Increasing human activities have caused significant global ecosystem disturbances at various scales. There is an increasing need for effective techniques to quantify and detect ecological changes. Remote sensing can serve as a measurement surrogate of spatial changes in ecological conditions. This study has improved a newly-proposed remote sensing based ecological index (RSEI) with a sharpened land surface temperature image and then used the improved index to produce the time series of ecological-status images. The Mann–Kendall test and Theil–Sen estimator were employed to evaluate the significance of the trend of the RSEI time series and the direction of change. The change vector analysis (CVA) was employed to detect ecological changes based on the image series. This RSEI-CVA approach was applied to Fujian province, China to quantify and detect the ecological changes of the province in a period from 2002 to 2017 using Moderate Resolution Imaging Spectroradiometer (MODIS) data. The result shows that the RSEI-CVA method can effectively quantify and detect spatiotemporal changes in ecological conditions of the province, which reveals an ecological improvement in the province during the study period. This is indicated by the rise of mean RSEI scores from 0.794 to 0.852 due to an increase in forest area by 7078 km². Nevertheless, CVA-based change detection has detected ecological declines in the eastern coastal areas of the province. This study shows that the RSEI-CVA approach would serve as a prototype method to quantify and detect ecological changes and hence promote ecological change detection at various scales.

Tasseled cap transformation (TCT) is a commonly used remote-sensing technique and has been successfully used in various remote sensing-related applications. However, the TCT coefficient set is sensor-specific, and therefore, in this article, we developed the TCT coefficients specifically for Sentinel-2 multispectral instrument at-sensor reflectance data. A total of ten synchronous image pairs of Sentinel-2 and Landsat-8, collected from the different parts of the world, were used for this approach. Instead of using the traditional Gram–Schmidt orthogonalization (GSO) method, we derived the coefficients using a principal component-based Procrustes analysis (PCP) method. This was done by rotating principal component axes of Sentinel-2 data to align to Landsat-8 Operational Land Imager tasseled cap axes via the Procrustes analysis. The results show that the TCT coefficients derived from our PCP method can effectively enhance brightness, greenness, and wetness characteristics of the Sentinel-2 imagery. The results have also been compared with those of a previous study that derived the Sentinel-2 TCT data using the GSO method instead. Landsat-8 and moderate resolution imaging spectroradiometer (MODIS)-derived TCT data have been used as references for comparison. The comparison shows that the PCP method outperforms the GSO method, as the PCP's results generally have lower root mean square errors and higher correlation coefficients (R) when compared with the corresponding TC components of Landsat-8 and MODIS data, respectively. The greatest difference between the PCP and GSO methods lies in the wetness component. The PCP method can correctly highlight vegetation and soil moisture, as well as water features in the component.

In this study, a non-destructive testing method for crack defects of self-lubricating sliding bearing coating based on terahertz time-domain spectroscopy was proposed. The self-lubricating coating materials were revealed to have good penetration and characteristic response in the terahertz band through experiments. To solve the problem of difficulty in signal feature extraction caused by overlap, the broad learning system was used to classify and predict time-domain signals of crack defects. The identification accuracy for crack defects is 96.08%, and the mean relative errors of prediction for interface and internal cracks (5 to 95 μm in size) are 4.16% and 3.40%, respectively. The method proved the applicability for qualitative and quantitative evaluation in crack defects, which is considered a new idea for the non-destructive testing of self-lubricating coating.

