Land surface temperature (LST) plays an important role in Earth energy balance and water/carbon cycle processes and is recognized as an Essential Climate Variable (ECV) and an Essential Agricultural Variable (EAV). LST products that are issued from satellite observations mostly depict landscape-scale temperature due to their generally large footprint. This means that a pixel-based temperature integrates over various components, whereas temperature individual components are better suited for the purpose of evapotranspiration estimation, crop growth assessment, drought monitoring, etc. Thus, disentangling soil and vegetation temperatures is a real matter of concern. Moreover, most satellite-based LSTs are contaminated by directional effects due to the inherent anisotropy properties of most terrestrial targets. The characteristics of directional effects are closely linked to the properties of the target and controlled by the view and solar geometry. A singular angular signature is obtained in the hotspot geometry, i.e., when the sun, the satellite and the target are aligned. The hotspot phenomenon highlights the temperature differences between sunlit and shaded areas. However, due to the lack of adequate multi-angle observations and inaccurate portrayal or neglect of solar influence, the hotspot effect is often overlooked and has become a barrier for better inversion results at satellite scale. Therefore, hotspot effect needs to be better characterized, which here is achieved with a three-component model that distinguishes vegetation, sunlit and shaded soil temperature components and accounts for vegetation structure. Our work combines thermal infrared (TIR) observations from the Sea and Land Surface Temperature Radiometer (SLSTR) onboard the LEO (Low Earth Orbit) Sentinel-3, and two sensors onboard GEO (geostationary) satellites, i.e. the Advanced Himawari Imager (AHI) and Spinning Enhanced Visible and Infrared Imager (SEVIRI). Based on inversion with a Bayesian method and prior information associated with component temperature differences as constrained, the findings include: 1) Satellite observations throughout East Asia around noon indicate that for every 10 degrees change in angular distance from the sun, LST will on average vary by 0.6 K; 2) As a better constraint, the hotspot effect can benefit from multi-angle TIR observations to improve the retrieval of LST components, thereby reducing the root mean squared error (RMSE) from approximately 3.5 K, 5.8 K, and 4.1 K to 2.8 K, 3.5 K, and 3.1 K, at DM, EVO and KAL sites, respectively; 3) Based on a dataset simulated with a threedimensional radiative transfer model, a significant inversion error may result if the hotspot is ignored for an angular distance between the viewing and solar directions that is smaller than 30 degrees. Overall, considering the hotspot effect has the potential to reduce inversion noise and to separate the temperature difference between sunlit and shaded areas in a pixel, paving the way for producing stable temperature component products.

Forest management and tree felling in the stand change the structural characteristics, which causes changes in the microclimate conditions. The microclimate is a key in sustainable forest management because soil temperature and moisture regimes regulate nutrient cycling in forest ecosystems. The aim of this research was to determine the changes in air and soil temperatures in pedunculate oak forest stands in different stages of shelterwood that stimulate natural regeneration. The research was conducted in pedunculated oak forests in Spa & ccaron;va area. The microclimatic parameters were measured in a mature old forest stand without shelterwood cutting and in stands with preparatory cut, seed cut, and final cut. The intensity of shelterwood had an impact on the amplitudes and values of air and soil temperatures. The highest average air temperature was in the stand with a preparatory cut. Extreme values of air and soil temperatures were measured in the stands with a final cut. The highest air and soil temperature amplitudes were in the stand with a final cut, with the exception of most of the winter, when the highest soil temperature amplitude was in the stand with a seed cut. The highest number of icy, cold, and hot days was in the stand with a final cut. SARIMA models establish that the difference between microclimatic parameters is not accidental.

The accelerated warming in the Arctic poses serious risks to freshwater ecosystems by altering streamflow and river thermal regimes. However, limited research on Arctic River water temperatures exists due to data scarcity and the absence of robust methodologies, which often focus on large, major river basins. To address this, we leveraged the newly released, extensive AKTEMP data set and advanced machine learning techniques to develop a Long Short-Term Memory (LSTM) model. By incorporating ERA5-Land reanalysis data and integrating physical understanding into data-driven processes, our model advanced river water temperature predictions in ungauged, snow- and permafrost-affected basins in Alaska. Our model outperformed existing approaches in high-latitude regions, achieving a median Nash-Sutcliffe Efficiency of 0.95 and root mean squared error of 1.0 degrees C. The LSTM model learned air temperature, soil temperature, solar radiation, and thermal radiation-factors associated with energy balance-were the most important drivers of river temperature dynamics. Soil moisture and snow water equivalent were highlighted as critical factors representing key processes such as thawing, melting, and groundwater contributions. Glaciers and permafrost were also identified as important covariates, particularly in seasonal river water temperature predictions. Our LSTM model successfully captured the complex relationships between hydrometeorological factors and river water temperatures across varying timescales and hydrological conditions. This scalable and transferable approach can be potentially applied across the Arctic, offering valuable insights for future conservation and management efforts.

