GISD30: global 30-m impervious surface dynamic dataset from 1985 to 2020 using time-series Landsat imagery on the Google Earth Engine platform

Accurately mapping impervious surface dynamics has great scientific significance and application value for urban sustainable development research, anthropogenic carbon emission assessment and global ecological environment modeling. In this study, a novel and accurate global 30 m impervious surface dynamic dataset (GISD30) for 1985 to 2020 was produced using the spectral generalization method and time-series Landsat imagery, on the Google Earth Engine cloud-computing platform. Firstly, the global training samples and corresponding reflectance spectra were automatically derived from prior global 30 m land-cover products after employing the multitemporal compositing method and relative radiometric normalization. Then, spatiotemporal adaptive classification models, trained with the migrated reflectance spectra of impervious surfaces from 2020 and pervious surface samples in the same epoch for each 5° × 5° geographical tile, were applied to map the impervious surface in each period. Furthermore, a spatiotemporal consistency correction method was presented to minimize the effects of independent classification errors and improve the spatiotemporal consistency of impervious surface dynamics. Our global 30 m impervious surface dynamic model achieved an overall accuracy of 91.5 % and a kappa coefficient of 0.866 using 18,540 global time-series validation samples. Cross-comparisons with four existing global 30 m impervious surface products further indicated that our GISD30 dynamic product achieved the best performance in capturing the spatial distributions and spatiotemporal dynamics of impervious surfaces in various impervious landscapes. The statistical results indicated that the global impervious surface has doubled in the past 35 years, from 5.116 × 105 km2 in 1985 to 10.871 × 105 km2 in 2020, and Asia saw the largest increase in impervious surface area compared to other continents, with a total increase of 2.946 × 105 km2. Therefore, it was concluded that our global 30 m impervious surface dynamic dataset is an accurate and promising product, and could provide vital support in monitoring regional or global urbanization as well as in related applications. The global 30 m impervious surface dynamic dataset from 1985 to 2020 generated in this paper is free to access at http://doi.org/10.5281/zenodo.5220816 (Liu et al., 2021b).

Altered spatiotemporal consistency in pediatric bipolar disorder patients with and without psychotic symptoms

Objective Psychotic symptoms are quite common in patients with pediatric bipolar disorder (PBD) and may affect the symptom severity and prognosis of PBD. However, the potential mechanisms are less well elucidated until now. Thus, the purpose of this study was to investigate the brain functional differences between PBD patients with and without psychotic symptoms. Method A total of 71 individuals including: 27 psychotic PBD (P-PBD), 25 nonpsychotic PBD (NP-PBD), and 19 healthy controls were recruited in the present study. Each subject underwent 3.0 Tesla functional magnetic resonance imaging scan. Four-dimensional (spatiotemporal) Consistency of local neural Activities (FOCA) was employed to detect the local brain activity changes. Analyses of variance (ANOVA) were used to reveal brain regions with significant differences among three groups groups of individuals, and inter-group comparisons were assessed using post hoc tests. Results The ANOVA obtained significant among-group FOCA differences in the left triangular inferior frontal gyrus, left supplementary motor area, left precentral gyrus, right postcentral gyrus, right superior occipital gyrus, and right superior frontal gyrus. Compared with the control group, the P-PBD group showed decreased FOCA in the left supplementary motor area and bilateral superior frontal gyrus and showed increased FOCA in the left triangular inferior frontal gyrus. In contrast, the NP-PBD group exhibited decreased FOCA in the right superior occipital gyrus and right postcentral gyrus and showed increased FOCA in the left orbital inferior frontal gyrus. Compared to the NP-PBD group, the P-PBD group showed decreased FOCA in the right superior frontal gyrus. Conclusion The present findings demonstrated that the two groups of PBD patients exhibited segregated brain functional patterns, providing empirical evidence for the biological basis of different clinical outcomes between PBD patients with and without psychotic symptoms.

Retrieval of Solar-induced Chlorophyll Fluorescence from TanSat Measurements: Comparison of SIF between TanSat and OCO-2

Solar-induced chlorophyll fluorescence (SIF) is emitted during photosynthesis in plant leaves. It constitutes a small additional offset to reflected radiance and can be observed by sensitive instruments. The Chinese global carbon dioxide monitoring satellite (TanSat), as its mission, acquires greenhouse gas column density. The advanced technical characteristics of the hyper-spectrum grating spectrometer (ACGS) onboard TanSat enable SIF retrieval from space observations in the O2-A band. In this study, one-year SIF data was processed from Orbiting Carbon Observatory-2 (OCO-2) and TanSat using a physical-based algorithm. A comparison between the SIF retrieved from OCO-2 and its official product shows their strong linear relationship (R2 > 0.85) and suggests the reliability of the algorithm. The global distribution showed that the SIF retrieved from the two satellites shared the same spatial pattern for all seasons with the grided SIF difference less than 0.3 W m−2 μm−1 sr−1, and they also agreed with the official OCO-2 SIF product. The retrieval uncertainty of seasonal-grided TanSat SIF is less than 0.03 W m−2 μm−1 sr−1 whereas the uncertainty of each sounding ranges from 0.1 to 0.6 W m−2 μm−1 sr−1. The relationship between SIF and terrestrial gross primary productivity was also estimated for data quality testing. The spatiotemporal consistency between TanSat and OCO-2 and their comparable data quality make the comprehensive usage of the two mission products possible. Data supplemented by TanSat observations are expected to contribute to the development of global SIF maps with more spatiotemporal detail, which will advance global research on vegetation photosynthesis.

