Search Results (207)

To address the limitations of traditional forest above-ground biomass (AGB) retrieval methods—namely, the restricted accuracy of physical models and the limited generalization ability of purely data-driven models—this study proposes a differentiable physical modeling (DPM) approach for forest AGB estimation. The method adopts the water cloud model (WCM) as a physics-based framework, grounded in radiative transfer theory, and integrates C-band synthetic aperture radar (SAR) data with multispectral imagery. Within the PyTorch tensor computation framework, automatic differentiation (AD) is employed to seamlessly couple the WCM with the deep fully connected neural network (DFCNN), enabling a differentiable implementation of the WCM. Using mean squared error (MSE) as the loss function, the neural network parameters are optimized through backpropagation and gradient descent, thereby constructing an end-to-end trainable DPM model that effectively retrieves forest AGB while preserving physical interpretability and generalization capability. To validate the proposed method, two representative test sites were selected: Simao in Pu’er, Yunnan Province, and Genhe in Inner Mongolia. GF-3 PolSAR and RADARSAT-2 data were used to extract backscattering coefficients and compute the radar vegetation index (RVI), while Landsat 8 OLI imagery was employed to calculate the normalized difference vegetation index (NDVI), difference vegetation index (DVI), and soil-adjusted vegetation index (SAVI). These datasets, together with ASTER GDEM, field-measured biomass, and other relevant datasets, were integrated to construct a multisource dataset combining remote sensing and ground observations. The performance of the DPM model was then compared with the traditional WCM and several data-driven models, including the fully connected neural network (FNN), generalized regression neural network (GRNN), RF, and Adaptive Boosting (AdaBoost). The results indicate that the DPM model achieved R² = 0.60, RMSE = 24.23 Mg/ha, Bias = 0.4 Mg/ha, and ubRMSE = 22.43 Mg/ha in Simao, and R² = 0.48, RMSE = 33.29 Mg/ha, Bias = 0.87 Mg/ha, and ubRMSE = 33.28 Mg/ha in Genhe, demonstrating consistently better performance than both the WCM and all tested data-driven models. The DPM model demonstrated consistent performance across ecologically contrasting forest regions. It alleviated the systematic overestimation bias of purely data-driven models and overcame the limitations in predictive accuracy resulting from the simplified structure of the WCM. The differentiability of the WCM enables the loss function errors to be backpropagated through the neural network, thereby allowing the optimization of the physical model parameters. Overall, the DPM framework integrates the advantages of both physical models and data-driven approaches, providing an estimation method with acceptable accuracy for forest AGB retrieval. It also offers theoretical and practical insights for the integration of deep learning and physical knowledge in other research fields. Full article

Ice clouds play a significant role in the Earth’s radiation balance due to their unique microphysical and radiative properties, which vary with formation mechanisms and regions and influence the local energy budget. In this study, six years of Ka-band Zenith Radar (KAZR) observations from the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) and the Southern Great Plains (SGP) sites, combined with the Fu–Liou radiative transfer model, were used to examine the macrophysical and microphysical properties of ice clouds, their radiative effects, and contributions to the surface energy budget. The results show that the frequency of ice cloud occurrence at SACOL is 40%, significantly higher than the 27% observed at SGP. At both sites, ice cloud altitudes exhibit an increasing trend in the context of recent warming, with a more pronounced increase at SGP. Seasonal variations are evident, with spring characterized by relatively thick and widespread ice clouds, while summer is dominated by high-altitude, optically thin clouds. Ice cloud occurrence peaks at night and decreases during the day at both sites; however, cloud diurnal variations in summer are much greater at SGP than at SACOL. Radiative analysis indicates that longwave radiation-induced warming dominates ice cloud radiative forcing. Net radiative forcing at the top of the atmosphere is 6.08 W/m² at SACOL and 3.06 W/m² at SGP, contributing to atmospheric heating within and beneath cloud layers. At the surface, sensible heat dominates the energy budget at SACOL (over 63%) due to its arid climate, whereas latent heat dominates at SGP (about 67%) because of abundant moisture; and ice clouds have the greatest impact in winter, reducing surface net radiation by 29% at SACOL and 26% at SGP, producing a cooling effect. Full article

