Landmarks serve as critical reference points for determining spatial orientations. Owing to the complexity and diversity of the shapes of landmark buildings, numerous fine visual details can hinder the clear identification of three-dimensional (3D) landmark models, posing a challenge for their automatic generation. To address this issue, we propose a method based on topological perception to simplify 3D landmark models, focusing on enhancing global perception features by exaggerating topology-related features. This method involves three key steps: voxelization, hole exaggeration and model generation. We evaluated the effectiveness of exaggeration and conducted a quantitative analysis of its degree of application in landmark buildings. The results demonstrate that topology-based exaggeration significantly improves the perception of 3D landmark models, and the degree of exaggeration is inversely correlated with the proportion of topology-related visual features in the models. Furthermore, a comparative analysis of four commonly used simplification algorithms shows that our method outperforms the other methods across five key evaluation metrics.

Jitter is a critical factor affecting the geometric accuracy of Earth observation satellites. After a satellite is launched, it is essential to detect and mitigate platform jitter to minimize its impact. The Ziyuan-3 (ZY3) series, China’s first civilian stereo mapping satellite constellation, meets the 1:50000 scale mapping accuracy requirement without ground control points. Detecting and suppressing jitter during the satellite’s orbit is a key technology for ensuring mapping accuracy. This letter investigates the causes of platform jitter across three satellites in the ZY3 series by analyzing gyroscope data using fast Fourier transform (FFT) at different stages of the satellite lifecycle. It compares the frequency and amplitude of jitter across different platforms and time periods. The results show that ZY3-01 and ZY3-02 exhibit consistent jitter patterns. They have significant amplitude in the 0.6 Hz region early in orbit and less later on, correlating with the satellites’ lifespans. In contrast, the 0.2 Hz region remains relatively stable. The ZY3-03 satellite, equipped with a highly stable platform, shows no significant jitter, evidencing the effectiveness of such platforms in jitter suppression.

Broadband emissivity (BBE) is an important variable in the evaluation of the energy budget and can be provided by the remote sensing products. As one of the commonly used BBE products, global land surface satellite (GLASS) AVHRR BBE and MODIS BBE are quite different. In this study, a new framework of AVHRR BEE estimation based on GLASS MODIS BBE is developed by introducing the more detailed soil datasets, the consideration of hemisphere and season, and the global selection of sampling points into the modeling. After our modification of the original GLASS AVHRR BBE, the modified BBE significantly eliminates the discrepancies between GLASS AVHRR and MODIS BEEs during 2001–2019, especially in summer and winter (0.004 decline of discrepancies), in the extreme arid and moist region [0.002 decline of discrepancies when aridity index (AI)4], in the high altitude area (0.01 decline of discrepancies when DEM >5000 m) and in some desert regions (0.005 decline of discrepancies when albedo >0.5). In addition, the application of our framework can significantly improve the performance of the original GLASS AVHRR BBE before 2000 when the GLASS MODIS BEEs is unavailable. Our framework is helpful for the reliable application of GLASS BBE and can provide a more satisfactory BBE product in a long time series (near to 40 years).

Land Surface Temperature (LST) is an important parameter representing surface energy, which is of great significance for monitoring urban heat islands, agricultural drought, and global climate. The high-resolution LST observations will address new applications in hydrology. The spatiotemporal fusion method can generate LST with high temporal and spatial resolution. However, missing data due to cloud cover becomes a main limitation to improving the accuracy of spatiotemporal fusion models. The purpose of the fusion of LST is image prediction and generation, and deep learning generative models provide an effective idea to solve this problem. Therefore, we proposed a conditional generative model fusion network (CGMFN) for LST generation in this paper. Firstly, based on the generated model, we construct an unsupervised generation network that simultaneously learns and iterates, which can generate fine-spatiotemporal-resolution LST data from reference images with missing values. Then, spectral normalization was applied to generators and discriminators to stabilize the training process. The pre-training mechanism was adopted to improve the iteration efficiency of the model. We tested and evaluated the model in the Heihe River Basin using FY-4A LST and MODIS LST datasets. Compared to different methods, CGMFN produces a lower RMSE (average0.97). In practical applications, CGMFN can reduce the influence of reference image missing values on fusion results and generate land surface temperature products with reliable accuracy.

