discharge. In order to compute them, several simplified approaches have been developed, using ideal models for the surrounding geometry and for the electrical parameters of the soil. This works provides the evaluation of the lightning electromagnetic fields generated by a lightning event striking on the sea taking into account the possible elevation of the terrain with respect to the sea level and the inclination of the seabed. The analysis has been carried out taking into account several distances between the coast and the striking location as well as different observation points, in order to evaluate the induced effects on a structure located close to the sea–land interface as well as in a more distant location. The analysis, implemented with the aid of the finite element method, leads to important conclusions on the components of the electric and magnetic field that are more affected by the realistic characteristics of the surrounding orography. [ABSTRACT FROM AUTHOR]

Exploring the premonsoonal land thermal predictor of the Indian summer monsoon is a hot topic under the background of global warming, and West Asia is one of the regions with the most significant warming in spring. In this study, we investigated the impact of anomalous spring land surface warming over West Asia on early summer (June) Indian monsoon precipitation as well as its possible mechanisms based on statistical analysis and numerical simulations. It has been found that spring land surface anomalous warming over West Asia corresponds to the enhancement of the leading mode of early summer precipitation in the Indian subcontinent, especially in its northern part. Further analysis indicates that an anomalously warm land surface over West Asia can advance the transition of atmospheric conditions toward the warm season by heating the atmosphere above. The increased land–sea meridional thermal contrast favors the intensification of the low-level jet and monsoon trough, further inducing anomalous moisture convergence and ascending motion over northern India. Additionally, the heat-driven anomalous upper-tropospheric anticyclone over West Asia favors the intensification of the tropical easterly jet and the northwestward development of the South Asian high (SAH). The enhanced SAH dynamically couples with the lower- to middle-level cyclonic circulation over northern India, resulting in a stronger monsoon and increased precipitation. These findings are helpful for better understanding and prediction of Indian early summer monsoon. Significance Statement: The land surface thermal condition is critical to the monsoon activity and exploring the premonsoonal land thermal predictor of Indian summer monsoon remains a hot topic. The purpose of this study is to explore how spring land surface thermal anomalies over West Asia impact Indian monsoon activity in early summer (June). The anomalous land surface warming over West Asia can lead to a stronger Indian monsoon in early summer by heating and driving the atmosphere, which benefits the precipitation increase over northern India. Our results provide a further scientific basis for the prediction of early summer Indian precipitation. [ABSTRACT FROM AUTHOR]

The surface albedo of Arctic sea ice is a critical variable in the Earth’s energy budget, and various Arctic sea ice albedo datasets have been derived from remote sensing data. However, the influences of reflectance anisotropy and melt ponds were not well considered in previous studies. To improve the estimation accuracy and calculation efficiency, we developed a method for estimating the surface albedo of Arctic sea ice using an ensemble back-propagation neural network (EBPNN) model. A bidirectional reflectance distribution function (BRDF)/albedo dataset of snow, white ice, and melt ponds was constructed using the asymptotic analytical radiative transfer (AART) model, and the nonlinear relationships between the surface reflectance of Moderate Resolution Imaging Spectroradiometer (MODIS) and surface albedo were established using the EBPNN model (prediction correlation coefficient $(R)= 0.996$ and root mean square error (RMSE) = 0.023). Our results are consistent with the in situ measurements and the Medium Resolution Imaging Spectrometer (MERIS) product ($R= 0.916$ , RMSE = 0.085, and bias = 0.001), in which the reflectance anisotropy of the snow/ice and the influence of melt ponds were fully considered, and the calculation efficiency was significantly improved by using the EBPNN model. The results of this study provide new insights for estimating the Arctic sea ice albedo from satellite observations, and the proposed EBPNN method can be used to generate a long-term Arctic sea ice albedo dataset with superior estimation accuracy and calculation efficiency. [ABSTRACT FROM AUTHOR]

Different-resolution change detection (DRCD) is now becoming an urgent problem to be solved, which is of great potential in rapid monitoring, such as disaster assessment and urban expansion. In DRCD tasks, bitemporal inputs are given in the form of different resolutions, and thus, conventional change detection (CD) methods cannot be applied directly. Previous studies have attempted to deal with this problem by reconstructing the low-resolution (LR) image into a high-resolution (HR) one, including interpolation and super-resolution (SR). However, these solutions are limited by the availability of training data, making it hard to meet different kinds of needs. Besides, these image-level strategies have also ignored the interaction and alignment of high-level features. Therefore, we propose a new approach based on multimodel Transformers (MM-Trans), which solves the resolution gaps of bitemporal inputs in DRCD tasks from the perspective of feature alignment. In the MM-Trans, a weight-unshared feature extractor is first utilized to precisely capture the features of the different-resolution inputs. Then, a spatial-aligned Transformer (sp-Trans) is introduced to align the LR-image features to the same size of the HR-image ones, which can be optimized in a learnable way by an auxiliary token loss. After that, a semantic-aligned Transformer (se-Trans) is adopted, in which the bitemporal features can be further interacted and aligned semantically. Finally, a prediction head is employed to obtain fine-grained change results. Experiments conducted on three common CD datasets, CDD, S2Looking, and HTCD dataset, have shown the advancement of the MM-Trans and fully demonstrated its potential in DSCD tasks. [ABSTRACT FROM AUTHOR]

