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Recent studies have suggested a close relationship between early summer precipitation over Northeast China and spring land surface thermal anomalies in West Asia. However, is this relationship the same over the multidecadal timescale? This study aims to identify the long-term variation in this relationship and the accompanying atmospheric circulation anomalies by using singular value decomposition, correlation analysis, and linear regression based on the ECMWF Reanalysis v5 (ERA5) atmospheric data, ERA-Land reanalysis, and CN05 gridded observations during 1961–2020 (60 yr). It is found that an interdecadal transition of the relationship between the spring surface temperature/thermal anomalies in West Asia and early summer precipitation over Northeast China occurred around 1990, and the temperature–rainfall relationship intensified after 1990. Based on the Mann–Kendall test, the study period was divided into P1 (1961–1990) and P2 (1991–2020). Further analysis indicated significant differences in the corresponding atmospheric circulation before and after the interdecadal transition. During P2, spring land surface warming in West Asia corresponded to a significantly enhanced early summer Circumglobal Teleconnection (CGT), which in turn suppressed the Northeast China cold vortex (NECV). The changes in circulation patterns further resulted in weakened moisture transport, strengthened subsidence, reduced precipitation triggering, and eventually, weakened precipitation. Additionally, the results suggest that the interdecadal transition of the relationship and the changes in the corresponding atmospheric circulation may be related to activities of the westerly jet stream. The second principal component (PC2) mode of empirical orthogonal function (EOF) of zonal wind in June over Asia demonstrated a pattern similar to that of the atmospheric circulation corresponding to land surface thermal anomalies. In addition, during P2, the PC2 mode of the westerly jet stream in June showed a strong positive correlation with the NECV, thereby suppressing precipitation over Northeast China. Therefore, it is concluded that the westerly jet stream may have affected the interdecadal transition of the temperature–rainfall relationship around 1990. [ABSTRACT FROM AUTHOR]

Assessing the daily water requirements of crops and understanding the severity of drought necessitates precise estimation of potential evapotranspiration (PET), particularly in regions with arid climates such as Egypt. In the present study, the RegCM4 regional climate model was used to investigate the sensitivity of the PET of Egypt to two land surface schemes and boundary layer parameterizations. The land surface schemes are the Biosphere Atmosphere Transfer System (BATS) and the Community Land Model version 4.5 (CLM45). The boundary layer schemes considered are the HOLTSLAG (HOLT) and University of Washington (UW). To accomplish this task, four 32-year simulations were conducted spanning from 1979 to 2010, with the first two years considered as spin up. The ERA-Interim reanalysis was used to downscale the RegCM4 model. The simulated PET was evaluated with respect to the high-resolution ERA5-land PET-based product (hPET). The results showed that the BATS showed a bias of −0.8 to −1.8 mm day−1, while the CLM45 showed a bias of −0.8 to −3 mm day−1. Also, fine-tuning the coefficient of the daily mean air temperature succeeded in reducing the PET bias. Additionally, the UW had a lower PET bias than that noted in HOLT. To further reduce the PET bias, the linear-scaling (LS) bias-correction method was used. The LS showed its potential skills in reducing the mean bias of the PET from −2.2 to +0.4 mm day−1 in the evaluation period and to ±0.2 mm day−1 in the validation period. Furthermore, the added value of the LS was confirmed concerning the climatological annual cycle in different locations representing different climate zones of Egypt. In conclusion, accurate estimation of the PET can be ensured using the BATS, the UW schemes, and the LS technique in the present climate or under different warming scenarios. [ABSTRACT FROM AUTHOR]

Rapid urbanization creates complexity, results in dynamic changes in land and environment, and influences the land surface temperature (LST) in fast-developing cities. In this study, we examined the impact of land use/land cover (LULC) changes on LST and determined the intensity of urban heat island (UHI) in New Town Kolkata (a smart city), eastern India, from 1991 to 2021 at 10-a intervals using various series of Landsat multi-spectral and thermal bands. This study used the maximum likelihood algorithm for image classification and other methods like the correlation analysis and hotspot analysis (Getis–Ord Gi* method) to examine the impact of LULC changes on urban thermal environment. This study noticed that the area percentage of built-up land increased rapidly from 21.91% to 45.63% during 1991–2021, with a maximum positive change in built-up land and a maximum negative change in sparse vegetation. The mean temperature significantly increased during the study period (1991–2021), from 16.31°C to 22.48°C in winter, 29.18°C to 34.61°C in summer, and 19.18°C to 27.11°C in autumn. The result showed that impervious surfaces contribute to higher LST, whereas vegetation helps decrease it. Poor ecological status has been found in built-up land, and excellent ecological status has been found in vegetation and water body. The hot spot and cold spot areas shifted their locations every decade due to random LULC changes. Even after New Town Kolkata became a smart city, high LST has been observed. Overall, this study indicated that urbanization and changes in LULC patterns can influence the urban thermal environment, and appropriate planning is needed to reduce LST. This study can help policy-makers create sustainable smart cities. [ABSTRACT FROM AUTHOR]

Between 1979 and 2021, global ocean regions experienced a decrease in dry season precipitation, while the trend over land areas varied considerably. Some regions, such as southeastern China, the Maritime Continent, eastern Europe, and eastern North America, showed a slight increasing trend in dry season precipitation. This study analyzes the potential mechanisms behind this trend by using the fifth major global reanalysis produced by ECMWF (ERA5) data. The analysis shows that the weakening of downward atmospheric motions played a critical role in enhancing dry season precipitation over land. An atmospheric moisture budget analysis revealed that larger convergent moisture fluxes lead to an increase in water vapor content below 400 hPa. This, in turn, induced an unstable tendency in the moist static energy profile, leading to a more unstable atmosphere and weakening downward motions, which drove the trend toward increasing dry season precipitation over land. More water vapor in the low troposphere is because of higher moisture convergence and moisture transport from ocean to land regions. In summary, this study demonstrates the intricate elements involved in altering dry season rainfall trends over land, which also emphasizes the importance of comprehending the spatial distribution of the wet-get-wetter and dry-get-drier paradigm. Significance Statement: This study found that global land precipitation during the dry season slightly increased from 1979 to 2021, while precipitation over oceans declined. Moist static energy analysis showed a trend toward less stability in areas with increased dry season precipitation and the opposite trend in regions with declining precipitation. Water vapor content trends and dynamic components were the primary controlling mechanism for precipitation trends. Furthermore, the hotspots with pronounced increases or decreases in dry season precipitation reflect local circulation changes influenced by anthropogenic or natural factors. [ABSTRACT FROM AUTHOR]

Frost is damaging to plants when air temperature drops below their tolerance threshold. The set of mechanisms used by cold-tolerant plants to withstand freezing is called “hardening” and typically take place in autumn to protect against winter damage. The recent incorporation of a hardening scheme in the demographic vegetation model FATES opens up the possibility to investigate frost mortality to vegetation. Previously, the hardening scheme was used to improve hydraulic processes in cold-tolerant plants. In this study, we expand upon the existing hardening scheme by implementing hardiness-dependent frost mortality into CLM5.0-FATES to study the impacts of frost on vegetation in temperate and boreal sites from 1950 to 2015. Our results show that the original freezing mortality approach of FATES, where each plant type had a fixed freezing tolerance threshold—an approach common to many other dynamic vegetation models, was restricted to predicting plant type distribution. The main results emerging from the new scheme are a high autumn and spring frost mortality, especially at colder sites, and increasing mid-winter frost mortality due to global warming, especially at warmer sites. We demonstrate that the new frost scheme is a major step forward in dynamically representing vegetation in ESMs by for the first time including a level of frost tolerance that is responding to the environment and includes some level of cost (implicitly) and benefit. By linking hardening and frost mortality in a land surface model, we open new ways to explore the impact of frost events in the context of global warming.

