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This study characterizes the marginally known angular-dependent polarization difference (PD) of the land surface emissivity (LSE), using the microwave humidity sounder (MWHS) onboard China’s Sun-synchronous satellite FengYun-3B (FY-3B). The instrument has two quasi-polarization channels at 150 GHz in addition to the three water-vapor absorption channels in the vicinity of 183 GHz, allowing observations at quasi-vertical and quasi-horizontal polarizations with its cross-track scanning geometry. The Saharan and Taklimakan deserts are taken as examples due to the relatively homogeneous surface conditions. In the two 150-GHz channels of MWHS, the polarization bias is corrected according to the measurements at the Earth incidence angle (EIA) of 0°. The monthly mean quasi-polarization difference (QPD) at 150 GHz is found to be varying with the EIA and exhibits seasonal variations over deserts. The QPD reaches a maximum value at the EIA of ~35°, while the zero value appears near the EIA of ~53° [equal to the satellite viewing angle (VA) of 45°] as expected. In contrast to the relatively stable and low QPD over Sahara, the QPD, quasi-polarized emissivity difference, and pure polarized emissivity difference (PPED) are generally larger in the Taklimakan Desert, where the maximum monthly QPD is ~4 K and the polarized emissivity difference increases monotonously to ~0.2 at the EIA of 50°. The complex surface parameters and temporal variabilities impinge on the uncertainties of the land polarization. The increasing soil moisture due to precipitation damps the dichroic ability of the land surface, while the frozen surface due to snowfalls in cold seasons enhances the polarization. [ABSTRACT FROM AUTHOR]

Spatiotemporal reflectance fusion has received considerable attention in recent decades. However, various challenges remain despite varying levels of success, especially regarding the recovery of spatial details with complex land cover changes. Taking the blending of Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) images as an example, this article presents a locally weighted unmixing-based spatiotemporal image fusion model (LWU-STFM) that focuses on recovering complex land cover changes. The core idea is to redefine the land use class of each pixel featuring land cover change at the prediction date. The spatial unmixing process is enhanced using a proposed geographically spectrum-weighted regression (GSWR), and then, we optimize similar neighboring pixels for the final weighted-based prediction. Experiments are conducted using semisimulated and actual time-series Landsat–MODIS datasets to demonstrate the performance of the proposed LWU-STFM compared with the classic spatial and temporal adaptive reflectance fusion model (STARFM), flexible spatiotemporal data fusion (FSDAF), two enhanced FSDAF models (SFSDAF and FSDAF 2.0), and a virtual image pair-based spatiotemporal fusion model for spatial weighting (VIPSTF-SW). The results reveal that the proposed LWU-STFM outperforms the other five models with the best quantitative accuracy. In terms of the relative dimensionless global error (ERGAS) index, the errors of Landsat-like images generated using LWU-STFM are 2.8%–63.4% lower than those of other models. From visual comparisons, LWU-STFM predictions illustrate encouraging improvements in recovering spatial details of pixels with complex land cover changes in heterogeneous landscapes and, thus, advancing applications of spatiotemporal image fusion for continuous and fine-scale land surface monitoring. [ABSTRACT FROM AUTHOR]

The complex terrain in mountainous areas distorts solar illumination, which brings a strong topographic effect on optical remote-sensing observations. Although many efforts have been done to correct this effect via normalizing solar illumination induced differences, there are still high uncertainty, especially for poor illuminated surfaces. In this study, a downwelling shortwave radiation (DSR)-based correction (DSRC) method was proposed. The topographic effects were accounted by normalizing DSR differences at different topographic conditions, and a stratified correction strategy was applied by separating the image into different groups according to normalized difference vegetation index (NDVI) to consider the spectral differences of different land-cover types. The DSRC method was applied to nine Landsat 8 scenes with high-resolution DSR data acquired by downscaling the Meteosat Second Generation (MSG) DSR product. The performance analysis indicates that the correlation coefficient between the corrected surface reflectance and illumination conditions notably decreased. Compared with SCS + C, empirical rotation, Statistical-Empirical, and Modified Minnaert methods, the DSRC method well retains inherent spectral pattern and provides good advantages in normalizing the aspect differences of surface reflectance. Furthermore, the comparison of NDVI values before and after correction indicated that DSRC preserved the original values and successfully corrected the overestimated NDVI values of poor illuminated surfaces. The corrected NDVI time series provide more reasonable cycle of the phenology of vegetated surfaces than the original series. In summary, the DSRC method has a strong potential for reducing topographic effects that currently limit the applications of remotely sensed data in mountainous areas. [ABSTRACT FROM AUTHOR]

Circumboreal forests are currently experiencing rapid climate warming which is altering their structure, productivity, and status as a carbon sink. Very high-resolution (VHR; < 2 m) stereo-derived digital surface models are available to monitor these forests, but a similar resolution digital terrain model (DTM) is required to extract information about tree height, which is often used to estimate carbon content. To the best of our knowledge, no openly available VHR DTM currently exists. To address this need, we developed approaches to extract DTMs by filtering VHR stereo point clouds (PCs) in sparse canopies of Alaska. Our evaluation consisted of two stereo processing methods with three PC search radii at six different tree canopy cover (TCC) intervals. We found that VHR DTMs were robust for estimating bare ground at TCC intervals less than 40% with vertical errors < 1.6 m using airborne small footprint light detection and ranging (LiDAR) as reference. [ABSTRACT FROM AUTHOR]

Accurate estimation of land surface temperature (LST) is crucial for ecological environment monitoring and climate change studies in mountainous areas. The current LST retrieval algorithms were developed without accounting for the topographic effect, which can only be used to retrieve LST over relatively flat surfaces. Due to the impact of 3-D structure of mountainous surfaces, rugged terrain makes the processes of thermal radiation more complex. In this study, a radiative transfer equation (RTE)-based single-channel algorithm was proposed to retrieve LST with topographic effect correction from the Landsat 8 thermal infrared (TIR) data in mountainous areas. This algorithm accounts for the changes in the thermal radiation components in the TIR RTE caused by the topographic effect. According to the analysis of simulation data, sky-view factor (SVF), atmospheric water vapor content, surface emissivity of target pixel, and average LST of the surrounding terrain have significant influence on the magnitude of the topographic effect. The differences between the LST retrieved without/with topographic effect correction from the Landsat 8 TIR data are related to SVF. The topographic effect should be taken into account in the LST retrieval algorithm when SVF is smaller than 0.7. The largest LST difference of approximately 1 K occurs in the deep valley. The results indicate that LST without topographic effect correction could be overestimated to be as high as 1 K. Due to a lack of in situ LST measurements, the performance of the LST retrieval algorithm in mountainous areas was only evaluated by comparing the brightness temperature (BT) at the top of the atmosphere (TOA) simulated by the DART+MODTRAN model and the TIR RTE over mountainous surfaces at three subregions. There is a good consistency between BT at the TOA simulated by the DART+MODTRAN model and the TIR RTE over mountainous surfaces at the three subregions, with a root-mean-squared error (RMSE) of less than 0.23 K. [ABSTRACT FROM AUTHOR]

