Surface longwave radiation (SLR) plays an important role in the energy budget of the Earth's climate system. Remote sensing provides various data sources to retrieve SLR on a large scale and with ...high spatial resolution (e.g., 1 km). Multiple retrieval methods of surface downward longwave radiation (SDLR) based on satellite thermal infrared data produce different retrieval results for the same scenario. Therefore, validating these models is necessary to understand their characteristics and limitations. To this end, ground-based measurements were used to provide independent validation of six widely used SDLR models for clear sky conditions over 41 Baseline Surface Radiation Network (BSRN) stations worldwide. The Wang2020 model had the best overall performance (bias of −5.480 W/m2, root-mean-square errors RMSE of 23.226 W/m2, R2 of 0.879), and Tang2008 model had similar retrieval capability. The errors of LST had limited influence on the retrieval accuracy of SDLR models. When using the near-surface air temperature, the retrieval accuracy of the Zhou2007 model was significantly improved with a range of ~9.5 W/m2 for the RMSE. The uncertainty of TCWV had significant effect on all model performances, wherein the Zhou2007 model had stronger error resilience of TCWV. Moreover, MODIS TIR TCWV data provided better performance than NIR TCWV in most situations, and thereby are preferred to use in the SDLR retrieval. Surface altitude had a lesser impact on SDLR retrieval than terrain effects. All models overestimated SDLR for peak stations in mountainous areas, with biases reaching 56.614 W/m2 and RMSE reaching 63.909 W/m2. Land cover type also had a significant effect on retrieval accuracy; model performances were poorer in the desert and barren where atmospheric conditions are extremely dry and hot. Remote sensing SDLR data with high accuracy are needed for hydrological, agricultural, and climate change applications. The results of this study provide a reference for the SDLR retrieval accuracy based on clear-sky models.
•Six SDLR models were validated using measurements from 41 BSRN stations worldwide.•Comprehensive validation was performed for different influencing factors.•The Wang2020 model had the best overall performance (RMSE of 23.226 W/m2).•Near-surface air temperature can significantly improve the accuracy of Zhou2007 model.•MODIS TIR TCWV data are preferred in SDLR retrievals with better performance.
Detecting various anomalies using optical satellite data prior to strong earthquakes is key to understanding and forecasting earthquake activities because of its recognition of ...thermal-radiation-related phenomena in seismic preparation phases. Data from satellite observations serve as a powerful tool in monitoring earthquake preparation areas at a global scale and in a nearly real-time manner. Over the past several decades, many new different data sources have been utilized in this field, and progressive anomaly detection approaches have been developed. This paper reviews the progress and development of pre-seismic anomaly detection technology in this decade. First, precursor parameters, including parameters from the top of the atmosphere, in the atmosphere, and on the Earth's surface, are stated and discussed. Second, different anomaly detection methods, which are used to extract anomalous signals that probably indicate future seismic events, are presented. Finally, certain critical problems with the current research are highlighted, and new developing trends and perspectives for future work are discussed. The development of Earth observation satellites and anomaly detection algorithms can enrich available information sources, provide advanced tools for multilevel earthquake monitoring, and improve short- and medium-term forecasting, which play a large and growing role in pre-seismic anomaly detection research.
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•AIRS data revealed pre-seismic surface temperature anomalies.•The high commission rate reduces the practicality of earthquake prediction.•The accuracy and omission rates of ...forecasting in Eastern Japan reached their peaks.•It’s reasonable to apply this framework for assessing earthquake forecasting ability.
There are growing observational evidences that various geophysical anomalies precede large earthquakes. However, the reliability of these anomalies for earthquake forecasting is controversial, and therefore more consistent assessment of forecasting ability is required. A framework for investigating pre-seismic anomaly detection using essential statistical indicators before global earthquakes is proposed. Surface temperature (ST) data from the Atmospheric Infrared Sounder (AIRS) sensor were used to realize this framework. First, seismic-related ST anomalies were identified, and then the statistical characteristics of forecasting ability for three indicators (accuracy, missed detection, and false alarm) were calculated in retrospective and prospective ways. The ST anomalies displayed some aggregation effects. Negative anomalies mainly concentrated on epicenters and to the north, while positive anomalies were found on the periphery; neither were strongly dependent on earthquake magnitude. The temporal evolution of forecasting metrics was relatively stable for the period 2010–2018. Mean accuracy, missed detection, and false alarm ratios were 6.01%, 1.60%, and 92.39%, respectively. Accuracy and missed detection ratios showed some spatial correlation and both peaked in the same area (e.g., eastern Japan); however, most areas showed very high false alarm ratios. Based on our findings, the combination of AIRS ST data and the Z-score anomaly detection algorithm to predict short-term earthquakes is currently not practical; the possibility of earthquake forecasting based on satellite thermal infrared measurements remains a huge challenge. However, our results confirmed the efficiency of this framework for quantitatively evaluating earthquake forecasting ability. This approach could be applied to various geophysical parameters and anomaly detection methods.
