Near-surface air temperature (Ta2M), defined as the temperature at 2 m above the ground, is influenced by various dynamical, radiative, and surface-atmosphere exchange processes. Despite numerous methods are developed to estimate daily or monthly mean temperatures, high spatiotemporal resolution products remain scarce. Longwave radiation, land surface temperature (LST), and total column water vapor (TCWV) are considered driving factors for retrieving Ta2M. This study proposes a new method that integrates the strengths of multiple products to generate a balanced global hourly averaged Ta2M dataset. Initially, this study proposes a reconstruction approach to derive longwave downward radiation (LWDR) based on random forest regression (RFR) by combining remote sensing and reanalysis datasets. Furthermore, a multiple linear regression approach is employed to model the interactions among Ta2M, longwave radiation, LST, and TCWV, resulting in a global long-term Ta2M product characterized by a spatial resolution of 0.05° and an hourly temporal resolution. The produced dataset exhibits reasonable accuracy, with root mean square error (RMSE) less than 3.1 K and bias less than 0.4 K. The method proposed in this study offers innovative strategies for estimating Ta2M from remote sensing and reanalysis datasets. It provides valuable insights into the dynamics of radiative and surface-atmosphere exchange processes, contributing to a more comprehensive understanding of Earth’s climate system.

Aboveground biomass (AGB) of rice is crucial for monitoring growth and predicting yields. While deep learning algorithms, such as deep convolutional neural networks (DCNNs), show compelling performance in estimating crop parameters, gathering sufficient ground-truth samples for model training poses a significant challenge, leading to the “small sample problem.” To address this, we propose a framework that utilizes a hybrid inversion model based on the PROSAIL-PRO radiative transfer model (RTM) combined with machine learning techniques [XGBoost and random forest (RF)]. This framework incorporates active learning optimization and the spectral angle mapper (SAM) method to select simulated samples that closely match real-world conditions, simultaneously assigning geographic location information to the samples. Using these qualified samples, we constructed both single-branch and multibranch DCNN models that integrate uncrewed aerial vehicle (UAV)-based hyperspectral principal components (PCs), canopy height (CH) information from the canopy surface model (CSM), and canopy temperature derived from thermal infrared (TIR) images. The effectiveness of this approach was validated across two experimental sites. The single-branch DCNN achieved the highest accuracy at site A ($R^{2} =0.816$and root-mean-square error (RMSE) =61.608 g/m2) with PCs, TIR, and CSM as inputs, while the multibranch DCNN performed best at site B ($R^{2} =0.784$and RMSE =65.533 g/m2), using PCs and TIR as inputs. Results indicate that simulated samples have considerable potential for practical applications. PCs were the primary contributors to the model, with TIR playing a more significant role than CSM. Overall, this study demonstrates high-precision estimation of rice AGB despite limited measured samples, offering valuable insights for crop monitoring under small sample conditions.

Remote sensing scene classification is critical in a wide range of applications. A significant number of studies have adopted deep learning methods for scene classification from remote sensing image datasets. While two-dimensional (2D) information has been fully exploited, three-dimensional (3D) structures of different remote sensing scenes are often overlooked by existing studies, which greatly limits the classification accuracies of scenes, especially in complex urban areas. To tackle this issue, in this paper, we present a novel Image Point Scene Network (IP-SceneNet) to extract features from multimodal heterogeneous data of high spatial resolution (HSR) images and aerial LiDAR point clouds for scene classification. The IP-SceneNet solves three key challenges that occur in multimodal scene classification. It first employs a voxel preprocessing scheme to compress the point clouds to address the information scale imbalance between the two modalities. Second, a dense pooling module is introduced to convert sparse point cloud voxel features into dense representations, mitigating the feature density imbalance. Third, a cross-attention mechanism, termed Image Voxel Attention (IVA), is implemented for the fusion of point cloud and image features, which addresses the problem of inter-modality correlations and redundancies. While we were developing IP-SceneNet, two datasets, named 2D3DScene-Netherlands and 2D3DScene-France, were established for tasks of multimodal scene classification. Experiments show that our proposed method achieved overall accuracies of 86.11% and 87.33% on the 2D3DScene-Netherlands and the 2D3DScene-France datasets, significantly outperforming both unimodal and other existing multimodal methods. Further ablation experiments confirmed the significant contributions of HSR images and LiDAR point clouds to scene classifications. In addition, the effectiveness of dense pooling module and IVA module in IP-SceneNet has also been validated. Our code and the above two datasets are available at https://github.com/treemanzzz/IP-SceneNet.

Leaf area index (LAI) and leaf chlorophyll content (LCC) are key indicators for monitoring rice growth dynamics. While UAV-based hyperspectral data is widely used, its high redundancy poses challenges for efficient information extraction. To address this, we propose a two-step generic framework. First, synthetic spectra generated by a field-constrained PROSAIL model are used to train a one-dimensional convolutional neural network with a self-attention mechanism that derives Spectral Composite Variables (SCVs) from redundant hyperspectral data. Then, the SCVs are combined with canopy temperature (from thermal infrared sensors) and crop height (derived from UAV-based LiDAR and RGB imagery) to develop a retrieval model, validated through both within-site and cross-site strategies. Results showed that the SCVs generated exhibited strong correlations with LAI and LCC, averaging 0.83 and 0.85, respectively. Moreover, the proposed framework achieved high retrieval accuracy across all growth stages (e.g., booting, heading, filling), with mean R² values of 0.76 for LAI and 0.71 for LCC. Specifically, both estimations reached peak performance during the heading stage, with an R² of 0.83 and RMSE of 0.47 m²/m² for LAI, and an R² of 0.77 and RMSE of 4.13 μg/cm² for LCC. Cross-site validation confirmed the model’s robustness and transferability, with the best performance consistently observed during the heading stage. Benefiting from this framework, spatial predictions of LAI and LCC at centimeter-level resolution closely aligned with observed patterns, enabling precise monitoring of rice growth. Overall, this study presents a robust and transferable solution for overcoming hyperspectral redundancy and enhancing crop growth estimation accuracy.