The Sea and Land Surface Temperature Radiometer (SLSTR) onboard the two Sentinel-3 satellites provides daily global coverage observation at daytime and nighttime. The split-window (SW) algorithm is currently used to retrieve the land surface temperature (LST) from SLSTR images; however, this algorithm has to utilize visible and near-infrared (VNIR) images and land cover to determine pixel emissivity. For nighttime observation, VNIR cannot be observed, and this limitation complicates the LST retrieval from nighttime images using the SW algorithm. This article proposed a three-channel temperature–emissivity separation (TES) algorithm that estimates the nighttime LST and emissivity from one middle-infrared (MIR) and two thermal-infrared (TIR) nighttime Sentinel-3 SLSTR images. The sensitive analysis showed that the algorithm could theoretically retrieve the LST and emissivity with errors less than 0.8 K and 0.015, respectively. Ground validation showed that the nighttime LST retrieval error was approximately 1.84 K and the bias was approximately −0.33 K. Finally, the TES algorithm was applied to obtain the LST and emissivity images over northern China as an example. The emissivity retrieved from the nighttime observation can be used in the daytime SW algorithm to improve its feasibility in the LST retrieval process. [ABSTRACT FROM AUTHOR]

Estimation of downward shortwave radiation (DSR) is of great importance in global energy budget and climatic modeling. Although various algorithms have been proposed, effective validation methods are absent for rugged terrains due to the lack of rigorous methodology and reliable field measurements. We propose a two-step validation method for rugged terrains based on computer simulations. The first step is to perform point-to-point validation at local scale. Time-series measurements were applied to evaluate a three-dimensional (3-D) radiative transfer model. The second step is to validate the DSR at pixel-scale. A semiempirical model was built up to interpolate and upscale the DSR. Key terrain parameters were weighted by empirical coefficients retrieved from ground-based observations. The optimum number and locations of ground stations were designed by the 3-D radiative transfer model and Monte Carlo method. Four ground stations were selected to upscale the ground-based observations. Additional three ground stations were set up to validate the interpolated results. The upscaled DSR was finally applied to validate the satellite products provided by MODIS and Himawari-8. The results showed that the modeled and observed DSR exhibited good consistency at point scale with correlation coefficients exceeding 0.995. The average error was around 20 W/m2 for the interpolated DSR and 10 W/m2 for the upscaled DSR in theory. The accuracies of the satellite products were acceptable at most times, with correlation coefficients exceeding 0.94. From an operational point of view, our method has an advantage of using small amount of ground stations to upscale DSR with relatively high accuracy over rugged terrains. [ABSTRACT FROM AUTHOR]

The widespread presence of mixed pixels in remotely sensed images is a pressing challenge for accurate target detection and classification. Linear spectral mixture analysis (LSMA) is commonly used to address this problem by deriving remotely sensed information at the subpixel level. In the implementation of LSMA, the effects of mixed-pixel spectral interference need to be taken into account; mixed spectra would exhibit as a pure spectral characteristic when the abundance of one endmember in a mixed pixel exceeds a specific threshold. However, the thresholds of endmember abundance resulting in mixed-pixel spectral interference remain unclear. Thus, this study designed an experiment to analyze the effect of the spectral interference of mixed pixels and to identify the thresholds causing such interference by spectral similarity measures (spectral angle and spectral distance). Four types of pure endmember spectra (vegetation, high-albedo impervious surface (HIS), low-albedo impervious surface (LIS), soil) and corresponding representative mixed spectra with endmember abundances of 95%–5% at intervals of 5% were collected from Earth Observing-1 Hyperion imagery. Spectral similarity measures among the pure endmember spectra and representative mixed spectra were used to determine the thresholds of endmember abundance that cause spectral interference. The results verified the effect of the spectral interference of mixed pixels. The thresholds of abundance causing mixed-pixel spectral interference in vegetation, HIS, LIS, and soil endmembers were 70%, 75%, 80%, and 70%, respectively. Therefore, when the endmember abundance within mixed pixels exceeds the abovementioned thresholds, these mixed spectra are interfered and would exhibit as a pure spectral characteristic. Accordingly, interfered mixed pixels have to be removed before applying LSMA or other unmixing methods to avoid the effect of spectral interference. [ABSTRACT FROM AUTHOR]

Estimating lake dynamics is vital for the accurate evaluation of climate change and water resources monitoring. However, it remains a challenge to estimate the lake mass budget due to extremely scarce in situ data, especially for alpine regions. In this article, multimission remote sensing observations were blended to examine recent lake variations and their responses to climate change around the Altai Mountains during 2001–2009 and 2010–1018. First, the multitemporal Landsat images were used to enable the detailed monitoring of the surface extent of 43 lakes (> 5 km2) around the Altai Mountains from 2001 to 2018. The results presented that the total lake surface extent shrunk from 9835 km2 in 2001 to a minimum of 9652 km2 in 2009, while subsequently rose to 9714 km2 in 2018. By combining the lake area with the water level derived from the ICESat and CryoSat-2 altimetry data, the water storage of seven lakes covering ∼84% of the overall lake area in the region was obtained. The total water storage was detected with a decrease of 4.86 ± 1.17 km3 from 2003 to 2009 and a decrease of 3.65 ± 1.16 km3 from 2010 to 2018, respectively. Although most of the glaciers in this region had a significant mass loss in the past decades, the factor analysis indicated that most of the lakes had maintained a steady or slightly changing tendency because the glacial melting water was counteracted by the negative impact of high evapotranspiration amount. For the lakes with a few glacier melting supplies, e.g., the Uvs lake and Hyargas lake, the significant water budget loss was caused by the increasing evapotranspiration, decreased precipitation, and developed animal husbandry, which mainly dominated the overall decreasing trend of lake water storage in the Altai Mountains. [ABSTRACT FROM AUTHOR]

The Landsat 1–5 multispectral scanner system (MSS) collected records of land surface mainly during 1972–1992. Investigations on MSS have been relatively limited compared with the numerous investigations on its successors, such as Thematic Mapper (TM) and Enhanced TM Plus (ETM+). The benefits of the Landsat program are not fully accomplished without the inclusion of MSS archives. Investigations on the Landsat 1–5 MSS channel reflectance characteristics wereperformed followed by derived vegetation spectral indices and the Tasseled Cap (TC) transformed features mainly using a collection of synthesized records. On average, the Landsat 4 MSS is generally comparable to the Landsat 5 MSS. The Landsat 1–3 MSSs show disagreement in channel reflectance compared with the Landsat 5 MSS, especially for the red channel (600–700 nm) and the near-infrared channel (700–800 nm). Meanwhile, the relative differences for vegetation spectral indices of the Landsat 3 MSS are mainly from −16% to −5% with the median about −11.5%, while those of the Landsat 2 MSS are mainly from −15% to −7%. Cross-validation tests and two case applications suggested that between-sensor consistency was improved generally through the transformation models generated by ordinary least-squares regression. To improve the consistency of the vegetation indices and the TC greenness, direct strategy employing respective transformation models was more effective than calculations based on the transformed channel reflectance. Considering the shortages of the Landsat MSS archives, further efforts are needed to improve its comparability with observations by other successive Landsat sensors. [ABSTRACT FROM AUTHOR]

Understanding land–atmosphere interactions in extremely hot environment offers insights on how such interactions will change in a warmer world. For this reason, scientists from a wide range of fields, including hydrology, meteorology, ecology and geology, have been interested in identifying the hottest places on Earth. A study back in 2006 based on the moderate resolution imaging spectroradiometer (MODIS) land surface temperature (LST) data identified the Lut Desert in Iran as the “thermal pole of the Earth.” Since then, Lut Desert has been regarded as the hottest place on Earth with the record temperature of 70.7 °C observed in 2005. Using the latest MODIS-derived LST collection 6 which offers an improved LST estimates with a high spatial resolution (1 km), we investigate the hottest temperatures, as well as its diurnal variability in Lut Desert. The results show that Lut Desert is much hotter than previously thought with a record LST of 80.83 °C in 2018 (approximately 10 °C higher than previously reported LST) mainly due to improvements in the new LST estimations from space, and use of higher spatial resolution of the data. Further, our results show that Lut Desert has an incredible diurnal variability range, up to around 71 °C depending on the season. [ABSTRACT FROM AUTHOR]

