The spatiotemporal analysis of crop spectral features has become a mainstream method for identifying crop stresses. However, current spatiotemporal feature extraction methods fragment the relationship between time and space, resulting in low accuracy in identifying different stresses in crops. The spatiotemporal cube (ST-cube) model has the advantage of integrating spatiotemporal features by unifying time and space modeling. This study proposes an ST-cube model to mine spatiotemporal changes under different rice stresses and identify heavy metal stress in rice. First, the stress information in the enhanced vegetation index (EVI) time series of rice pixels was extracted. Second, the ST-cube was segmented to obtain continuous spatiotemporal units of the stressed rice. Finally, an intraannual global spatiotemporal stability index (GSTSintra) and an interannual global spatiotemporal stability index (GSTSinter) were constructed to evaluate the spatiotemporal stability of rice under stresses. A case study was performed on a rice planting area in Zhuzhou, Hunan, China, during 2019–2021 using Sentinel-2A images and field measurement data. The results indicated that rice under the stress exhibits significant spatial clustering. Regardless of the year, the spatial distribution and proportion of GSTSintra values were similar. In the three-year analysis, most GSTSinter values were close to 1, indicating that the sources of the stress experienced during the years were similar. GSTSintra and GSTSinter accurately revealed the stability of continuous spatiotemporal distributions of rice under stress, which can open up new avenues for identifying heavy metal stress in rice under complex stress conditions.

Deep learning (DL) models for change detection (CD) are affected by the changed/unchanged and hard/easy sample imbalance during the training process. Most of the loss functions for solving the sample imbalance problem are a static loss which is difficult to adapt to the variation of data distribution. In this paper, we propose a dynamic method termed dynamic adaptive focal loss function (DAFL). Specifically, we first statistically count the number of changed/unchanged samples in different batches of training data, and a dynamic weighting factor is constructed to dynamically and adaptively balance their proportions. Furthermore, a dynamic modulation factor is proposed to suppress the hard/easy sample imbalance. In addition, we employ a CD model based on Progressive Scale Expansion Network (PSENet), which is trained by using DAFL for remote sensing images. Experimental results on three CD datasets (CDD, SYSU-CD and LEVIR-CD) indicate that DAFL outperforms all baseline approaches. Our proposed method achieves the maximum improvement, with an F1-score of 0.33%,0.8% and 0.94% for sufficient sample size, and 2.15%, 2.61% and 3.89% for small-sample size, respectively. This advancement is crucial for the application of CD, which provides an alternative method for solving sample imbalance in the condition of varying sample size, especially small sample condition that is common in the real scenario.

The remote sensing time-series change detection algorithm based on the pixel or single landscape patch ignores the change analysis of spatial structure information. Inspired by graph convolutional network (GCN) modeling, a set of landscapes (nodes) and their relationships (edges) are proposed. In this study, a parallel strategy in spatial GCN and progressive strategy in temporal GCN, called the hybrid GCN model network as a holistic framework, was proposed to accurately capture both spatial and temporal variations in landscape patterns based on yearly Landsat time series. A super-patch (i.e., fixed patch class surrounded by a one-hop neighbor patch) was selected as the input of the GCN network. First, a spatial GCN model with three parallel graph convolutional layers was adopted to classify landscape pattern types. Three dominant categories of landscape patterns over the past three decades have been identified. Second, four landscape metrics in super-patches were proposed for the quantitative characterization of changes in landscape patterns. Finally, a temporal GCN model with two progressive graph convolutional layers was used to detect six types of patch changes, which were applied to continuously detect the landscape pattern evolution processes. Regardless of the spatial GCN and temporal GCN, they provided satisfactory performance using the training and validation sets with overall accuracy > 92% and Kappa coefficient > 0.90, and loss values converging to 0.049 and 0.128, respectively, based on NLLLoss function until 500 epochs. It is believed that the hybrid GCN model has great potential for mining possible implicit spatiotemporal relationships and future evolution of landscape patterns.