Shortwave net radiation (SWNR) plays an important role in the surface radiation balance and serves as the primary driving force for the exchange of surface and atmospheric materials. Although numerous algorithms exist for estimating SWNR, most of them tend to ignore the influence of aerosols and digital elevation model (DEM) on SWNR. Specifically, the impact of different aerosol types on SWNR can vary significantly, and the SWNR also exhibits considerable variations at different altitudes. It is true that many algorithms demonstrate higher accuracy in low-altitude regions with less polluted rural aerosol (nonabsorbent aerosol) areas. However, their accuracy tends to decrease when applied to high-altitude areas and heavily polluted urban aerosol (absorbent aerosol) regions. In this study, an improved all-sky parameterized algorithm is proposed to estimate SWNR by fully considering solar zenith angle (SZA), DEM, and different aerosol types, and rural and urban aerosol types are distinguished by a random forest (RF) method. The new algorithm is verified versus surface radiation budget network (SURFRAD) and baseline surface radiation network (BSRN) observations and compared with the traditional algorithms (Tang-2006 and Li-1993) and Clouds and the Earth’s Radiant Energy System (CERES) products. The results reveal that the new algorithm exhibits excellent accuracy at both instantaneous and hourly scales. For rural and urban aerosol types under all-sky conditions, the bias and root mean square error (RMSE) of the new algorithm are both less than 3.5 and 106.5 W/m on the instantaneous scale and less than 12 and 77 W/$\text{m}^{2}$on hourly scale, respectively. However, the existing algorithms show a significant overestimation (bias > 50 W/$\text{m}^{2}$) for the urban aerosol type under various atmospheres conditions. For the CERES single scanner footprint (SSF) (instantaneous) and CERES SYNergy (CERES SYN, 1-hourly) products, the overestimation phenomenon is also detected under urban aerosol type, with bias greater than 40 and 15 W/$\text{m}^{2}$, respectively. Compared with the existing algorithms, the new algorithm demonstrates superior applicability under larger SZA conditions. When the SZA exceeds 70°, the rate of estimated effective value can be increased by up to 14%. In addition, the new algorithm can effectively solve the problem of underestimation in high-altitude areas, which frequently occurs in most existing algorithms (bias$< -16$W/$\text{m}^{2}$). The improved accuracy and applicability of the new algorithm, along with its strategy of distinguishing aerosol types, can provide valuable insights for the SWNR estimation from space.

Longwave downward radiation (LWDR) is an important parameter that modulates the earth’s radiation and energy balance, and is also a key variable that affects the global warming. Currently, although many reanalysis LWDR products and satellite-based algorithms are available, their coarse spatio-temporal resolutions as well as the difficulties in organizing the corresponding driving parameters seriously limit their applications. As China’s new generation geostationary satellite, Fengyun-4A (FY-4A) provides higher spatial and temporal resolutions (4 km @nadir, 15min at full disk mode) at longwave infrared channels which can routinely monitor the changes of the earth’s radiation in near real-time and therefore provide great potentials in generating various high-accuracy radiation products. Unfortunately, the existing official LWDR products of FY-4A can only provide estimates under clear skies, and its accuracy still has much room for improvement. For above-mentioned points, an improved general all-sky parameterization algorithm is proposed based on readily available input variables, such as land surface temperature (LST), column water vapor (CWV) and cloud-top temperature (CTT). Then the new algorithm is applied to FY-4A aiming to derive believable all-sky LWDR. The validation results show that, the new algorithm does show a noticeable improvement over the original one by reducing the relatively large errors in LWDR under conditions of extremely cold and dry (flux range <150 W/m²), as well as the large bias in the polar and high altitude regions. Moreover, the new method can generate more reliable LWDR than that of FY-4A official product in terms of both spatio-temporal continuity and accuracy, with RMSE less than 22 W/m² and bias less than 0.5 W/m² under all-sky conditions. The easy-to-use and believable performance of the new algorithm provide an opportunity to accurately derive all-sky LWDR from FY-4A and similar satellite missions with high resolutions.

In the above article[1], the following corrections to text citations should be noted. In Sections II “DATA” and IV “RESULTS AND ANALYSIS,” the citation [13] is changed to [24] and all text citations for [24] through [45] link to the latter citation.Table Iprovides the incorrect citation as shown in the published article along with the reference to which it should direct. In addition, the citation [27] in the above article[1]is revised to[4]in this Errata.

Shortwave downward radiation (SWDR) plays a major role in the material and energy balance of the Earth’s climate system. However, most of existing SWDR research and products assume that the surface is flat, ignoring the effect of topography. This approach introduces significant uncertainties in the calculated fluxes and smooths the spatial distribution of SWDR. This paper proposes a uniform shortwave topographic radiation model (USWTRM) based on the principle of energy conservation. To evaluate the USWTRM, we compared it with the large-scale remote sensing data and image simulation framework (LESS). The USWTRM performed better than the traditional method in most conditions. For clear-sky, when the SZA=0°, the relative root-mean-square error (rRMSE), relative bias (rbias), and R2of the USWTRM at 1-km were 0.1 %, 0.0 %, and 1.000, respectively. At SZAs of 20°, 40°, and 60°, the USWTRM also showed better results than the traditional method. Moreover, the USWTRM performed similarly at 3-km and 5-km as that of 1-km. For cloudy-sky, the rRMSE and rbias of the USWTRM at fine-scale were 3.5%, and 0.0%, respectively. At 1-km, the rRMSE and rbias of the USWTRM were 0.9%, and 0.5%, respectively. In particular, the USWTRM outperformed previous studies in accurately quantifying the SWDR over rugged areas, under both clear and cloudy skies. Overall, the analysis reveals that the USWTRM works well over mountainous regions in terms of reliable accuracy, applicability, and generalization. It provides a new perspective for accurately deriving topographic SWDR at various scales and significantly reduces radiation uncertainties over rugged terrain.