The potential applications of high spatiotemporal resolution land surface temperature (LST) products are extensive. However, the tradeoff between spatial and temporal resolutions of remote sensing data has significantly constrained the availability of such LST products. Existing spatiotemporal fusion methods appear to encounter certain limitations. This article proposes a two-stage hierarchical spatiotemporal fusion network (THSTNet) to fuse MODIS LST products and Landsat LST products. THSTNet adopts the shift windows (Swin) transformer architecture and employs a two-stage process to reconstruct the fine-resolution image at the target time. The innovation within THSTNet is multifaceted. It combines spatiotemporal mapping (ST mapping) with deep learning, enhancing the extraction of global information for fusion results; leveraging self-attention computation, it adopts a two-stage structure to improve the understanding of intricate LST changes. In addition, it incorporates a texture converter module aimed at enhancing spatial details within the reconstruction results. Validation of the model’s predictions was conducted using actual images and ground observations, affirming the high reliability of THSTNet’s predictive outcomes. Compared with two traditional methods [spatial and temporal adaptive reflectance fusion model (STARFM) and enhanced STARFM (ESTARFM)] and four deep learning-based methods [enhanced deep convolutional spatiotemporal fusion network (EDCSTFN), generative adversarial network (GAN)-based spatiotemporal fusion model (GANSTFM), spatiotemporal temperature fusion network (STTFN), and multistream fusion network (MSNet)], THSTNet demonstrated superior performance (average root-mean-square error (RMSE) is below 1.3 K and average structural similarity index (SSIM) is 0.939). The prediction results of THSTNet also maintain high consistency with ground observations (the average RMSE is 2.3 K and the average$R^{2}$is 0.9). The code will be available athttps://github.com/HuPengHua2021/THSTNet.

Land surface temperature (LST) is a crucial parameter linked to water and carbon cycles. However, due to the wide swath widths of polar-orbiting satellites, the temporal inconsistencies in each pixel observation lead to significant LST variations, resulting in temporal incomparability in the current LST products acquired from these satellites. Therefore, a physical method based on the surface energy balance and Bowen ratio (SEB-BR) was developed to correct the temporal effects on the LST. The advantage of the SEB-BR approach lies in its ability to establish a physical connection between various physical parameters and the LST, quantifying each parameter’s contribution to LST variation and thereby mechanically linking physical variables that contribute to LST variation. A diurnal temperature cycle (DTC) model named GOT01-dT and random forest regression (RFR) were introduced for comparison. Results showed that the SEB-BR model achieved higher accuracy with an average RMSE (bias) of 2.85 (1.26) K, outperforming the RFR model (2.96 (1.36) K) and GOT01-dT model (2.94 (0.39) K). The SEB-BR method also demonstrated better agreement with in-situ LST across various local solar time differences, particularly for temporal differences between 0.8–0.9 hours, where the RMSE decreased significantly from 3.54 K to 2.63 K. The improvements in accuracy across all seasons, reflected in the RMSE reductions from 3.56 (2.08) K to 3.39 (1.80) K, were significant. These results highlight the effectiveness of the SEB-BR approach in correcting temporal effects and its potential for large-scale temporal normalization, offering strong interpretability and spatiotemporal extensibility.

To address limitations in regional-scale applications of passive microwave remote sensing soil moisture (SM) products, various downscaling methods have been proposed to enhance the spatial resolution of SM. Nonetheless, these downscaled methods, which are based on optical and thermal infrared data, face challenges in vegetated areas and are prone to inaccuracies due to cloud cover affecting ancillary data. In response to these challenges, we introduced a novel approach termed the vegetation-temperature-radiation-composite (VTRC) method. This method recognizes the robust interaction between vegetation and SM and also appreciates the high sensitivity of surface temperature variation to surface net solar radiation in relation to SM. Besides, the VTRC method combines ERA5 data with its high temporal resolution and MODIS data to counteract data loss due to cloud coverage. Applied to downscale the ESA CCI SM products from 0.25° to 0.01° spatial resolution, the VTRC method is validated over Castilla y León (Spain) and Anhui province (China). The VTRC method demonstrates a significant improvement compared to the feature space-based downscaled method and the original ESA CCI products, achieving higher correlation coefficient (R) of 0.56 and 0.66 m3/m3in humid and semi-arid regions with ground observations, respectively. Furthermore, it maintains a consistent unbiased root mean square error (ubRMSE) of 0.05 m3/m3in both regions. Additionally, the inclusion of vegetation information notably improves the accuracy in comparison to solely using land surface temperature and surface net solar radiation. Significantly, evaluations across varying vegetation cover surfaces revealed enhanced accuracy, especially in regions with abundant vegetation.

Towards fully exploring multidisciplinary research topics under global warming, the spatiotemporal discontinuities of land surface temperature (LST) products due to cloud contamination still challenge such research topics. Data fusion that seeks the best compromise between multiple data sources plays a key role in providing gapless LST data. However, most models only use information about the variation of LST at different times or the difference between different LST products without effectively combining the two pieces of information. With the rapid development of deep learning methods, powerful modeling capabilities can solve this problem. This article proposes a novel multi-information fusion network (MIFN) based on convolutional neural networks (CNNs) and attention mechanisms to map gapless all-sky LST, taking temporal-changing (TC) and data-differentiated (DD) information into consideration. Temporal normalization is used as a pre-processing step to match the time of Moderate Resolution Imaging Spectroradiometer (MODIS) and European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis fifth Generation (ERA5) LSTs. The MIFN extracts multiscale multi-temporal TC and DD features by network constraints. Then, the weights of the target LST features reconstructed by TC and DD features are assigned through an attention mechanism to fuse them reasonably. Finally, we design a loss function that combines TC, DD, and LST reconstruction to improve the accuracy of LST prediction further. We performed comprehensive data experiments to validate the performance of the new method. Our technique is more advantageous in generating MODIS-like LSTs than four state-of-the-art methods and two CNN models using a single piece of information.