Surface albedo in the rural and urban environment is one of the most influencing parameters in the evaluation of the radiative forcing of the land surface. Satellite remote sensing is an efficient tool for surface broadband albedo estimation with various spatial resolutions. Different retrieval algorithms were proposed and tested in the literature. This letter focuses on the narrow-to-broadband conversion algorithm applied to Sentinel-2 reflective data, using its best spatial resolution (10 m). New narrow-to-broadband coefficients for albedo retrieval were computed. Their accuracy was tested with ground-based measurements in two different environments and compared with another algorithm introduced in the literature. The application of these coefficients for the albedo estimation requires the assumption of Lambertian surface and clear sky conditions. The first test was performed on six field stations of the Surface Radiation Budget Network (SURFRAD), and the second test was carried out within an urban area (Perugia, Central Italy), to better evaluate the impact of the spatial resolution on the albedo estimation from spaceborne sensors. The obtained root mean square error (RMSE) for the two tests presented very good values for both algorithms (around 0.02), with a slightly better performance of the proposed coefficients, especially for the higher albedo values. In the urban test, the retrieved albedo from Sentinel-2 with a 10-m pixel size provided considerably better results than the Landsat 8 estimation (30-m), highlighting the benefit of using a finer resolution in heterogeneous environments. [ABSTRACT FROM AUTHOR]

Anisotropy information from moderate-to-coarse-resolution sensors [e.g., 500-m Moderate Resolution Imaging Spectroradiometer (MODIS)] is widely applied to estimate high-resolution surface albedo. Simulated albedos using MODIS bidirectional reflectance distribution function (BRDF) parameters as prior knowledge based on the kernel-driven model are employed to build and assess the lookup table (LUT) of the direct-estimation method, which is then used to estimate high-resolution albedos directly from top-of-atmosphere (TOA) reflectance data (e.g., Landsat albedo). Previously, the errors in the simulated albedos were not considered in building and assessing the LUT. In this article, daytime time-series (30 min) of snow-free albedo measurements with sufficient solar zenith angles (SZAs) were introduced to build the LUT for snow-free Landsat TM surface shortwave broadband albedo (TM albedo) retrievals, together with TOA-simulated reflectance by concurrent daily MODIS BRDF parameters. The assessment utilizes an independent data set and shows larger discrepancies between the estimated and measured albedos [root-mean-square errors (RMSEs) of >0.03 at SZAs ≥ 60°] than those in previous articles. To reduce inconsistencies between the MODIS BRDF parameters and the observed albedos, as well as possible spatial resolution differences between the MODIS and Landsat data, we adopted a correction strategy that first linearly adjusts the MODIS BRDF parameters to match the albedo measurements by a magnitude method, and second, the TOA reflectance simulations were further corrected by concurrent TM reflectances. The developed algorithm shows a significant improvement after using such corrections as a priori (RMSE < 0.02 at SZA ≤ 75°). The validation indicates improved accuracies in the TM albedo estimation. These improvements may provide potential albedo estimations for nadir-viewing high-resolution sensors using coarse-resolution anisotropy information. [ABSTRACT FROM AUTHOR]

The characterization of fine temporal-resolution land surface dynamics from broadband optical satellite sensors is constrained by sparse acquisitions of high-quality imagery; interscene variation in radiometric, phenological, atmospheric, and illumination conditions; and subpixel variability in heterogeneous environments. In this letter, we address these concerns by developing and testing the automatic adaptive signature generalization and regression (AASGr) algorithm. Provided a robust reference map corresponding to the date of one image, AASGr automates the prediction of continuous fields maps from imagery time series that is adaptive to the spectral and radiometric characteristics of each target image and thereby requires neither atmospheric correction nor data normalization. We tested AASGr on a 22-year Landsat time series to quantify subannual impervious fractional cover dynamics in Houston, TX—an area characterized by a high degree of spatial heterogeneity in surface cover and high frequency in land cover change. The map time series achieved high accuracy in a three-part validation procedure and reveals spatio-temporal dynamics of urban intensification and extensification at a level of detail previously elusive in discrete classifications or coarse temporal-resolution map products. The automation of continuous fields time series is enabling a new generation of land surface products capable of characterizing precise morphologies along a continuum of spatio-temporal change. While AASGr was applied here to predict subpixel impervious fractional cover from Landsat imagery, the method is generalizable to a range of imagery and applications requiring dense continuous fields time series with uncertainty estimates of geophysical and biochemical characteristics, such as leaf area index, biomass, and albedo. [ABSTRACT FROM AUTHOR]

An analysis of the coherency properties of specular scattering from ocean and land surfaces as observed in global navigation satellite system-reflectometry (GNSS-R) and signals of opportunity systems is presented. This analysis applies existing approximate models for the coherent and incoherent contributions. Approximate expressions are developed for when one component of the return is likely to dominate as a function of surface and observing system properties. The model developed is then applied for sea surface returns, and the relative contribution of the coherent term is expressed as a function of the receiver height, frequency, incidence angle, and wind speed. For L-band spaceborne measurements, it is shown that coherence is expected only for wind speeds less than 2–3 m/s, while for P-band spaceborne measurements, coherence can dominate returns for wind speeds up to 5–7 m/s. For land surface measurements from space, it is shown that the surface rms height needs to be sufficiently low for coherent components to dominate returns. Coherence dominates for roughness values not exceeding a range of 5–7 cm for the L-band and 15–30 cm for the P-band. For the L-band, these conditions over land are likely to be created primarily by inland water bodies. A model for the specular scattering from a water body, including earth curvature effects, is then developed to highlight the strong dependence of the resulting coherent field on the shape of the water body and any offset in its location from the specular point. These results further clarify the significant variability that should be expected in coherent scattering from inland water bodies. [ABSTRACT FROM AUTHOR]

Validation of remote sensing albedo products involves comparisons between point-scale in situ observations and footprint-scale satellite retrievals. However, the observed differences between product and in situ observations are not only attributable to intrinsic errors of satellite products but also to inadequate spatial representativeness of in situ observations. Here, representativeness errors of in situ observations and their effects on validation results were quantitatively explored. Furthermore, the contributors and their influences on representativeness errors were quantified. In the case of large representativeness errors, validation result errors are mainly controlled by representativeness errors. When representativeness errors are small, validation result errors are likely affected by other factors and can be so large that cannot be ignored. Surface heterogeneity is most positively related to representativeness errors, followed by the deviation distance of in situ site from the pixel center. The representative area surrounding in situ sites only shows a weak negative correlation with representativeness errors. The range seems to be not a good indicator of spatial representativeness of in situ sites since there is almost no relationship between them. When these factors are combined, surface heterogeneity contributes more to representativeness errors on the 500-m pixel scale, while quantitative impacts of the representative area and location deviation of in situ sites are not fully understood because magnitudes of these effects are dependent on the choice of high-resolution data set. These findings enhance our understanding about spatial representativeness of in situ observations and improve the quality of validation results based on single in situ observations. [ABSTRACT FROM AUTHOR]