Land surface temperature (LST) is a pivotal parameter in many study areas. At present, numerous algorithms are available to retrieve accurate LST from different satellite thermal infrared (TIR) observations. However, rare studies focus on simultaneous LST and land surface emissivity (LSE) retrieval from the TIR measurements of MERSI-II onboard Chinese FengYun-3D satellite designed with a spatiotemporal resolution of 250 meters and five days, that just bridges the Terra/Aqua-MODIS and Landsat-TIRS observations. Although simultaneous LST and LSE retrieval could be achieved by the existing temperature-emissivity separation (TES) method, only two of the three MERSI-II TIR channels are with the spatial resolution of 250 meters leading to the difficulty of directly applying the traditional TES method. Inspired by the TES method and the theory of temperature-independent spectral indices (TISI), this study proposed a new scheme for deriving the 250-meters LST and LSE simultaneously from the MERSI-II TIR data. T-based validations using the ground measurements indicated that the LST retrieval accuracy was about 3.19 K and 2.51 K in the daytime and nighttime, respectively. Cross-validations taking MODIS LST product as the references showed that errors in the retrieved LST was <2.1 K in the daytime while <1.4 K during the nighttime. Overall, results showed that the proposed method can be used to retrieve global LST and LSE from the MERSI-II data, which can facilitate their applications in relevant fields.

Land surface temperature (LST) is a crucial parameter needed to study the thermal environment in urban areas. Currently, it can be restored from thermal infrared (TIR) measurements based on various LST retrieval algorithms. But the expected urban LST retrieval accuracy of <1 K is difficult to achieve because knowledge is lacking on how to correct the impact from the surface 3-D structures and the sunlit-shadow temperature contrast. Although an Analytical TIR radiative transfer Model Over Urban area (ATIMOU) has been proposed, the temperature contrast between sunlit and shadowed areas has been not well managed yet, thus lead to its inapplicability in daytime TIR observations. This study develops an Extended ATIMOU (E_ATIMOU) that considers the impact from both 3-D structures and sunlit-shadow temperature contrast. According to the simulations based on E_ATIMOU, if such impact is not properly accounted for, a 4.43 K bias can be potentially introduced to the ground brightness temperature of a street canyon under the condition of wavelength of 10 μm, ratio “sunlit-road area/total-road area” of 0.5, shadowed wall and road temperature of 300 K, and the sunlit-shadow temperature contrast of 5 K, which emphasizes the necessity of addressing this impact during the LST retrieval in urban areas. Moreover, E_ATIMOU has also been validated by intercomparing with the discrete anisotropic radiative model (DART). The discrepancy between the two models for the calculated ground brightness temperatures is found to be <0.1 K for various urban scenarios, indicating that the E_ATIMOU is in good agreement with DART.

Surface longwave downward radiation (LWDR) plays an important role in modulating greenhouse effect and climate change. Constructing a global longtime series LWDR dataset is greatly necessary to systematically and in-depth study the LWDR effect on the climate. However, the current multi-source LWDR products (satellite and reanalysis) show large differences in terms of both spatio-temporal resolutions and accuracy in various regions. Therefore, it is necessary to fuse multi-source datasets to generate more accurate LWDR with high spatio-temporal resolution on a global scale. To this end, a downscaling strategy is firstly proposed to generate LWDR dataset with 0.25° resolution from CERES-SYN data with 1° scale, by incorporating the Land Surface Temperature (LST), Total Column Water Vapor (TCWV) and Elevation. Then a machine learning-based fusion method is provided to generate a global hourly LWDR dataset with spatial resolution of 0.25° by combing three products (CERES-SYN, ERA5 and GLDAS). Compared with ground measurements, the performance of generated LWDR product reveals that the correlation coefficient (R), mean bias error (BIAS), and root mean square error (RMSE) were 0.97, -0.95 W/m2 and 22.38 W/m2 respectively over the land, and 0.99, -0.88 W/m2 and 10.96 W/m2 over the ocean. Specially, it shows improved accuracy in the low and middle latitude regions compared with other LWDR products. Considering its better accuracy and higher spatio-temporal resolution, the new LWDR product can provide essential data for deeply understanding the global energy balance and even the global warming. Moreover, the proposed fusion strategy can be enlightening for readers in the fields of multi-source data combination and big data analysis.

Ice/snow surface temperature (I/SST) is an essential parameter in many research fields such as the climate change, energy, and matter balance of the South pole regions. Recently, many algorithms have been developed for various satellite observations to derive the I/SST. However, rare studies focus on accurate I/SST retrieval from the observations of Chinese MEdium Resolution Spectral Imager II (MERSI-II) instrument onboard the FY-3D satellite with the spatial resolution of 250 m and temporal resolution of about five days, which just bridges the specifications of the Aqua-MODerate-resolution Imaging Spectroradiometer (MODIS) (1000-m pixel size and 0.5-day revisit cycle) and Landsat-Thermal InfraRed Sensor (TIRS) (100-m pixel size and 16-day revisit cycle) instruments. In this study, a new method with correction of striping noise and consideration of angular emissivity effect is developed for the MERSI-II data to accurately retrieve the I/SST. The performance of the proposed method is assessed by using both MODIS product and ground I/SST measurements. The results show that the FY-3D I/SST retrieval accuracy is comparable to the MODIS product, with a discrepancy of < 1.6 K. Ground-based validation reveals that the proposed method could be used to accurately retrieve the I/SST with a root-mean-square error (RMSE) of < 1.5 K. Overall, this study proposes a method for accurate I/SST retrieval from the FY-3D MERSI-II data with the pixel size of 250 m, implying the possibilities in improving the spatio-temporal resolutions of the current I/SST products. Moreover, the proposed method is also helpful to improve our understandings of the polar regions.