Thermal anisotropy is an important phenomenon in thermal infrared remote sensing as it restricts the retrieval accuracy of surface longwave radiation (SLR). Topography is an essential controlling ...factor for the directionality of SLR for high-relief regions (e.g., mountain regions) where there is land surface heterogeneity and non-isothermal properties at pixel scales. However, satellite sensors can only receive radiance from a specific surface object at a small number of simultaneous viewing angles, which makes the quantitative modeling of thermal anisotropy challenging. Therefore, we developed the topographic longwave radiation model (TLRM) to describe the directionality of SLR components taking into account the variability of both subpixel topography and thermal anisotropy in high-relief regions. The reliability of TLRM was validated using the Discrete Anisotropic Radiative Transfer (DART) model over two typical geomorphic areas: a valley scene and a peak scene. The preliminary validation shows good agreement in terms of surface upward longwave radiance, which confirms the potential of TLRM for capturing the anisotropic patterns of land surfaces. The radiance values simulated by the DART model were first revised for the spectral mismatch. Then, they are used to correct residual deviation for TLRM using linear regressions. The root mean square error (RMSE) and coefficient of determination (R2) were 0.830 W/(m2 ∙ sr) and 0.746 for the valley scene, respectively, and 0.239 W/(m2 ∙ sr) and 0.711 for the peak scene, respectively. Compared with TLRM, models that do not consider terrain effects generate significant discrepancies in high relief SLR components. The differences in downward longwave radiation can reach −60 W/m2 in valleys without considering terrain effects. Based on the reference of hemispherical upward longwave radiation, surface upward longwave radiation estimated by the direct estimation method had a bias of 11.41 W/m2 and standard deviation (STD) of 7.30 W/m2, while the directional upward longwave radiation had a bias of 5.99 W/m2 and STD of 4.08 W/m2, showing lower estimation variation. The discrepancy between surface net longwave radiation (NLR) and terrain-corrected NLR ranged between 50 and −130 W/m2 with clear negative biases mainly occurring in valleys. With higher spatial resolutions of remotely sensed imagery, the influence of complex terrain on land surface radiative flux has become more significant. This parameterization scheme is expected to better represent the topographic effects on SLR, enhance understanding of thermal anisotropy in non-isothermal mixed pixel areas of high relief, and improve the inversion accuracy of SLR.
•Terrain effects on retrieval of surface longwave radiation (SLR) are significant.•Large scale SLR can be estimated by the topographic longwave radiation model (TLRM).•TLRM integrates terrain, effective emissivity and broadband thermal modeling.•Subgrid radiation parameterization calculates SLR at multiple spatial scales.•TLRM has the capability to capture anisotropy patterns of SLR.
•New SLR retrieval model was developed for hyperspectral infrared sounders.•An AIRS footprint geometrical model was proposed to match MODIS cloud masks.•Site spatial representativeness was important ...for validating low resolution products.•The SLR model was insensitive to AIRS instrument noise.•Significant diurnal variation of retrieval errors should be considered.
Surface longwave radiation (SLR) derived from remotely sensed data facilitates understanding of the SLR in global climate change. Hyperspectral infrared sounders aboard space platforms provide information on the surface and vertical structure of Earth’s atmosphere. However, currently, SLR products estimated from these observations are unavailable, which hampers their application potential for Earth’s radiation budget in the context of global warming. To address this issue, we developed simple and effective SLR model under clear-sky conditions using at-sensor spectral radiances from Atmospheric Infrared Sounder (AIRS). The model was found to be insensitive to AIRS instrument noise, and showed good performances based on a simulation dataset. The AIRS footprint geometrical model was proposed to match the AIRS and Moderate Resolution Imaging Spectroradiometer (MODIS) data to estimate the cloud fraction. Validation against ground-based measurements found that the surface upward longwave radiation model has a bias of 3.18 W/m2, root-mean-square error (RMSE) of 30.51 W/m2, and R2 of 0.84; the surface downward longwave radiation model has a bias of 0.77 W/m2, RMSE of 29.09 W/m2, and R2 of 0.78. The large validation biases at two ground sites reflect the limited spatial representativeness for AIRS footprints. Terrain-induced altitude differences and spatial inhomogeneity can redistribute the spatial distributions of SLR. Moreover, the model performances were weakly dependent on seasonal variation. The results indicate that the proposed model provides a foundation for the further development of operational SLR products.
