Change detection in time series is among the most critical problems in Earth monitoring and attracts extensive attention in the remote sensing community. The task is, however, nontrivial because available images are irregularly collected due to interference by clouds and shadows. Traditional recurrent neural networks neglect such information and, thus, degrade the possibility of distinguishing pseudochanges (caused by intra-annual and interannual dynamics) and real changes. To this end, we proposed an unsupervised time-distance-guided convolutional recurrent network (UTRNet) for change detection in irregularly collected images. UTRNet is distinctive because the influence of pseudochanges can be suppressed by adopting a novel time-distance-guided long short-term memory (TLSTM) unit, in which input and forget gates are modified to adapt to irregular time distances. To the best of our knowledge, this is the first time that the influence of pseudochanges can be suppressed using irregular time distances. Moreover, to make UTRNet more applicable, a weighted prechange detection model is proposed to extract the most reliable training samples automatically. In the training process, unlike existing approaches that only care about changed and unchanged sample imbalance, our UTRNet also pays attention to imbalance between hard and easy samples, and proposes a new focal weighted cross-entropy loss, which helps to make the training process focus on hard changed samples. The proposed UTRNet is validated on Landsat 8 time-series data collected over nine typical scenes in the 2013–2021 period. Qualitative and quantitative comparisons with several state-of-the-art methods suggest the superior performance of UTRNet. Our dataset and codes are available at https://github.com/thebinyang/UTRNet .

We analyzed the impacts of islands on suspended sediment concentration (SSC) in Zhoushan Coastal waters based on data from HY-1C, which was launched in September 2018 in China, carrying Coastal Zone Imager (CZI) and Chinese Ocean Color and Temperature Scanner (COCTS) on it for offshore observation. A new SSC retrieved model was established based on the relationship between in situ SSC and the reflectance in red and near infrared bands of CZI image. Fifteen CZI images obtained from October to December 2019 were applied to retrieve SSC in Zhoushan coastal waters. The results show that SSC in study area is 100–1600 mg·L⁻¹. The SSC near islands changes obviously. Upstream of the islands, SSC is lower than downstream. During the flood and ebb, when the current passes through the islands, circumfluence will appear, under certain geophysical factors, generating Karman vortex streets downstream of the islands. The sediments were stirred by the fast speed current at the outer side of vortex street to the sea surface inducing higher SSC at the outer side of the vortex street, while the central sediments of the vortex street were lower. In the direction of ocean currents, the SSC of the vortex street downstream of islands is changing regularly, i.e., increasing, then decreasing and increasing again and then decreasing in a snaking vortex street whose length downstream is between 1000 and 8000 m long.

We analyzed the distribution of suspended sediments concentration (SSC) in the Yellow River Estuary based on data from GaoFen-1 (GF-1), which is a high-resolution satellite carrying a wide field-of-view (WFV) sensor and panchromatic and a multispectral (PMS) sensor on it. A new SSC retrieval model for the wide field-of-view sensor (M-WFV) was established based on the relationship between in-situ SSC and the reflectance in blue and near infrared bands. SSC obtained from 16 WFV1 images were analyzed in the Yellow River Estuary. The results show that (1) SSC in the study area is mainly 100–3500 mg/L, with the highest value being around 4500 mg/L. (2) The details of suspended sediment injection phenomenon were found in the Yellow River Estuary. The SSC distribution in the coastal water has two forms. One is that the high SSC water evenly distributes near the coast and the gradient of the SSC is similar. The other is that the high SSC water concentrates at the right side of the estuary (Laizhou Bay) with a significantly large area. Usually, there is a clear-water notch at the left side of the estuary. (3) Currents clearly influenced the SSC distribution in the Yellow River Estuary. The SSC gradient in the estuary was high against the local current direction. On the contrary, the SSC gradient in the estuary was small towards the local current direction. Eroding the coast and resuspension of the bottom sediments, together with currents, are the major factors influencing the SSC distribution in nearshore water in the Yellow River Estuary.

The China France Oceanography Satellite (CFOSAT) and Haiyang‐2B (HY‐2B) satellites were successively launched in China on the 29^th and 25^th of October 2018, respectively. HY‐2B carries the second Chinese radar altimeter along with three other microwave sensors, whereas CFOSAT contains the first Surface Waves Investigation and Monitoring (SWIM) instrument and a fan beam rotating scatterometer. As missions for measuring the dynamic marine environment, both satellites can measure the nadir significant wave height (SWH). For data obtained from a newly launched satellite to be utilized with confidence, the quality of SWH from the two satellites requires validation. In this study, the HY‐2B altimeter and CFOSAT nadir SWHs have been validated against the National Data Buoy Center (NDBC) buoys and the Jason‐3 altimeter SWH data, respectively, which resulted in CFOSAT nadir SWH having the best accuracy and HY‐2B having the best precision. The SWHs of the two missions are also calibrated by Jason‐3 and NDBC buoys. Following calibration, the root mean square error (RMSE) of CFOSAT and HY‐2B are 0.21 and 0.27 cm, respectively, when compared to Jason‐3, and 0.23 and 0.30 cm, respectively, compared to the buoys. Our results show that the two missions can provide good‐quality SWH and can be relied upon as a new data resource of global SWH.

The comparison and verification of ocean wave spectrum by remote sensing and in situ measurements at the spectral level are quite rare because the use of the traditional comparison method leads to very limited spatiotemporal matching pairs. In this article, a new comparison method is proposed. With this method, under different sea conditions (wind wave mainly/swell mainly) and sea surface conditions (wind speed smaller than 20 m/s and significant wave height from 1 to 7 m), mean directional wave height spectra from surface waves investigation and monitoring (SWIM) are compared at the spectral level to the buoy counterparts, in different classes of the sea state. This includes the comparison of the omnidirectional wave height spectrum and the directional function at the peak wavenumber. The comparison results show that, under medium and high sea conditions, wave directional spectra provided by the SWIM beams at 8° and 10° incidence have a high consistency with those from buoy data. Under low sea conditions, the measurement bias of SWIM wave directional spectra mainly comes from three phenomena that are, by order of importance, an abnormal lifting of spectral energy caused by nonwave components at low wave numbers (parasitic peak), from the nonlinear surfboard effect in the radar imaging mechanism, and from a slight underestimation of speckle noise spectral density.