Estimation of downward shortwave radiation (DSR) is of great importance in global energy budget and climatic modeling. Although various algorithms have been proposed, effective validation methods are absent for rugged terrains due to the lack of rigorous methodology and reliable field measurements. We propose a two-step validation method for rugged terrains based on computer simulations. The first step is to perform point-to-point validation at local scale. Time-series measurements were applied to evaluate a three-dimensional (3-D) radiative transfer model. The second step is to validate the DSR at pixel-scale. A semiempirical model was built up to interpolate and upscale the DSR. Key terrain parameters were weighted by empirical coefficients retrieved from ground-based observations. The optimum number and locations of ground stations were designed by the 3-D radiative transfer model and Monte Carlo method. Four ground stations were selected to upscale the ground-based observations. Additional three ground stations were set up to validate the interpolated results. The upscaled DSR was finally applied to validate the satellite products provided by MODIS and Himawari-8. The results showed that the modeled and observed DSR exhibited good consistency at point scale with correlation coefficients exceeding 0.995. The average error was around 20 W/m2for the interpolated DSR and 10 W/m2for the upscaled DSR in theory. The accuracies of the satellite products were acceptable at most times, with correlation coefficients exceeding 0.94. From an operational point of view, our method has an advantage of using small amount of ground stations to upscale DSR with relatively high accuracy over rugged terrains.

Optical and thermal infrared remote sensing images highly integrate spatial heterogeneity information (land surface soil, vegetation and water). This paper evaluated the capacity of Landsat-8 and Moderate-resolution Imaging Spectroradiometer (MODIS) remote sensing indices and empirical relationship models for soil moisture estimations at different depths. The results show that (1) compared with other Landsat-8 indices, shortwave infrared based Surface Water Capacity Index (SWCI) has higher correlation with 10-50 cm depth soil moisture. The comparison based on MODIS daily indices confirms that SWCI can monitor 20 cm soil moisture with more stability; (2) The quadratic polynomial model based on Land Surface Temperature (LST) and SWCI possessed highest accuracy among all empirical models. The average coefficient of determination (R2) increases to 0.257 from 0.150 based on LST-NDVI linear model and 0.176 based on LST-SWCI linear model. Soil moisture analysis at both 30 m and 1 km spatial scale suggest that optical remote sensing could indirectly reflect soil moisture variation with higher precise and more stability in root layer rather than top-most layer.

In this article, we developed a hybrid method to estimate surface shortwave radiation (SSR) for the new-generation Himawari-8 geostationary satellite. This hybrid method combines the advantages of a deep neural network (DNN) with high speed and radiative transfer model (RTM) to achieve high accuracy: the RTM provides training data for the DNN under various cloud and aerosol conditions (including heavy aerosol loadings). Moreover, our hybrid method can simultaneously output the byproducts of photosynthetically active radiation (PAR), ultraviolet A (UVA), and Ultraviolet B (UVB), the direct and diffuse components at the surface, and the upward solar radiation at the top-of-atmosphere (TOA). The trained DNN was applied to the Himawari-8 satellite atmospheric products for 2016 and comprehensively validated using a total of 118 stations from four networks located in the full-disk regions of Himawari-8. The results showed an RMSE of 125.9 Wm-2for instantaneous SSR, 105.4 Wm-2for hourly SSR, 31.9 Wm-2for daily SSR, and respective mean bias error (MBE) scores of 8.1, 27.6, and 12.3 Wm-2. The hybrid method developed in this study performed well, achieving high accuracy and high speed, and it is capable of providing near-real-time SSR estimates for many applied energy fields.

In this study, the Chang'E-2 microwave radiometer (CELMS) data are employed to study the thermophysical features of the regolith in the western lunar farside, including Giordano Bruno - the youngest lunar crater of its size. The results are as follows. Firstly, the distribution of the rocks with depth is different in Bruno, King, and Necho craters. Secondly, abundant cold anomalies and one hidden hot anomaly are discovered. Thirdly, at least seven hidden linear structures are discovered, some of which hinting the impacting process of Bruno crater.

Cloud plays a significant role in the study of the Earth's radiation balance. Particularly, it is still a big challenge to derive longwave radiation under cloudy-sky conditions for the community for a long time. In this paper, a scheme to estimate the upward longwave radiation (LWUR) under cloudy skies is proposed by accounting for the solar-cloud-satellite geometry (SCSG) effect. The application of the new method to MODIS data indicates that the new scheme can work well and the LWUR under the cloud layers can be successfully recovered. And accordingly, the fraction of valid LWUR for a typical image is correspondingly improved. Although MODIS data was employed here, this method can be applied to any optical remote sensing images as long as the required parameters for correcting the SCSG effect are provided.

Land surface shortwave downward radiation (SWDR) is generally defined as incident solar energy over land surfaces in the shortwave spectrum (300-3000nm). As one of important parameter of the land surface radiation budget (SRB) and many land process models, it is usually required as part of the input variables to address a large variety of scientific application issues in fields of agricultural management, climate trends, ecological forecasting, reusable energy production, public health and so on. Currently, regional or global SWDR can be estimated from polar-orbiting or geostationary satellite observations based on empirical or physical-based retrieval models. These remote-sensed SWDR products with fine spatial resolution provide us valuable information about a series of critical Earth science problems and enable us to gain more insight into the planet we live on. In this paper, an improved method was proposed based on a look-up table approach via MODTRAN-5 simulations to estimate clear-sky instantaneous SWDR and its direct component from TOA radiance of Moderate Resolution Imaging Spectrometer (MODIS) observation. Ground measurements from seven SURFRAD sites are used for validating the algorithm and the result shows good accuracy. This approach is suitable for producing clear-sky instantaneous SWDR and its direct component at the spatial resolution of finer than 5 km.

Rugged terrain, as a high percent of the Earth's terrestrial surface, can cause the directionality of the surface thermal radiation, and affect the retrieved land surface temperature (LST) and longwave radiation (SLR) from satellite measurements due to the limited instantaneous field of view and observation angles. New directional brightness temperature (DBT) and equivalent brightness temperature (EBT) models were established considering terrain effects. The biases between them were also analyzed based on a simulated scene using the Advanced Spacebome Thermal Emission and Reflection Radiometer (ASTER) LST, emissivity and topographic data. The results show that BTs at the valley and peak points are clearly anisotropic, while this directionality at the cropland point is not obvious. The DBT shows hotspot effects which is closely related to the solar position. The range of DBTs can reach up to about 9 K in the valley point and the standard deviation of this difference in all view directions is 1.05 K. Thus, it can be concluded that it is hard to meet the requirement of retrieval accuracy of LST or SLR over rugged terrain if ignoring the three-dimensional structure of mountainous region and its angular thermal radiation.