Understanding interactions between low clouds and land surface fluxes is critical to comprehending Earth's energy balance, yet their relationships remain elusive, with discrepancies between observations and modeling. Leveraging long‐term field observations over the Southern Great Plains, this investigation revealed that cloud‐land interactions are closely connected to cloud‐land coupling regimes. Observational evidence supports a dual‐mode interaction: coupled stratiform clouds predominate in low sensible heat scenarios, while coupled cumulus clouds dominate in high sensible heat scenarios. Reanalysis data sets, MERRA‐2 and ERA‐5, obscure this dichotomy owing to a shortfall in representing boundary layer clouds, especially in capturing the initiation of coupled cumulus in high sensible heat scenarios. ERA‐5 demonstrates a relatively closer alignment with observational data, particularly in capturing relationships between cloud frequency and latent heat, markedly outperforming MERRA‐2. Our study underscores the necessity of distinguishing different cloud coupling regimes, essential to the understanding of their interactions for advancing land‐atmosphere interactions. Plain Language Summary: Low cloud interactions with the Earth's surface in the Southern Great Plains are examined to understand the coupling between low clouds and the land surface. Clouds play a major role in Earth's radiation energy balance and therefore the climate system, yet the cloud‐land interaction relationship is complicated and not well‐understood. Based on analyses of long‐term field observations, we find that cloud‐land interactions in this region are closely related to cloud‐land coupling regimes. Observational evidence supports a dual‐mode interaction: coupled stratiform clouds that dominate in low sensible heat states and coupled cumulus that dominate in high sensible heat states. The reanalysis data sets exhibit a common shortcoming of a deficit in boundary layer cloud parameterization, with a poorer representation of the initiation of coupled cumulus especially. This study highlights the importance of understanding the different cloud coupling regimes that interact with the land, which is essential for advancing weather and climate models. Key Points: This study develops a diagnostic approach for untangling cloud‐land relationships across distinct cloud coupling regimesField observations are utilized to assess performances of reanalysis data in representing cloud‐land interaction across different regimesFindings emphasize the importance of differentiating cloud coupling regimes in observational and modeling studies of boundary layer clouds [ABSTRACT FROM AUTHOR]

Soil moisture (SM) and soil freeze–thaw (FT) are two relatively active surface parameters that are significant to the sustainable development of the water–land–air–plant–human nexus. Over time, regional or global SM and FT datasets with different spatial resolutions have been developed. In response to the requirements of multiscale product validation and multisource uncertainty tracking, a soil moisture and soil temperature (ST) monitoring network in the Qinghai Lake Basin (QLB-NET) was established in September 2019. The QLB-NET is characterized by densely distributed in situ sites (82 sites) measuring SM and ST at 5-, 10-, and 30-cm depths, with 60 sites in a large-scale network covering an area of 36 km × 40 km and 22 sites evenly distributed across two small-scale 1 km × 1 km networks. Quantitative analyses of the in situ measurements show that the QLB-NET can provide stable and reliable ground truth for SM and FT over coarse grid scales, e.g., 36 km × 36 km, 25 km × 25 km, and 0.25° × 0.25°. When statistics are correspondingly performed over 50 out of 54, 25 out of 29, and 25 out of 28 sites, the results are described as follows: 1) the standard deviation of the mean SM varies between 0.0127 and 0.0196 m3 m−3, with the corresponding difference between the upper and lower quartiles being less than 0.02 m3 m−3; 2) the ground freeze–thaw state can be correctly identified with high probabilities ranging from 85.3% to 100% on two freeze–thaw transitional dates. The QLB-NET observed datasets are distributed online and will be continuously updated through cooperation with the National Tibetan Plateau Data Center (http://data.tpdc.ac.cn), facilitating product validation and uncertainty tracking, spatiotemporal analysis of SM change and FT transition, optimization of the SM and FT retrieving algorithms and scaling methods, and development of the mountainous microwave radiative transfer model. SIGNIFICANCE STATEMENT: This study aims to introduce the newly constructed QLB-NET in detail, including the site deployment strategy, the installation and maintenance, the sensor calibration, and the characteristics and quality of the in situ SM and ST measurements. Quantitative analyses of the in situ measurements show that the QLB-NET can provide stable and reliable ground truth for SM and FT over coarse grid scales, e.g., the SMAP 36 km × 36 km grid. The high-quality QLB-NET measurements will facilitate research of product validation and uncertainty tracking, spatiotemporal analysis of SM change and FT transition, optimization of the SM and FT retrieving algorithms and scaling methods, and development of the mountainous microwave radiative transfer model. [ABSTRACT FROM AUTHOR]

Rapid urbanization creates complexity, results in dynamic changes in land and environment, and influences the land surface temperature (LST) in fast-developing cities. In this study, we examined the impact of land use/land cover (LULC) changes on LST and determined the intensity of urban heat island (UHI) in New Town Kolkata (a smart city), eastern India, from 1991 to 2021 at 10-a intervals using various series of Landsat multi-spectral and thermal bands. This study used the maximum likelihood algorithm for image classification and other methods like the correlation analysis and hotspot analysis (Getis–Ord Gi* method) to examine the impact of LULC changes on urban thermal environment. This study noticed that the area percentage of built-up land increased rapidly from 21.91% to 45.63% during 1991–2021, with a maximum positive change in built-up land and a maximum negative change in sparse vegetation. The mean temperature significantly increased during the study period (1991–2021), from 16.31°C to 22.48°C in winter, 29.18°C to 34.61°C in summer, and 19.18°C to 27.11°C in autumn. The result showed that impervious surfaces contribute to higher LST, whereas vegetation helps decrease it. Poor ecological status has been found in built-up land, and excellent ecological status has been found in vegetation and water body. The hot spot and cold spot areas shifted their locations every decade due to random LULC changes. Even after New Town Kolkata became a smart city, high LST has been observed. Overall, this study indicated that urbanization and changes in LULC patterns can influence the urban thermal environment, and appropriate planning is needed to reduce LST. This study can help policy-makers create sustainable smart cities.

Assessing the daily water requirements of crops and understanding the severity of drought necessitates precise estimation of potential evapotranspiration (PET), particularly in regions with arid climates such as Egypt. In the present study, the RegCM4 regional climate model was used to investigate the sensitivity of the PET of Egypt to two land surface schemes and boundary layer parameterizations. The land surface schemes are the Biosphere Atmosphere Transfer System (BATS) and the Community Land Model version 4.5 (CLM45). The boundary layer schemes considered are the HOLTSLAG (HOLT) and University of Washington (UW). To accomplish this task, four 32-year simulations were conducted spanning from 1979 to 2010, with the first two years considered as spin up. The ERA-Interim reanalysis was used to downscale the RegCM4 model. The simulated PET was evaluated with respect to the high-resolution ERA5-land PET-based product (hPET). The results showed that the BATS showed a bias of −0.8 to −1.8 mm day−1, while the CLM45 showed a bias of −0.8 to −3 mm day−1. Also, fine-tuning the coefficient of the daily mean air temperature succeeded in reducing the PET bias. Additionally, the UW had a lower PET bias than that noted in HOLT. To further reduce the PET bias, the linear-scaling (LS) bias-correction method was used. The LS showed its potential skills in reducing the mean bias of the PET from −2.2 to +0.4 mm day−1 in the evaluation period and to ±0.2 mm day−1 in the validation period. Furthermore, the added value of the LS was confirmed concerning the climatological annual cycle in different locations representing different climate zones of Egypt. In conclusion, accurate estimation of the PET can be ensured using the BATS, the UW schemes, and the LS technique in the present climate or under different warming scenarios.