The concept of local climate zone (LCZ) has standardized the calculation of urban heat island (UHI) intensity (UHII) and established the connection among urban morphology, surface property, and UHI. In addition, LCZ has provided a new insight into the studies on urban thermal environment. This study selected Nanjing, China, as the study area and utilized a combined method that comprised remote sensing based and geographic information system based methods based on random forest classifier for LCZ classification. Overall accuracy reached 92%, and kappa coefficient was 0.911. In addition, the seasonal and diurnal differences of land surface temperature (LST) were analyzed via LST retrieval from Landsat data and computational fluid dynamics model simulation, respectively. Results confirmed the warmest and coldest zones in four seasons. The LST distribution characteristics of land cover and built types were basically the same during the four seasons. Moreover, the UHII difference of an LCZ class in various seasons and the UHII difference of a season in various LCZs were investigated. The daily LSTs of the simulated LCZs (1 to 6) within 24 h showed the same variation law but different variation extents in a day. The LST variations of built types were related to building elements, such as building height, building density, building layout, and green ratio. This study identified an existing relation between LST and LCZ and analyzed LST on the basis of LCZs from seasonal and diurnal scales, which provides guidance for future researchers. [ABSTRACT FROM AUTHOR]

In this study, to improve the accuracy of land surface temperature (LST) products over barren surfaces, we present an operational algorithm to retrieve the LST from Moderate-Resolution Imaging Spectroradiometer (MODIS) thermal infrared data using physically retrieved emissivity products. The LST algorithm involved two steps. First, the emissivity in the two MODIS split-window (SW) channels was estimated using the vegetation cover method, with the bare soil component emissivity derived from the ASTER global emissivity data set. Then, the LST was retrieved using a modified generalized SW algorithm. This algorithm was implemented in the MUlti-source data SYnergized Quantitative (MuSyQ) remote sensing product system. The MuSyQ MODIS LST product and the Collection 6 MODIS LST product (MxD11_L2) were compared and validated using ground measurements collected from four barren surface sites in Northwest China during the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) experiment from June 2012 to December 2015. In total, 2268 and 2715 clear-sky samples were used in the validation for Terra and Aqua, respectively. The evaluation results indicate that the MuSyQ LST products provide better accuracy than the C6 MxD11 product during both daytime and nighttime at all four sites. For the daytime results, the LST is underestimated by the C6 MxD11 products at all four sites, with a mean bias of −1.78 and −2.86 K and a mean root-mean-square error (RMSE) of 3.16 and 3.94 K for Terra and Aqua, respectively, whereas the mean biases of the MuSyQ LST products are within 1 K, with a mean bias of −0.26 and −1.03 K and a mean RMSE of 2.45 and 2.71 K for Terra and Aqua, respectively. For the nighttime results, the LST is also underestimated by the C6 MxD11 products at all four sites, with a mean bias of −1.60 and −1.26 K and a mean RMSE of 1.93 and 1.60 K for Terra and Aqua, respectively, whereas the mean biases of the MuSyQ LST products are 0.16 and 0.58 K and the mean RMSEs are 1.12 and 1.25 K for Terra and Aqua, respectively. The results indicate that the underestimation of the C6 MxD11 LST product at all four sites mainly results from the overestimation of the emissivities in MODIS bands 31 and 32. This study demonstrates that physically retrieved emissivity products are a useful source for LST retrieval over barren surfaces and can be used to improve the accuracy of global LST products. [ABSTRACT FROM AUTHOR]

This paper investigates surface temperature variables as they relate to passive microwave-derived surface freeze/thaw (FT) state and assesses the accuracy of such FT products relative to surface temperature. Utilizing retrievals from the soil moisture active/passive (SMAP), advanced microwave scanning radiometer, and special sensor microwave imager instruments, surface FT records have previously been derived globally. Moderate Resolution Imaging Spectroradiometer skin temperature, North American Land Data Assimilation System (NLDAS) skin, 0–10-cm soil layer, and 2-m air temperatures are compared to the various FT state products (FTSPs) by defining the threshold for FT state transitions at 0 °C. This paper utilizes the 2015–2016 overlap period in FT records within the NLDAS domain. Spatial variability of classification accuracy (CA) is then investigated over the study area. A proportional differencing method also enables the identification of biases between FTSPs and surface temperature variables. Additionally, by analyzing probability distribution functions of FTSPs associated with temperature values, we assess the distribution of temperature variables as they relate to FT classifications. Classification agreement is shown to vary with sensor configuration, seasonality, and retrieval time. Air temperature is found to have the highest CAs across FTSPs (81%–91%), while NLDAS soil exhibits a close relationship to FTSPs, especially in regard to SMAP products. Finally, ascending (p.m.) retrievals are shown to be increasingly linked to the selected temperature parameters as compared to descending (a.m.) observations. This paper contributes to an improved understanding of current FTSPs and will benefit efforts to enhance future FT products. [ABSTRACT FROM AUTHOR]

This letter summarizes substantial modifications made to the Surface Water Microwave Product Series (SWAMPS), a coarse-resolution (~25 km) global inundated area fraction data record derived from active and passive microwave remote sensing. SWAMPS is the most temporally dense, long-term record of global surface water dynamics publicly available today. This update improves upon the original release by: 1) incorporating a customized, consistent resampling and assembly of the Special Sensor Microwave Imager and Special Sensor Microwave Imager Sounder brightness temperature record; 2) eliminating signal contamination from ocean waters along coastlines; 3) inclusion of permanent surface waters as a component of the data record; and 4) reducing anomalous inundation retrievals over arid and semiarid regions. This update provides for the enhanced scientific utility of the full 25+ years of data records. Remaining uncertainties in the surface water fraction retrievals are principally in areas with bare, sandy surface cover and in areas with dense vegetation cover that diminishes radiometric sensitivity to surface water. This data record and associated documentation are freely available through the Alaska Satellite Facility, Fairbanks, AK, USA. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is an important input parameter to characterize urban environmental heat change. Existing satellite-borne thermal infrared sensor technology cannot completely support the applications using high spatial resolution LST, such as analysis of urban thermal environment and energy consumption assessment. Downscaling LST is an alternative method to retrieve LST of high spatial resolution. In this paper, we propose an improved multi-factor geographically weighted regression (MFGWR) algorithm for LST downscaling. More factors were incorporated into geographically weighted regression method by taking into account different land covers and temporal variation so that the downscaled LST at urban areas with complicated land cover at various seasons was improved. It was applied to four urban areas with large difference on land cover at different seasons. Taking into account different factors, the temperature distribution of MFGWR reproduced additional spatial detail. Compared with the major statistical LST downscaling methods including thermal image sharpening algorithm (TsHarp), multiple scale factors with adaptive thresholds algorithm (MSFAT), support vector machine regression combined with gradient boosting (SVR-GB), and GWR, MFWGR showed a stable performance and higher accuracy. [ABSTRACT FROM AUTHOR]