Reanalysis is a valuable potential data source for permafrost studies. The latest-generation reanalysis of the Japanese Reanalysis for three quarters of a century (JRA-3Q) benefits from improved snow and soil schemes and demonstrates encouraging performance for soil temperature in permafrost regions compared to its predecessor, JRA-55, and other state-of-the-art reanalyses. We find JRA-3Q to have an overall mean annual air temperature bias of-0.17 degrees C, with-0.55 degrees C in permafrost regions. The snow depth was underestimated by-5.5 cm. In permafrost regions, the mean annual ground temperature bias was about-0.09 degrees C. The estimated permafrost area from JRA-3Q is between 10.8 and 15.8 x 106 km2. The active layer thickness is substantially overestimated by about 0.65 m. The JRA-3Q soil temperature exhibits a pronounced warm bias in Alaska, which is very likely due to the overestimated snow insulation and simplified soil organic content. The decoupled energy conservation parameterization (DECP) method employed in the JRA-3Q soil scheme restricts its suitability for the interpretation of detailed permafrost phenomena, such as zero-curtain effects. This DECP method is used in many stateof-the-art land surface models; our results demonstrate the need for additional contributions to improve the representation of permafrost-specific processes.

BackgroundBiochar is widely recognized for its capacity to capture and store carbon in soil attributed to its stable structure. However, in most field studies examining the effects of biochar application on soil respiration, the impact of rainfall events on the experimental outcomes has not been taken into account. To address the existing gap in this research field, we conducted a one-year study on soil respiration in an urban camphor forest and collected the data of soil respiration, soil temperature, soil moisture, and the rainfall events closest to the soil respiration monitoring time. We specifically examined how different stages of rainfall events influenced soil respiration in relation to biochar application.ResultsThis study found that the annual average soil respiration rate increased with the doses of biochar application, and the soil respiration rate under the biochar application at the dose of 45 t/ha showed a significant rise. The stages of rainfall events, rainfall amount, and the interaction effect of the two, and biochar doses significantly affected soil respiration. The parameters in the regression model for soil respiration, soil temperature and moisture varied with the different stages of rainfall events and the doses of biochar application. The biochar application eliminated the significant effect of soil moisture on soil respiration during one day after rainfall events. The significant correlation between soil moisture and the temperature sensitivity of soil respiration (Q10) was eliminated by biochar application, both during one day after rainfall events and more than eight days after rainfall events.ConclusionsOur findings indicated that the rice straw biochar application has a short-term positive effect on soil respiration in urban camphor forests. The rainfall events affect the field soil respiration monitored in the biochar applications, possibly by affecting the soil respiration response to soil temperature and moisture under different doses of biochar application. The impact of rainfall events on soil respiration in biochar application experiments should be considered in future forest monitoring management and practice.

Freeze-thaw cycles (FTC) alter soil function through changes to physical organization of the soil matrix and biogeochemical processes. Understanding how dynamic climate and soil properties influence FTC may enable better prediction of ecosystem response to changing climate patterns. In this study, we quantified FTC occurrence and frequency across 40 National Ecological Observatory Network (NEON) sites. We used site mean annual precipitation (MAP) and mean annual temperature (MAT) to define warm and wet, warm and dry, and cold and dry climate groupings. Site and soil properties, including MAT, MAP, maximum-minimum temperature difference, aridity index, precipitation as snow (PAS), and organic mat thickness, were used to characterize climate groups and investigate relationships between site properties and FTC occurrence and frequency. Ecosystem-specific drivers of FTC provided insight into potential changes to FTC dynamics with climate warming. Warm and dry sites had the most FTC, driven by rapid diurnal FTC close to the soil surface in winter. Cold and dry sites were characterized by fewer, but longer-duration FTC, which mainly occurred in spring and increased in number with higher organic mat thickness (Spearman's rho = 0.97, p < 0.01). The influence of PAS and MAT on the occurrence of FTC depended on climate group (binomial model interaction p (chi(2)) < 0.05), highlighting the role of a persistent snowpack in buffering soil temperature fluctuations. Integrating ecosystem type and season-specific FTC patterns identified here into predictive models may increase predictive accuracy for dynamic system response to climate change.

Sprinkler irrigation is an effective method for protecting economic crops from frost damage; however, current research on its impacts is insufficient and lacks comprehensive evaluation. This research investigated the effects of sprinkler irrigation for frost protection on the air, soil, and tea plants in the tea garden. Sprinkler frost protection experiments were conducted in the tea garden, where temperature sensors measured the air and soil temperatures, and Monitoring-PAM was used to measure the chlorophyll fluorescence parameters (Fv/Fm) of the tea plants. The results indicated that lower initial ambient temperatures or smaller droplet sizes accelerate the rate of air temperature increase and slow the cooling rate. Under conditions of heavy frost, ice formation from irrigation water acts as an insulating layer, protecting the inter-row soil. Additionally, the Fv/Fm values of tea leaves protected by sprinkler irrigation ranged from 0.6 to 0.7, and were significantly higher than those of leaves exposed to frost damage. The results also showed that air and soil temperature and tea Fv/Fm can be used to perform a comprehensive assessment of sprinkler frost protection effectiveness.