Land surface processes are strongly associated with heat waves (HWs). However, how the uncertainties in land surface processes owing to inaccurate physical parameters influence subseasonal HW predictions has rarely been explored. To examine the impact of parameter errors of land surface processes on the uncertainty of subseasonal HW predictions, five strong and long‐lasting HW events over the middle and lower reaches of the Yangtze River are investigated. Based on the Weather Research and Forecasting model, the conditional nonlinear optimal perturbation related to parameters (CNOP‐P) approach is employed to address the aforementioned issues. Numerical results demonstrate that the CNOP‐P type errors of physical parameters cause large prediction errors for five HW event onsets. Two types of CNOP‐Ps are obtained for HW events, called the type‐1 CNOP‐P and the type‐2 CNOP‐P. The type‐1 (type‐2) CNOP‐P causes an approximately 3°C (2°C) warm (cold) bias during the HW period. Surface sensible and latent heat flux errors, especially flux exchange between vegetation canopy and canopy air, provide considerable uncertainty in subseasonal HW predictions. The type‐1 (type‐2) CNOP‐P exhibits an underestimation (overestimation) of transpiration. Furthermore, it should be noted that the type‐1 CNOP‐P results in a substantial difference in soil moisture, a phenomenon that is demonstrated to be challenging to observe in the type‐2 CNOP‐P. The results indicate that understanding vegetation‐atmosphere dynamics is crucial for improving subseasonal HW predictions. Jointly lowering soil‒atmosphere and vegetation‐atmosphere uncertainty can notably improve subseasonal HW prediction skills. Plain Language Summary: Land surface processes play critical roles in subseasonal heat wave (HW) predictions. In this study, a rarely explored problem is examined: how uncertainties in land surface processes (i.e., model parameters) influence subseasonal HW predictions. The conditional nonlinear optimal perturbation related to parameters (CNOP‐P) approach is employed to identify parameter errors causing the largest prediction errors. Five strong and long‐lasting HW events over the middle and lower reaches of the Yangtze River (MLYR) are evaluated by the Weather Research and Forecasting model. There are two types of CNOP‐Ps of HW events, named type‐1 and type‐2 CNOP‐P. The type‐1 CNOP‐P overestimates the temperature, whereas the type‐2 CNOP‐P underestimates it. Both types of CNOP‐Ps lead to notable forecast uncertainties. Surface sensible heat flux and latent heat flux errors are the main causes leading to large uncertainties in subseasonal HW predictions. Vegetation transpiration is important in the two types of CNOP‐Ps. In contrast to the type‐2 CNOP‐P, the type‐1 CNOP‐P also underestimates soil evaporation, which results in soil moisture deficits. Low soil moisture conditions are critical for exacerbating HWs. This study indicates that accurately describing the soil‐vegetation‐atmosphere feedback processes is an effective way to improve the subseasonal prediction of severe HWs over the MLYR. Key Points: The uncertainties in the physical parameters of the land surface process cause considerable errors in subseasonal heat wave (HW) predictionsThe combined effects of vegetation transpiration and soil evaporation are crucial for predictions of HW onset and evolutionThe conditional nonlinear optimal perturbation related to parameters method can be used to explore the subseasonal predictability of HWs [ABSTRACT FROM AUTHOR]

This study presents a novel classification model, the Normalized Difference Vegetation Index (NDVI), degree of polarization (DOP), texture classification model (NDTCM), for both regional and global scales, utilizing a synergistic approach that combines the NDVI from optical data with the DOP and its associated texture from synthetic aperture radar data. This integration effectively enhances land use and land cover (LULC) mapping. Specifically, Multi-date Moderate Resolution Imaging Spectroradiometer/Landsat images for NDVI extraction and dual polarization Phased-Array L-band Synthetic Aperture Radar data for DOP are employed in this study. This integration enables the NDTCM to effectively classify land cover into five categories: forest, shrubland, urban, cultivated land, and bare surface. Applied to Mediterranean land cover mapping, the NDTCM achieved high accuracy, with rates of 93.3% for forests, 57.5% for shrublands, 64.4% for urban areas, 76.8% for cultivated lands, and 92.8% for bare surfaces. Compared with global land-cover models, such as GlobCover, the NDTCM showed superior performance in forest and shrubland classification, exceeding GlobCover's accuracy of 84.3% for forests and 35.4% for shrublands, in this study case. The contribution of each data source to the classification results was significant. NDVI data were instrumental in identifying vegetative cover. The DOP and texture information enriched the model's capability to discern land cover types by providing insights into the physical structure and heterogeneity of the surfaces, critical for distinguishing between different land covers, such as forest and shrubland. This comprehensive integration demonstrates the NDTCM's potential as a robust framework for future advancements in LULC mapping and environmental studies. [ABSTRACT FROM AUTHOR]

Evaporative fraction (EF) is the proportion of latent heat flux in surface net energy and reflects the moisture status of an ecosystem. However, the effects of different meteorological factors and vegetation changes on EF are not known. In this study, 142 flux tower observations were used to analyze the spatial and temporal variations of EF in different vegetation types and to analyze the response of meteorological factors and vegetation changes to EF. The results showed that evergreen broadleaf forests (EBFs) had the highest multiyear mean EF (0.61), while open shrublands (OSH) had the lowest multiyear mean EF (0.32). Areas with higher EF values were generally found in temperate continental, mediterranean, and oceanic climates. Seasonally, EFs were generally high in the northern and southern hemispheres during the summer and relatively low in the spring and autumn. The correlation analysis showed that EF increased significantly with increasing precipitation and leaf area index (LAI) for all six vegetation types. Deciduous broadleaf forests ($R$ = 0.37, RSS = 6.96, and $P < 0.01$) and wetlands ($R$ = 0.26, RSS = 7.03, and $P < 0.01$) showed a significant increasing trend with increasing vapor pressure deficit (VPD), and two vegetation types, grasslands ($R$ = −0.35, RSS = 13.44, and $P < 0.01$), and EBF ($R$ = −0.19, RSS = 3.77, and $P < 0.01$) showed a significant decrease trend with increasing VPD. The monthly mean EF of three vegetation types, grasslands ($R$ = 0.26, RSS = 14.20, and $P < 0.01$), deciduous broadleaf forests ($R$ = 0.34, RSS = 7.11, and $P < 0.01$), and wetlands ($R$ = −0.40, RSS = 6.37, and $P < 0.01$) decreased significantly with increasing wind speed. Our findings provide a scientific basis for assessing surface water conditions and contribute to the understanding of EF interactions with climate and vegetation change. [ABSTRACT FROM AUTHOR]

Abstract Understanding interactions between low clouds and land surface fluxes is critical to comprehending Earth's energy balance, yet their relationships remain elusive, with discrepancies between observations and modeling. Leveraging long‐term field observations over the Southern Great Plains, this investigation revealed that cloud‐land interactions are closely connected to cloud‐land coupling regimes. Observational evidence supports a dual‐mode interaction: coupled stratiform clouds predominate in low sensible heat scenarios, while coupled cumulus clouds dominate in high sensible heat scenarios. Reanalysis data sets, MERRA‐2 and ERA‐5, obscure this dichotomy owing to a shortfall in representing boundary layer clouds, especially in capturing the initiation of coupled cumulus in high sensible heat scenarios. ERA‐5 demonstrates a relatively closer alignment with observational data, particularly in capturing relationships between cloud frequency and latent heat, markedly outperforming MERRA‐2. Our study underscores the necessity of distinguishing different cloud coupling regimes, essential to the understanding of their interactions for advancing land‐atmosphere interactions.