The Water Cloud Model (WCM) provides a simple and effective framework for quantifying vegetation canopy absorption and scattering components and has been widely applied to active microwave-based soil moisture retrieval on vegetated areas. However, under conditions of dense vegetation, the WCM neglects soil–vegetation interaction scattering, which limits its retrieval accuracy. To mitigate this limitation, this study analyzes the active microwave radiative transfer process under vegetated conditions and proposes an approach to explicitly quantify soil–vegetation interaction scattering by incorporating the first-order soil–vegetation-scattering component into the WCM, thereby enhancing the performance of the WCM at high vegetation coverage. The effectiveness of the proposed model is validated using in situ observations from three study areas with different vegetation characteristics: (a) a pure farmland area, (b) a mixed landscape with small forest and shrubland patches and large cropland areas, and (c) a mixed landscape with large forest and shrubland patches and small cropland areas. Data from 2020–2022 were used for model training and parameter calibration, while independent datasets from 2023 and 2024 were employed to validate the model performance. In both the model training and validation phases, the proposed model improved the soil moisture retrieval accuracy across all study areas while exhibiting slight differences in the backscatter simulation performance. During the model training period, the root-mean-square error (RMSE) between simulated and measured backscatter in study area (a) increased slightly by 1.9%, whereas it decreased by 2.79% and 2.0% in study areas (b) and (c), respectively. In terms of soil moisture retrieval, the RMSEs in study areas (a), (b), and (c) decreased by 6.66%, 1.18%, and 6.03%, respectively. In the validation experiments, for the year 2023, the RMSEs of simulated versus observed backscatter in study areas (a), (b), and (c) were reduced by 9.6%, 1.51%, and 4.35%, respectively, while the corresponding soil moisture retrieval RMSEs decreased by 12.6%, 4.53%, and 7.24%. For the year 2024, the backscatter RMSE in study area (a) increased by 6.07%, whereas it decreased by 2.17% and 6.47% in study areas (b) and (c), respectively; meanwhile, the soil moisture retrieval RMSEs were reduced by 2.81%, 3.69%, and 9.45%, respectively. In summary, this study improves the accuracy of active microwave remote sensing-based soil moisture retrieval in areas with different vegetation cover by explicitly quantifying soil–vegetation interaction scattering. Full article

Google Earth Engine (GEE) has revolutionised remote sensing. The GEE cloud platform lets users quickly analyse large satellite imagery datasets with custom programmes, enhancing global-scale analysis. Crop condition monitoring using GEE would greatly help in decision-making and precision agriculture. Estimating canopy chlorophyll content (CCC) is an effective way to monitor crops using remote sensing because leaf chlorophyll is a key indicator. A hybrid model that combines radiative transfer models (RTMs), such as PROSAIL, with Gaussian Process Regression (GPR) can effectively estimate crop biophysical parameters using remote sensing images. GPR has proven to be one of the best methods for this purpose. This study aimed to develop a hybrid model to estimate CCC from S2 imagery and transfer it to the GEE platform for efficient data processing. In this work, the CCC (g/cm²) data from the S2 biophysical processor toolbox for the S2 imagery of the ICAR-Indian Agricultural Research Institute (IARI) on 23 February 2023 were used as observation data to train the hybrid algorithm. The hybrid model was successfully validated against the 155 input data with an R2 of 0.94, RMSE of 10.02, and NRMSE of 5.04%. The model was integrated into GEE to successfully generate a CCC-estimated map of IARI using S2 imagery from 23 February 2023. An R2 value of 0.96 was observed when GEE-estimated CCC values were compared against CCC values estimated locally. This establishes that the GEE-based CCC estimation with the PROSAIL + GPR hybrid model is an effective and accurate method for monitoring vegetation and crop conditions over large areas and extended periods. Full article