High-frequency aerosol observation from a new-generation geostationary meteorological satellite is capable to capture and monitor the spatiotemporal dynamic variation of aerosols, which is of vital significance to environmental research and climate studies. Due to the diversity and complexity of land cover, it is a challenge to retrieve aerosol properties with high accuracy over land, especially over heterogeneous land surfaces. In this study, a geometry-discrete minimum reflectance aerosol retrieval algorithm (GeoMRA) has been proposed to retrieve 10-min high temporal resolution aerosol optical depth (AOD) datasets for geostationary Himawari-8 Advanced Himawari Imager (AHI) sensor, aiming at providing universal bidirectional reflectance distribution function (BRDF) descriptions for different land surfaces with different heterogeneous extent. The AOD retrievals from GeoMRA demonstrate good consistency against the ground-based AERONET measurements in East Asia from 2015 to 2020, with a correlation coefficient (${R}$) of 0.883 and approximately 65.6% of matchups falling within the expected error envelope of ±(0.05% +15%). Intercomparison between the GeoMRA retrieved AOD and other operational AOD products shows that the GeoMRA AOD retrievals, which generally possess similar spatial distribution and accuracy as Moderate Resolution Imaging Spectroradiometer (MODIS) AOD products, have better performances than the Japan Aerospace Exploration Agency (JAXA) AOD products by providing more accurate AOD retrievals with higher spatial coverage. Moreover, the AOD bias analyses further demonstrate the robustness of GeoMRA algorithm, and an extreme haze event shows that the continuous GeoMRA AOD images illustrate smoother temporal variations than JAXA AOD products, demonstrating its efficacy and reliability in capturing the process of haze transport and monitoring the continuous spatiotemporal variation of aerosol. The above results suggest the considerable accuracy of GeoMRA algorithm for scientific application requirement and demonstrate the robustness of the proposed BRDF scheme in describing heterogeneous surfaces with diverse reflectance distribution. [ABSTRACT FROM AUTHOR]

This article proposes a closed-form asymptotic solution for the bistatic coherent scattering of a multilayered rough surface structure based upon its effective dielectric constant (EDC) in specular direction. The EDC is modeled by establishing an equivalence of the coherent scattering between the multilayered rough surface structure and a half-space homogeneous medium. The scattering solution is then solved using the scalar Kirchhoff approximation (SKA) method. This new method is referred to as the SKA-EDC method, and it is applied to analyze the sensitivity of the EDC and coherent reflectivity to bare soils with realistic parameters at P- and L-bands. The result indicates that EDC can give different responses to the soil moisture variations with respect to the coherent reflectivity, enabling the potentials of root-zone soil moisture retrieval. At incidence angle smaller than 35°, EDC gives the same value for both polarizations, and the coherent reflectivity can show a significant response to soil at depths <15–50 cm at 0.80 GHz and <5–15 cm at 1.57 GHz, depending on soil moisture. The simulation also demonstrates that subsurface roughness has a trivial effect on the EDC and coherent reflectivity for three rough surfaces separated by continuously dielectric profiles. Subsurface can, thus, be assumed to be flat for reducing uncertainty in soil moisture inversion algorithms when the subsurface roughness is unknown. [ABSTRACT FROM AUTHOR]

The derivation of microwave land surface emissivity (MLSE) under various weather conditions from the microwave radiometer plays a crucial role in acquiring land surface and atmospheric parameters. Nevertheless, currently, most existing studies mainly focus on the clear-sky scenarios due to a lack of cloudy-sky land surface temperature (LST) and uncertainties in simulating the scattering and emission properties of atmospheric hydrometeors. Under this background, with satellite observations and the random forest (RF) model, this study proposes a method to estimate the MLSE under cloudy skies. First, clear-sky MLSEs with satisfactory accuracy are retrieved by using the brightness temperatures (BTs) from the Advanced Microwave Scanning Radiometer-Earth sensor, LSTs from the Moderate Resolution Imaging Spectroradiometer (MODIS), and atmospheric profiles from the ERA5 reanalysis. Then, the relation among the clear-sky MLSE and related impact factors is built with the RF and extended to the cloudy-sky environment for generating all-weather MLSEs with a 0.25°. The results show that the input datasets present a considerable impact on the calculation of instantaneous MLSE, and a 5.73 K bias of ERA5 LST may generate a 0.014–0.021 error in the MLSE from 6.9- to 89-GHz horizontal polarization, while the impacts of BT and profile uncertainties on the MLSE are smaller. The retrieved clear-sky MLSE is coincident with the existing MLSE for the spatiotemporal variations, and there is an average difference range from −0.035 to 0.035 in January 2008. Meanwhile, the constructed RF model can successfully apply to cloudy-sky status and recover the MLSE image gaps affected by cloud contamination. [ABSTRACT FROM AUTHOR]

This study characterizes the marginally known angular-dependent polarization difference (PD) of the land surface emissivity (LSE), using the microwave humidity sounder (MWHS) onboard China’s Sun-synchronous satellite FengYun-3B (FY-3B). The instrument has two quasi-polarization channels at 150 GHz in addition to the three water-vapor absorption channels in the vicinity of 183 GHz, allowing observations at quasi-vertical and quasi-horizontal polarizations with its cross-track scanning geometry. The Saharan and Taklimakan deserts are taken as examples due to the relatively homogeneous surface conditions. In the two 150-GHz channels of MWHS, the polarization bias is corrected according to the measurements at the Earth incidence angle (EIA) of 0°. The monthly mean quasi-polarization difference (QPD) at 150 GHz is found to be varying with the EIA and exhibits seasonal variations over deserts. The QPD reaches a maximum value at the EIA of ~35°, while the zero value appears near the EIA of ~53° [equal to the satellite viewing angle (VA) of 45°] as expected. In contrast to the relatively stable and low QPD over Sahara, the QPD, quasi-polarized emissivity difference, and pure polarized emissivity difference (PPED) are generally larger in the Taklimakan Desert, where the maximum monthly QPD is ~4 K and the polarized emissivity difference increases monotonously to ~0.2 at the EIA of 50°. The complex surface parameters and temporal variabilities impinge on the uncertainties of the land polarization. The increasing soil moisture due to precipitation damps the dichroic ability of the land surface, while the frozen surface due to snowfalls in cold seasons enhances the polarization. [ABSTRACT FROM AUTHOR]

