Aboveground biomass (AGB) serves as a crucial indicator of the carbon sequestration capacity of coastal wetland ecosystems. Conducting extensive field surveys in coastal wetlands is both time-consuming and labor-intensive. Unmanned aerial vehicles (UAVs) and satellite remote sensing have been widely utilized to estimate regional AGB. However, the mixed pixel effects in satellite remote sensing hinder the precise estimation of AGB, while high-spatial resolution UAVs face challenges in estimating large-scale AGB. To fill this gap, this study proposed an integrated approach for estimating AGB using field sampling, a UAV, and Sentinel-2 satellite data. Firstly, based on multispectral data from the UAV, vegetation indices were computed and matched with field sampling data to develop the Field–UAV AGB estimation model, yielding AGB results at the UAV scale (1 m). Subsequently, these results were upscaled to the Sentinel-2 satellite scale (10 m). Vegetation indices from Sentinel-2 data were calculated and matched to establish the UAV–Satellite AGB model, enabling the estimation of AGB over large regional areas. Our findings revealed the AGB estimation model achieved an R² value of 0.58 at the UAV scale and 0.74 at the satellite scale, significantly outperforming direct modeling from field data to satellite (R² = −0.04). The AGB densities of the wetlands in Xieqian Bay, Meishan Bay, and Hangzhou Bay, Zhejiang Province, were 1440.27 g/m², 1508.65 g/m², and 1545.11 g/m², respectively. The total AGB quantities were estimated to be 30,526.08 t, 34,219.97 t, and 296,382.91 t, respectively. This study underscores the potential of integrating UAV and satellite remote sensing for accurately assessing AGB in large coastal wetland regions, providing valuable support for the conservation and management of coastal wetland ecosystems.

Exploring the spatiotemporal patterns of urban thermal environments is crucial for mitigating the detrimental effects of urban heat islands (UHI). However, the long-term and fine-grained monitoring of UHI is limited by the temporal and spatial resolutions of various sensors. To address this limitation, this study employed the Google Earth Engine (GEE) platform and a multi-source remote sensing data fusion approach to generate a densely time-resolved Landsat-like Land Surface Temperature (LST) dataset for daytime observations spanning from 2001 to 2020 in Shanghai. A comprehensive analysis of the spatiotemporal patterns of UHI was conducted. The results indicate that over the past 20 years, the highest increase in average LST was observed during spring with a growth coefficient of 0.23, while the lowest increase occurred during autumn (growth coefficient of 0.12). The summer season exhibited the most pronounced UHI effect in the region (average proportion of Strong UHI and General UHI was 28.73%), while the winter season showed the weakest UHI effect (proportion of 22.77%). The Strong UHI areas gradually expanded outward over time, with a noticeable intensification of heat island intensity in the northwest and coastal regions, while other areas did not exhibit significant changes. Impervious surfaces contributed the most to LST, with a contribution of 0.96 °C, while water had the lowest contribution (−0.42 °C). The average correlation coefficients between LST and NDVI, NDWI, and NDBI over 20 years were −0.4236, −0.5128, and 0.5631, respectively.

Automatic and accurate detection of clouds and cloud shadows is a critical aspect of optical remote sensing image preprocessing. This paper provides a time series maximum and minimum mask method (TSMM) for cloud and cloud shadow detection. Firstly, the Cloud Score+S2_HARMONIZED (CS+S2) is employed as a preliminary mask for clouds and cloud shadows. Secondly, we calculate the ratio of the maximum and sub-maximum values of the blue band in the time series, as well as the ratio of the minimum and sub-minimum values of the near-infrared band in the time series, to eliminate noise from the time series data. Finally, the maximum value of the clear blue band and the minimum value of the near-infrared band after noise removal are employed for cloud and cloud shadow detection, respectively. A national and a global dataset were used to validate the TSMM, and it was quantitatively compared against five other advanced methods or products. When clouds and cloud shadows are detected simultaneously, in the S2ccs dataset, the overall accuracy (OA) reaches 0.93 and the F1 score reaches 0.85. Compared with the most advanced CS+S2, there are increases of 3% and 9%, respectively. In the CloudSEN12 dataset, compared with CS+S2, the producer’s accuracy (PA) and F1 score show increases of 10% and 4%, respectively. Additionally, when applied to Landsat-8 images, TSMM outperforms Fmask, demonstrating its strong generalization capability.

Spectral superresolution (SSR) aims to improve the spectral resolution of images from multispectral imagery or even RGB images. However, the majority of existing SSR methods do not fully exploit the spatial and texture features in RGB images, which would lead to the image unreal, distorted of the high frequency details in the reconstructed SSR images. In this study, a hybrid spectral and texture attention pyramid network (STANet) is proposed to reconstruct hyperspectral images (HSI) with RGB bands of remote sensing image as input. More specifically, a learnable texture feature extraction module is proposed, aiming to make full use of the texture features in the RGB images which is important in the subsequent spectral reconstruction. Furthermore, to better reconstruct the correlations between various spectral channels, a spatial-spectral constrained cross-attention module is introduced. Finally, a novel spectral-texture fusion method is proposed, which successfully alleviates the problem of insufficient deep interaction among multiple deep features. On three remote sensing datasets, STANet demonstrates state-of-the-art performance, with its Peak Signal-to-Noise Ratio (PSNR) exceeding the sub-optimal methods by 0.7266dB, 0.6724dB, and 0.6dB respectively. The results of the land cover classification experiment using the reconstructed HSI further demonstrated the performance of the STANet algorithm.

Cloud cover and long revisit cycle of satellites can cause gaps in optical images and pose a significant obstacle to the consistency of Earth observation missions. Recently, synthetic aperture radar (SAR)-to-optical image translation (S2OIT) has become an emerging approach to reconstruct the missing information of optical remote sensing images. However, the previous studies ignored the mechanism difference between SAR and optical data and produced color distortion, image blurriness, and texture detail loss in the generated optical images. To tackle these challenges, we propose a multi-temporal SAR-to-optical image translation network (MTS2ONet) for high-quality optical image generation. The proposed model comprises two subnetworks: change feature extraction subnetwork (Change_Extractor) and S2O_IT subnetwork (S2O_Translator). The first subnetwork is tasked with extracting change features from SAR images captured at dates T and T+1, and then translating them from the SAR domain to the optical domain. Subsequently, the S2O_Translator integrates the optical image at date T+1 with the change features extracted by the Change_Extractor to generate the optical image at date T. Additionally, we produce a dual-temporal SAR-optical dataset called DTSEN1-2 for model evaluation. Experiments on the DTSEN1-2 dataset reveal that our method is superior to the state-of-the-art (SOTA) methods with the metrics PSNR (36.0435), SSIM (0.9896), LPIPS (0.0443), RMSE (0.0174) and exhibits preferable results in visual effects. Our dataset and codes can be accessed via the following link: https://github.com/hopeupup/MTS2ONet.