Optical remote sensing time series are optimal for understanding and monitoring biochemical changes of key phenological parameters, which is essential for the assessment of vegetation health. However, due to cloud contamination, optical images often lack several days’ to months’ worth of information. Therefore, reconstructing optical time series based on the synthetic aperture radar (SAR) is necessary, which has the advantage of the production of continuous images under all weather conditions. In this study, an improved sequence-to-sequence (Seq2Seq) model was proposed, which integrated teacher forcing (TF) with the attention mechanism to handle input and output time series with unequal lengths. The proposed model could be used to reconstruct optical full-band time series using SAR time series data and reduce sample requirements based on the modification of the loss calculation method. To explore the spatiotemporal scalability of the proposed model, the test samples were divided into three categories: same time but different spatial domain, different time but same spatial domain, and both different temporal and spatial domains. The major findings can be summarized as follows: 1) 72.7% of the generated Landsat 8 sequence values had an absolute error of less than 0.05; 2) The mean absolute error of all bands was less than 0.0812; 3) The mean squared errors of all samples were lower than 0.015 regardless of the type of the test sample; and 4) TF was introduced to the model to improve the accuracy of the generated Landsat 8 sequence, yielding an increase of more than 5.97%. We concluded that the proposed model had good robustness both temporally and spatially. It performed well in the reconstruction of optical image time series, provided a basis for the use of time series pairs with unequal lengths, and can be applied to cloud removal and vegetation index reconstruction.

Detecting the evolution of large-area landscape patterns using long-term remote-sensing images is helpful in supporting research on the relationship between landscape patterns and ecological processes, as well as the development of ecological process simulations and spatiotemporal interaction models. However, detection methods have generally been developed as separate applications, each with a separate type of landscape pattern change; remote-sensing images are acquired at epochal timesteps. Consequently, in practical applications, many omission changes for some types of pattern changes and inaccurate evolution time are presented in the detected map. In this article, state-and-evolution detection models (SEDMs) are promoted to obtain complete information about the evolution of landscape patterns based on yearly land cover data. In the proposed framework, we first define the major categories of landscape pattern changes to comprehensively reveal the characteristics of landscape pattern changes associated with real change cases. Next, a morphological rule-based pattern recognition approach is proposed for quantitative discrimination among these categories. This approach is then applied in annual land cover data to continuously detect landscape pattern evolution processes and evolution time. Finally, the detected evolution time in different evolution processes is applied to measure the timestep between two disparate types. The performances of the SEDMs are presented by Landsat-derived land cover evolution in Shanxi, China. The detected results are indirectly verified by the land cover conversion matrix and connect index, indicating strong robustness and generalization ability of the SEDMs.

Mapping forest disturbances using dense time series can timely identify disturbances at the subannual scale. However, these change detection methods using dense time series may be infeasible when not enough temporal observations are available. In this article, an online change detection algorithm that identifies forest disturbances at a subannual scale using spatial context from the sparse Landsat time series was proposed. First, the spatial normalized index that removed forest seasonality was prepared for establishing a simplified model instead of the harmonic model, thereby reducing the requirements for a high temporal frequency of clear observations for model initialization. Second, by using the spatial errors model to establish the simplified model, the normally distributed residual time series that removed the spatial autocorrelation were obtained. Third, the spatial statistic$t$time series transformed from residual time series within a$3\times3$spatial window were subsequently subjected to the exponentially weighted moving average$t$chart (EWMA-t), which is a statistical process control chart for a short cycle corresponding to sparse Landsat time series. Fourth, disturbed pixels were labeled if the chart values persistently deviated from the control limits of the chart. The proposed algorithm was applied to a subtropical forest with low Landsat data availability and yielded an overall accuracy of 86% in the spatial domain and temporal accuracy of 93.7%, achieving accurate and timely identification of forest disturbances. The proposed method called the EWMA-t change detection (EWMATCD) algorithm provides an alternative for disturbance detection at the subannual scale in regions with low data availability.

Canopy water content (CWC) is one of the most important biochemical properties of plants, which can be estimated from remote sensing data conveniently by using vegetation water indices. This paper started from the analysis of some existing indices and then proposed two novel indices to estimate CWC. First, the area under part of near infrared and shortwave infrared reflectance curve were calculated. Then two indices, Area-based Normalized Index (ABNI) and Area-Based Ratio Index (ABRI) were developed by using ratio method and normalization method, respectively. From the validation results, the new indices were found to exponentially correlate with CWC more significantly than some classical indices, and the determination coefficient (R2) and root mean square error (RMSE) of the new method were 0.89 and 0.04, which indicated that the novel indices provided a promising way to monitor CWC.