We evaluated NASA soil moisture active–passive (SMAP) soil moisture (SM) and freeze-thaw (FT) datasets for the utility to identify FT and SM conditions as precursors to a 2017 spring-melt flood event in the Red River of the North Basin. SMAP FT and SM datasets were analyzed at basin-scale and at specific North Dakota Agricultural Weather Network (NDAWN), National Oceanic and Atmospheric Administration (NOAA), and Red River of the North U.S. Geological Survey (USGS) stations prior to and during the observed flood. Results indicate that SMAP FT dataset had better agreement with NDAWN and NOAA air temperature measurements than with soil temperature. SMAP FT and SM were able to observe FT states and saturated soil conditions at basin-level and significant increases in SM content up to five days before USGS gauge height increase and the manifestation of the flood event. A Spearman's rank ($R_{s}$) cross-correlation coefficient lag function was applied to SMAP SM and USGS river gauge heights and the strength of the relationship varied by location and lead time. Downstream locations near and in the flood area (North Grand Forks, Oslo, North Drayton) displayed moderate to strong relationships at 1-, 3-, and 4-day lead times (Rs = 0.67, 0.84, 0.71; p <.05), respectively. Pembina had the strongest relationship (4-day lead time; $R_{s}= 0.88; p<.05$), well during the flood event recorded. This study suggests that SMAP SM and FT datasets can potentially provide useful information on surface state conditions to spring-melt floods in the Red River of the North Basin. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) plays an important role in a wide variety of scientific studies. Several methodologies to retrieve LST and correct the atmospheric effects for thermal infrared satellite imagery have been developed and all of them require prior knowledge of the land surface emissivity (LSE). The techniques developed for LSE and LST retrieval need to be validated with field measurements. However, in situ measurements are a challenge, being essential to investigate the particularities of each instrument to verify the best approach to collect data and validate the algorithms. Fourier Transform Infrared (FT-IR) spectrometer has been widely used to obtain emissivity of different targets and calculate temperature. The instrument may be used to validate remote sensing data. Moreover, FT-IR allows to collect both emissivity and temperature at the laboratory, thus being an alternative to field validation. We investigated the emissivity dependence of the temperature. In addition, we evaluated the possibility of replacing in situ measurements by laboratory-controlled measurements. We have chosen two single-channel methods to calculate LST and perform the analysis in a Landsat 8 image. We also performed field measurements at the same time as the satellite overpass. FT-IR showed great potential to validate remotely sensed data. However, the instrument requires some time to acquire stability and attention in the calibration process. Laboratory measurements can replace field data, producing approximately 2% of the difference in the LSE. Both single-channel methods provide good accuracy for LST retrieval. Nonetheless, the improved single-channel has superior performance for the study area conditions. [ABSTRACT FROM AUTHOR]

Most retrieval functions used in remote sensing assume that the land surface is homogeneous. When those functions are used at a coarse spatial resolution for heterogeneous surfaces, scale effects might appear. This paper tries to develop an improved computational geometry method (ICGM) upscaling model that takes into consideration the actual distribution of surface measurements by using dynamic weights for the upper and lower envelopes of a convex hull. By aggregating to a series of simulated data at coarse spatial resolution, the weight coefficients can be determined via a least square method. To evaluate the proposed upscaling model, the leaf area index (LAI) is used as an example. The results for three sites with different degrees of heterogeneity show that the ICGM upscaling model can effectively correct for the scale effects of the LAI, and in most cases, achieve an accuracy that is comparable to that of traditional upscaling models. The relative error of the estimated LAI for the selected sites decreases from 3.35%, 11.01%, and 19.62% to an average of 0.28%, 1.48%, and 5.16%, respectively, at kilometer scale. A determination of whether retrieval functions are continuous or derivable is no longer required. Furthermore, there is no need to rely upon synchronous high spatial resolution data. Because the weight coefficients vary little at different scales, those coefficients are thought to be insensitive to different scales and can be taken as constants for a given study site. This study indicates that the proposed method is promising and feasible even for a heterogeneous landscape. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is described as one of the most important environmental parameters of the land surface biophysical process. Commonly, the remote-sensed LST products yield a tradeoff between high temporal and high spatial resolution. Thus, many downscaling algorithms have been proposed to address this issue. Recently, downscaling with machine learning algorithms, including artificial neural networks (ANNs), support vector machine (SVM), and random forest (RF), etc., have gained more recognition with fast operation and high computing precision. This paper intends to make a comparison between machine learning algorithms to downscale the LST product of the moderate-resolution imaging spectroradiometer from 990 to 90 m, and downscaling results would be validated by the resampled LST product of the advanced spaceborne thermal emission and reflection radiometer. The results are further compared with the classical algorithm—thermal sharpening algorithm (TsHARP), using images derived from two representatives kind of areas of Beijing city. The result shows that: 1) all machine learning algorithms produce higher accuracy than TsHARP; 2) the performance of TsHARP on urban area is unsatisfactory than rural because of the weak indication of impervious surface by normalized difference vegetation index, however, machine learning algorithms get the desired results on both two areas, in which ANN and RF models perform well with high accuracy and fast arithmetic, SVM also gets a good result but there is a smoothing effect on land surface; and 3) additionally, machine learning algorithms are promising to achieve a universal framework which can downscale LST for any area within the training data from long spatiotemporal sequences. [ABSTRACT FROM AUTHOR]

Water storage change of the lakes in the Tibetan Plateau is regarded as one of the most critical regional hydrological consequences owing to climate change. In this study, we investigate the water storage changes in 22 lakes in the Tibetan Plateau based on sequential remote sensed lake area and water level, which are derived from moderate resolution imaging spectroradiometer (MODIS) surface reflectance and Laboratoire D’Etudes en Géophysique et Océanographie Spatiales (LEGOS) altimetry data, respectively. Water storage of the lake is estimated on the basis of the relationship between lake area and water level. The method can be seen as an alternative to the conventional hydrological approaches. The results show that, during 2001–2017, most of the studied lakes in the Tibetan Plateau have shown significant increasing trends in water storage accompanied with larger lake area and higher water level. The changes in lake water storage are found in close relation to variations of climate factors such as precipitation, potential evaporation, and temperature in most lakes. The climate change impacts, however, can be amplified or attenuated by other environmental factors in some lake catchments. [ABSTRACT FROM AUTHOR]

Research on extraction of impervious surface has developed for decades, but it is still quite challenging to obtain impervious surface information with high accuracy, especially from multispectral remote sensing imageries. Linear spectral mixture analysis (LSMA) is a major method for estimating impervious surface areas. When LSMA is conducted, an important phenomenon is often neglected: the spectral signature is frequently interfered by the dominant component (impervious surface, vegetation, or soil) within a mixed pixel, namely the effects of mixed-pixel spectral interference. The primary objective of this study is to design a hierarchical extracting method based on normal difference vegetation index (NDVI) thresholding to eliminate these effects and improve mapping accuracy. In this method, NDVI was used and divided into different values within the range of 0.1–0.9 at intervals of 0.05. Every NDVI value was used to divide the Landsat OLI data into two segmented layers that were respectively unmixed using LSMA. The critical step was to seek out an optimal NDVI threshold by accuracy assessment, through which, a segmented layer that produced impervious surface extraction with best mapping accuracy could be identified. The results demonstrated that NDVI = 0.70 was the optimal threshold, indicating that Landsat image with NDVI ≤ 0.70 was not affected by spectral interference. Therefore, LSMA was implemented to extract impervious surface based on the Landsat image with NDVI ≤ 0.70, and the remaining area was classified by Gaofen-2 imagery to produce a complete map using object-based image analysis. This hierarchical method is effective to remove or minimize the effects of mixed-pixel spectral interference with a promising accuracy improvement of 29.3% on average. Furthermore, it provides a novel idea to enhance urban impervious surface mapping by LSMA based on NDVI thresholding. [ABSTRACT FROM AUTHOR]

