Characterized by high concentrations and significant diurnal dynamics, total suspended particulate matter (TSM) in Hangzhou Bay (HZB) can influence the water quality. Both cruise sampling and polar-orbiting satellite remote sensing make it difficult to synoptically delineate such matter in coastal regions due to insufficient spatiotemporal resolution. However, near-real-time monitoring from the Geostationary Ocean Color Imager (GOCI), the world's first geostationary satellite ocean color sensor, now allows hourly observations of target areas. Here, we developed an empirical TSM retrieval algorithm for the GOCI, based on the spectral absorption index (SAI). Additionally, validations of the atmospheric correction algorithm and SAI TSM algorithm were conducted, with the GOCI-retrieved normalized water-leaving radiance (Lwn) and TSM well-matched with in situ values. Moreover, the diurnal dynamics corresponded to tidal phases, with a maximum of 5000 mg/L in central HZB. The seasonal variations in TSM were also determined by freshwater inputs and sediment loads, with correlation coefficients of −0.77 and 0.84, respectively. The increased wind speed in winter enhanced vertical mixing and favored the resuspension of sediment to the upper layer, and therefore TSM was higher in winter than in summer. These results highlight the feasibility of using geostationary ocean color remote sensing to monitor short-term and regional oceanic phenomena in coastal waters. Furthermore, this study contributes to our understanding of related sediment dynamics and physical biogeochemical processes.

Fourier transform imaging spectrometers (FTISs) are widely used in global hyperspectral remote sensing due to the advantages of high stability, high throughput, and high spectral resolution. Spectrum reconstruction (SpecR) is a classic problem of FTISs determining the acquired data quality and application potential. However, the state-of-the-art SpecR algorithms were restricted by the length of maximum optical path difference (MOPD) of FTISs and apodization processing, resulting in a decrease in spectral resolution; thus, the applications of FTISs were limited. In this study, a deep learning SpecR method, which directly learned an end-to-end mapping between the interference/spectrum information with limited MOPD and without apodization processing, was proposed. The mapping was represented as a fully connected U-Net (FCUN) that takes the interference fringes as the input and outputs the highly precise spectral curves. We trained the proposed FCUN model using the real spectra and simulated pulse spectra, as well as the corresponding simulated interference curves, and achieved good results. Additionally, the performance of the proposed FCUN on real interference and spectral datasets was explored. The FCUN could obtain similar spectral values compared with the state-of-the-art fast Fourier transform (FFT)-based method with only 150 and 200 points in the interferograms. The proposed method could be able to enhance the resolution of the reconstructed spectra in the case of insufficient MOPD. Moreover, the FCUN performed well in visual quality using noisy interferograms and gained nearly 70% to 80% relative improvement over FFT for the coefficient of mean relative error (MRE). All the results based on simulated and real satellite datasets showed that the reconstructed spectra of the FCUN were more consistent with the ideal spectrum compared with that of the traditional method, with higher PSNR and lower values of spectral angle (SA) and relative spectral quadratic error (RQE).

Recently, few-shot learning-based methods have achieved impressive results in cross-domain hyperspectral classification. However, existing approaches often ignore differences in spectral information caused by varing spectral range across different datasets, and encounter limitations due to the constrained parameter size of the models. Furthermore, the substantial differences between RGB and hyperspectral images present significant challenges in applying foundation models (e.g., SAM, DINOv2) to the hyperspectral domain. This paper proposes a novel framework for cross-domain few-shot hyperspectral classification that leverages parameter-efficient fine-tuning, which we apply to DINOv2 to construct SpectralDINO. Different spectral ranges reflect different physical properties of the target. To enhance the consistency of the spectral features extracted by the model from different domains, we introduce a source domain spectral alignment (SDSA) strategy to align the spectral ranges and bands of the source domain data to the target domain. SpectralDINO employs the visual foundation model to enhance its ability to generalize cross-domain knowledge. Additionally, we propose a dual mixture-of-subspaces low-rank adaptation (Dual-MoS LoRA) method to address the structural limitation of the low-rank adaptation methods in distinguishing domain-specific features from multi-domain inputs. Only 1.14% of the 21.37M parameters need to be trained to perform fine-tuning. Extensive experimental results on three public datasets demonstrate the superiority of SpectralDINO.