We present an analytical formulation of the correlation coefficient of the electromagnetic fields scattered at near-specular direction by a rough or gently undulating surface and measured at two spatially separated positions occupied by a moving receiver at slightly different times. This allows us obtaining an explicit expression of the correlation time of the received signal in terms of radar and surface parameters. This work provides a contribution to the discussion, currently ongoing in the Global Navigation Satellite System Reflectometry (GNSS-R) scientific community, about the behavior of received signal fluctuations, especially when surface profile variations are such that the scattering is neither coherent nor completely incoherent. The scattering surface is here modeled as randomly rough, and the Kirchhoff approximation (KA) or the first-order small slope approximation (SSA1) is employed to compute the scattered field. In fact, the expression of the correlation coefficient is the same for both approximations. The obtained closed-form expression shows that as the surface correlation length increases, the degree of coherence smoothly increases from the value obtained with the expression already available in the literature for very rough surfaces to a value close to unity for gently undulating surfaces. The obtained behavior of correlation time as a function of surface parameters, system resolution, and observation geometry is in agreement with numerical simulations available in the literature. In general, obtained analytical results are in agreement with the observed behavior of GNSS-R signals over flat land surfaces. [ABSTRACT FROM AUTHOR]

As the successors of the Huanjing Jianzai-1 (HJ-1) series satellites in the Chinese Environmental Protection and Disaster Monitoring Satellite Constellation, the first two Huanjing Jianzai-2 (HJ-2) A/B satellites have been successfully launched on September 27, 2020. The polarized scanning atmospheric corrector (PSAC) sensors, onboard the HJ-2 A/B satellites, are served as the synchronously atmospheric correction instrument requiring a high-speed and accurate aerosol optical depth (AOD) algorithm. For this purpose, we proposed a neural network-based AOD retrieval model (named the AODNet) that takes full advantage of the multispectral measurements of PSAC for AOD retrieval at a high speed. The training of AODNet is conducted by the simulated observation data (currently applicable for the China region) from the forward calculation using the radiative transfer model. In this way, the land surface reflectance (LSR) is no need for our well-trained model. It is expected to be one of the effective ways to solve the ill-pose problem in the decoupling of the atmosphere and surface information in AOD retrieval. Either the Sun-sky radiometer Observation NETwork (SONET) AOD or the AErosol RObotic NETwork (AERONET) AOD was used to validate the AODNet AOD. The correlation coefficient is higher than 0.85, and more than 60% of the AODNet AOD can fall into the expected error envelope of ±(0.05+20%). The cross-comparison shows that the AODNet has better accuracy than MODIS dark target (DT) and deep blue (DB) algorithm. The air pollution episode is well characterized by the AODNet AOD using PSAC data. [ABSTRACT FROM AUTHOR]

The existence of global warming is common knowledge. However, it can be predicted that there may be cooling zones worldwide based on the mechanism of terrestrial biophysical processes. Here, the Theil–Sen median trend, the Mann–Kendall trend test method, continuous wavelet transformation, and the Hurst exponent were used to study the cooling trends, abrupt change times, periodicity, and future sustainability of temperature changes in different cooling zones since the 1900s based on the CRU dataset. We found an amazing result; 8,305,500 km2 of land surface had been cooling since the 1900s, covering five continents and 32 countries, accounting for 86% of land area in China, and distributed over 16 zones. The average cooling rate of the cooling zones was −0.24 °C/century. The maximum cooling rate was −1.40 °C/century, and it was 1.43 times the average rate of global land warming (0.98 °C/century). The cooling zones near the sea were greatly influenced by ocean currents and were mainly affected by a small time scale periodicity of less than 30 years, whereas the cold zones located relatively far from the sea and less affected by ocean currents were mainly affected by medium time scales of more than 30 years. Moreover, 32.33% of the cooling zones, involving 2,684,900 km2, will be continuously cooling in the future, and the rest will probably warm up in 2114, 2041, 2096, 2099, 2119, 2073, 2048, and 2101, respectively. The study will help us to further understand the essential characteristics of global climate change, and to find more theoretical bases for mitigating global warming and exploring surface cooling mechanisms.

Atmospheric disturbance, sensor malfunctions, and other factors can cause serious gap pixels in the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference snow index (NDSI) products. In this article, MODIS NDSI gap pixels are reconstructed in a highly heterogeneous area with drastic snow accumulation and melting changes using a long short-term memory (LSTM) network. Three LSTM-based MODIS NDSI gap pixel reconstruction schemes, i.e., forward, backward, and bidirectional LSTM networks that separately use earlier, subsequent, and integrated earlier and subsequent timestamp information, are developed. NDSI information for the gap pixel is restored using the long-term spatiotemporal information for this pixel and its adjacent pixels. A case study of NDSI reconstruction in the source area of the Yellow River, northwestern China, during the 2018–2019 snow season, demonstrates that all three LSTM-based schemes can reliably generate spatiotemporally continuous NDSI data with an accuracy comparable to that of the original MODIS NDSI products under clear-sky conditions. The bidirectional LSTM-based scheme, which has the best performance, can achieve a desirable overall accuracy of 89.93%, with an omission error of 3.82% and a commission error of 6.25%, in terms of dichotomous evaluation based on in situ snow depth observations. The $R^{2}$ , average RMSE, overestimation error, and underestimation error are 0.95, 5.13%, 5.39, and 6.40%, respectively, in terms of the continuous value assessment based on the gap pixels assumption. Our results demonstrate the reliability and feasibility of the LSTM-based schemes in recovering the missing values in MODIS NDSI products by deeply excavating the spatial continuity and long-time series dependence of the snow cover. [ABSTRACT FROM AUTHOR]