The advanced Himawari imager (AHI) aboard the Himawari-8 geostationary satellite provides high-frequency observations with broad coverage, multiple spectral channels, and high spatial resolution. In this paper, AHI data were used to develop an algorithm for joint retrieval of aerosol optical depth (AOD) over land and land surface bidirectional reflectance. Instead of performing surface reflectance estimation before calculating AOD, the AOD and surface bidirectional reflectance were retrieved simultaneously using an optimal estimation method. The algorithm uses an atmospheric radiative transfer model coupled with a surface bidirectional reflectance factor (BRF) model. Based on the assumption that the surface bidirectional reflective properties are invariant during a short time period (i.e., a day), multiple temporal AHI observations were combined to calculate the AOD and surface BRF. The algorithm was tested over East Asia for year 2016, and the AOD retrieval results were validated against the aerosol robotic network (AERONET) sites observation and compared with the Moderate Resolution Imaging Spectroradiometer Collection 6.0 AOD product. The validation of the retrieved AOD with AERONET measurements using 14 713 colocation points in 2016 over East Asia shows a high correlation coefficient: $R = 0.88$ , root-mean-square error = 0.17, and approximately 69.9% AOD retrieval results within the expected error of $\pm 0.2\cdot {\mathrm {AOD}}_{\mathrm {AERONET}}\pm 0.05$. A brief comparison between our retrieval and AOD product provided by Japan Meteorological Agency is also presented. The comparison and validation demonstrates that the algorithm has the ability to estimate AOD with considerable accuracy over land. [ABSTRACT FROM AUTHOR]

Multitemporal airborne and satellite imagery data with frequent repeat coverage provide great capability for change detection (CD). When comparing two images taken at different times of day or in different seasons for CD, the variation of topographic shades and shadows caused by the change of sunlight angle can be so significant that it overwhelms the real object and environmental changes, making automatic detection unreliable. An effective CD algorithm, therefore, has to be robust to the illumination variation. In this paper, the robustness of phase correlation (PC) to shadow effects is proven via mathematical analysis, and then, an illumination-invariant change detection (IICD) metric is proposed based on pixel-wise dense PC matching. In the proposed IICD method, a graph-based visual saliency map is introduced for the initial CD followed by an active contour-based segmentation to precisely quantize the change region. Compared to the state-of-the-art CD algorithms, experiments using daily images of a landscape model and Landsat satellite images demonstrate that only the proposed method can effectively detect and precisely segment appearance changes under daily and seasonal sunlight changes. [ABSTRACT FROM AUTHOR]

Latent heat is the energy released or absorbed by a substance through phase change without changing its temperature. Its flux is a crucial element of the hydrological cycle at the land–air interface. Many water-related indexes have been proposed as indicators for latent heat flux extraction from satellite imagery, while the extraction accuracy still remains a space to improve nowadays. In this paper, a new multiband index, called normalized difference latent heat index (NDLI), is proposed for remote sensing of land surface heat flux. It utilizes the reflectance observations of three bands from Landsat 8 operational land imager, including red, green, and shortwave infrared channels. Its performance in terms of deriving latent heat flux through their incorporation into the commonly used surface energy balance algorithm for land (SEBAL) is then compared with the other three existing water-related indexes with two of them using normalized difference water index and normalized difference vegetation index. Results show that NDLI exhibits the strongest correlation with the SEBAL-derived latent heat flux among the used water-related indexes with correlation coefficient $r = 0.75$. They also indicate that the NDLI is the most sensitive and reliable index outperforming the previously developed indexes to determine the characteristics of water content in different land cover types. It is concluded that NDLI can be used as a good indicator to represent the potential latent heat flux at the earth’s surface. [ABSTRACT FROM AUTHOR]

The spatial scale variability of surface shortwave radiation (SSR) budget is dominated by clouds. Therefore, a prerequisite step for accurate estimation of the instantaneous SSR components under all-sky conditions, especially partly cloudy conditions, is to determine a proper spatial scale. In this study, a modified one-dimensional (1-D) radiative transfer (RT) model is developed to study the effect of cloud cover on SSR field, in which cloud fraction (CF) is introduced to improve the classic 1-D plane-parallel RT equations and a global sensitivity analysis (GSA) is performed to quantitatively understand the effect of CF on SSR under both optically thick and optically thin cloudy conditions. An artificial neural network approach is then employed to generate multiple SSR components based on extensive RT simulations using the modified 1-D RT model and the GSA results. The optimal spatial scale is quantitatively determined through eight cloudy scenarios over the Tibetan Plateau, with different amounts and spatial distribution patterns of clouds. The GSA results show that the top three vital parameters for the modified 1-D RT model are solar zenith angle, CF, and land surface albedo. The optimal spatial scale for applying the modified 1-D RT model is about 20 km, which is surprisingly consistent with some theoretically and technically complicated simulation studies. The finding of the current study adds new evidence to the growing body of knowledge about the spatial scale consideration for estimating all-sky instantaneous SSR with 1-D RT theory. [ABSTRACT FROM AUTHOR]

The land surface shortwave downward radiation (SWDR), also known as surface incident solar radiation, is usually defined as the total solar spectral energy between 300 nm and 3000 nm that arrives at the earth's surface. The SWDR is an essential variable for numerous land models and thus need to be accurately mapped using various approaches. Although numerous efforts have been made to assess the applicability and accuracy of the SWDR datasets produced by satellite and numeric models, few studies have evaluated the performance of the SWDR products from Himawari-8. In this paper, the Himawari-8 SWDR products with both 1 km and 5 km spatial scales were evaluated using ground measurements. The validation results indicate that both of the Himawari-8 SWDR products are in close agreement with ground measurements, even though obvious biases are found. Also, an in-depth intercomparison between the Himawari-8 SWDR product at a 5 km resolution and three other SWDR radiation datasets (CERES-SYN, MERRA-2, and ERA-Interim) was conducted. Here, the Himawari-8 showed the highest levels of accuracy among the four SWDR datasets. Considering its higher temporal and spatial resolutions, the Himawari-8 SWDR products are more detailed and provide the unprecedented potential to improve land surface modeling and reveal the cloud-radiation interactions, even at a 10-min temporal scale. [ABSTRACT FROM AUTHOR]

The importance of the bidirectional reflectance distribution function (BRDF) has been well documented in quantitative remote sensing. The semiempirical, kernel-driven BRDF model is widely used to generate operational BRDF/albedo products due to its simplicity and accuracy. However, the effect of topography is rarely coupled with a kernel-based BRDF model. In this paper, a new kernel-driven reflectance model for sloping terrain (KDST) was developed based on the framework of the RossThick-LiSparse-Reciprocal (RTLSR) model. The slope, aspect, geotropic nature of the tree crown governed by gravity, component spectra contrasts, and diffuse irradiance were considered in the KDST model. The performance of KDST was evaluated by 3-D discrete anisotropic radiative transfer (DART) simulations, in situ measurements, and HJ-1A/B constellation charge-coupled device satellite observations. Using DART simulations, KDST reduces the maximum biases of the RTLSR from 15.6% and 29.7% to 7.1% and 4.8% for the surface bidirectional reflectance factor and hemispherical-direction reflectance factor, respectively. Compared with in situ measurements, KDST improves the reflectance simulation accuracy for the red and near-infrared bands from 0.0172 (18.65%) and 0.022 (4.77%) to 0.0060 (6.51%) and 0.0043 (1.05%), respectively. The preliminary comparison results indicate that KDST is promising for reflectance simulation over rugged terrain. [ABSTRACT FROM AUTHOR]

