Dense time-series remote sensing images have transformed the traditional bitemporal land-cover change detection to continuous monitoring. Previous work mostly employs linear fitting, prediction, or decomposition methods, and the detection accuracy is not high. The latest progress of deep learning (DL) shows its advantages in time-series change monitoring. However, DL models are computationally expensive and require lots of labeled samples, resulting in often employed prediction-threshold-based unsupervised change detection method. However, the determination of a reasonable threshold has always been a big problem. Therefore, we proposed the similarity-measurement-based deep transfer learning for time-series adaptive change detection (SDTL-TSACD) model. First, a standard dynamic time warping (SDTW) distance was proposed and used to cluster large-scale time series into multiple subcategories with high time-series similarity. Second, a time convolutional network (TCN) was used for nonlinear time-series fitting and prediction, and an early stop strategy was used to prevent overfitting. Then, the trained TCN model would be transferred and performed pixel-by-pixel time-series prediction within the same category, and the SDTW was also used to evaluate the prediction accuracy. Finally, the Otsu adaptive threshold was used to detect change points, and the spatial neighbor relationship was used to eliminate the pseudo-change points. Change detection results using 132 benchmark datasets showed that the SDTL-TSACD performed well in both accuracy and efficiency. In addition, the MOD13Q1-EVI images from 2001 to 2020 were used to study the land-cover change of the Loess Plateau, and the SDTL-TSACD also showed a good ability to solve practical problems.

Many models for change point detection from time series remote sensing images have been developed to date. For forest ecosystems, fire disturbance detection models have always been an important topic. However, due to a lack of benchmark datasets, it is difficult to determine which model is appropriate. Therefore, we collected and generated a benchmark dataset specifically for forest fire disturbance detection, named CUG-FFireMCD1. The CUG-FFireMCD1 contains a total of 132 pieces of MODIS MOD13A2 time series, and each time series contains at least one fire disturbance. The occurrence time for a forest fire disturbance was determined using the National Cryosphere DesertDataCenter(NCDC) website, and the precise latitude and longitude coordinates were determined using the FireCCI51 dataset. In addition, we selected four commonly used time series change detection models and validate the advantages and limitations of the four models through dataset analysis. Finally, we use the detection results of the models and their applicable scenarios to label the additional change points. The four models we used are breaks for additive season and trend (BFAST), Prophet, continuous change detection and classification (CCDC), and Landsat-based detection of trends in disturbance and recovery (LandTrendR). The experiments show that the BFAST outperformed the other three models in forest fire disturbance detection from MOD13A2 time series, with the successful-detection-proportion rate of 96.2% with the benchmark dataset. The detection effect of the Prophet model is not as good as that of BFAST, but it also performs well, with the successful-detection-proportion rate of 87.9%. The detection results of CCDC and LandTrendR are similar, and the detection success rate is lower than that of BFAST and Prophet, but their detection results can be used as data support for labeling work. However, to apply them perfectly to MOD13A2 time series change detection, it is best to do some model adaptation. In summary, the CUG-FFireMCD1 data were verified using different types of time series change detection models, and the change points we marked are credible. The CUG-FFireMCD1 will surely provide a reliable benchmark for model optimization and the accuracy verification of remote sensing time series change detection.

Nowadays, in the field of data mining, time series data analysis is a very important and challenging subject. This is especially true for time series remote sensing classification. The classification of remote sensing images is an important source of information for land resource planning and management, rational development, and protection. Many experts and scholars have proposed various methods to classify time series data, but when these methods are applied to real remote sensing time series data, there are some deficiencies in classification accuracy. Based on previous experience and the processing methods of time series in other fields, we propose a neural network model based on a self-attention mechanism and time sequence enhancement to classify real remote sensing time series data. The model is mainly divided into five parts: (1) memory feature extraction in subsequence blocks; (2) self-attention layer among blocks; (3) time sequence enhancement; (4) spectral sequence relationship extraction; and (5) a simplified ResNet neural network. The model can simultaneously consider the three characteristics of time series local information, global information, and spectral series relationship information to realize the classification of remote sensing time series. Good experimental results have been obtained by using our model.

Land surface temperature (LST) is widely used in the field of time-series remote sensing. However, due to the influence of cloud cover, the large area of LST data observation is missing, which seriously affects the later data analysis. In the past research, various effective interpolation methods have been developed, but they usually cannot effectively interpolate the image data with large observation missing. In this article, a new method for interpolating these missing data called Hilbert tensor rearrangement with Bayesian temporal tensor factorization (HTR-BTTF) is proposed. This method requires tensor rearrangement of remote sensing data, combined with BTTF method for interpolation. In order to evaluate the performance of our method, we select three real study areas with different climates, Wuhan, Harbin, and Kunming LST data during day and night, and add cloud covers with different sizes to the cold and warm season layers each year. BTTF, inverse distance weighted (IDW), harmonic analysis of time series (HANTS), and GapFill are used as comparison methods. Root-mean-square error (RMSE) is a comprehensive evaluation index of interpolation results. Experiments have shown that HTR-BTTF is an effective method for interpolating missing observations, which is better than other methods. In the simulation experiment of the largest cloud cover size, on average, the RMSE of the data filled using the HTR-BTTF method was 17.2% lower than that of BTTF and 54.9% lower than that of the GapFill method, and it shows good robustness and high accuracy.

Remote sensing images often suffer from substantial data loss due to factors such as thick cloud cover and sensor limitations. Existing methods for imputing missing values in remote sensing images fail to fully exploit spatiotemporal auxiliary information, which restricts the accuracy of their reconstructions. To address this issue, this paper proposes a novel deep learning-based approach called MS²TAN (Multi-Scale Masked Spatial-Temporal Attention Network) for reconstructing time-series remote sensing images. First, we introduce an efficient spatiotemporal feature extractor based on Masked Spatial-Temporal Attention (MSTA) to capture high-quality representations of spatiotemporal neighborhood features surrounding missing regions while significantly reducing the computational complexity of the attention mechanism. Second, a Multi-Scale Restoration Network composed of MSTA-based Feature Extractors is designed to progressively refine missing values by exploring spatiotemporal neighborhood features at different scales. Third, we propose a “Pixel-Structure-Perception” Multi-Objective Joint Optimization method to enhance the visual quality of the reconstructed results from multiple perspectives and to preserve more texture structures. Finally, quantitative experimental results under multi-temporal inputs on two public datasets demonstrate that the proposed method outperforms competitive approaches, achieving a 9.76%/9.30% reduction in Mean Absolute Error (MAE) and a 0.56 dB/0.62 dB increase in Peak Signal-to-Noise Ratio (PSNR), along with stronger texture and structural consistency. Ablation experiments further validate the contribution of the core innovations to imputation accuracy.

China launched its first hyperspectral remote sensing satellite for geological resource and environmental survey and monitoring, Geology-1, on 17 May 2025. Weighing just 83 kg, the satellite features a compact and integrated design with 26 spectral bands covering visible to shortwave infrared wavelengths. Cutting-edge optical technologies minimize interference and enhance image clarity. Geology-1 represents a major breakthrough in creating lightweight, specialized, and cost-effective satellites for the geological industry.