The freeze-thaw (FT) status of soil regulates ecological and hydrologic processes and is, therefore, a vital component of land surface models. This study utilizes a hidden Markov model (HMM) to retrieve surface FT status from L-band [Soil Moisture Active Passive (SMAP)] and Ka-band [Advanced Microwave Scanning Radiometer (AMSR)] satellite microwave brightness temperatures in cold-constrained lands north of $45 ^\circ \text{N}$. The HMM, parameterized on a per-gridcell basis such that there are two possible states and emissions probabilities are assumed to be a two-component Gaussian Mixture, produces the posterior probability (a continuous variable between 0 and 1) that the surface is frozen given the radiometer input. HMM classification accuracy, averaged over five core validation networks, is acceptable (Ap > 80%) when judged against in situ air and soil temperature measurements from individual validation sites and is comparable to that of current FT products that produce a discrete state. Patterns in performance across variable land class, open water fraction, and elevation are assessed from 91 sparse network weather stations within 87 gridcells in the domain. The resulting satellite data record provides a continuous variable estimate of the daily probability of frozen conditions over northern land areas experiencing widespread thawing of permafrost and a shrinking frozen season due to global warming. [ABSTRACT FROM AUTHOR]

To understand the temporal decorrelation of the near-specular component of land-scattered signals in global navigation satellite system reflectometry (GNSS-R) and describe the nature of the scattering considering spaceborne receivers at arbitrary altitudes, we propose here an analytical solution of the covariance of the field under the Kirchhoff approximation. Both cases of infinite illumination and finite illumination on the ground are studied. Surfaces with gentle undulations are considered, i.e., those having small slopes and showing slow variations of the profiles over the horizontal scale. This allows for investigating scattered fields that can be neither coherent nor completely incoherent over land surfaces that are nearly flat. In a recent work from the authors, an extensive numerical evaluation of the decorrelation of the near-specular land scattering was presented. The phenomenology of the problem was studied and discussed numerically, solving, for airborne receivers, the relevant scattering integral, both as a function of the geometry of the system and the statistical parameters of the illuminated surface. Such numerical results are used here to validate the proposed closed-form formulation. It is demonstrated how the near-specular scattering, collected over land targets by a GNSS-R receiver from space, decorrelates as a function of the receiver movement and the statistical parameters describing the illuminated surface (namely, height standard deviation and correlation length). The proposed analysis provides information of interest for the design of future GNSS-R missions. The interpretation of GNSS-R data from space, which typically shows strong fluctuations, can also be supported by this approximated analytical study. [ABSTRACT FROM AUTHOR]

Spatio-temporal fusion is a feasible way to provide synthetic satellite images with high spatial and high temporal resolution simultaneously. Due to its practicability, spatio-temporal fusion has gotten increasing attention, for which many spatio-temporal fusion approaches have been developed. Most spatio-temporal fusion methods follow the “base fine image guided” (BFIG) fusion mode, resulting in the fact that their fusion results are similar to the base fine images. Therefore, these methods can perform well in areas with limited surface changes due to high similarity between the base and the predicted fine images. However, they might not be applicable in areas with intense surface changes. In this article, we develop a pansharpening-based spatio-temporal fusion model (PSTFM) by introducing the pansharpening fusion mode, which is “coarse image guided” (CIG), into spatio-temporal fusion. PSTFM first trains a pansharpening convolutional neural network (CNN), which then fuses the coarse images and reconstructed panchromatic (Pan) images of the predicted time to recover the missing fine images. The newly proposed PSTFM is compared with three representative BFIG spatio-temporal fusion methods on two Landsat–Moderate Resolution Imaging Spectroradiometer (MODIS) datasets, both of which contain intense surface changes. After that, the experimental results are analyzed and discussed in detail. The experiments and the analysis demonstrate that the newly proposed PSTFM has remarkably qualitative and quantitative performance in predicting the intense surface changes while it is mediocre in areas with low surface change intensity. [ABSTRACT FROM AUTHOR]

Initialization methods are needed for geophysical components of Earth system prediction models. These methods are needed from medium-range to decadal predictions and also for short-range Earth system forecasts in support of safety (e.g., severe weather), economic (e.g., energy), and other applications. Strongly coupled land–atmosphere data assimilation (SCDA), producing balanced initial conditions across the land–atmosphere components, has not yet been introduced to operational numerical weather prediction (NWP) systems. Most NWP systems have evolved separate data assimilation (DA) procedures for the atmosphere versus land/snow system components. This separated method has been classified as a weakly coupled DA system (WCDA). In the NOAA operational short-range weather models, a moderately coupled land–snow–atmosphere assimilation method (MCLDA) has been implemented, a step forward from WCDA toward SCDA. The atmosphere and land (including snow) variables are both updated within the DA using the same set of observations (aircraft, radiosonde, satellite radiances, surface, etc.). Using this assimilation method, land surface state variables have cycled continuously for 6 years since 2015 for the 3-km NOAA HRRR model and with CONUS cycling since 1997. Month-long experiments were conducted with and without MCLDA for both winter and summer seasons using the 13-km Rapid Refresh model with atmosphere (50 levels), soil (9 levels), and snow (up to 2 layers if present) on the same horizontal grid. Improvements were evident for 2-m temperature for all times of day out to 6–12 h for both seasons but stronger in winter. Better temperature forecasts were also shown in the 1000–900-hPa layer corresponding roughly to the boundary layer. Significance Statement: Accuracy of weather models depends on accurate initial conditions for soil temperature and moisture as well as for the atmosphere itself. This paper describes a moderately coupled data assimilation method that modifies soil conditions based on forecast error corrections indicated by atmospheric observations. This method has been tested for a month-long period in summer and winter and shown to consistently improve short-range forecasts of 2-m temperature and moisture. This coupled data assimilation method is used already in NOAA operational short-range models to improve its prediction skill for clouds, convective storms, and general weather conditions. [ABSTRACT FROM AUTHOR]