Decades of urbanization have created sprawling, complex, and vulnerable cities, half of which are located in water-scarce areas. With the looming effects of climate change, including increasing droughts and water shortages, there is an urgent need to better understand how urbanization impacts the water cycle at city scale. Impervious surfaces disrupt the natural flow of water, affecting groundwater recharge in water-scarce cities, such as Los Angeles, looking to harness local water resources. In the face of growing water demand, informing on opportunities to maximize potential groundwater recharge can help increase cities’ resilience. WRF–Hydro, a physics-based hydrological modeling system, capable of resolving atmospheric, land surface, and hydrological processes at city scale, is adapted to represent urban impervious surfaces. The modified model is used to assess the hydrological implications of historical urbanization. Pre- and post-urban scenarios are used to quantify the impacts of impervious surfaces on the local water budget. Our results show that urbanization in LA has vastly decreased the potential for groundwater recharge, with up to half of the water inflow being redirected from infiltration in highly urbanized watersheds, while doubling surface runoff’s share of the city’s water budget, from ~15% to 30%. This study not only sheds light on the role of imperviousness on groundwater recharge in water-scarce cities, but also offers a robust and transferable tool for the management of urban land and water resources.

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. [ABSTRACT FROM AUTHOR]

Remote sensing is the technology by which the characteristics of the analyzed objects can be identified, measured and analyzed without direct contact, but from a distance. Remote sensing technology to identify objects and understand environmental conditions is the uniqueness of reflection and emission. Landsat 8 is an American Earth observation satellite launched on February 11, 2013. It is the eighth satellite in the Landsat program and the seventh to successfully reach orbit. This satellite system provides medium resolution images from 15 meters to 100 meters of the land surface and polar regions. It works in the visible spectrum, near infrared, short wave infrared and thermal infrared. In the present study, the Landsat 8 method was used to collect spectral data. These data help us understand the evolution of many natural processes, the monitoring of agricultural and forest land, can allow the longest continuous data recording of the Earth's surface seen from space. Landsat data allow the monitoring of climate change, which leads to the improvement of human health and biodiversity. Satellite images have a very important role in the analysis of the earth's surface and important sources of extraction of stored geographical information. The study area is located in the west of Romania and is made up of the following administrative-territorial units: Ohaha Lungă, Mănăștiur, Dumbrava, Făget, Margina, Curtea, Fîrdea and Tomești. Based on the spectral information, four normalized differentiation indices for land surface analysis, namely NDMI, NDBR, NDWI and SAVI, were calculated for each study area. [ABSTRACT FROM AUTHOR]

The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides global, 9-km resolution, 3-hourly surface and root-zone soil moisture from April 2015 to the present with a mean latency of 2.5 days from the time of observation. The L4_SM algorithm assimilates SMAP L-band (1.4 GHz) brightness temperature (Tb) observations into the NASA Catchment land surface model as the model is driven with observation-based precipitation. This paper describes and evaluates the use of satellite- and gauge-based precipitation from the NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) products in the L4_SM algorithm beginning with L4_SM Version 6. Specifically, IMERG is used in two ways: (i) The L4_SM precipitation reference climatology is primarily based on IMERG-Final (Version 06B) data, replacing the Global Precipitation Climatology Project Version 2.2 data used in previous L4_SM versions, and (ii) the precipitation forcing outside of North America and the high latitudes is corrected to match the daily totals from IMERG, replacing the gauge-only, daily product or uncorrected weather analysis precipitation used there in earlier L4_SM versions. The use of IMERG precipitation improves the anomaly time series correlation coefficient of L4_SM surface soil moisture (versus independent satellite estimates) by 0.03 in the global average and by up to ∼0.3 in parts of South America, Africa, Australia, and East Asia, where the quality of the gauge-only precipitation product used in earlier L4_SM versions is poor. The improvements also reduce the time series standard deviation of the Tb observation-minus-forecast residuals from 5.5 K in L4_SM Version 5 to 5.1 K in Version 6. Significance Statement: Soil moisture links the land surface water, energy, and carbon cycles. NASA Soil Moisture Active Passive (SMAP) satellite observations and observation-based precipitation data are merged into a numerical model of land surface water and energy balance processes to generate the global, 9-km resolution, 3-hourly Level-4 Soil Moisture (L4_SM) data product. The product is available with ∼2.5-day latency to support Earth science research and applications, such as flood prediction and drought monitoring. We show that a recent L4_SM algorithm update using satellite- and gauge-based precipitation inputs from the NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) products improves the temporal variations in the estimated soil moisture, particularly in otherwise poorly instrumented regions in South America, Africa, Australia, and East Asia. [ABSTRACT FROM AUTHOR]

Ensemble data assimilation (DA) is an efficient approach to reduce snow simulation errors by combining observation and land surface modeling. However, there is a small spread between ensemble members of simulated snowpack, which typically occurs for a long time with 100% snow cover fraction (SCF) or snow-free conditions. Here, we apply a hybrid DA method, in which direct insertion (DI) is a supplement of the ensemble square root filter (EnSRF), to assimilate the spaceborne SCF into a land surface model, driven by China Meteorological Administration Land Data Assimilation System high-resolution climate forcings over northern China during the snow season in 2021/22. Compared to the open-loop experiment (without SCF assimilation), the root-mean-square error (RMSE) of SCF is reduced by 6% through the original EnSRF and is even lower (by 14%) in the combined DI and EnSRF (EnSRFDI) experiment. The results reveal the ability of both EnSRF and EnSRFDI to improve the SCF estimation over regions where the snow cover is low, while only EnSRFDI is able to efficiently reduce the RMSE over areas with high SCF. Moreover, the SCF assimilation is also observed to improve the snow depth and soil temperature simulations, with the Kling–Gupta efficiency (KGE) increasing at 60% and 56%–70% stations, respectively, particularly under conditions with near-freezing temperature, in which reliable simulations are typically challenging. Our results demonstrate that the EnSRFDI hybrid method can be applied for the assimilation of spaceborne observational snow cover to improve land surface simulations and snow-related operational products. Significance Statement: Due to the small spread between the seasonal snowpack of ensemble simulations, ensemble snow cover fraction (SCF) data assimilation (DA) proves to be ineffective. Therefore, we apply a hybrid method that combines the direct insertion (DI) and ensemble square root filter (EnSRF) to assimilate the spaceborne SCF into a land surface model (LSM) driven by high-resolution climate forcings. Our results reveal the applicability of the EnSRFDI to further improve snow cover simulations over regions with high SCF. Furthermore, the DA experiments were validated through a large number of in situ observations from the China Meteorological Administration. The uncertainties of snow depth and soil temperature simulations are also slightly reduced by the SCF DAs, particularly over regions with a poor LSM performance. [ABSTRACT FROM AUTHOR]

Thresholds of soil moisture exist below which the atmosphere becomes hypersensitive to land surface drying, inducing thermal feedbacks that can exacerbate heatwaves. Realistic representation of threshold transitions in forecast models could improve extreme heat predictability and understanding of the role of land–atmosphere coupling. This study evaluates the performance of several forecast models from the Subseasonal Experiment (SubX) and several prototype versions of the Unified Forecast System (UFS) in their representation of threshold transitions by validation against reanalysis data. A metric of skill (true skill score) is applied to soil moisture breakpoint values, which mark the transition to heatwave hypersensitivity for drying soils. Forecast models have poor skill at being initialized on the correct side of the breakpoint, but show improvement when normalized to account for deficiencies in their soil moisture climatologies. Regionally, models performed best in the U.S. Northwest and worst in the Southwest. They effectively capture the tendency of western regions to spend more summer days in the hypersensitive regime than the eastern United States. Models represent well extreme heat as a consequence of atmospheric initial state for the first week of the forecast, but struggle to represent the soil moisture feedback regime. Forecast models generally perform better at extreme heat prediction when they are already dry and in the hypersensitive regime, even when erroneously so, implying that errors or biases exist in model parameterizations. Nevertheless, composite analysis shows encouraging model performance of the "hit" category, suggesting that an improvement in soil moisture initialization could further improve extreme heat forecast skill. [ABSTRACT FROM AUTHOR]