The vertical structure and optical–microphysical properties of ice clouds determine their radiative effects. With an average altitude above 3000 m above mean sea level (AMSL) and unique thermal circulation, the Tibetan Plateau forms ice clouds with seasonally varying microphysical characteristics. In this study, satellite lidar observations from CALIPSO and ERA5 reanalysis from 2006 to 2023 reveal significant seasonal variation in ice clouds over the Tibetan Plateau and adjacent regions. In winter, maximums of the backscatter coefficient (β₅₃₂) and ice water content (IWC) were found south of the Qinling-Huaihe Line, as well as in the Sichuan Basin and the Yangtze Plain. In summer, these maximums move onto the Plateau, and the cloud height rises by about 1 km. The altitude of the β₅₃₂ maximum rises from about 4 km in winter to nearly 6 km in summer. Among four cloud categories defined by joint geometric and optical thickness thresholds, clouds with small geometric thickness and large optical thickness (thin and dense clouds) are the most radiatively important. While these clouds are seldom observed over the Tibetan Plateau in winter, they contribute to over thirty percent of local ice cloud occurrences during summer. Their preferred altitude rises from 3–4 km to 6–7 km, occurring under comparatively warmer environmental temperatures. Although limited in geometric depth, the thin and dense clouds exhibit the highest β₅₃₂ and IWC, the lowest multiple scattering coefficient (η₅₃₂), and the highest depolarization ratio (δ₅₃₂). They contribute about thirty percent of the total extinction and backscatter, despite representing only ten to twenty percent of all cases. Full article

Cloud feedback is a major source of uncertainty in climate projections. In particular, Arctic clouds, arguably one of the most poorly understood aspects of the climate system, strongly modulate radiative energy fluxes from the Earth’s surface to the top of the atmosphere. In situ and satellite observations reveal the existence of ubiquitous optically thin ice clouds (TICs) in the Arctic during polar nights, whose influence on atmospheric energy is still poorly understood. This study quantifies the effect of TICs on the atmospheric energy budget during polar winter. A reanalysis-driven simulation based on the Canadian Regional Climate Model version 6 (CRCM6) was used with the Predicted Particle Properties (P3) scheme (2016) to produce an ensemble of 3 km mesh simulations. This set is composed of three simulations: CRCM6 (reference, the original dynamically coupled cloud formation), CRCM6 (nocld) (clear-sky) and CRCM6 (100%cld) (overcast, 100% cloud cover as a forcing perturbation). Using the regional energetic equations (Nikiema and Laprise), we compare the three cases to assess TIC forcing. The results show that TICs cool the atmosphere, with the difference between two simulations (cloud/no clouds) reaching up to −2 K/day, leading to a decrease in temperature on the order of ~−4 KMonth⁻¹. The energetics cycle indicates that the time-mean enthalpy generation term G_M and baroclinic conversion dominate Arctic circulation. The G_M acting on the available enthalpy reservoir (A_M) increased by a maximum value of ~5 W·m⁻² (58% on average) due to the effects of TICs, increasing in energy conversion. TICs also lead to average changes of 9% in time-mean available enthalpy and −5.9% in time-mean kinetic energy. Our work offers valuable insights into the Arctic winter atmosphere and provides the means to characterize clouds for radiative transfer calculations, to design measurement instruments, and to understand their climate feedback. Full article