The arid and semiarid areas of the Loess Plateau are extremely sensitive to climate change. Land–atmosphere interactions of these regions play an important role in the regional climate. However, most present land surface models (LSMs) are not reasonable and accurate enough to describe the surface characteristics in these regions. In this study, we investigate the effects of three key land surface parameters including surface albedo, soil thermal conductivity, and additional damping on the Noah LSM in simulating the land surface characteristics. The observational data from June to September from 2007 to 2009 collected at the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) station in northwest China are used to validate the Noah LSM simulations. The results suggest that the retrieved values of surface albedo, soil thermal conductivity, and additional damping based on observations are in closer agreement with those of the MULT scheme for surface albedo, the J75_NOAH scheme for soil thermal conductivity, and the Y08 scheme for additional damping, respectively. Furthermore, the model performance is not obviously affected by surface albedo parameterization schemes, while the scheme of soil thermal conductivity is vital to simulations of latent heat flux and soil temperature and the scheme of additional damping is crucial for simulating net radiation flux, sensible heat flux, and surface soil temperature. A set of optimal parameterizations is proposed for the offline Noah LSM at the SACOL station when the MULT scheme for surface albedo, the J75_NOAH scheme for soil thermal conductivity, and the Y08 scheme for additional damping are combined simultaneously, especially in the case of sensible heat flux and surface soil temperature simulations. [ABSTRACT FROM AUTHOR]

The confusion matrix has long been adopted as the 'de facto' and 'de jure' standard method of reporting on the thematic accuracy assessment of any land surface geospatial dataset. This type of data supports decision‐making in many different fields, so suitable quality is therefore essential in order to take the best decisions. Nevertheless, the creation and exploitation of the confusion matrix remains as an open topic with issues related to sampling design, quantitative indices derived from the matrix, statistical hypotheses that could be applied, etc. In connection with the latter, a confusion matrix dataset would be useful for a researcher in this matter. We have developed such a dataset retrieving confusion matrices from the literature, mainly research articles published in scientific journals included in WoS. We have collected almost 200 matrices in a database. This allows us to access the complete matrices and query different interesting properties of them and of the project where they were developed such as matrix size, sample size, location, year of data capture, labels of the classes, quality indices used, and extension and location of the project (where available). [ABSTRACT FROM AUTHOR]

The quantitative evaluation of the intensity of regional human activity can reflect changes in the relationship between people and the environment and provide a scientific basis for the formulation of land-use policy and for the construction of the ecological environment. This paper adopted the ratio of the area of construction land to the total area to calculate human activity intensity in Inner Mongolia at the league/city, banner/county, and pixel levels, respectively. On this basis, the overall change in human activity intensity and its spatiotemporal differentiation, geographical unit change, and spatial autocorrelation were analyzed. The results are as follows: (1) The intensity of human activity increased from 2000 to 2020. The intensity of human activity in Inner Mongolia was 8.71% in 2000, and it increased to 8.73% in 2010 and to 9.71% in 2020. The growth rate has accelerated since 2010. (2) The areas with high and medium intensity of human activities are mainly concentrated in the area south of the Hinggan–Yinshan–Helan Mountains. The intensity in the Hulunbeier grassland area and Alxa desert area is relatively low, and the diffusion effect gradually becomes prominent in the proximal local area. (3) The results for the detection of influencing factors indicate that the livestock density, multi-year average precipitation conditions, and population density have significant driving effects on the changes in intensity of human activity in Inner Mongolia. [ABSTRACT FROM AUTHOR]

Spatiotemporal reflectance fusion has received considerable attention in recent decades. However, various challenges remain despite varying levels of success, especially regarding the recovery of spatial details with complex land cover changes. Taking the blending of Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) images as an example, this article presents a locally weighted unmixing-based spatiotemporal image fusion model (LWU-STFM) that focuses on recovering complex land cover changes. The core idea is to redefine the land use class of each pixel featuring land cover change at the prediction date. The spatial unmixing process is enhanced using a proposed geographically spectrum-weighted regression (GSWR), and then, we optimize similar neighboring pixels for the final weighted-based prediction. Experiments are conducted using semisimulated and actual time-series Landsat–MODIS datasets to demonstrate the performance of the proposed LWU-STFM compared with the classic spatial and temporal adaptive reflectance fusion model (STARFM), flexible spatiotemporal data fusion (FSDAF), two enhanced FSDAF models (SFSDAF and FSDAF 2.0), and a virtual image pair-based spatiotemporal fusion model for spatial weighting (VIPSTF-SW). The results reveal that the proposed LWU-STFM outperforms the other five models with the best quantitative accuracy. In terms of the relative dimensionless global error (ERGAS) index, the errors of Landsat-like images generated using LWU-STFM are 2.8%–63.4% lower than those of other models. From visual comparisons, LWU-STFM predictions illustrate encouraging improvements in recovering spatial details of pixels with complex land cover changes in heterogeneous landscapes and, thus, advancing applications of spatiotemporal image fusion for continuous and fine-scale land surface monitoring. [ABSTRACT FROM AUTHOR]

The land surface is an important component of numerical weather and climate forecast models due to their effect on energy–water balances and fluxes, and it is essential for forecasts on a seasonal scale. The present study aimed to understand the effects of land surface processes on initialization of seasonal forecasts in the austral spring, in particular soil moisture. We built forecasts with the Brazilian global Atmospheric Model hindcast from 2000 to 2010, with a configuration similar to those used in the operational environment. To improve it, we developed a new initial condition of the land surface using the Land Information System over South America and the Global Land Data Assimilation System for the rest of the globe and used it as the input in the forecast model. The results demonstrated that the model is sensitive to changes in soil moisture and that the new high–resolution soil moisture dataset can be used in model initialization, which resulted in an increase in the correlation of precipitation over part of South America. We also noticed an improvement in the representation of surface fluxes and an increase in soil moisture content and specific humidity at medium and low levels of the atmosphere. The analysis of the coupling between the land surface and the atmosphere showed that, for Central Brazil, the states of the continental surface define the surface fluxes. For the Amazon and La Plata Basins, the model did not correctly represent the coupling because it underestimated the soil moisture content.