Surface energy budget has great influence on earth surface biogeophysical and biogeochemical processes. As one of the components of surface energy budget, the longwave downward radiation (LWDR) is essential to many land applications. In this research, a new LWDR estimation scheme under clear sky by integrating MODIS thermal bands radiance and in situ measurements through the multivariate adaptive regression splines (MARS) has been proposed. The MODIS products and corresponding multinetwork in situ LWDR observations under clear skies in 2004 were extracted for model training and cross-validation. The in situ LWDR observations from seven SURFRAD sites in 2008 were used to test the new method. In training phase, the cross-validation RMSE, bias, and R-square of the new method are, respectively, 24.66 W/m2, −0.446 W/m2, and 0.88 in the daytime and 23.16 W/m2, −0.272 W/m2, and 0.92 in the nighttime. The test results at the seven SURFRAD sites show that the proposed method has good performance, with RMSE, bias, and R-square of 17.15 W/m2, 0.028 W/m2, and 0.91 in the daytime and 14.08 W/m2, −3.609 W/m2, and 0.94 in the nighttime, respectively. More importantly, the error analysis reveals that the predicted residual errors of the proposed method have a weak correlation with related meteorological parameters at global scale, which reflects the stability and robustness of the new method. [ABSTRACT FROM AUTHOR]

Accurate cloud and cloud shadow detection in multispectral remote sensing imager due to the high spectral variations of clouds and the complexities of underlying landscapes, especially for images without thermal bands. In this paper, we proposed a multitemporal integrated cloud z-score (MTICZ) method for cloud and cloud shadow detection for multitemporal optical images. First, an integrated cloud z-score (ICZ) index was designed to identify clouds and cloud shadows, and measure the likelihood of a pixel being either a cloudy or shadowed pixel. Clouds and cloud shadows were then detected by differencing the ICZ values between the reference image and the cloudy image, and they were refined using a cloud and shadow matching algorithm. Finally, the MTICZ method was evaluated through cloud fraction estimation and 3,000 random validation samples from the cloudy Landsat scenes. The results indicate that MTICZ method achieved a significant agreement with reference cloud fractions $({{R^2} = {\text{0.97}},{\text{RMSE}} = {\text{4.31}\%}})$ and performed well under complicated land surface conditions, with an average overall accuracy of 91.65%. In addition, the MTICZ method was compared with two popular cloud detection methods, the Fmask method and the multitemporal cloud detection (MTCD) algorithm. This comparison reveals some improvements for cloud identification in complex landscapes, such as impervious surfaces and mixed areas of snow and cloud. Therefore, we argue that MTICZ provides an effective multitemporal method to detect cloud and cloud shadow for optical imagery in complicated landscapes. [ABSTRACT FROM AUTHOR]

High-resolution optical imagery can provide detailed information of urban land objects for impervious surface extraction, while airborne light detection and ranging (LiDAR) data can provide height features of land objects. Therefore, synergistic use of high-resolution imagery and LiDAR data is considered as an effective method to improve impervious surfaces extraction. In this paper, a novel hierarchical multiscale super-pixel-based classification method is proposed and applied to the urban impervious surfaces extraction from WorldView-2 and normalized digital surface model (nDSM) images derived from airborne LiDAR data. Three subsets in rural, rural–urban, and urban subsets are selected as the study areas. First, we split nonground and ground objects based on nDSM thresholds. Second, a hierarchical multiresolution segmentation method is used to generate nonground and ground super pixels. Then, we determine the multiscale input images based on the size of super pixels. Third, we construct optimal deep residual network (ResNet) and Spatial Pyramid Pooling (SPP-net) to train the model using multiscale input images. Finally, we use our deep models to predict hierarchically total super pixels in three subsets and generate the classification and impervious surfaces results. Our proposed method adopts hierarchical classification based on LiDAR nDSM height, which significantly improves the impervious surfaces extraction accuracies. Then, the deep residual network is applied further on multispectral and height fused data to extract urban impervious surfaces. Moreover, we propose an adaptive method to determine multiscale input images based on the segmentation of super pixels, which are inputs into the ResNet+SPP-net to train the deep model. Our proposed method reduces the uncertainty of multiscale input images and extracts better multiscale features. The results of the experiment show that our proposed method has a significant superiority to traditional pixel-based method and single scale method for urban impervious surfaces extraction. [ABSTRACT FROM AUTHOR]

This letter presents a new approach to incorporate topographic relief effect in land data assimilation system over mountainous terrain. The conventional radiative transfer model (RTM) used for assimilation of microwave brightness temperature (Tb) is subjected to systematic bias owing to its flat earth model assumption. This is theoretically important for direct Tb assimilation system since a difference between simulated and observed Tb may disarray the assimilation system. Here, we consider three crucial relief effects, namely: 1) change in local incidence angle; 2) rotation of the plane of polarization; and 3) effect of pixels in shadow not visible to radiometer. Results indicate that the RTM formulation with relief effects (Topo) significantly reduce the bias as compared to the conventional RTM (Flat). The simulated Tb for the Topo case shows improved sensitivity toward soil moisture as compared to the Flat case. The proposed land surface model (LSM)–RTM assimilation framework has an immense potential source for initialization of LSM for seasonal climate prediction over mountainous terrain. [ABSTRACT FROM AUTHOR]

Thermal sensors onboard Landsat satellites have been underutilized due to the lack of consistent and accurate methodologies for retrieving the land surface temperature (LST) at global scales over all land cover types. We present an operational algorithm for generating Landsat LST consistently for all sensors that will be implemented by the United States Geological Survey/The National Aeronautics and Space Administration and made available at the Land Processes Distributed Active Archive Center. The LST algorithm involves three steps. The observed thermal radiance is atmospherically corrected using a radiative transfer model and reanalysis data. The Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Emissivity Data Set version 3 is spectrally adjusted and then modified to account for vegetation phenology and snow cover using Landsat visible-shortwave infrared data. The LST is retrieved by inverting the atmospherically and emissivity corrected Landsat radiances with a lookup-table approach. Landsat-derived emissivities were validated at two pseudoinvariant sand dune sites within an average absolute error of 0.54% when compared with laboratory measurements. The Landsat LST retrievals were validated with in situ observations from four surface radiation budget network (SURFRAD) sites, and two inland water bodies (Salton Sea and Lake Tahoe) in the USA. The LST retrievals for Landsat 5 and 7 had a mean bias (root mean square error) of 0.7 K (2.2 K) and 0.9 K (2.3 K) for the SURFRAD sites, and −0.3 K (0.6 K) and 0.4 K (0.7 K) for the inland water bodies, respectively. The operational algorithm will provide a consistent LST record from four decades of historical Landsat thermal data enabling the long-term monitoring of temperature and trends, land cover and land use changes, and improved utilization in models. [ABSTRACT FROM AUTHOR]