Cloud Top Height (CTH), defined as the altitude of the highest cloud layer above mean sea level, is a crucial geophysical parameter for quantifying cloud radiative effects, analyzing severe weather systems, and improving climate models. To enhance the accuracy of CTH retrieval from Fengyun-4A (FY-4A) satellite data, this study proposes a multi-stage deep learning framework that progressively refines cloud parameter estimation. The method utilizes cloud information from the FY-4A/AGRI (Advanced Geosynchronous Radiation Imager) Level 1 calibrated scanning imager radiance data product to construct a multi-source data fusion neural network model. The model inputs combine multi-channel radiance data with cloud parameters, including Cloud Top Temperature (CTT) and Cloud Top Pressure (CTP). We used the CTH measurement data from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite as a reference to verify the model output. Results demonstrate that the proposed multi-stage model significantly improves retrieval accuracy. Compared to the official FY-4A CTH product, the Mean Absolute Error (MAE) was reduced by 49.12% to 2.03 km, and the Pearson Correlation Coefficient (PCC) reached 0.85. To test the applicability of the model under complex weather conditions, we applied it to the CTH inversion of the double typhoon event on 10 August 2019. The model successfully characterized the spatial distribution of CTH within the typhoon regions. The results are consistent with the National Satellite Meteorological Centre (NSMC) reports and clearly reveal the different intensity evolutions of the two typhoons. This research provides an effective solution for high-precision retrieval of high-level cloud CTH at a large scale, using geostationary meteorological satellite remote sensing data. Full article

Owing to their high vertical resolution, remote sensing data from meteorological satellite hyperspectral infrared sounders are well-suited for the identification, monitoring, and early warning of high-impact weather events. The effective utilization of full field-of-view (FOV) observations from satellite infrared sounders in high-impact weather applications remains a major research focus and technical challenge worldwide. This study proposes a generalized variational retrieval framework to estimate full FOV cloud fraction and precipitable water vapor (PWV) from observations of the Geostationary Interferometric Infrared Sounder (GIIRS) onboard the Fengyun-4A (FY-4A) satellite. Based on this method, experiments are performed using high-frequency FY-4A/GIIRS observations during the landfall periods of Typhoon Lekima (2019) and Typhoon Higos (2020). A three-step channel selection strategy based on information entropy is first designed for FY-4A/GIIRS. A constrained generalized variational retrieval method coupled with a cloud cost function is then established. Cloud parameters, including effective cloud fraction and cloud-top pressure, are initially retrieved using the Minimum Residual Method (MRM) and used as initial cloud information. These parameters are iteratively optimized through cost-function minimization, yielding full FOV cloud fields and atmospheric profiles. Full FOV brightness temperature simulations are conducted over cloudy regions to quantitatively evaluate the retrieved cloud fractions, and the derived PWV is further applied to the identification and analysis of hazardous weather events. Experimental results demonstrate that incorporating cloud parameters as auxiliary inputs to the radiative transfer model improves the simulation of FY-4A/GIIRS brightness temperature in cloud-covered areas and reduces brightness temperature biases. Compared with ERA5 Total Column Water Vapour (TCWV) data, the PWV derived from full FOV profiles containing cloud parameter information shows closer agreement and, at certain FOVs, more effectively indicates the occurrence of high-impact weather events. The simplified methodology proposed in this study provides a robust basis for the future assimilation and operational utilization of infrared data over cloud-affected regions in numerical weather prediction models. Full article

High-speed impact is a critical mechanism for structural damage. The infrared signatures generated during fragment formation provide essential data for damage assessment, protective system design, and target identification. This study investigated an aluminum alloy blunt projectile penetrating a target plate by employing smoothed particle hydrodynamics to simulate the debris ejection thermal and infrared properties. The infrared signatures of the debris clouds were calculated using Mie scattering theory under a spherical particle approximation. The reverse Monte Carlo methodology was applied to solve the radiative transfer equations and quantify the infrared emission characteristics. The infrared radiation characteristics of the debris cloud were investigated for projectile impact velocities of 800, 1000, and 1200 m/s. The results showed that the anterior debris regions reached peak temperatures of approximately 1200 K, with a temperature rise of 150–200 K per 200 m/s velocity increase behind the target. The medium-wave (3–5 μm) infrared intensity of the debris cloud was higher than the long-wave (8–12 μm) infrared intensity. The development of debris clouds enhanced the dispersion effect and slowed the increase in infrared radiation intensity in the same time interval. This study provides theoretical foundations for the dynamic infrared radiation characteristics of fragments generated by high-velocity projectile impacts. The infrared radiation characteristics within typical spectral bands can be utilized to assess hit probability and kill effectiveness. Full article