In general, the chloroplast genomes of angiosperms are considered to be highly conserved and affected little by adaptive evolution. In this study, we tested this hypothesis based on sequence ...differentiation and adaptive variation in the plastid genomes in the order Dipsacales. We sequenced the plastid genomes of one Adoxaceae species and six Caprifoliaceae species, and together with seven previously released Dipsacales chloroplasts, we determined the sequence variations, evolutionary divergence of the plastid genomes, and phylogeny of Dipsacales species. The chloroplast genomes of Adoxaceae species ranged in size from 157,074 bp (
) to 158,305 bp (
), and the plastid genomes of Caprifoliaceae varied from 154,732 bp (
var.
) to 156,874 bp (
). The differences in the number of genes in Caprifoliaceae and Adoxaceae species were largely due to the expansion and contraction of inverted repeat regions. In addition, we found that the number of dispersed repeats (Adoxaceae = 37; Caprifoliaceae = 384) was much higher than that of tandem repeats (Adoxaceae = 34; Caprifoliaceae = 291) in Dipsacales species. Interestingly, we determined 19 genes with positive selection sites, including three genes encoding ATP protein subunits (
,
, and
), four genes for ribosome protein small subunits (
,
,
, and
), four genes for photosystem protein subunits (
,
,
, and
), two genes for ribosome protein large subunits (
and
), and the
,
,
,
,
, and
genes. These gene regions may have played key roles in the adaptation of Dipsacales to diverse environments. In addition, phylogenetic analysis based on the plastid genomes strongly supported the division of 14 Dipsacales species into two previously recognized sections. The diversification of Adoxaceae and Caprifoliaceae was dated to the late Cretaceous and Tertiary periods. The availability of these chloroplast genomes provides useful genetic information for studying taxonomy, phylogeny, and species evolution in Dipsacales.
Plant nitrogen assimilation and use efficiency in the seedling's root system are beneficial for adult plants in field condition for yield enhancement. Identification of the genetic basis between root ...traits and N uptake plays a crucial role in wheat breeding. In the present study, 198 doubled haploid lines from the cross of Yangmai 16/Zhongmai 895 were used to identify quantitative trait loci (QTLs) underpinning four seedling biomass traits and five root system architecture (RSA) related traits. The plants were grown under hydroponic conditions with control, low and high N treatments (Ca(NO3)2·4H2O at 0, 0.05 and 2.0 mmol L−1, respectively). Significant variations among the treatments and genotypes, and positive correlations between seedling biomass and RSA traits (r=0.20 to 0.98) were observed. Inclusive composite interval mapping based on a high-density map from the Wheat 660K single nucleotide polymorphisms (SNP) array identified 51 QTLs from the three N treatments. Twelve new QTLs detected on chromosomes 1AL (1) in the control, 1DS (2) in high N treatment, 4BL (5) in low and high N treatments, and 7DS (3) and 7DL (1) in low N treatments, are first reported in influencing the root and biomass related traits for N uptake. The most stable QTLs (RRS.caas-4DS) on chromosome 4DS, which were related to ratio of root to shoot dry weight trait, was in close proximity of the Rht-D1 gene, and it showed high phenotypic effects, explaining 13.1% of the phenotypic variance. Twenty-eight QTLs were clustered in 12 genetic regions. SNP markers tightly linked to two important QTLs clusters C10 and C11 on chromosomes 6BL and 7BL were converted to kompetitive allele-specific PCR (KASP) assays that underpin important traits in root development, including root dry weight, root surface area and shoot dry weight. These QTLs, clusters and KASP assays can greatly improve the efficiency of selection for root traits in wheat breeding programmes.
Rugged terrain, as a large percentage of the Earth's terrestrial surface, is frequently reported to cause directionality of land surface thermal radiation (LSTR), and seriously affects the retrieval ...accuracy of land surface temperature (LST) and surface longwave radiation from satellite measurements. Therefore, modeling topographic effects on surface thermal anisotropy is essential to understand surface radiative processes. The directional brightness temperature (DBT) and equivalent brightness temperature (EBT) models at the pixel scale are proposed to indicate thermal anisotropy, considering viewing geometry, topographic effects, and subpixel variations based on the thermal infrared radiative transfer equation. A simulated data set of DBT and EBT at the 1-km resolution was obtained based on LST, emissivity, and terrain data with 30-m resolution. The terrain, coupled with solar and viewing geometries and subgrid variation, significantly affects the directionality of LSTR, and results in a remarkable bias between DBT and EBT. For the nadir observation, the bias is from -0.8 to 1 K, and reaches -5 to 2 K when viewing zenith angle becomes 50°. The maximal deviation is about 9 K over the most rugged mountains, which causes 57.6 W/m 2 bias of longwave radiation based on a 300 K blackbody. Furthermore, when LST is retrieved from DBT, the uncertainty of broadband emissivity of 0.01 causes LST bias of ~0.35 K. The models are considered to be very helpful in exploring terrain-induced thermal anisotropy, and enlightening in reducing estimation bias of remote sensing products over complex terrain.