Water is a crucial component of ecosystems and a critical resource that cannot be replaced for social progress or human life. In this study, Agulu Lake, an inland water body located in Anambra, southeast Nigeria, was mapped, classified, and delineated with remotely sensed data so as to monitor the spatial-temporal changes that occurred in the lake’s surface water every 15 years, in 1985, 2000, and 2015, in order to achieve sustainable development by 2030. The Sustainable Development Goals (SDGs) of the United Nations emphasize the need to manage the marine environment. Some of the goals of the SDGs have some connection to open surface water, but goal 6a and indicator 6.6.1 are significant to this study. The study adopted Landsat 5 TM (1985), ETM+ (2000), Landsat 8 OLI (2015), ArcGIS 10.5 software, and the maximum likelihood classifier to create various classification maps. The Google Earth image (2015) was also used to show the general overview of Agulu Lake and its environs. The findings demonstrate that during the study period, the land surface class grew while the water surface class (Agulu Lake) shrank.

Wildfire has significant impact on plant phenology. The plants’ phenological variables, derived from time series satellite data, can be monitored and the changes in satellite imagery may be used to identify the beginning, peak, and end of the growing season. This study investigated the use of remote sensing data and land surface phenology (LSP) parameters to evaluate the impacts of fire. The LSP parameters included the start of growing season (SOS), the length of the growing season (LOS), the end of the growing season (EOS), maximum greenness of the season (Gmax), and minimum greenery in the season (Gmin) in the fire-impacted, semiarid oak forests of Iran. These LSP parameters were extracted from multitemporal normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI2) data, acquired from MODIS sensor images in Zagros of the Ilam province in western Iran. By extracting LSP indices from the NDVI and EVI2 data, the indices were compared between burned forest areas, areas surrounding the burned forests, and unburned areas and for timesteps representing pre-fire, fire (i.e., year of fire), and post-fire (i.e., 2 years) conditions. It was found that for the burned area, there were significant differences in Gmax and the day that Gmax occurred. Furthermore, there was also a significant difference in Gmin between the pre- and post-fire conditions when NDVI was used and a significant difference between Gmax when EVI2 was used. The results also showed that in both time series there was a significant difference between the burned and control area in terms of Gmax. In general, the results showed that the fire had a negative effect on LSP, but in the two years after the fire, there were signs of forest restoration. This study provides necessary information to inform forest and resource conservation and restoration programs.

Intraseasonal soil moisture variability has the potential to feed back onto the West Africa monsoon circulation through its influence on surface turbulent fluxes and planetary boundary‐layer characteristics. Using satellite observations and an atmospheric reanalysis, we investigate intraseasonal soil moisture–atmosphere feedbacks triggered by large‐scale dynamics within the West African monsoon. Surprisingly, even though the surface response across the Sahel to strong convection is short‐lived (days) and precipitation accumulations are spatially and temporally heterogeneous, a coherent regional‐scale surface response to intraseasonal variability is observed. This surface response then feeds back onto the West African monsoon circulation. For example, during a dry intraseasonal event, Sahelian surface soil moisture significantly decreases, which elevates surface temperatures by 1.5 °C and shifts the monsoon circulation southward by approximately 1.5° latitude. Also, during a wet event the surface moistens and cools which leads to a northward monsoon shift. Alongside a low‐level wind response, the African Easterly Jet (AEJ) also responds to surface changes due to variations in the meridional temperature gradient. For example, an increased temperature gradient during a dry event intensifies and shifts the AEJ southward. The combined response of low‐level monsoon westerlies and the AEJ impacts low‐level shear and characteristics of strong convection. Elevated low‐level shear during a dry event promotes an intensification of deep convection across southern West Africa. This study provides new insight into the sensitivity of the West African monsoon circulation to intraseasonal soil moisture feedbacks and encourages similar research in other regions. An improved understanding and model representation of soil moisture–atmosphere feedbacks has the potential to improve forecasts beyond daily time‐scales and enhance early warning systems. [ABSTRACT FROM AUTHOR]

Remotely sensed soil moisture (SM) with high accuracy and high spatial–temporal resolution is crucial to meteorological, agricultural, hydrological, and environmental applications. The Cyclone Global Navigation Satellite System (CYGNSS) is the first constellation that uses the L-band signal transmitted by the GNSS satellites to develop daily SM data products. In this study, a physics-based algorithm is proposed to couple CYGNSS surface reflectivity (SR) and Soil Moisture Active Passive (SMAP) brightness temperature estimates for accurate SM retrieval. The algorithm is based on the radiative transfer model and the SMAP data to derive a combined parameter of the vegetation optical depth ($\tau$) and the surface roughness parameter ($h$). The CYGNSS L1 Version 2.1 data of the years 2017–2018 and 2019–2020 are used for calibration and validation, respectively. The SM estimates agree and correlate well with the SMAP SM and in situ SM data on a global scale ($R =0.679$ , RMSE $=0.051\,\,\text{m}^{3}\text{m}^{-3}$ , and MAE $=0.045\,\,\text{m}^{3}\text{m}^{-\mathrm {3}}$ against SMAP SM; $R = 0.729$ against in situ SM). The proposed algorithm makes contributions from two aspects. First, the proposed algorithm provides a physics-based algorithm using SMAP brightness temperature to calibrate the attenuation due to vegetation and surface roughness on the CYGNSS-derived SR. Unlike attenuation models that have been explored previously in the context of CYGNSS, this algorithm executes the calibration without relying on observations of $h$ or vegetation biophysical parameters as inputs but with the SMAP brightness temperature as the only observations. Second, the proposed algorithm provides a new way for the combined usage of CYGNSS and SMAP to improve the temporal and spatial coverages of global SM with temporal coverage increased by 38.2% and spatial coverage increased by 31.6%. [ABSTRACT FROM AUTHOR]