Apollo Basin locates within the large South Pole-Aitken Basin (SPA). The study on Apollo Basin will provides some interesting information about the composition and thermal state of the shallow Moon crust. In this paper, the normalized brightness temperature (nTB) maps and the (FeO + TiO2) abundance (FTA) were systematically combined to study the thermal behaviors of Apollo Basin. The results firstly indicate a strong correlation between the nTB behaviors and the FTA. Secondly, the low nTB behaviors hint the homogeneity of the Moon crust in the thermophysical parameters. Finally, the abnormally high nTB behaviors in the western of the Basin floor probably imply the existence of the pyroclastic deposits.

Knowledge of the thermal state will provide essential information to better understand the thermal evolution of the Moon. In this paper, four typical Copernican craters, including Copernicus, Aristarchus, Tycho and Jackson, are selected and their thermal behaviors are evaluated with the CE-2 CELMS data. The results indicate that: (1) There exists a strong correlation between the TBdistribution at noon and the topography. (2) The changes of the regolith thermophysical parameters with depth are rather complex in the four typical craters. (3) The TBat midnight is more suitable to study the regolith thermophysical features. (4) The shallow layer of the lunar crust is likely cold.

Land surface shortwave downward radiation (SWDR), as one of major components of the surface radiation budget (SRB), plays an important role in the fields of atmospheric, oceanic, and land processes, and ultimately influences the Earth's climate as well as the matter and energy cycle of the earth system. Currently, regional or global SWDR can be obtained either from reanalysis products or from satellite observations based on statistical or physical-based retrieval models. Although great efforts have been made to assess the applicability and accuracy of those different SWDR datasets, few studies have been conducted to evaluate the performance of the Clouds and the Earth's Radiant Energy System Synoptic (CERES-SYN) Edition 3a and Himawari-8 SWDR datasets over the Tibetan Plateau. In this study, the both SWDR datasets are validated against in-situ data at 11 ground sites from the China Meteorological Administration (CMA). It is found that the Himawari-8 SWDR product has a slightly higher accuracy in these two SWDR datasets but with a significantly higher spatial resolution (5km). The mean bias is 1.7 W/m2for CERES-SYN and -1.6 W/m2for Himawari-8, respectively, the root mean square errors (RMSE) are 31.3 W/m2for CERES-SYN and 31.2 W/m2for Himawari-8, respectively. Mean coefficient of determination (R2) of the two datasets are both over 0.8. It is clearly that CERES-SYN tends to overestimate SWDR somewhat while the Himawari-8 has slight underestimation over the Tibetan Plateau. The findings in this paper can be valuable for hydrological, ecological, agrometeorological and biogeochemical applications and researches.

In this study, a quantitative Freeze/thaw (F/T) index from passive microwave observations is defined, and is assumed to be linearly correlated with land surface temperature from thermal infrared observations. Thus, a linear regression method is proposed and verified to be effective over a multiscale network of Naqu of the Tibetan Plateau. Then, we implement and test the proposed approach to generate daily F/T state maps at a 5-km spatial resolution through the fusion of AMSR2 and MODIS data. It is found the high resolution F/T maps agreed well with ground reference observations of 0-cm soil temperature, with an overall accuracy of ~86.6%. This study provides new insights for high-resolution F/T mapping beyond the (Soil Moisture Active Passive) SMAP mission.

Land surface temperature (LST) is a key variable in the processes of energy and water balance between Earth's surface and atmosphere. To date, considerable researches have been focused on this issue, especially the thermal infrared (TIR) methods. Whereas TIR measurements are only limited to clear-sky condition, no observation is possible under cloudy conditions. While, passive microwave (PMW) as an alternative to the TIR measurements can penetrate the clouds. In this paper, the optical LST was derived from AMSR-E brightness temperatures by building a strong linear relationship over the continental United States. Unlike the previous studies, the algorithm was conducted by further using the corresponding ground measured LSTs which consist of both clear and cloudy conditions. The linear relationships were built on three sub-regions which is defined by NDVIs. The results show that the root-mean-square error (RMSE) of derived LST ranges from 2.50K to 3.23K for ascending overpass and 1.54K to 2.71K for descending track. This accuracy is proven to be better than existing work.

Surface radiation balance plays a vital role in the earth surface system and affects many biogeophysical processes. As one of components of surface energy balance, longwave downward radiation (LWDR) is considered as the most poorly estimated radiation component, and its uncertainty is regarded as substantially larger than other terms of surface energy budget. In this paper, we applied the multivariate adaptive regression splines (MARS) method to derive LWDR based on MODIS thermal infrared bands top of atmosphere radiances and ground-based LWDR measurements. In model fitting process, the RMSE, bias and R-square value are 25.49 W/m2, -0.000 W/m2and 0.88, respectively; and in model validation stage, the RMSE, bias and R-square value are 25.63 W/m2, 0.481 W/m2and 0.87, respectively. The newly proposed model demonstrates comparable accuracy with other LWDR estimating methods and proves that MARS method is very useful in remote sensing based LWDR estimation.

Land surface longwave (LW) radiation (or longwave radiative flux), including longwave upwelling (LWUR), downward (LWDR) and net radiation (LWNR), are key components of the total energy that drives the surface energy balance at the interface between the earth's surface and the atmosphere. The importance of LW radiation in regulating air temperature and balancing surface energy is enlarged especially under cloudy-sky conditions. Unfortunately, to date, a tremendous attempts have been made to derive LW radiation from space only valid under clear-sky conditions leading to difficulty of utility of remote sensing-based LW radiation products in most land models due to their spatial discontinuity. Although few studies focused on LW radiation estimation under cloudy-sky conditions, while their global application are still problematic. In this paper, novel strategies are proposed aiming to derive high resolution cloudy-sky LWDR and LWUR by fusing collocated optical and microwave satellite data. The results reveal that the new approaches work rather well, thus, more importantly, providing unprecedented possibilities for generating high resolution global LW radiation datasets.

