The cloud-free monthly composite of the global nighttime light (NTL) data derived from the Suomi National Polar orbiting Partnership with the Visible Infrared Imaging Radiometer Suite (VIIRS) day/night band (DNB) has gained popularity for detecting anthropogenic and socioeconomic activities. However, the monthly VIIRS DNB composite suffers from a data missing problem induced by continuous cloud cover. The full potential of the VIIRS DNB time series is consequently hindered by low-quality and missing observations. This article proposes a spatiotemporal statistical method (STSM) to predict the VIIRS DNB imagery in severe absence of valid observations' situation. The polynomial with the harmonic model was applied to describe the long-term trends and seasonal cycles in time series. A spatial marginal semivariogram was established to quantify the data dependence in space; we then used spatial interpolation to correct the predicted results from temporal curve fitting. The final predicted values were validated with the actual values based on cross-validation. The results suggest that the STSM is suitable for predicting with a high coefficient of determination (R² = 0.922) and a relatively low root-mean-square error (RMSE = 3.40 nW/cm²/sr). We extended the proposed method to forecast future imagery for a five-month period, the performance of which was more stable, with the highest R²/RMSE (0.158 ± 0.010), compared with two other methods. Therefore, the STSM is effective and stable for modeling and predicting the VIIRS DNB monthly composite and will help address the data missing issue.

The xCELLigence real-time cell analysis (RTCA) system has the potential to detect cellular proliferation, migration, cytotoxicity, adherence, and remodeling. Although the RTCA system is widely recognized as a noninvasive and efficient tool for real-time monitoring of cellular fate, it cannot describe detailed cell morphological parameters, such as length and intensity. Transforming growth factor beta(TGF-β) induced the epithelial–mesenchymal transition (EMT), which produces significant changes in cellular morphology, so we used TGF-β to treat A549 epithelial cells in this study. We compared it with lipopolysaccharide (LPS) and cigarette smoke extract (CSE) as stimulators. We developed an efficient algorithm to quantify the morphological cell changes. This algorithm is comprised of three major parts: image preprocessing, Hough transform (HT), and post-processing. We used the RTCA system to record the A549 cell index. Western blot was used to confirm the EMT. The RTCA system showed that different stimulators produce different cell index curves. The algorithm determined the lengths of the detected lines of cells, and the results were similar to the RTCA system in the TGF-β group. The Western blot results show that TGF-β changed the EMT markers, but the other stimulator remained unchanged. Optics-based computer vision techniques can supply the requisite information for the RTCA system based on good correspondence between the results.

With the rapid urbanization process, the construction of lighting facilities is increasing, whereas artificial light at nighttime (ALAN) negatively affects organisms in protected areas and threatens ecosystems. Therefore, a deep research of ALAN within protected areas is significant for better preserving biodiversity by scientific ALAN management. Taking the ecological conservation redline (ECR) in Zhejiang Province as a case study, we consistently applied remotely sensed ALAN data from 2000 to 2020 for exploring spatiotemporal changing characteristics of ALAN. More importantly, both human living and ecological safety were considered to classify ALAN status in 2019 in order to propose rational suggestions for management. The results showed ALAN intensified and expanded, increasing from 3.05 × 10¹² nW·sr⁻¹ to 5.24 × 10¹³ nW·sr⁻¹ at an average growth rate of 2.35 × 10¹² nW·sr⁻¹·year⁻¹. Hotspot analysis and bivariate spatial clustering identified the aggregation situation of ALAN and the population. They showed that statistically significant ALAN hotspots accounted for only 20.40% of the study area while providing 51.82% of the total ALAN. Based on the mismatches between human demand and ALAN supply, two crucial areas were identified where regulation is needed most, and targeted policy recommendations were put forward. The study results can contribute to the effective regulation of ALAN in protected areas.

Integrating satellite observations into hydrologic models has the potential to enhance soil moisture (SM) retrievals at high spatial and temporal resolutions. The hydrological model SWAT can produce spatiotemporally continuous soil moisture product with high resolution. This study proposed to physically couple the SWAT and Tau-omega radiative transfer model to retrieve SM with a spatial resolution of 30 m. First, the SWAT model integrated a physically-based SM module was used to simulate hourly SM, from which the brightness temperature ( TB ) at 30 m was obtained by forwardly simulating the Tau-omega model. Then, the simulated TB at fine resolution and SMAP TB at coarse resolution were merged by Kalman filter into a disaggregated TB. Finally, SM at 30m resolution was retrieved from the disaggregated TB . The proposed methods were evaluated over the Lanjiang River Basin. The results show that the retrieved SM is highly correlated with two official SMAP SM products and that it has a better accuracy than five SMAP SM products. Particularly, due to the high resolution, the retrieved product captured the heterogeneity of SM within and among fields, compared to the individual use of the hydrological model or satellite observation. Our proposed approach can aid the decision-making in agricultural management at the field scale.