The escalating heatwave risks over the Tibetan Plateau (TP) highlight unresolved gaps in understanding multitype mechanisms and diurnal urban canopy heat island (UCHI) responses. Using Xining’s high-density observational network (2018–2023) and by employing comparative analysis (urban–rural, heatwave versus non-heatwave days) and composite analysis, we found: During the record-breaking July 2022 heatwave across the TP, Xining reached an extreme UCHI peak (z-score: 3.0). Critically asymmetric UCHI responses as daytime heatwaves amplify mean intensity by 0.35 °C via extreme value shifts, whereas nighttime events suppress it by 0.31 °C. Crucially, heatwaves induce negligible daytime UCHI modulation but drive comparable magnitude nighttime UCHI intensification (during daytime events) and reduction (during nighttime events), demonstrating type-dependent and diurnally asymmetric urban thermal sensitivities. Heatwaves driven by distinct synoptic patterns; daytime events are controlled by an anomaly anticyclone (cloudless, dry conditions), while nighttime events occur under plateau-north anticyclones (cloudy, humid conditions). These patterns fundamentally reshape heatwave–UCHI interactions through divergent mechanisms: Daytime/nighttime heatwaves amplify/suppress nocturnal UCHI through enhanced/reduced urban heat storage and accelerated/inhibited rural radiative cooling. Our case study demonstrates that although heatwaves generally amplify nocturnal UCHI, in dry regions, their synoptic drivers significantly modify this nighttime synergy. The nocturnal UCHI during heatwave is not only driven by humidity effects but also modulated by cloud cover-regulated rural radiative cooling and urban thermal storage. These findings establish a mechanistic framework for heatwaves–UCHI interactions and provide actionable insights for heat-resilient planning in high-altitude arid cities. Full article

Radiometric consistency across satellite platforms is fundamental to producing high-quality Climate Data Records (CDRs). Because different cross-calibration methods have distinct advantages and limitations, comparative evaluation is necessary to ensure record accuracy. This study presents a comparative assessment of two widely applied calibration approaches—Simultaneous Nadir Overpass (SNO) and Double Difference (DD)—for the thermal infrared (TIR) bands of FY-3D MERSI. MODIS/Aqua serves as the reference sensor, while radiative transfer simulations driven by ERA5 inputs are generated with the Advanced Radiative Transfer Modeling System (ARMS) to support the analysis. The results show that SNO performs effectively when matchup samples are sufficiently large and globally representative but is less applicable under sparse temporal sampling or orbital drift. In contrast, the DD method consistently achieves higher calibration accuracy for MERSI Bands 24 and 25 under clear-sky conditions. It reduces mean biases from ~−0.5 K to within ±0.1 K and lowers RMSE from ~0.6 K to 0.3–0.4 K during 2021–2022. Under cloudy conditions, DD tends to overcorrect because coefficients derived from clear-sky simulations are not directly transferable to cloud-covered scenes, whereas SNO remains more stable though less precise. Overall, the results suggest that the two methods exhibit complementary strengths, with DD being preferable for high-accuracy calibration in clear-sky scenarios and SNO offering greater stability across variable atmospheric conditions. Future work will validate both methods under varied surface and atmospheric conditions and extend their use to additional sensors and spectral bands. Full article