Based on our previous studies, a simple fusion algorithm is proposed to estimate surface shortwave fluxes with polar-orbiting and geostationary satellite data. A shortwave flux component of one geostationary moment can be retrieved by only five inputs which include the known flux of one polar-orbiting moment, solar zenith angles and cloud fractions of the two moments. The preliminary validations are performed in terms of both the simulated and realistic datasets. The R2for each component is higher than 0.90 in the simulated case. The accuracy is relatively lower for the more complicated realistic situations. All of the validation results show that the simple and practical fusion algorithm has the potential to estimate surface shortwave fluxes with acceptable accuracy. With the combination of polar-orbiting (MODIS) and geostationary (Fengyun-2C) satellite data, surface shortwave fluxes with the temporal resolution of one hour were retrieved over the Tibetan Plateau.

Water vapor plays an important roles in the Earth's energy and water cycles. Compared to optical remote sensing, microwave remote sensing has the advantage to acquire information of atmosphere under cloudy condition. Up to now, there is no published reliable total precipitable water product over land from AMSR2 due to effect of high land surface emissivity in microwave band. In this study, an improved total precipitable water retrieved algorithm for AMSR2 will be developed based on previous studies. In the retrieval algorithm, a land surface emissivity parameter estimation model is developed using combination of AMSR2 and MODIS observation. The precisely estimated surface emissivity parameter is the key parameter in the retrieval of total precipitable water. Finally, the total precipitable water was retrieved using a look-up table, and it is validated using total precipitable water observed from global distributed GPS.

So far, the Greenhouse Gases Observing Satellite (GOSAT) and the Orbiting Carbon Observatory-2 (OCO-2) are the only two missions designed to measure the column-averaged CO2dry air mole fraction (XCO2). To improve our understanding of global carbon source and sink, these two XCO2products are compared in this study. The result reveals that the OCO-2 XCO2product show the wider spatial coverage than those of GOSAT from 30°S ∼ 90°N latitude. At the same time, GOSAT and OCO-2 XCO2products shows a good agreement with correlation coefficient (R2) of 0.69 and bias of −1.0 ppm. However, the discrepancy is still existed in some region. The discrepancy between these two products implies that it is necessary to make them complement each other to better improve our knowledge of global carbon cycle or even climate change.

Infrared Difference Dust Index (IDDI) is often used as a satellite dust product to detect the change of mineral dust aerosols in the atmosphere. And aerosol optical depth (AOD) is also a main measurement for mineral dust aerosol. To qualify dust loading on the regional or global scale, it is very necessary to understand the relation between IDDI and AOD. Therefore, this study investigates the impact of sensitivity factors including surface temperature and surface type to the IDDI by using MODTRAN (Moderate Resolution Transmittance code) model to better evaluate the relation between IDDI and AOD. The result shows that the simulated IDDI from MODTRAN are extremely sensitive to the surface temperature and surface type. The IDDI is growing with the increased surface temperature. And the sensitivity of farm and forest type to IDDI is similar and their difference is very small. But the sensitivity of desert type and ocean to IDDI is obviously different from other two types and the surface type is also a key parameter to IDDI. The result also implies that there is an exponential relationship between IDDI and AOD. These results will be very helpful to further establish the relation between IDDI and AOD.

In this study, a novel approach is proposed to simultaneously detect clouds and cloud shadows for remotely sensed images. Unlike the existing methods that based on spectral tests, it is based on the simulated band radiance, so that it can be applied to any remotely sensed images. The results showed that it very effective compared to existing algorithms.

Frozen ground is soil or rock in which part or all of the pore water has turned into ice. Freeze/thaw state is simply water-ice phase change, but it is an important sign like a giant on-off “switch” of the land surface processes. The freezing of soil greatly reduces the water infiltration and migration in the soil, and in consequence generates a substantial increase in snowmelt runoff. The seasonal cycles of freezing and thawing significantly influence the surface energy exchanges with atmosphere. Therefore, freeze/thaw state monitoring is becoming essential under the context of global changes. The WCOM integrates all the advantages of previous satellites, and is expected to provide more accurate information of freeze/thaw state through the synergy use of active and passive, high and low resolution measurements.

Estimate surface air temperature (Ta) accurately in fine scale is very necessary for hydrological simulation, especial in glacierized basins. The purpose of this paper is to present a framework to mapping the Ta using multi-source remote sensing data and reanalysis dataset. The main content includes two parts: (a) filling the gaps in remotely sensed land surface temperature (LST) using spatial-temporal Kriging method and (b) developing a semi-empirical method to relate Ta and LST that is applicable in glacierized basins. The framework is further tested in the Langtang valley, Nepal which is a glacierized basin in the central Hindu-Kush-Himalaya (HKH) region. The validation results show that the estimated Ta has generally good spatial and temporal variations. The RMSE of Ta at Langtang Kyangjin station is 9.1K and 7.7K at 10:30 and 13:30, respectly.

Clouds are the strongest modulator of the solar radiation absorbed by the earth-atmosphere system. In this study, a regional cloud fraction (RCF) is involved to modify the classic one-dimensional radiative transfer model, in order to study the effect of inner-pixel broken clouds on the radiation field at large scale. A global sensitivity analysis (GSA) is performed to quantitatively understand the effect of 11 parameters on each surface shortwave radiation component. The GSA results show that three most influential parameters for the modified model are RCF, land surface albedo and solar zenith angle. Three less important parameters are ground altitude, visibility and cloud extinction coefficient which is the case only for the situation of optically thin clouds. The other five factors can be considered as not important. These findings will enhance our knowledge on how to accurately model the surface shortwave radiation fluxes at large scale for partial cloudy conditions.

In recent years, global warming caused by increased atmospheric CO2has greatly drawn widespread attention from the public. Although satellite observations and model-simulation offer us two effective approaches to monitor and assess the global atmospheric CO2, quantification of the differences between these two different CO2data is not fully investigated yet. In this paper, these CO2products including satellite observations and model-simulation are inter-compared in terms of magnitude and their spatiotemporal distributions. The results reveal that these CO2data from different data source show a good agreement all over the world, whereas many discrepancies still exist between satellite observations and model-simulation, especially in the Northern Sphere.