Due to the development of spectral remote sensing imaging technology, hyperspectral data in different spectral ranges, such as visible and near-infrared, short-wave infrared, etc., can be acquired simultaneously. Data fusion between these nonoverlapping spectral ranges’ hyperspectral data has become an urgent task. Most existing hyperspectral data fusion methods focus on two types of hyperspectral data with overlapping spectral ranges, requiring spectral response functions as a necessary condition, which is not applicable to this task. To address this issue, we propose the combined spectral unmixing fusion (CSUF) method, an unsupervised method with certain physical significance. It effectively solves the problem of hyperspectral data fusion with nonoverlapping spectral ranges through the two hyperspectral data point spread function estimation and combined spectral unmixing. Experiments on airborne datasets and HJ-2 satellite data show that, compared with various leading methods, our method achieves the best performance in terms of reference evaluation indicators such as the PSNR and SAM, as well as the non-reference evaluation indicator the QNR. Furthermore, we deeply analyze the spectral response relationship and the impact of the ratio of spectral bands between the fused data on the fusion effect, providing references for future research.

Compared with traditional single-view and radiometric-only observations, multiangle polarimetric observations of water-leaving radiation play a crucial role in enhancing the retrieval of ocean constituents and aerosol microphysical properties. In this study, the impacts of uncertainties in the degree of polarization (DOP) of water-leaving radiance (L_w) on the retrieval of oceanic constituents and inherent optical properties (IOPs) were investigated via global radiative transfer (RT) simulations and the fully connected U-Net (FCUN) model. The uncertainties in the retrieval of oceanic constituents and IOPs were further investigated with various sensor azimuth angles. The results indicated that the global mean absolute percentage errors (MAPEs) for differing oceanic constituents and IOPs significantly decreased as the number of observation angles increased. Taking the retrieval of Chla as an example, the global MAPEs between the FCUN predictions and RT simulation inputs for Chla concentrations under differing observation angles were 7.41%, 3.76%, 2.70%, 2.44%, 2.62%, and 1.82%. Moreover, the MAPEs at sensor azimuth angles of 0° and 30° were significantly lower than those at other azimuth angles for the single-view observations. As the number of observation angles increased, the variation in MAPEs with the sensor azimuth angle gradually weakened. Furthermore, the impact of errors in the L_w DOP on the retrieval uncertainties decreased as the number of observation angles increased, and the global MAPEs of Chla after adding the various random instrument noises were 46.56% (46.91%), 6.59% (7.21%), 5.21% (5.79%), 4.72% (4.98%), 3.99% (4.52%), and 3.64% (4.03%). Overall, the multiangle polarimetric observations can suppress or balance the impact of uncertainties in the L_w DOP on the retrieval of oceanic constituents and IOPs.

Although the fusion of multispectral (MS) and hyperspectral (HS) images in remote sensing has become relatively mature, and different types of fusion methods have their own characteristics in terms of fusion effect, data dependency, and computational efficiency, few studies have focused on the impact of radiance extreme areas, which widely exist in real remotely sensed scenes. To this end, this paper proposed a novel method called radiance extreme area compensation fusion (RECF). Based on the architecture of spectral unmixing fusion, our method uses the reconstruction of error map to construct local smoothing constraints during unmixing and utilizes the nearest-neighbor multispectral data to achieve optimal replacement compensation, thereby eliminating the impact of overexposed and underexposed areas in hyperspectral data on the fusion effect. We compared the RECF method with 11 previous published methods on three sets of airborne hyperspectral datasets and HJ2 satellite hyperspectral data and quantitatively evaluated them using 5 metrics, including PSNR and SAM. On the test dataset with extreme radiance interference, the proposed RECF method achieved well in the overall evaluation results; for instance, the PSNR metric reached 47.6076 and SAM reached 0.5964 on the Xiong’an dataset. In addition, the result shows that our method also achieved better visual effects on both simulation and real datasets.