Phenology provides important information for wheat growth management and the estimation of wheat yield and quality. The relative threshold method has been widely used to retrieve phenological metrics from remotely sensed data owing to its simplicity. However, the thresholds vary substantially among phenological metrics and locations, hampering us from effectively detecting spatial and temporal variations in winter wheat phenology. In this study, we developed a calibrated relative threshold method based on ground phenological observations. Compared with the traditional relative threshold method, our method can minimize the bias and uncertainty caused by unreasonable thresholds in determining phenological dates. On this basis, seven key phenological dates and three growth periods of winter wheat were estimated from long-term series (1981–2016) of the remotely sensed Normalized Difference Vegetation Index for North China (106°18′–122°41′E, 28°59′–39°57′N). Results show that the pre-wintering phenological dates of winter wheat (i.e., emergence and tillering) occurred in December in the south and in mid- to late- October in the north, while the post-wintering phenological dates (i.e., green-up onset, jointing, heading, milky stage, and maturity) exhibited the opposite pattern, that is, January to May in the south and February to June in the north. Consequently, the vegetative growth period increased from 49 days in the south to 77 in the north, and the reproductive growth period decreased from 51 days to 29 days. At the regional scale, all winter wheat phenological dates predominantly advanced, with the most pronounced advancement being for green-up onset (–0.10 days/year, p > 0.1), emergence (–0.09 days/year, p > 0.1), and jointing (–0.08 days/year, p > 0.1). The vegetative growth period and reproductive growth period at the regional scale predominantly extended by 0.03 (p > 0.1) and 0.09 (p < 0.001) days/year, respectively. In general, the later phenological events (i.e., heading, milky stage, and maturity) tended to advance with higher temperature, while the earlier phenological events (i.e., emergence, tillering, green-up onset, and jointing) showed a weak correlation with temperature, suggesting that the earlier events might be mainly affected by management while later ones were more responsive to warming. These findings provide a critical reference for improving winter wheat management under the ongoing climate warming.

Satellite top-of-atmosphere (TOA) reflectance has been validated as an effective index for estimating PM_2.5 concentrations due to its high spatial coverage and relatively high spatial resolution (i.e., 1 km). For this paper, we developed an emsembled random forest (RF) model incorporating satellite top-of-atmosphere (TOA) reflectance with four categories of supplemental parameters to derive the PM_2.5 concentrations in the region of the Yangtze River Delta-Fujian (i.e., YRD-FJ) located in east China. The landscape pattern indices at two levels (i.e., type level and overall level) retrieved from 3-year land classification imageries (i.e., 2016, 2018, and 2020) were used to discuss the correlation between county-based PM_2.5 values and landscape pattern. We achieved a cross validation R² of 0.91 (RMSE = 9.06 μg/m³), 0.89 (RMSE = 10.19 μg/m³), and 0.90 (RMSE = 8.02 μg/m³) between the estimated and observed PM_2.5 concentrations in 2016, 2018, and 2020, respectively. The PM_2.5 distribution retrieved from the RF model showed a trend of a year-on-year decrease with the pattern of “Jiangsu > Shanghai > Zhejiang > Fujian” in the YRD-FJ region. Our results also revealed that the landscape pattern of farmland, water bodies, and construction land exhibited a highly positive relationship with the county-based average PM_2.5 values, as the r coefficients reached 0.74 while the forest land was negatively correlated with the county-based PM_2.5 (r = 0.84). There was also a significant correlation between the county-based PM_2.5 and shrubs (r = 0.53), grass land (r = 0.76), and bare land (r = 0.60) in the YRD-FJ region, respectively. Three landscape pattern indices at an overall level were positively correlated with county-based PM_2.5 concentrations (r = 0.80), indicating that the large landscape fragmentation, edge density, and landscape diversity would raise the PM_2.5 pollution in the study region.

Structural defects in self-lubricating sliding bearings would lead to local stress concentration and service life reduction. The absence of accurate detection technology for micro defects in self-lubricating coating has seriously limited their application in mechanical equipment. A total variation (TV) fusion terahertz imaging method is proposed toward the unidentifiable micro defect induced by overlapping terahertz echoes. First, a range of indicators is developed to quantify the alterations of signal characteristics in different stages of the defect. Subsequently, the TV fusion based on these indicator images could clearly identify different defects. After that, a multinomial regression model is established through the mathematical relation of defect thicknesses and indicators, thus the defect thickness could be obtained accurately. The experimental results for surface defect detection and internal delamination measurement demonstrate the high accuracy and excellent robustness of the proposed method, making it attractive for defect detection and quality assessment of self-lubricating coating.