The root, a key organ for sensing the soil environment, is easily damaged by environmental stresses such as low soil temperature. Although the exact mechanism is unknown, exogenous sucrose can mitigate the oxidative damage to the root caused by low temperatures in the root zone. In this study, we examined how exogenous sucrose affected the transcriptome and physiology of Malus baccata Borkh. seedling roots at sub-low root-zone temperature (LRT). The exogenous sucrose treatment was more effective than other treatments in mitigating LRT stress injury. This was achieved by decreasing reactive oxygen species (O 2 .- and H2O2) 2 O 2 ) and malondialdehyde content, increasing the activities of antioxidant enzymes (SOD, POD, CAT, APX, GR, and MDHAR), increasing AsA and GSH content, and increasing soluble sugar content. Transcriptome analysis revealed that alpha-linolenic acid metabolism, fatty acid biosynthesis, phenylpropane biosynthesis, and glycolysis/gluconeogenesis were the primary areas of enrichment for the differentially expressed genes identified under the LRT treatment. Exogenous sucrose may enhance the tolerance of Malus baccata Borkh. to LRT by regulating the expression of differentially expressed genes ( GST, LOX, SS, PFK, ADH, , and 4CL) ) related to the antioxidant system, carbohydrate metabolism, alpha-linolenic acid metabolism, and phenylpropane biosynthesis pathways. These results offer a foundation for additional investigation into the molecular mechanism underlying the modulation of the root response to low temperature by exogenous sucrose.

Introduction: Permafrost and seasonally frozen soil are widely distributed on the Qinghai-Tibetan Plateau, and the freezing-thawing cycle can lead to frequent phase changes in soil water, which can have important impacts on ecosystems.Methods: To understand the process of soil freezing-thawing and to lay the foundation for grassland ecosystems to cope with complex climate change, this study analyzed and investigated the hydrothermal data of Xainza Station on the Northern Tibet from November 2019 to October 2021.Results and Discussion: The results showed that the fluctuation of soil temperature showed a cyclical variation similar to a sine (cosine) curve; the deep soil temperature change was not as drastic as that of the shallow soil, and the shallow soil had the largest monthly mean temperature in September and the smallest monthly mean temperature in January. The soil water content curve was U-shaped; with increased soil depth, the maximum and minimum values of soil water content had a certain lag compared to that of the shallow soil. The daily freezing-thawing of the soil lasted 179 and 198 days and the freezing-thawing process can be roughly divided into the initial freezing period (November), the stable freezing period (December-early February), the early ablation period (mid-February to March), and the later ablation period (March-end of April), except for the latter period when the average temperature of the soil increased with the increase in depth. The trend of water content change with depth at all stages of freezing-thawing was consistent, and negative soil temperature was one of the key factors affecting soil moisture. This study is important for further understanding of hydrothermal coupling and the mechanism of the soil freezing-thawing process.

Improved modeling of permafrost active layer freeze-thaw plays a crucial role in understanding the response of the Arctic ecosystem to the accelerating warming trend in the region over the past decades. However, modeling the dynamics of the active layer at diurnal time scale remains challenging using the traditional models of freeze-thaw processes. In this study, a physically based analytical model is formulated to simulate the thaw depth of the active layer under changing boundary conditions of soil heat flux. Conservation of energy for the active layer leads to a nonlinear integral equation of the thaw depth using a temperature profile approximated from the analytical solution of the heat transfer equation forced by ground heat flux. Temporally variable ground heat flux is estimated using non-gradient models when field observations are not available. Validation of the proposed model conducted against field data obtained from three Arctic forest and tundra sites demonstrates that the model is able to simulate both thaw depth and soil temperature profiles accurately. The model has the potential to estimate regional variability of the thaw depth for permafrost related applications. The seasonally thawed layer on top of the permafrost (active layer) is a key component of the Arctic system affected by the strong warming trend over the past decades. This soil layer experiences a pronounced seasonal cycle of freezing and thawing processes caused by the availability of Sun's energy. Mathematical modeling of the thaw depth of the active layer has remained challenging. This study formulates a novel model for the simulation of the diurnal cycle of thawing process. The formulation is developed using innovative models of heat flux that goes into the soil and soil temperature profile. Ground heat flux is derived from available energy at the land surface using a theory of surface heat flux partition. The soil temperature profile is expressed using ground heat flux within the active layer. The proposed model has been validated against field observations during thawing season. The model simulation and field observations of the thaw depth are in a good agreement at three Arctic study sites with forest and tundra surface conditions. The proposed formulation can be used for modeling freeze-thaw cycles of the active layer at the regional scales since data on surface available energy can be obtained from remote sensing observations. The proposed model is highly effective in modeling thawing depth at higher time resolution and representing the soil energy budget Non-gradient models demonstrate a strong capability to model soil energy budget in data-sparse harsh environments