High-resolution aerosol retrieval is of great significance for understanding the impact of aerosols on air pollution and climate change. In this study, an algorithm for aerosol retrieval at a spatial resolution of 60 m over complex urban areas is developed using Sentinel-2 images. The proposed algorithm has two assumptions: 1) the blue–red surface reflectance ratio does not change temporally in a single season and 2) surface reflectance over bright areas is invariant over three months. Then, the aerosol optical depth (AOD) is retrieved from the surface reflectance correlations with a combination of temporal signatures over the vegetated areas and bright areas. The aerosol robotic network (AERONET) measurements in Beijing and its surrounding from 2017 to 2019 are collected and used to validate the retrieved 60-m Sentinel-2 AODs; 77% of the retrieved Sentinel-2 AODs fall within the expected error (EE), and the correlation coefficient is of 0.927. MODerate resolution Imaging Spectroradiometer (MODIS) AOD products at 1- and 10-km resolutions (MCD19A2 and MOD04_L2, respectively) are acquired to compare with the retrieved Sentinel-2 AODs. Comparison results show that the retrieved Sentinel-2 AODs are superior to the MOD04_L2 dark target (DT) and MCD19A2 AODs, and slightly better than the MOD04_L2 deep blue (DB), and DT and DB combined (DTBC) AODs. The validation and comparison results indicate that the proposed algorithm is able to describe aerosol distributions at high resolution continuously. However, further work is needed to apply the proposed algorithm on a global scale. [ABSTRACT FROM AUTHOR]

Soil temperature (ST) is one of the key variables in land–atmosphere interactions. The response of ST to atmospheric changes and subsequent influence of ST on atmosphere can be recognized as the processes of signals propagation. Understanding the storing and releasing of atmospheric signals in ST favors the improvement of climate prediction and weather forecast. However, the current understanding of the lagging response of ST to atmospheric changes is very insufficient. The analysis based on observation shows that both the storage of air temperature signals in deep ST even after 4 months and the storage of precipitation signals in shallow ST after 1 month are widespread phenomena in China. Air temperature signals at 2 m can propagate to the soil depths of 160 and 320 cm after 1 and 2 months, respectively. The storages of antecedent air temperature and precipitation signals in ST are slightly weaker and stronger during April–September, respectively, which is related to more precipitation during growing season. The precipitation signals in ST rapidly weaken after 2 months. Moreover, the effects of accumulated precipitation and air temperature on the signal storage in ST have significant monthly variations and vary linearly with soil depth and latitude. The storage of antecedent air temperature or precipitation signals in ST exhibits an obvious decadal variation with a period of more than 50 years, and it may result from the modulation of the global climate patterns which largely affect local air temperature and precipitation. [ABSTRACT FROM AUTHOR]

Surface-based inversions (SBIs) are significant and common natural phenomena in the planetary boundary layer, and they play essential roles in weather and climate. This study used radiosonde data from 493 radiosonde stations worldwide from the Integrated Global Radiosonde Archive during 1989–2019 to investigate the variations in surface-based inversions from a global perspective. The results indicated that, from 1989 to 2019, the SBI frequency increased and the SBI strength variations with fluctuations and SBI depth decreased over the study period. However, the spatial distribution of frequency, strength, and depth did not have consistent trends. In comparison with the Southern Hemisphere, SBIs in the Northern Hemisphere occurred more frequently and were stronger and deeper. In terms of stations over land and the ocean, we found that the SBI frequency over the ocean has increased faster than that over land in the past 15 years and that the SBI strength over land was almost 2 times that of the ocean. The amplitudes of the annual cycle of SBI characteristics over land were greater than over the ocean in both hemispheres, and the frequency, strength, and depth were greater over land. This study investigated surface-based inversions from a global perspective and filled a gap in the current research on SBIs. [ABSTRACT FROM AUTHOR]

Rainfall and snowfall have different effects on energy balance calculations and land–air interactions in terrestrial models. The identification of precipitation types is crucial to understand climate change dynamics and the utilization of water resources. However, information regarding precipitation types is not generally available. The precipitation obtained from meteorological stations across China recorded types only before 1979. This study parameterized precipitation types with air temperature, relative humidity, and atmospheric pressure from 1960 to 1979, and then identified precipitation types after 1980. Results show that the main type of precipitation in China was rainfall, and the average annual rainfall days (amounts) across China accounted for 83.08% (92.55%) of the total annual precipitation days (amounts). The average annual snowfall days (amounts) in the northwestern region accounted for 32.27% (19.31%) of the total annual precipitation days (amounts), which is considerably higher than the national average. The average annual number of rainfall and snowfall days both displayed a downward trend while the average annual amounts of these two precipitation types showed an upward trend, but without significance at 0.1 levels. The annual number of rainfall and snowfall days in the southwestern region decreased significantly (−2.27 and −0.31 day decade−1, p < 0.01). The annual rainfall amounts in the Jianghuai region increased significantly (40.70 mm decade−1, p < 0.01), and the areas with the most significant increase in snowfall amounts were the northwestern (3.64 mm decade−1, p < 0.01). These results can inform our understanding of the distribution and variation of precipitation with different types in China. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) is a remote sensing technique for mapping of soil moisture with high spatial resolution. C-band SAR can resolve features at field scale, or better, but responds to moisture only within the top 1 to 2 cm of the soil. When validating SAR-derived soil moisture products against standard in situ measurements at 5 to 10 cm depth, the greater moisture variability at the soil surface may be inaccurately categorized as measurement error. An alternative method was developed where the C-band SAR product is validated against soil moisture simulated at 2 cm depth by the HYDRUS-1D model. This reproduces soil moisture depth profiles from daily meteorological observations, leaf area index, and soil hydraulic parameters. The model was fitted at 13 COSMOS-UK sites so that the model output at 10 cm depth closely reproduced the cosmic ray neutron sensor data. At ten of the sites studied, there was an improvement of up to 8% in root-mean-squared difference by validating the Copernicus surface soil moisture (SSM) product at 2 cm compared to 10 cm. This suggests that Copernicus SSM and other C-band SAR surface soil moisture algorithms may be more accurate than have hitherto been acknowledged.