The NASA CERES project conducts an independent assessment of the calibration stability of MODIS and VIIRS reflective solar bands to ensure consistency in CERES-derived clouds and radiative flux products. The assessment includes the use of tropical deep convective cloud invariant targets (DCC-IT), identified using a simple brightness temperature threshold. For visible bands, the collective DCC pixel radiance probability density function (PDF) was negatively skewed. By tracking the bright inflection point, rather than the PDF mode, and applying an anisotropic adjustment suited for the brightest DCC radiances, the lowest trend standard errors were obtained within 0.26% for NPP-VIIRS and within 0.36% for NOAA20-VIIRS and Aqua-MODIS. A kernel density estimation function was used to infer the PDF, which avoided discretization noise caused by sparse sampling. The near 10° regional consistency of the anisotropic corrected PDF inflection point radiances validated the DCC-IT approach. For the shortwave infrared (SWIR) bands, the DCC radiance variability is dependent on the ice particle scattering and absorption and is band-specific. The DCC radiance varies regionally, diurnally, and seasonally; however, the inter-annual variability is much smaller. Empirical bidirectional reflectance distribution functions (BRDFs), constructed from multi-year records, were most effective in characterizing the anisotropic behavior. Due to the distinct land and ocean as well as regional radiance differences, land, ocean, and regional BRDFs were evaluated. The regional radiance variability was mitigated by normalizing the individual regional radiances to the tropical mean radiance. Because the DCC pixel radiances have a Gaussian distribution, the mean radiance was used to track the DCC response. The regional BRDF-adjusted DCC-IT mean radiance trend standard errors were within 0.38%, 0.46%, and 1% for NOAA20-VIIRS, NPP-VIIRS, and Aqua-MODIS, respectively. Full article

The current study focuses on the interconnection between wildfires and the atmosphere and more precisely on the radiative effect of wildfire emissions on a global scale. Specifically, the effective radiative forcing (ERF) of present-day wildfire emissions relative to pre-industrial conditions is determined. Atmosphere-only simulations were performed using EC-Earth3, and three wildfire-emission datasets were introduced: BB4CMIP6 and two reconstructed alternatives, one derived from the BB4CMIP6 dataset and one derived from the fire model LPJ-LMfire. Our simulations indicate that the main drivers of ERF are the changes in cloud cover and surface albedo caused by the present-day wildfire emissions. Full article

Aviation contrails significantly impact climate via radiative forcing, but their segmentation in thermal infrared satellite images is challenged by thin-layer structures, blurry edges, and cirrus cloud interference. We propose MFcontrail, a deep learning model integrating multi-axis attention and frequency-domain enhancement for precise contrail segmentation. It uses a MaxViT encoder to capture long-range spatial features, a FreqFusion decoder to preserve high-frequency edge details, and an edge-aware loss to refine boundary accuracy. Evaluations on OpenContrails and Landsat-8 datasets show that MFcontrail outperforms state-of-the-art methods: compared with DeepLabV3+, it achieves a 5.03% higher F1-score and 5.91% higher IoU on OpenContrails, with 3.43% F1-score and 4.07% IoU gains on Landsat-8. Ablation studies confirm the effectiveness of frequency-domain enhancement (contributing 69.4% of IoU improvement) and other key components. This work provides a high-precision tool for aviation climate research, highlighting frequency-domain strategies’ value in satellite cloud image analysis. Full article

Monitoring and accurately forecasting ultraviolet (UV) radiation is of great importance especially due to its adverse effects on human health. In this study, we develop a numerical model to estimate the UV surface solar radiation with the overarching goal of providing a fully automated UV forecasting tool in the region of Epirus, Greece, and especially at the city of Ioannina. The UV surface solar radiation (SSR) is estimated based on detailed radiative transfer (RT) calculations. To ensure their accuracy, we employ the well-established UVSPEC model included in the libRadtran RT routines. LibRadtran provides a variety of options to set up and modify an atmosphere with molecules, aerosol particles, water and ice clouds and a surface as the lower boundary. As a first step, we performed a sensitivity study of the surface solar UV radiation with respect to ozone, precipitable water, aerosol optical properties and surface albedo. Our calculations are performed initially under clear-sky conditions to eliminate the uncertainties induced by clouds. All our calculations are performed spectrally within the UV spectral range, for a specific date and time at Ioannina, Epirus. Full article