The assimilation of two surface-sensitive channels of the AMSU-A instruments on board the NOAA-15/NOAA-18/NOAA-19 and MetOp-A/MetOp-B satellites over land was achieved in the China Meteorological Administration Global Forecast System (CMA_GFS). The land surface emissivity was calculated by 1) the window channel retrieval method and 2) the Tool to Estimate Land Surface Emissivities at Microwave frequencies (TELSEM2). Quality controls for these satellite microwave observations over land were conducted. The predictors and regression coefficients used for oceanic satellite data were retained during the bias correction over land and found to perform well. Three batch experiments were implemented in CMA_GFS with 4D-Var: 1) assimilating only the default data, and adding the above data over land with land surface emissivity obtained from 2) TELSEM2 and 3) the window channel retrieval method. The results indicated that the window channel retrieval method can better reduce the departure between the observed and simulated brightness temperature. Over most land types, the positive impacts of this method exceed those of TELSEM2. Both TELSEM2 and the window channel retrieval method improve the humidity analysis near the ground, as well as the forecast capability globally, particularly in those regions where the land coverage is greater, such as in the Northern Hemisphere. The data utilization of the two surface-sensitive channels increase by 6% and 12%, respectively, and the additional data every 6 h can cover most land, where there was no surface-sensitive data assimilated before. This study marks the beginning of near-surface channel assimilation over land in CMA_GFS and represents a breakthrough in the assimilation of other surface-sensitive channels in other satellite instruments. Significance Statement: Surface-sensitive microwave channels are difficult to assimilate in NWP due to the lack of both direct measurement and appropriate modeling for instantaneous land surface emissivity. This paper discusses a method that improves the surface emissivity estimates, which has allowed the utilization of surface-sensitive microwave channels in CMA_GFS. Those capabilities have resulted in better data utilization, improved forecasts of temperature, geopotential height, and winds in the Northern Hemisphere at 3–7 days, and represent an incremental and important improvement to CMA_GFS. [ABSTRACT FROM AUTHOR]

Changes in surface water and energy balance can influence weather through interactions between the land and lower atmosphere. In convecting atmospheres, increases in convective available potential energy (CAPE) at the base of the column are driven by surface turbulent fluxes and can lead to precipitation. Using two global satellite datasets, we analyze the impact of surface energy balance partitioning on convective development by tracking CAPE over soil moisture drydowns (interstorms) during the summer, when land–atmosphere coupling is strongest. Our results show that the sign and magnitude of CAPE development during summertime drydowns depends on regional hydroclimate and initial soil moisture content. On average, CAPE increases between precipitation events over humid regions (e.g., the eastern United States) and decreases slightly over arid regions (e.g., the western United States). The soil moisture content at the start of a drydown was found to only impact CAPE evolution over arid regions, leading to greater decreases in CAPE when initial soil moisture content was high. The effect of these factors on CAPE can be explained by their influence principally on surface evaporation, demonstrating the importance of evaporative controls on CAPE and providing a basis for understanding the soil moisture–precipitation relationship, as well as land–atmosphere interaction as a whole. Significance Statement: Land–atmosphere coupling is a long-standing topic with growing interest within the climate and modeling communities. Understanding and characterizing the feedbacks between the land surface and lower atmosphere has important implications for weather and climate prediction. One component of land–atmosphere coupling not yet fully understood is the soil moisture–precipitation relationship. Our work quantifies the land influence on one pathway for precipitation, convection, by tracking the evolution of atmospheric convective energy as soils dry between storms. Using global satellite observations, we find clear spatial and temporal trends that link summertime convective development to soil moisture content and evaporation. Our observational results provide a benchmark for evaluating how well weather and climate models capture the complex coupling between land and atmosphere. [ABSTRACT FROM AUTHOR]

In this study screen‐level analyses of air temperature and humidity are used to objectively evaluate the impact of a new land‐surface package being considered for implementation in Environment and Climate Change Canada (ECCC)'s medium‐range global deterministic numerical weather prediction (NWP) system. Through its control of heat, moisture, and momentum fluxes to the atmosphere, the land surface has a substantial impact on near‐surface meteorology and on the atmospheric boundary layer. The approach examined in this study is based on the comparison between model forecasts and screen‐level analyses. It demonstrates the impact on medium‐range NWP of a new land‐surface package that includes (i) a new set of databases to specify soils and land‐cover characteristics, (ii) improved land‐surface initial conditions obtained by the assimilation of space‐based remote‐sensing observations, and (iii) a more sophisticated scheme for land‐surface modelling. The evaluation method is shown to provide useful information on the impact of the new land‐surface package, including lead‐time‐averaged difference maps as well as plots showing the evolution with lead time of the standard deviation of errors (STDE) and of the temporal correlation between forecasts and analyses. The new land‐surface package has a positive impact on near‐surface forecasts of air temperature and humidity for a summertime period, with smaller STDE and larger temporal correlation for both variables. The improvement is greater for humidity than for air temperature. The maximum impact is found around seven‐day lead time, with substantial gains in absolute and relative values for STDE and temporal correlation. The positive impact is also quantified in terms of prediction hours, with gains of about one day at the medium range. Details of the pros and cons for this objective evaluation approach are discussed. [ABSTRACT FROM AUTHOR]

In semi‐arid environments, rainfall‐driven soil moisture fluctuations exert a strong influence on surface turbulent fluxes. Intraseasonal rainfall variability can therefore impact low‐level atmospheric temperatures and influence regional circulations. Using satellite observations and an atmospheric reanalysis, we investigate whether rainfall variability induced by the Madden–Julian oscillation (MJO) triggers land–atmosphere feedbacks across East Africa. We identify that surface fluxes during the East African wet seasons (March–May and October–December) are sensitive to MJO‐induced precipitation variations across low‐lying regions of South Sudan and highland regions of Uganda and southwest Kenya. For example, during MJO phases 6 to 8, when rainfall is suppressed, surface temperatures and sensible heat fluxes increase, whilst evapotranspiration decreases. Spatial variations in the surface flux response to rainfall variability feed back onto the atmosphere through amplifying MJO‐associated pressure anomalies. During dry MJO events for instance, surface warming across the exit region of the Turkana channel increases the low‐tropospheric along‐channel pressure gradient and intensifies the Turkana jet. We conclude that average surface‐driven temperature fluctuations during a single day are responsible for approximately 12% of MJO‐associated variability of the Turkana jet speed. However, we expect that the accumulation of heat over multiple days to the west of the East African Highlands further amplifies anomalies in the pressure gradient and jet intensity. Modelling experiments are required to quantify the accumulated impact of the surface forcing. Surface‐driven Turkana jet variations influence the East African moisture budget and affect the intensity and inland propagation of coastal convection. Not only is this the first study to investigate the importance of intraseasonal land–atmosphere feedbacks across East Africa, but it is also the first to show that Turkana jet characteristics are partly driven by surface conditions. This work motivates an investigation into whether subseasonal forecast models fully harness the predictability from surface‐induced jet variations. [ABSTRACT FROM AUTHOR]