Century-long observed gridded land precipitation datasets are a cornerstone of hydrometeorological research. But recent work has suggested that observed Northern Hemisphere midlatitude (NHML) land mean precipitation does not show evidence of an expected negative response to mid-twentieth-century aerosol forcing. Utilizing observed river discharges, the observed runoff is calculated and compared with observed land precipitation. The results show a near-zero twentieth-century trend in observed NHML land mean runoff, in contrast to the significant positive trend in observed NHML land mean precipitation. However, precipitation and runoff share common interannual and decadal variability. An obvious split, or breakpoint, is found in the NHML land mean runoff-precipitation relationship in the 1930s. Using runoff simulated by six land surface models (LSMs), which are driven by the observed precipitation dataset, such breakpoints are absent. These findings support previous hypotheses that inhomogeneities exist in the early-twentieth-century NHML land mean precipitation record. Adjusting the observed precipitation record according to the observed runoff record largely accounts for the departure of the observed precipitation response from that predicted given the real-world aerosol forcing estimate, more than halving the discrepancy from about 6 to around 2 W m-2. Consideration of complementary observed runoff adds support to the suggestion that NHML-wide early-twentieth-century precipitation observations are unsuitable for climate change studies. The agreement between precipitation and runoff over Europe, however, is excellent, supporting the use of whole-twentieth-century observed precipitation datasets here.

We explore whether and how the land characteristics, especially surface drag, affect the squall line, in an idealized modeling framework. We find that the balance between the ambient low‐level shear and cold‐pool‐induced shear in the squall line can be modified by surface drag: it not only changes the ambient shear if near‐surface environmental wind is nonzero, but also reduces the cold‐pool‐induced shear by both weakening the low‐level wind and deepening the cold pool. The land surface impact on squall lines is larger with lower convective available potential energy (CAPE), partly because of stronger convection with higher CAPE. The cold pool intensity (vertically integrated negative buoyancy) could not explain the reduced cold pool shear induced by the land. Furthermore, land characteristics—such as roughness lengths—help maintain the shear balance and potentially make the squall line long‐lived, suggesting that they be included in convective (or cold pool) parameterizations to improve climate and Earth system models. Key Points: Surface drag affects squall lines by weakening near‐surface wind and deepening the cold poolThe widely used cold pool intensity no longer characterizes the cold‐pool‐induced shear when surface drag is includedDynamical effects of the land surface are missed in thermodynamic‐based cold pool parameterizations [ABSTRACT FROM AUTHOR]

Validation of satellite albedo products is an essential step because their quantitative application lie in their ability to record the real state of the earth surface. Upscaling in situ measurements to the corresponding pixel scale is necessary due to the spatial scale mismatch between in situ and satellite measurements. Machine learning-based models have been increasingly used for upscaling because they can yield more reliable results than traditional methods. Nevertheless, the main controlling factors on upscaled results have rarely been discussed. This article explores the control factors that bring uncertainties to the upscaled results based on machine learning models. Three machine learning models, including random forest (RF), $k$ -nearest neighbor (KNN), and Cubist models, were selected to upscale single site in situ-based albedo to the coarse pixel scale. The upscaled results were carefully assessed through comparison with pixel scale albedo reference. The results indicate that the accuracy of upscaled results depends on the machine learning models, the inclusion of key variables related to albedo, the dataset selection of these variables, the amount of training data, and the sensitivity of machine learning models to these factors. Despite the dependence on control factors, the machine learning-based upscaling methods generally have excellent applicability across different spatial scales and over other untrained areas. Therefore, they open the door to generating a time series of globally, spatially continuous distributed reference datasets with sufficient length, consistency, and continuity to adequately fulfill the requirement of a comprehensive validation. [ABSTRACT FROM AUTHOR]

Downward longwave radiation (DLR) at the surface is a key variable of interest in fields, such as hydrology and climate research. However, existing DLR estimation methods and DLR products are still problematic in terms of both accuracy and spatiotemporal resolution. In this article, we propose a deep convolutional neural network (DCNN)-based method to estimate hourly DLR at 5-km spatial resolution from top of atmosphere (TOA) brightness temperature (BT) of the Himawari-8/Advanced Himawari Imager (AHI) thermal channels, combined with near-surface air temperature and dew point temperature of ERA5 and elevation data. Validation results show that the DCNN-based method outperforms popular random forest and multilayer perceptron-based methods and that our proposed scheme integrating multisource data outperforms that only using remote sensing TOA observations or surface meteorological data. Compared with state-of-the-art CERES-SYN and ERA5-land DLR products, the estimated DLR by our proposed DCNN-based method with physical multisource inputs has higher spatiotemporal resolution and accuracy, with correlation coefficient (CC) of 0.95, root-mean-square error (RMSE) of 17.2 W/m2, and mean bias error (MBE) of −0.8 W/m2 in the testing period on the Tibetan Plateau. [ABSTRACT FROM AUTHOR]