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•Consecutive statistical evaluation framework for earthquake forecasting was updated.•Retrospective analyses reveal accuracy and missed detection rates of ∼80% and 20%.•Accuracy rates ...can be >10% within some regions in Japan and Indonesia.•Overall temporal average Matthews correlation coefficients range from −0.48 to 0.21.•Earthquake-prone regions are better experiment areas to test forecasting capability.
Short-term earthquake prediction remains a challenge. In this study, we investigated earthquake predictability using a consecutive statistical evaluation framework (CSEF). Two widely used anomaly detection methods—Z-score (ZS) and Robust Satellite Techniques (RST)—were evaluated using the Atmospheric Infrared Sounder surface temperature data based on global M ≥ 6 earthquakes with focal depths of ≤70 km from 2006 to 2020. Retrospective correlation analyses reveal accuracy and missed detection rates of 80.33% & 19.67% and 80.93% & 19.67% for ZS and RST, respectively. For earthquake forecasting performance in seismically active regions, accuracy rates are within 0–1% and false alarm rates are up to 50–80%. Areas near earthquake-prone regions have the highest accuracy rates. The accuracy rates can be >10% within some regions in Japan and Indonesia. Overall temporal average Matthews correlation coefficients (MCC) range from −0.48 to 0.21; global spatial average MCCs for each day from 2006 to 2020 are between −0.1 and 0.1. After 2012, the ZS method yields higher MCC values than the RST method. Our results confirm the reliability of CESF for assessment of earthquake forecasting capability, and the possibility of forecasting earthquakes at earthquake-prone areas. This approach can be applied to long-term analyses of precursory parameters, anomaly detection methods, and hypotheses, all of which are essential to the ultimate goal of routine and consistent earthquake forecasting.
The sky view factor (SVF) is a crucial variable widely used to quantify the characteristics of surface structures and estimate surface radiation budget. Many SVF models based on raster data have been ...developed but not yet evaluated in a more quantitative and uniform manner. In this paper, four typical SVF models (Dozier‐Frew (D‐F), Manners, Lindberg‐Grimmond (L‐G), and Helbig_h) are evaluated using the SVF derived from simulated fisheye images based on the digital surface model (DSM) and digital elevation model data. The SVF calculated by D‐F method using DSM data has the best accuracy, with a mean bias error of −0.007, root‐mean‐square error of 0.069, and coefficient of determination (R2) of 0.914. For the SVF value derived from digital elevation model data, L‐G method shows good performance, with an mean bias error of 0.013, root‐mean‐square error of 0.032, and R2 of 0.897. The pixels near the edges of buildings, within the valley or along ridgelines, have higher SVF deviations. In addition, the slope angle calculated using DSM data has some artificial defects that make the significant impact on the SVF biases due to their calculation method and the discontinuous surface in urban areas. Thus, L‐G and Helbig_h methods are more applicable for the DSM data due to the difficulty in defining slope and aspect angles. Moreover, the high accordance of SVFs between Helbig_h and L‐G methods implies that the Helbig_h method is an alternative in virtue of its simpler form and lower computation cost than L‐G method.
Plain Language Summary
Four typical sky view factor (SVF) models are evaluated and compared using simulated SVF data based on two kinds of raster terrain data to estimate models' accuracies and their different spatial characteristics. They show that different calculation accuracies and large SVF bias are mainly near the edges of buildings, along the valley and ridgeline. The slope angle can cause larger SVF biases than the aspect angle, and the influence on the SVF derived from DSM data is more significant than that derived from digital elevation model data. This paper evaluates these methods in a more quantitative and uniform manner, which offers accuracy levels that are important for SVF's application in various fields. The new findings and implications for possible improvements can benefit the estimation of the urban heat island, surface solar radiation, and melting of the polar icecap.
Key Points
Four different SVF models are evaluated and compared using urban DSM and montane DEM data in a quantitative and uniform manner
The slope angle calculated using DSM data has some artificial defects that cause large SVF biases due to the discontinuous surface in urban areas
The L‐G and Helbig_h methods are more applicable for urban DSM data because defining slope and aspect angles is difficult for the DSM data
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