Light scattering models of snow are very important for the remote sensing of snow. Many previous models have used unrealistic assumptions about the snow particle shape and microstructure. In this paper, a new model is proposed, wherein a bicontinuous medium is used to simulate the snow microstructure, and geometric optics theory is used in combination with the Monte Carlo method to simulate the scattering properties of snow. Then, using the radiative transfer equation, the snow reflectance, including the polarized reflectance, can be simulated. Unlike other models that use Monte Carlo ray tracing, the new model is computationally efficient and can be used for massive simulations and practical applications. The simulation results of the new model are compared with the ground measurements and simulation results of a traditional model based on the Mie theory. Through validations and comparisons, the new model is shown to demonstrate a significantly improved capability in simulating the bidirectional reflectance of snow. The importance of the grain shape and microstructure modeling in the light scattering models of snow is confirmed by the comparison of the simulation results.

Accurate microwave brightness temperature is important for the retrieval of land surface parameter. However, the existence of atmosphere affect acquisition of brightness temperature by microwave sensor onboard satellite. In this paper, atmosphere sensitivity of each band of AMSR-E is analyzed and an atmosphere correction method is developed with ancillary water vapor and cloud liquid water data for both clear and cloudy condition. As a validation, time series of microwave vegetation index is used to qualitatively verify the atmosphere corrected brightness temperature, and it shows that the atmosphere correction method make a good improvement on microwave vegetation index.

Currently, ascertaining and quantifying the global distribution of carbon dioxide from space-based measurements are greatly valuable for understanding the causes of global warming and predicting the tendency of climate change. Nevertheless, the number of valid XCO2data points from a single space-based sensor is generally limited on the earth. Based on this problem, a fused XCO2dataset is used to generate a continuous spatio-temporal distribution of global CO2concentration by combining GOSAT with SCIAMACHY in this study. And this dataset is also compared with a data assimilation system Carbon Tracker as well as ground-based TCCON sites. The results reveal that the spatial coverage of the fused data is wider than individual space-based XCO2measurements (GOSAT or SCIAMCHY) on the global scale. Meanwhile, compared to that of GOSAT or SCIAMACHY, the correlation between the fused data and Carbon Tracker is relatively better. In addition, the fused data show a good agreement with CO2retrieval of ACOS and BESD as well as that of TCCON sites although a little biases exist.

In our study, we proposed a gap-filled method based on ordinary kriging to generate a global land distribution map of carbon dioxide (CO2) by modeling the spatial correlation structures of column-averaged CO2dry air mole fractions (XCO2) on the global scale, using a fused data by combining GOSAT and SCIMACHY. The relationship between the distance and semi-variogram of XCO2is estimated and modeled by an exponential model with a nugget-effect component. The semi-variogram result indicates that there is a significant spatial correlation within the fused CO2data set. The prediction of XCO2using semi-variogram model is conducted within 1 degree×1 degree grids over the world. The results reveal that the global distribution of XCO2based on kriging method is the most extensive compared with other CO2products. Moreover, the monthly map from the kriging approach has less predicted uncertainties, most of which are less than 0.5% of XCO2value.

Longwave radiation is a key component of total energy that drives surface energy balance at the interface between the surface and atmosphere. To date, a number of algorithms have been developed toward accurately estimating surface longwave radiation from remotely sensed data. While most of these existing algorithms can only derive longwave radiation under clear-sky conditions due to the limited penetration of optical remote sensing thus leading to spatial incontinuity in derived radiation map. Wherein the land surface temperature (LST) play a key role in longwave radiation estimation, especially for surface emitted (upwelling) and net longwave flux. If LSTs under cloudy area can be recovered, the derivation of surface longwave ration under cloudy conditions would be straightforward. To this end, in this paper, a fusing strategy is proposed to combine the LST measurements from MODIS and AMSR-E. The results show that the proposed fusing strategy for combining microwave and optical space-based measurements in recovering surface LST under cloudy conditions is very effective. By fusion, the spatial coverage of valid LSTs over the globe is highly improved.

Shortwave (SW) radiative flux (usually within 0.3∼3μm) is the dominant energy source of our planet, which drives the climate as well as the matter and energy cycle of the Earth system. It is an indispensable component of surface total energy balance. Considering the importance of SW radiation, during the past decades, more and more studies have conducted for estimating surface SW radiation using satellite-based data, such as MODIS, CERES, GOES etc. Although great effort has been made, most researches neglect the topographic effect and mainly focus on the retrieval of SW radiation over ideal horizontal surfaces for both instantaneous and time-integrated radiation. For this point, we propose a topographic SW radiation model based on the existing studies. Based on this, the SW radiative flux components are derived from MODIS data by fully accounting for the surface topographic effect. The results show that the errors induced in the retrieved daily SW radiation can reach up to 400W/m2at 1km scale. For instantaneous radiation, the uncertainties of derived SW radiation can reach up to 300W/m2even at 5km scale due to topographic effect. The findings of this paper prove the importance of topographic modeling of surface radiation over rugged terrain.

Current and future satellite missions with L-band passive microwave radiometers could provide useful information for monitoring the soil moisture and freeze/thaw state at a global scale. The soil surface roughness plays a significant role in microwave emission from land surfaces. In this study, a simple parameterized model from exponentially correlated surface was developed. Results indicated the model can be very useful in understanding the effects of surface roughness on microwave emission.

Urban heat island (UHI) is one of the main factors influencing the weather, climate, and environment in urban and its surrounding areas. In this study, MODIS's MOD11A2 product over the year of 2000-2012 is used to investigate and monitoring the UHI of Dongguan, China. The result indicates that the higher UHI intensity at day-time is frequently occurred in the summer and fall, while the lower UHI intensity is often observed in spring, while no seasonal variation pattern could detect for night-time UHI. During the 13years, the region with higher LSTs is frequently distributed in areas covered by built, roads and bare soils etc., while regions with lower LSTs are mainly occurred in places dominated by waters, forests and parks etc.