The NASA Goddard Earth Observing System (GEOS) employs a hybrid four‐dimensional ensemble‐variational (Hybrid‐4DEnVar) atmospheric data assimilation system to provide global, near‐real time weather analysis and forecast products. This study introduces a land analysis into the GEOS Hybrid‐4DEnVar system to additionally assimilate L‐band (1.4 GHz) brightness temperature observations over land from the NASA Soil Moisture Active Passive (SMAP) satellite mission, which are highly sensitive to surface (∼0–5 cm) soil moisture. This weakly coupled land analysis impacts the simulated and forecast atmosphere through the land–atmosphere exchange processes encoded in the atmospheric model. Retrospective assimilation experiments for boreal summer 2017 were conducted with the system at 50 km horizontal resolution. The SMAP assimilation is shown to mitigate errors in screen‐level (2 m) specific humidity (q2m) and temperature (T2m), with regional reductions in the root‐mean‐square error (RMSE) of q2m and maximum daily T2m by up to 0.4 g·kg−1 and 0.3 K, respectively. These improvements are somewhat smaller than those found in a precursor study that used the atmospheric analysis in the simpler, 3‐dimensional variational (3DVar) atmospheric assimilation configuration of the current GEOS reanalysis; Hybrid‐4DEnVar provides overall better near‐surface background estimates than 3DVar and therefore leaves less room for improvement. Finally, in the Hybrid‐4DEnVar system with SMAP assimilation, forecasts of q2m and T2m have significantly improved anomaly correlation and RMSE (99% confidence) at lead times out to 5 days compared to the Hybrid‐4DEnVar system without SMAP assimilation, with medium‐range lead times extended by ∼3 hr for q2m and ∼2 hr for T2m. Slight but nevertheless significant improvements are also seen for temperature forecasts at the 925 and 850 hPa levels and lead times out to 4 days. Humidity forecasts at 925 and 850 hPa are improved out to 1.5‐day lead time with 90% confidence. [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. [ABSTRACT FROM AUTHOR]

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) onboard the International Space Station (ISS) provides standard land surface temperature (LST) products to meet community concerns on water stress and evapotranspiration. In 2019, an anomaly on the mass storage unit (MSU) changed the data acquisition mode of ECOSTRESS to a direct streaming one, eliminating three bands centered at 1.60, 8.29, and $9.20~\mu \text{m}$. This letter analyzes the feasibility of retrieving LST from ECOSTRESS data using split-window (SW) techniques which need only two thermal infrared (TIR) bands to serve as an alternative to produce LST products. Three different spilt-window algorithms have been developed and analyzed for all possible TIR band combinations using the simulated top-of-atmosphere (TOA) radiance dataset. Besides, the selected SW algorithms were validated using the intercomparison method with ECOSTRESS LST product data and the temperature-based method with surface radiation budget (SURFRAD) ground-based measurements. The result showed that SW algorithms provided higher accuracies than the temperature-emissivity separation (TES) algorithm, with the main advantage that the atmospheric profile data is not demanded prior. [ABSTRACT FROM AUTHOR]

The global ecosystem dynamics investigation (GEDI) contains a full waveform, multibeam laser altimeter, which provides vertical vegetation structure information. However, few studies have assessed the accuracy of using GEDI light detection and ranging (LiDAR) data in retrieving ground topography from dense forest environments. There has been no in-depth study assessing the accuracy of GEDI data with different kinds of elevation index in footprint samples from densely forested areas (canopy cover > 70%). To address this limitation, this study aims to assess the accuracy of ground topography estimates from dense forested terrain through the combination of airborne LiDAR data. The results show that under dense forest conditions, the mean elevation in the GEDI footprint performed more accurate than the max elevation, the min elevation, and the median elevation in the GEDI footprint. GEDI provides reasonable estimates of mean terrain height, with ${R} ^{2}$ of 0.99, root mean squared error (RMSE) of 6.08 m, and mean absolute error (MAE) of 3.92 m. In addition, dense forest ecosystems reduce the accuracy of the terrain height estimates. Overall, GEDI with its full-waveform technology represents promising spaceborne LiDAR for terrain height retrieval in dense forest environments. [ABSTRACT FROM AUTHOR]

Tremendous progress has been made in estimating soil moisture (SM) from satellite remote sensing data. Several global-scale coarse-resolution products have also been generated and released for various applications in the Earth system. However, high-resolution SM estimation is still in its infancy. Currently, two main methods are used for this purpose: downscaling approaches and direct retrieval from microwave and optical/thermal data. Several studies have attempted to comprehensively evaluate the performance of these approaches and have found that each method has its own strengths and weaknesses, with no single method outperforming the others. In this study, we aim to investigate the advantages of integrating optical, thermal, and microwave data to estimate SM by leveraging an intensive SM network and triple collocation (TC) method. First, we determined the best-performing coarse-resolution microwave SM product through the TC approach. Second, we generated 1-km SM using a downscaling approach based on land surface temperature and vegetation index, utilizing the best-performing SMAP L3 descending product. Third, we evaluated the high-resolution downscaled SM, Sentinel 1 SM, and SMAP/Sentinel 1 combined SM products using SM measurements from the REMEDHUS station network, ETOPO1 elevation, Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) precipitation, and the European Space Agency (ESA) CCI land cover map. Finally, we investigated and demonstrated the advantages of merging these products through point-scale evaluation and large-scale spatial pattern comparison. [ABSTRACT FROM AUTHOR]

Vegetation phenology is considered an essential biological indicator in understanding the behavior of ecosystems and how they respond to environmental cues. However, the potential of interannual variations of remotely sensed phenology signals to differentiate plant types remains poorly understood, especially in understudied systems with highly heterogeneous landscapes such as wetlands. This study presents a case study in a San Francisco Bay area marsh that investigates the usefulness of interannual variation, defined as the root-mean-square error of enhanced vegetation index (EVI) measurements against a fitted phenology curve, at the beginning, middle, and end of the growing season as indicators of plant types. The study found that altitude above sea level and certain land surface phenology metrics, such as the day-of-the-year of the end of the season, the mid-autumn day, and the greening rate before the summer peak, were significantly related to these interannual variation trends. These results indicate that a detailed time-series analysis at the beginning and end of growing seasons may enhance large-scale wetland characterization. Overall, the findings of this study contribute to our understanding of vegetation phenology and provide a framework for more accurate wetland classification in future studies. [ABSTRACT FROM AUTHOR]

In remote sensing, plenty of multispectral images are publicly available from various landcover satellite missions. Contrastive self-supervised learning is commonly applied to unlabeled data but relies on domain-specific transformations used for learning. When focusing on vegetation, standard transformations from image processing cannot be applied to the near-infrared (NIR) channel, which carries valuable information about the vegetation state. Therefore, we use contrastive learning, relying on different views of unlabeled, multispectral images to obtain a pretrained model to improve the accuracy scores on small-sized remote sensing datasets. This study presents the generation of additional views tailored to remote sensing images using atmospheric correction as an alternative transformation to color jittering. The purpose of the atmospheric transformation is to provide a physically consistent transformation. The proposed transformation can be easily integrated with multiple channels to exploit spectral signatures of objects. Our approach can be applied to other remote sensing tasks. Using this transformation leads to improved classification accuracy of up to 6%. [ABSTRACT FROM AUTHOR]

Spaceborne L-band data have the potential to monitor flooded and irrigated areas. However, further studies are needed to assess in real cases the impact of flood-irrigated crops on soil moisture and ocean salinity (SMOS) and soil moisture active passive (SMAP) surface soil moisture (SSM) data. This letter demonstrates the ability of SMOS/SMAP SSM retrievals to quantify the fraction of flood-irrigated areas at the seasonal scale and at a 25-km resolution in the Telangana State in southern India. Over irrigated areas, both SMOS level 3 (L3) SSM and SMAP L3 enhanced SSM products present a bimodal annual cycle, with a peak of SSM during the monsoon (wet) season corresponding to rainfall and irrigation, and a peak during the dry season due to irrigation activities solely. The second peak is absent or has a very small amplitude in areas where rice represents a small fraction (typically below 5%–10%). More importantly, the amplitude of the second SSM peak is significantly correlated with the rice cover fraction within $25\times25$ km2 pixels ($R = 0.81$ for SMOS and 0.77 for SMAP), showing its potential to assess crop fraction and hence the water used for irrigation. The SMOS/SMAP L3 SSM peak during the dry period occurs several months before the harvest, constituting an indicator for rice stocks at the end of the season. However, the irrigation signature is absent from the SMAP level 4 SSM product derived from the assimilation of SMAP brightness temperatures (Tbs) in a land surface model, which indicates that the data assimilation scheme is inefficient to restitute irrigation information. [ABSTRACT FROM AUTHOR]

