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Ecological connectivity in landscapes is crucial for plant diversity conservation. The barrier risk to ecological connectivity represents the risk to ecological connectivity loss or weakening, resulting from the barrier to biological information exchange among habitats. Therefore, clarifying the barrier risk to the ecological connectivity of plant diversity in space can reveal the spatial impacts of reduced ecological connectivity on plant diversity. This study analyzed effects of karst peak, river network, arable land, and impervious surface on plant diversity in karst natural, countryside, urban, and island landscapes in Guizhou Province with fragile environment. Then, we calculated the barrier distance of ecological connectivity to reveal the barrier risk to the ecological connectivity of plant diversity in space. The results showed that karst peak was the source of high plant diversity, and plant diversity could diffuse about 400 m around karst peaks. River network and arable land enhanced the connectivity among karst peaks to maintain plant diversity, and the effect on enhancing the connectivity was about 300 m and 450 m, respectively, while the weakening effect of impervious surface on connectivity was about 350 m. Based on the distance for plant diversity diffusing around karst peaks, the barrier distance of ecological connectivity was determined by the combination type of river network, arable land and impervious surface in landscapes. From low to high, the barrier risk to the ecological connectivity of plant diversity was about 1,110 m in the combination of river network and arable land, about 790 m in the combination of river network, arable land and impervious surface, about 520 min the combination of arable land and impervious surface, about 400 m in the combination of river network and impervious surface. Our findings clarify the barrier risk to the ecological connectivity of plant diversity in space, and provide a scientific basis for plant diversity conservation from the perspective of ecological connectivity.

We use optimal estimation (OE) to quantify methane fluxes based on total column CH 4 data from the Greenhouse Gases Observing Satellite (GOSAT) and the GEOS-Chem global chemistry transport model. We then project these fluxes to emissions by sector at 1 ∘ resolution and then to each country using a new Bayesian algorithm that accounts for prior and posterior uncertainties in the methane emissions. These estimates are intended as a pilot dataset for the global stock take in support of the Paris Agreement. However, differences between the emissions reported here and widely used bottom-up inventories should be used as a starting point for further research because of potential systematic errors of these satellite-based emissions estimates. We find that agricultural and waste emissions are ∼ 263 ± 24 Tg CH 4 yr -1 , anthropogenic fossil emissions are 82 ± 12 Tg CH 4 yr -1 , and natural wetland/aquatic emissions are 180 ± 10 Tg CH 4 yr -1. These estimates are consistent with previous inversions based on GOSAT data and the GEOS-Chem model. In addition, anthropogenic fossil estimates are consistent with those reported to the United Nations Framework Convention on Climate Change (80.4 Tg CH 4 yr -1 for 2019). Alternative priors can be easily tested with our new Bayesian approach (also known as prior swapping) to determine their impact on posterior emissions estimates. We use this approach by swapping to priors that include much larger aquatic emissions and fossil emissions (based on isotopic evidence) and find little impact on our posterior fluxes. This indicates that these alternative inventories are inconsistent with our remote sensing estimates and also that the posteriors reported here are due to the observing and flux inversion system and not uncertainties in the prior inventories. We find that total emissions for approximately 57 countries can be resolved with this observing system based on the degrees-of-freedom for signal metric (DOFS > 1.0) that can be calculated with our Bayesian flux estimation approach. Below a DOFS of 0.5, estimates for country total emissions are more weighted to our choice of prior inventories. The top five emitting countries (Brazil, China, India, Russia, USA) emit about half of the global anthropogenic budget, similar to our choice of prior emissions but with the posterior emissions shifted towards the agricultural sector and less towards fossil emissions, consistent with our global posterior results. Our results suggest remote-sensing-based estimates of methane emissions can be substantially different (although within uncertainty) than bottom-up inventories, isotopic evidence, or estimates based on sparse in situ data, indicating a need for further studies reconciling these different approaches for quantifying the methane budget. Higher-resolution fluxes calculated from upcoming satellite or aircraft data such as the Tropospheric Monitoring Instrument (TROPOMI) and those in formulation such as the Copernicus CO 2 M, MethaneSat, or Carbon Mapper can be incorporated into our Bayesian estimation framework for the purpose of reducing uncertainty and improving the spatial resolution and sectoral attribution of subsequent methane emissions estimates. [ABSTRACT FROM AUTHOR]

Methods developed to explore the luminescent properties of the moon facilitated the development of techniques to infer terrestrial solar‐induced chlorophyll fluorescence (SIF) from satellite instruments. While single SIF retrievals are inherently noisy, averaging many retrievals allows us to obtain highly accurate estimates. We analyzed several years of aggregated SIF data collected by the TROPOspheric Monitoring Instrument (TROPOMI) and the Orbiting Carbon Observatory‐2 (OCO‐2) over nonvegetated areas to explore the potential of SIF retrievals beyond the realm of photosynthesis. The fundamentally different retrievals at varying wavelengths in the near‐infrared reveal that about 10% of all barren surfaces are weakly luminescent, while a few areas luminesce strongly—amounts comparable to SIF from vegetation. By means of lithological maps, we attributed the strongest luminescence signals to exposed carbonate sedimentary rocks. Besides a detailed evaluation of the signal properties, we discussed implications for SIF data sets and other remote sensing products. Plain Language Summary: During the last decade, advancements in remote sensing techniques allowed us to infer solar‐induced chlorophyll fluorescence, a faint electromagnetic signal emitted by vegetation, from space‐borne measurements. Here, we explored the potential of such measurements to also observe mineral luminescence, a signal that is typically even weaker than fluorescence from vegetation, which explains why it has not been documented to be detectable from space. We identified luminescent regions by means of several years of vegetation fluorescence data derived from two satellites (OCO‐2 and Sentinel‐5P) over nonvegetated areas and analyzed the composition of luminescent surfaces. According to our analysis, about 10% of all barren areas on Earth are weakly luminescent, whereas some areas display surprisingly strong signals. Key Points: First report of mineral luminescence observed from spaceStrongest signals originate from carbonate sedimentary rocksEmission appears to be spectrally flat in the near‐infrared and Lambertian [ABSTRACT FROM AUTHOR]