It is well documented that soil moisture can be retrieved from passive microwave observations. A basic assumption of most passive microwave-based soil moisture retrieval algorithms is that vegetation temperatures (Tv) and soil temperatures (Ts) are equal (i.e., Tv=Ts), which however is not well satisfied in some cases, especially during daytime. In this study, we proposed a soil-vegetation temperature decomposition (SVTD) approach to avoid such an assumption, which can improve the accuracy of soil moisture retrievals from the Advanced Microwave Scanning Radiometer 2 (AMSR2) data. First, the SVTD was used to decompose the vegetation and soil temperatures of the soil-vegetation mixed pixels in the Tibetan Plateau (TP). Subsequently, the decomposed temperature was integrated into the soil moisture retrieval algorithm to correct the effects of soil and vegetation temperatures, and soil moisture is then retrieved following the same strategy adopted in the land parameter retrieval model (LPRM). Finally, the algorithm was validated against densely-instrumented soil moisture networks (Maqu, Naqu, and Ngari) built in the Tibetan Plateau, and was also compared with the LPRM AMSR2 soil moisture product. Results indicate the proposed algorithm performs much better than the original LPRM in soil-vegetation mixed areas. The proposed SVTD method is promising for soil moisture retrieval from passive microwave satellites, especially in the daytime when the difference between soil and vegetation temperatures is relatively large.

High spatiotemporal resolution land surface temperature (LST) data are essential for dynamic monitoring and prediction in climate change research. Due to the limitations of remote sensing instruments, the current platforms have difficulty achieving a compromise between high spatial and temporal resolutions for LST products. In this study, we propose a spatiotemporal fusion network for land surface temperature based on a conditional variational autoencoder (CVAE-LSTFM). First, an improved network is designed based on the CVAE by reconstructing an encoder and a decoder. To generate fine LST images based on dense time series, a variational inference model is formulated to integrate coarse and fine LST image pairs in variational autoencoded latent space. In addition, a new compound loss function for the proposed training method is designed to reduce the effects of noise and outliers. Then, a pretraining mechanism is adopted to optimize the network training process, and the parameters can be transferred to the training network of the CVAE to accelerate network convergence. Finally, a novel weighting strategy that considers spatiotemporal variations in LST (LST consistency weighting) is employed to solve the spatiotemporal heterogeneity problem caused by the rapid changes in LST. The method is quantitatively tested and evaluated in the Heihe River Basin using FY-4A LST and MODIS LST from September 2019. Compared with two traditional models and two deep learning-based models, CVAE-LSTFM yields lower RMSE (average<1.26 K) and LPIPS (learning perceptual image patch similarity, average<0.13) values and higher SSIM (structural similarity, average>0.96). In practice, CVAE-LSTFM can generate high spatiotemporal resolution LST values (hourly LST with a 1 km spatial resolution) with high accuracy, quality, and robustness.

Error source analyses are critical for the satellite-retrieved surface net longwave radiation products. In this study, we evaluate the error sources of the Clouds and the Earth's Radiant Energy System (CERES) project Single Scanner Footprint (SSF) net longwave radiation (NLW) product (monthly mean, 1° equal area) at 11 sites from July 2000 to December 2007 in China. Results show that cloud fraction, land surface temperature and atmospheric temperature error dominate the NLW error, with respective error contributions of ~-20, ~15, and ~10 W/m2, while the total precipitable water vapor has a weak influence. Spatially, due to strong algorithm sensitivity and large product errors, surface temperature and cloud fraction have the most contributions to error sources, especially in northern China, while the atmospheric temperature significantly affects NLW in southern China because of its large product errors. To improve NLW, the large errors of these parameters should be reduced. Our study identifies the dominant error sources and should be helpful to improving the CERES NLW in China.

Reliable estimation of regional evapotranspiration remains as a challenge. In our study, based on a nonparametric evapotranspiration approach, a retrieval algorithm using the MODIS products and climate drive datasets was proposed to retrieve evapotranspiration, in terms of latent heat flux (LE) over a semi-arid region under all sky. Surface observations were regarded as the validation references, which were obtained from six eddy-covariance sites with land covers of desert, Gobi, village, orchard, vegetable and wetland. Our results showed that the distribution of retrieved LE matched well with the desert-oasis ecosystems. And the accuracy of instantaneous LE retrievals varied with sites with a R2from 0.19 to 0.63, a bias from -129.46 W/m2to 56.73 W/m2, a relative error from 5.23% to 29.18%, and a RMSE from 95.44 W/m2to 150.29 W/m2. The validation supports the applicability of the algorithm in the arid/semi-arid region.