The land surface temperature (LST) of urban is a key factor in the field of urban environmental monitoring, and thermal infrared (TIR) remote sensing is an efficient method to obtain it. An important assumption of the traditional thermal radiance transfer model is that the land surface is flat, which has now proven difficult to hold under the urban landscape. Most of the existing urban thermal radiance transfer models have been developed for remote sensing images with a spatial resolution of tens of meters. Currently, airborne TIR sensors have the observation capability to acquire remote sensing images with an ultrahigh spatial resolution (1 cm–1 m), and the model needs to be improved. This letter proposed a new ultrahigh spatial resolution urban thermal radiance transfer model (UHURT) after analyzing the transfer processes within the urban canopy at ultrahigh spatial resolution. Various radiance components, the emitted radiance, reflected atmospheric downward radiance, and adjacent radiance, were modeled separately. The results of the traditional model and the UHURT model were compared with those of a ray-tracing computer simulation model, which showed that the new model successfully quantifies the multiple scattering and adjacent effects and obtained images closer to the computer simulation images. Besides, the LST retrieval of the computer-simulated images was performed using the traditional model and the UHURT model, and the proposed model successfully reduced the errors of the retrieval results and weakened the spatial correlation between the residual distribution and the geometric characteristics of the urban landscape. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) is an essential parameter in the water cycle and surface energy balance. Accurate estimation of daytime or daily ET is of great significance for many activities in human economic and social development. This study aims to propose a novel method for estimating daytime average ET by using temporal measurements of land surface temperature (LST) and net surface shortwave radiation (NSSR), following a previously developed elliptical relationship between diurnal cycles of LST and NSSR under cloud-free days. The method was primarily developed from the simulated data of a physics-based Atmosphere–Land Exchange (ALEX) model under different underlying surfaces and atmospheric conditions. Based on the simulated data, the proposed method showed considerable accuracy with the overall coefficient of determination ($R^{2}$) of 0.958 and the root mean square error (RMSE) of 25.3 Wm $^{-2}$. In addition, ground ET measurements at four Ameriflux sites (US-ARM, US-SRM, US-Whs, and US-Wkg) during the 2018 growing season were collected to assess the estimated daytime average ET. Results show an overall RMSE of 64.7 Wm $^{-2}$ for the estimated ET at the four sites, and the US-Whs site reveals the best accuracy ($R^{2}$ = 0.825 and RMSE = 44.4 Wm $^{-2}$). These results indicated a potential for generating daytime ET with geostationary satellite observations at regional scales in future development. [ABSTRACT FROM AUTHOR]

Quantifying and monitoring land surface evapotranspiration (ET) is an essential task for understanding the earth’s water, energy, and carbon cycles. ET, specifically plant transpiration ($T$), is closely linked to the photosynthesis, which is coupled through stomatal function. However, the mechanistic links between sun-induced chlorophyll fluorescence (SIF) information indicating canopy photosynthetic activity and $T$ are complex and difficult to derive empirically. An empirical SIF- $T$ relationship at ecosystem scale was developed and coupled to the two-source energy balance model (TSEB-SIF) to estimate the ET and its components, $T$ and soil evaporation, $E$. By comparing model predictions with observations from an irrigated cropland site located in a semiarid region, the TSEB-SIF model shows a slightly better performance to the TSEB model in estimating ET, especially under water deficit conditions. Moreover, the TSEB-SIF model more reliably partitioned the $T$ from ET, while the TSEB model tended to overestimate the contribution of $T$ to ET. [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. [ABSTRACT FROM AUTHOR]

Modeled global warming is often quantified using global near-surface air temperature (Tas). Meanwhile, long-term temperature datasets combine observations of Tas over land with sea surface temperature (SST) over ocean. Modeled ocean Tas warms more than SST, which can bias model--observation comparisons. Skin temperature (Ts), which is typically warmer than Tas, follows SST changes so the ocean surface temperature discontinuity δTs 5 Ts 2 Tas decreases with warming. Here I show that under CO2 forcing, decreased δTs is consistently simulated for nonpolar ocean within 6608S/N, but not for other regions. I investigate the causes of oceanic dTs decrease using a LongRunMIP climate simulation, radiative kernels, and standard methods for diagnosing forcing and feedbacks from the CMIP5 ensemble. CO2 forcing establishes longwave heating of the lower atmosphere and subsequent adjustments that result in a small Tas increase, and therefore a δTs decrease. During the subsequent warming in response to CO2 forcing, the model-mean surface evaporation feedback is 3.6 W m-2 °C-1 over oceans, which reduces Ts warming relative to Tas and further shrinks δTs. Present-day forcing and feedback contributions are of similar magnitude, and both contribute to small differences in model--observation comparisons of global warming rates when these differences are not accounted for. [ABSTRACT FROM AUTHOR]

Many plant phenological events are sensitive to temperature, leading to changes in the seasonal cycle of ecosystem function as the climate warms. To evaluate the current and future implications of temperature changes for plant phenology, researchers commonly use a metric of temperature sensitivity, which quantifies the change in phenology per degree change in temperature. Here, we examine the temperature sensitivity of phenology, and highlight conditions under which the widely used days-per-degree sensitivity approach is subject to methodological issues that can generate misleading results. We identify several factors, in particular the length of the period over which temperature is integrated, and changes in the statistical characteristics of the integrated temperature, that can affect the estimated apparent sensitivity to temperature. We show how the resulting artifacts can lead to spurious differences in apparent temperature sensitivity and artificial spatial gradients. Such issues are rarely considered in analyses of the temperature sensitivity of phenology. Given the issues identified, we advocate for process-oriented modelling approaches, informed by observations and with fully characterised uncertainties, as a more robust alternative to the simple days-per-degree temperature sensitivity metric. We also suggest approaches to minimise and assess spurious influences in the days-per-degree metric.

Intelligent design is a trend in geotechnical engineering. However, a middle link between demand identification and intelligent design is missing in anchor engineering, resulting in lower efficiency in terms of the interaction between the anchor components, geological body and modeling information. To address this challenge, this paper proposes a method for modeling a three-dimensional anchor engineering information model based on sparse boreholes. This method includes three parts: modeling the geological body, integrated modeling of the anchor components and the geological body, and multiple information assignments based on the anchor model. The feasibility was validated in actual engineering scenarios by using neural networks and rendering engines. The results showed that the prediction accuracy of the strata improved by 17.69%, the modeling efficiency increased by double, and engineering information was fit to the model well. This paper has the application potential to achieve intelligent design in anchor engineering.

Landform classification is crucial for a host of applications that include geomorphological, soil mapping, radiative and gravity-controlled processes. Due to the complexity and rapid developments in the field of landform delineation, this study provides a scoping review to identify trends in the field. The review is premised on the PRISMA standard and is aimed to respond to the research questions pertaining to the global distribution of landform studies, methods used, datasets, analysis units and validation techniques. The articles were screened based on relevance and subject matter of which a total of 59 articles were selected for a full review. The parameters relating to where studies were conducted, datasets, methods of analysis, units of analysis, scale and validation approaches were collated and summarized. The study found that studies were predominantly conducted in Europe, South and East Asia and North America. Not many studies were found that were conducted in South America and the African region. The review revealed that locally sourced, very high-resolution digital elevation model ( DEM) products were becoming more readily available and employed for landform classification research. Of the globally available DEM sources, the SRTM still remains the most commonly used dataset in the field. Most landform delineation studies are based on expert knowledge. While object-based analysis is gaining momentum recently, pixel-based analysis is common and is also growing. Whereas validation techniques appeared to be mainly based on expert knowledge, most studies did not report on validation techniques. These results suggest that a systematic review of landform delineation may be necessary. Other aspects that may require investigation include a comparison of different DEMs for landform delineation, exploring more object-based studies, probing the value of quantitative validation approaches and data-driven analysis methods.