Unsupervised Monocular Depth Estimation for Colonoscope System Using Feedback Network

A colonoscopy is a medical examination used to check disease or abnormalities in the large intestine. If necessary, polyps or adenomas would be removed through the scope during a colonoscopy. Colorectal cancer can be prevented through this. However, the polyp detection rate differs depending on the condition and skill level of the endoscopist. Even some endoscopists have a 90% chance of missing an adenoma. Artificial intelligence and robot technologies for colonoscopy are being studied to compensate for these problems. In this study, we propose a self-supervised monocular depth estimation using spatiotemporal consistency in the colon environment. It is our contribution to propose a loss function for reconstruction errors between adjacent predicted depths and a depth feedback network that uses predicted depth information of the previous frame to predict the depth of the next frame. We performed quantitative and qualitative evaluation of our approach, and the proposed FBNet (depth FeedBack Network) outperformed state-of-the-art results for unsupervised depth estimation on the UCL datasets.

The Sentinel-3 OLCI Terrestrial Chlorophyll Index (OTCI): Algorithm Improvements, Spatiotemporal Consistency and Continuity with the MERIS Archive

The Ocean and Land Colour Instrument (OLCI) on-board Sentinel-3 (2016–present) was designed with similar mechanical and optical characteristics to the Envisat Medium Resolution Imaging Spectrometer (MERIS) (2002–2012) to ensure continuity with a number of land and marine biophysical products. The Sentinel-3 OLCI Terrestrial Chlorophyll Index (OTCI) is an indicator of canopy chlorophyll content and is intended to continue the legacy of the Envisat MERIS Terrestrial Chlorophyll Index (MTCI). Despite spectral similarities, validation and verification of consistency is essential to inform the user community about the product’s accuracy, uncertainty, and fitness for purpose. This paper aims to: (i) describe the theoretical basis of the Sentinel-3 OTCI and (ii) evaluate the spatiotemporal consistency between the Sentinel-3 OTCI and the Envisat MTCI. Two approaches were used to conduct the evaluation. Firstly, agreement between the Sentinel-3 OTCI and the Envisat MTCI archive was assessed over the Committee for Earth Observation Satellites (CEOS) Land Product Validation (LPV) core validation sites, enabling the temporal consistency of the two products to be investigated. Secondly, intercomparison of monthly Level-3 Sentinel-3 OTCI and Envisat MTCI composites was carried out to evaluate the spatial distribution of differences across the globe. In both cases, the agreement was quantified with statistical metrics (R2, NRMSD, bias) using an Envisat MTCI climatology based on the MERIS archive as the reference. Our results demonstrate strong agreement between the products. Specifically, high 1:1 correspondence (R2 >0.88), low global mean percentage difference (−1.86 to 0.61), low absolute bias (<0.1), and minimal error (NRMSD ~0.1) are observed. The temporal profiles reveal consistency in the expected range of values, amplitudes, and seasonal trajectories. Biases and discrepancies may be attributed to changes in land management practices, land cover change, and extreme climatic events occurred during the time gap between the missions; however, this requires further investigation. This research confirms that Sentinel-3 OTCI dataset can be used along with the Envisat MTCI to provide a data coverage over the last 20 years.

Evaluating Prediction Models for Mapping Canopy Chlorophyll Content Across Biomes

Accurate measurement of canopy chlorophyll content (CCC) is essential for the understanding of terrestrial ecosystem dynamics through monitoring and evaluating properties such as carbon and water flux, productivity, light use efficiency as well as nutritional and environmental stresses. Information on the amount and distribution of CCC helps to assess and report biodiversity indicators related to ecosystem processes and functional aspects. Therefore, measuring CCC continuously and globally from earth observation data is critical to monitor the status of the biosphere. However, generic and robust methods for regional and global mapping of CCC are not well defined. This study aimed at examining the spatiotemporal consistency and scalability of selected methods for CCC mapping across biomes. Four methods (i.e., radiative transfer models (RTMs) inversion using a look-up table (LUT), the biophysical processor approach integrated into the Sentinel application platform (SNAP toolbox), simple ratio vegetation index (SRVI), and partial least square regression (PLSR)) were evaluated. Similarities and differences among CCC products generated by applying the four methods on actual Sentinel-2 data in four biomes (temperate forest, tropical forest, wetland, and Arctic tundra) were examined by computing statistical measures and spatiotemporal consistency pairwise comparisons. Pairwise comparison of CCC predictions by the selected methods demonstrated strong agreement. The highest correlation (R2 = 0.93, RMSE = 0.4371 g/m2) was obtained between CCC predictions of PROSAIL inversion by LUT and SNAP toolbox approach in a wetland when a single Sentinel-2 image was used. However, when time-series data were used, it was PROSAIL inversion against SRVI (R2 = 0.88, RMSE = 0.19) that showed greatest similarity to the single date predictions (R2 = 0.83, RMSE = 0.17 g/m2) in this biome. Generally, the CCC products obtained using the SNAP toolbox approach resulted in a systematic over/under-estimation of CCC. RTMs inversion by LUT (INFORM and PROSAIL) resulted in a non-biased, spatiotemporally consistent prediction of CCC with a range closer to expectations. Therefore, the RTM inversion using LUT approaches particularly, INFORM for ‘forest’ and PROSAIL for ‘short vegetation’ ecosystems, are recommended for CCC mapping from Sentinel-2 data for worldwide mapping of CCC. Additional validation of the two RTMs with field data of CCC across biomes is required in the future.