Wetlands' soil hydrology and temperature changes greatly affect methane emissions, and further affect climate. Wetland processes are not modeled well yet in the community land model (CLM) and further study is needed. Wetlands are represented as saturated organic soil, instead of being treated as water bodies only without soil and vegetation in CLM. Wetland soils are discretized to fibric, hemic, and sapric layers accounting for the typical variations in hydraulic characteristics of organic soils in the newly parameterized model. Spin-up is achieved by repeating the full range of available years 3 times (3 spin-up cycles). Daily model output is averaged to monthly output for analysis. The original model (CLM3_cntrl) and the modified wetland scheme model (CLM3_wet) are run independently. Sensible and latent heat fluxes and soil temperatures are validated. The results show that the state variables are improved through the wetland soils parameterization compared to the original CLM model.

Remote sensing of atmospheric CO2is essential to study global warming. To date, there are many instruments to detect CO2from space, such as, AIRS, GOSAT, SCIAMACHY and IASI etc., while quantification of the differences among these CO2products has not been fully investigated yet. In this study, the differences between CO2products from AIRS, GOSAT, SCIAMAMCHY (totally four products) have been compared. The results showed that although these CO2products are derived from different instruments, the complementarity in spatial coverage and relatively high correlation among them make it potentially possible to combine them, especially for GOSAT and SCIAMACHY.

Space-based monitoring of atmospheric CO2is very crucial for global carbon cycle studies and even global change. In this study, a method for physically fusing SCIAMACHY and GOSAT CO2measurements has been proposed by fully considering the averaging kernel and spatio-temporal variations as well as the CO2retrieval errors. The results revealed that the average global coverage of ACOS and BESD is around about 0.56% and 0.27% respectively at a daily scale. The monthly-mean coverage of such products accounts about 5.66% and 4.62% respectively. While spatial coverage of fused XCO2can reach up to 0.76% and 8.51 % on daily and monthly scale respectively. These findings in this paper proved the effectiveness of the proposed method.

Accurate spectral calibration of the in-flight sensors is crucial for processing and exploration of remotely sensed data. This paper developed a strategy to make spectral recalibration (i.e., spectral response function, central wavelength, and bandwidth) for in-flight broadband sensor using a device-responsivity-decomposition model with a priori knowledge and an optimization algorithm. Sensitivity analysis indicates that an accurate result requires the targets to be observed under a dry and clear atmospheric condition (column water vapor2and visibility > 23 km) and no more than 5% error is included in the measured data. The new strategy was used to retrieve the spectral parameters along with radiometric calibration coefficients for a multichannel camera onboard an unmanned aerial vehicle from simultaneously remotely sensed and ground measured data sets over 19 (15 color-scaled and four gray-scaled) man-made surface targets, and the retrieved results were validated with a similar data set over another four man-made targets. It demonstrated that the camera's spectral parameters were accurately retrieved and an error less than 3.5 W/m2/μm/sr was brought to the channel radiance.

Land surface emissivity can be used for several purposes including land surface characterization and atmospheric retrieval over land. It is quite challengeable to simulate passive microwave radiances over land. This paper focuses on land surface emissivity retrieval and radiance simulation under snow-free conditions on a global scale for AMSR-E sensor configurations. A surface emission model (Qp) is coupled within Community Radiative Transfer Model (CRTM) which takes volumetric scattering of dense medium into consideration. The Qp model has been proved that it has higher accuracy and more suitable for the high-frequency and high-incidence AMSR-E data analysis. The results show that estimated radiances are comparable to passive microwave observations from satellite for different land surface vegetation types. The Root Mean Square Errors (RMSEs) are less than 20K and the mean errors are generally less than 10K.

Quantifications of the differences among currently available CO2products are very necessary for deeply understanding each product and their joint use. A spatio-temporal matching strategy has been proposed in this work to allow the CO2products from AIRS, IASI, GOSAT and SCIAMACHY to be physically comparable by accounting for the a priori CO2profiles employed in retrieval stage, averaging kernel functions and atmospheric pressure profiles etc. Based on this, these CO2products are intercompared in terms of magnitudes of CO2concentrations and their spatio-temporal distributions. The results show that relative large discrepancies are detected among these products, both in specific values of CO2concentrations and the spatio-temporal distributions, implying more efforts should be made to fully understand the differences of such measurements and to better constrain the uncertainties in CO2retrievals from space in the future.

Net Surface Radiative Flux is the key parameter for global change studies. In this study, two models designed to directly estimate net surface radiative fluxes over horizontal surfaces are developed based on artificial neural network (ANN).These models not only avoid the error propagation involved in the existing algorithms, but also provide the necessary data for estimating fluxes over rugged terrain. The validation results show that the maximum root mean square error (RMSE) of the ANN models is less than 45W/m2and 25 W/m2for net shortwave and longwave fluxes, respectively. By coupling the outputs of ANN models, the shortwave and longwave topographic radiative models are subsequently proposed to derive the net surface fluxes over rugged terrain. The results indicate that great errors can be detected if the topographic effect is ignored over rugged area, especially for net shortwave radiative fluxes.

Accurate radiometric and spectral calibrations of hyperspectral remote sensing instruments are essential for optimum data processing and exploitation. Two improved methods for the refinement of the spectral calibration of air- and spaceborne imaging spectrometers are presented in this paper. Both spectral channel position and width can be retrieved by modeling the atmospheric absorption features around 760, 940, 1140, and 2060 nm without making use of external atmospheric or surface parameters. A sensitivity analysis based on synthetic data demonstrated that, for each of the two methods, the root-mean-square errors to be expected were less than 0.18 nm for the retrieval of channel wavelength center and less than 0.8 nm for channel full-width at half-maximum. The application of the proposed methods to a real Hyperion data set showed quite-similar cross-track variations in the spectral calibration for the two methods, although relatively large differences in magnitude were found near the 940- and 1140-nm H2O absorption features. The significant improvement of the reflectance spectra derived after the refinement of the instrument spectral calibration confirms the good performance of the proposed methods.