To characterize the effects of subgrid surface heterogeneity, the blending-height concept has been developed as a coupling strategy for surface parameterization schemes used in numerical weather prediction models. Previous modeling studies have tested this concept using stationary conditions with one-dimensional strips of surface roughness. Here, large-eddy simulations are used to examine the response of the blending height and effective surface roughness to tiled land-cover heterogeneity, or a two-dimensional chessboard pattern of alternating high and low vegetation given a diurnal cycle of solar irradiance in subarctic conditions. In each experiment, the length scale of the roughness elements is increased while the total domain fraction of each vegetation type is kept constant. The effective surface roughness was found to decrease with increasing length scale of surface cover heterogeneity, which is shown to have a significant impact on estimated wind turbine power calculated from logarithmic wind profiles. In stable conditions, the blending height in cases with large heterogeneity length scales was found to exist well above the surface layer. Because the behavior of the blending height has implications for coupled models, a simple model for the blending height as a function of heterogeneity length scale is introduced. [ABSTRACT FROM AUTHOR]

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In this study, we investigate the air temperature response to land-use and land-cover change (LULCC; cropland expansion and deforestation) using subgrid land model output generated by a set of CMIP6 model simulations. Our study is motivated by the fact that ongoing land-use activities are occurring at local scales, typically significantly smaller than the resolvable scale of a grid cell in Earth system models. It aims to explore the potential for a multimodel approach to better characterize LULCC local climatic effects. On an annual scale, the CMIP6 models are in general agreement that croplands are warmer than primary and secondary land (psl; mainly forests, grasslands, and bare ground) in the tropics and cooler in the mid–high latitudes, except for one model. The transition from warming to cooling occurs at approximately 40°N. Although the surface heating potential, which combines albedo and latent heat flux effects, can explain reasonably well the zonal mean latitudinal subgrid temperature variations between crop and psl tiles in the historical simulations, it does not provide a good prediction on subgrid temperature for other land tile configurations (crop vs forest; grass vs forest) under Shared Socioeconomic Pathway 5–8.5 (SSP5–8.5) forcing scenarios. A subset of simulations with the CESM2 model reveals that latitudinal subgrid temperature variation is positively related to variation in net surface shortwave radiation and negatively related to variation in the surface energy redistribution factor, with a dominant role from the latter south of 30°N. We suggest that this emergent relationship can be used to benchmark the performance of land surface parameterizations and for prediction of local temperature response to LULCC. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Landform classification is crucial for a host of applications that include geomorphological, soil mapping, radiative and gravity-controlled processes. Due to the complexity and rapid developments in the field of landform delineation, this study provides a scoping review to identify trends in the field. The review is premised on the PRISMA standard and is aimed to respond to the research questions pertaining to the global distribution of landform studies, methods used, datasets, analysis units and validation techniques. The articles were screened based on relevance and subject matter of which a total of 59 articles were selected for a full review. The parameters relating to where studies were conducted, datasets, methods of analysis, units of analysis, scale and validation approaches were collated and summarized. The study found that studies were predominantly conducted in Europe, South and East Asia and North America. Not many studies were found that were conducted in South America and the African region. The review revealed that locally sourced, very high-resolution digital elevation model (DEM) products were becoming more readily available and employed for landform classification research. Of the globally available DEM sources, the SRTM still remains the most commonly used dataset in the field. Most landform delineation studies are based on expert knowledge. While object-based analysis is gaining momentum recently, pixel-based analysis is common and is also growing. Whereas validation techniques appeared to be mainly based on expert knowledge, most studies did not report on validation techniques. These results suggest that a systematic review of landform delineation may be necessary. Other aspects that may require investigation include a comparison of different DEMs for landform delineation, exploring more object-based studies, probing the value of quantitative validation approaches and data-driven analysis methods. [ABSTRACT FROM AUTHOR]

Stable boundary layers are still a relatively problematic component of atmospheric modeling, despite their frequent occurrence. While general agreement exists that Monin–Obukhov similarity is not applicable in the stable boundary layer (SBL) due to the nonhomogeneous, nonstationary flow, no universal organizing theory for the surface SBL has been presented. The Stable Atmospheric Variability and Transport (SAVANT) field campaign took place in the fall of 2018 to explore under what conditions shallow drainage flow is generated. The campaign took place in an agricultural setting and covered the period of both pre- and postharvest, allowing for not only a basic exploration of the boundary layer but also a robust dataset for applied agricultural understanding of aerosol dispersion and impacts of changes in surface cover on drainage flows. This article provides a description of the field campaign. Examples of publicly available data products are presented, as well as examples of shallow drainage flow and corresponding lidar measurements of dispersion. Additionally, the field campaign was used to provide educational opportunities for students from several disciplines, and the outcomes of these joint educational ventures are discussed as models for future collaborations. [ABSTRACT FROM AUTHOR]

Abstract Frost is damaging to plants when air temperature drops below their tolerance threshold. The set of mechanisms used by cold‐tolerant plants to withstand freezing is called “hardening” and typically take place in autumn to protect against winter damage. The recent incorporation of a hardening scheme in the demographic vegetation model FATES opens up the possibility to investigate frost mortality to vegetation. Previously, the hardening scheme was used to improve hydraulic processes in cold‐tolerant plants. In this study, we expand upon the existing hardening scheme by implementing hardiness‐dependent frost mortality into CLM5.0‐FATES to study the impacts of frost on vegetation in temperate and boreal sites from 1950 to 2015. Our results show that the original freezing mortality approach of FATES, where each plant type had a fixed freezing tolerance threshold—an approach common to many other dynamic vegetation models, was restricted to predicting plant type distribution. The main results emerging from the new scheme are a high autumn and spring frost mortality, especially at colder sites, and increasing mid‐winter frost mortality due to global warming, especially at warmer sites. We demonstrate that the new frost scheme is a major step forward in dynamically representing vegetation in ESMs by for the first time including a level of frost tolerance that is responding to the environment and includes some level of cost (implicitly) and benefit. By linking hardening and frost mortality in a land surface model, we open new ways to explore the impact of frost events in the context of global warming.

Tropical deforestation can result in substantial changes in local surface energy and water budgets, and thus in atmospheric stability. These effects may in turn yield changes in precipitation. The Maritime Continent (MC) has undergone severe deforestation during the past few decades but it has received less attention than the deforestation in the Amazon and Congo rain forests. In this study, numerical deforestation experiments are conducted with global (i.e., Community Earth System Model) and regional climate models (i.e., Regional Climate Model version 4.6) to investigate precipitation responses to MC deforestation. The results show that the deforestation in the MC region leads to increases in both surface temperature and local precipitation. Atmospheric moisture budget analysis reveals that the enhanced precipitation is associated more with the dynamic component than with the thermodynamic component of the vertical moisture advection term. Further analyses on the vertical profile of moist static energy indicate that the atmospheric instability over the deforested areas is increased as a result of anomalous moistening at approximately 800-850 hPa and anomalous warming extending from the surface to 750 hPa. This instability favors ascending air motions, which enhance low-level moisture convergence. Moreover, the vertical motion increases associated with the MC deforestation are comparable to those generated by La Niña events. These findings offer not only mechanisms to explain the local climatic responses to MC deforestation but also insights into the possible reasons for disagreements among climate models in simulating the precipitation responses.

Abstract 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).