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).

The unmanned aerial vehicle (UAV)-borne hyperspectral imaging system has the advantages of high spatial resolution, flexible operation, under-cloud flying, and easy cooperation with ground synchronous tests. Because this platform often flies under clouds, variations in solar illumination lead to irradiance inconsistency between different rows of hyperspectral images (HSIs). This inconsistency causes errors in radiation correction. In addition, due to the accuracy limitations of the GPS/inertial measurement unit (IMU) and irregular changes in flight platform speed and attitude, HSIs have deformation and drift, which is harmful to the geometric correction and stitching accuracy between flight strips. Consequently, radiation and geometric error limit further applications of large-scale hyperspectral data. To address the above problems, we proposed an integrated solution to acquire and correct UAV-borne hyperspectral images that consist of illumination data acquisition, radiance and geometric correction, HSI, multispectral image (MSI) registration, and multi-strip stitching. We presented an improved three-parameter empirical model based on the illumination correction factor, and it showed that the accuracy of radiation correction considering illumination variation improved, especially in some low signal-to-noise ratio (SNR) bands. In addition, the error of large-scale HSI stitching was controlled within one pixel.

The polarization characteristics of water-leaving radiation, which contain rich information on oceanic constituents, have often been neglected. Due to the lack of suitable instruments and practical difficulties in removing strong contamination by polarized skylight, direct measurement of the polarization of water-leaving radiation remains a challenge. In this study, we designed an above-water instrument (named POLWR) to directly measure the polarization of water-leaving radiation and examined its field application in Qiandao Lake, China. Results showed that the Stokes components of water-leaving radiance ( $L_{w}$ ) measured by POLWR were consistent with the radiative transfer (RT) simulations, with a determination coefficient ( $R^{2}$ ) and mean relative error of 0.67 and 18.86%, respectively. The Qiandao Lake results revealed that the degree of polarization (DOP) of $L_{w}$ varied from 0.05 to 0.5 within the 412–865-nm range. Moreover, a good relationship between the polarized remote sensing reflectance ( $R_{\mathrm {rsp}}$ ), and DOP and chlorophyll-a ( Chla ) concentration was found at 368 nm in this productive lake, indicating great potential for the inversion of oceanic constituents from polarization signals. With its small size and direct measurement ability, the POLWR instrument should be widely applicable and could help improve our understanding of the polarization characteristics of water-leaving radiation and the underwater light field.

Single-photon LIDAR faces challenges in high-quality 3D reconstruction due to high noise levels, low accuracy, and long inference times. Traditional methods, which rely on statistical data to obtain parameter information, are inefficient in high-noise environments. Although convolutional neural networks (CNNs)-based deep learning methods can improve 3D reconstruction quality compared to traditional methods, they struggle to effectively capture global features and long-range dependencies. To address these issues, this paper proposes a multi-level efficient 3D image reconstruction model based on vision transformer (ViT). This model leverages the self-attention mechanism of ViT to capture both global and local features and utilizes attention mechanisms to fuse and refine the extracted features. By introducing generative adversarial ngenerative adversarial networks (GANs), the reconstruction quality and robustness of the model in high noise and low photon environments are further improved. Furthermore, the proposed 3D reconstruction network has been applied in real-world imaging systems, significantly enhancing the imaging capabilities of single-photon 3D reconstruction under strong noise conditions.

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.