In terms of traditional threshold methods, uniform thresholds are used for cloud detection based on remote sensing images; however, due to complex surface structures and cloud conditions, such an approach is typically difficult to effectively implement for high-precision cloud detection. To solve this problem, a new cloud detection algorithm is proposed based on global land cover data. Specifically, a high spatial-resolution at 30-m Global Land Cover Data set with global coverage was employed as background data for image inversions, which further supported cloud detection in remote sensing images. Notably, threshold settings can be varied for different land cover types. Such an algorithm can effectively improve the accuracy of cloud pixel identification for thin and broken clouds, even over bright areas. Moreover, Landsat 5 data are used to perform cloud detection experiments based on this algorithm. The thresholds are considering land cover variations. The thresholds of land cover types spatiotemporally vary, such as vegetation, differed by latitude and over time. In addition, six common land cover types are selected for cloud detection experiments. Then, validations analyses are conducted through visual interpretation and the results indicated that the algorithm is capable of achieving a high cloud detection accuracy. Specifically, the overall RMSE of cloud cover is 4.44%, and the accuracies of cloud and clear-sky pixel identifications is 86.5% and 98.7%, respectively. [ABSTRACT FROM AUTHOR]

Prior knowledge of the background land surface reflectance (LSR) constitutes one of the most important factors affecting the precision of cloud shadow detection. To resolve this problem, a surface reflectance-based cloud shadow detection (SRCSD) algorithm is proposed for multitemporal Landsat images. Monthly surface reflectance data sets constructed from MODIS surface reflectance products (MOD09A1) were used to provide the background LSR for cloud shadow detection. Based on the background LSR, the possible variation in the top of atmosphere (TOA) reflectance for each clear pixel can be estimated using the radiative transfer equation under different atmospheric conditions. If a pixel has a smaller TOA reflectance than the minimum value of the possible range under clear conditions, it is identified as being shadow covered. One hundred and twenty-five Landsat 8 Operational Land Imager scenes covered by various surface types were selected to evaluate the feasibility of the algorithm. A validation using manual cloud shadow masks showed that the average producer’s accuracy and user’s accuracy were approximately 0.805 and 0.893, respectively. A comparison of the results of the SRCSD algorithm with those of an object-based cloud shadow detection algorithm (Fmask) recently developed for Landsat images revealed that SRCSD generally detects cloud shadows better than Fmask. The most significant improvement of the SRCSD algorithm is the better detection capability for thin and broken cloud shadows, and this algorithm can be extended to multiple types of satellite data after proper modification. [ABSTRACT FROM AUTHOR]

High-resolution remote sensing plays an important role in the study of subtle changes on the Earth's surface. The newly orbiting Chinese GF-1 satellites are designed to observe the Earth surface on a regional scale; however, the satellite efficiency requires further investigation. In this paper, the efficiency of using GF-1 01 satellite images to monitor a complex surface is tested by considering supplementary information and different land cover classification methods. Our work revealed that the GF-1 satellite observations can efficiently detect land cover fragments. When the support vector machine method is applied, the overall classification accuracy based on multisource data reaches 90.5%. The “salt and pepper phenomenon” is effectively reduced in classification images. These results also indicate that the accuracy of the GF-1 image classification is superior to the results when using the same method with the Landsat 8 and Sentinel-2A images, with the overall classification accuracy increasing by 23.6% and 13.6%. Our study suggests that GF-1 satellite observations are suitable for land cover studies on complex land surfaces. This approach can benefit various related fields such as land resource surveys, ecological assessments, environmental evaluations, and so forth. [ABSTRACT FROM AUTHOR]

The correlation of spatial variation in land surface temperature with building height and density in Zhongshan District, Dalian, China, is investigated over the period 2003–2013. We utilize remote sensing data and multisource land-use data, which we evaluate using a combination of a single-window algorithm and correlation analysis. The results show that: 1) during 2003–2013, the number of high surface temperature regions increased by 4339 grid cells (out of a total of 53 601), with most of the high-temperature grid cells distributed along Jiefang Road and Zhongnan Road in the northern part of Zhongshan District. Ninety-eight percent of grid cells had temperature ranges of 293–309 K in July 2003, 296–310 K in August 2003, 295–308 K in July 2008, 296–311 K in August 2008, 305–314 K in July 2013, and 303–318 K in August 2013; 2) during 2003–2008, the number of low-rise buildings increased by 140%, and the number of multistory buildings increased by 100%. During 2008–2013, the number of buildings in all height categories remained relatively unchanged, with the exception of an 11% decrease in the number of low-rise buildings; 3) surface temperature showed weak correlation with building height (0.314, 0.346, and 0.361 in 2003, 2008, and 2013, respectively) but moderate correlation with building density (0.511, 0.533, and 0.563, respectively). [ABSTRACT FROM AUTHOR]

The objective of this letter is to investigate and demonstrate potential accuracy improvements in land cover classification using Landsat data, by integrating time-series features with a specially designed classification method. We present a new framework, mapping land cover types using annual time-series Landsat data (LandUTime), which adopts a two-stage approach. First, to generate pattern features, regression analysis is conducted on annual time-series Landsat data at the pixel level. Second, all features are packed into “blocks” and delivered to “UniBagging,” which organizes base classifiers in separated sets according to the feature subspace, and conducts classification by integrating the results of many base classifiers. To evaluate the effectiveness of LandUTime, we performed a series of experiments on six Landsat bands. The results show marked improvements in both overall classification accuracy and Cohen’s kappa coefficient. LandUTime is particularly promising for distinguishing objects whose spectral characteristics show temporal differences, such as deciduous or evergreen forest and cultivated crops. [ABSTRACT FROM PUBLISHER]

Accurate determination of the specular point is important for simulating and processing reflected global navigation satellite system signals for remote sensing applications. Existing methods for determining the specular point are based on both spherical and ellipsoidal approximations of the Earth and employ either Snell's law or Fermat's principle to formulate the problem. By analysis and simulation, it is shown that these methods produce significant errors at intermediate latitudes. In this paper, a novel formulation for the solution of the specular point is proposed that satisfies Snell's Law on the WGS84 ellipsoid. The proposed method is compared with the existing methods for various receiver orbit configurations and algorithm augmentations. It is shown that the proposed method is more accurate than the existing methods and more computationally efficient than the minimum path length (MPL) method. Additionally, the resultant grazing angles, MPL method errors, and specular point locations as a function of the receiver orbit are investigated, leading to the finding that the likelihood of an error is significant for geometries favored by reflectometry applications. [ABSTRACT FROM PUBLISHER]

Land surface temperature and emissivity (LST&E) determine the total amount of upward long-wave infrared radiation emitted from the Earth's surface, making them key variables in a wide range of studies, including climate variability, land cover/use change, and the energy balance between the land and the atmosphere. LST&E products are currently produced on a routine basis using data from the MODIS instruments on the NASA EOS platforms and by the VIIRS instrument on the Suomi-NPP platform that serves as a bridge between NASA EOS and the next-generation JPSS platforms. Two new NASA LST&E products for MODIS (MxD21) and VIIRS (VNP21) will be produced during 2017 using a new approach that addresses discrepancies in accuracy and consistency between the current suite of MODIS and VIIRS LST split-window-based products. The new approach uses a temperature emissivity separation (TES) algorithm, originally developed for the ASTER instrument, to physically retrieve both LST and spectral emissivity consistently for both sensors with high accuracy and well-defined uncertainties. This study demonstrates continuity between the new MYD21 and VNP21 LST products at the <±0.5 K level, with differences that are invariant to environmental conditions and land cover type. Furthermore, MYD21 and VNP21 retrieved emissivities matched closely in magnitude and temporal variation to within 1%–2% over two land validation sites consisting of quartz sands and grassland. Continuity between the new suite of MODIS and VIIRS LST&E products will ensure a consistent and well-characterized long-term LST&E data record for better monitoring and understanding trends in Earth system behavior. [ABSTRACT FROM PUBLISHER]

The land surface temperature (LST) drives many terrestrial biophysical processes and varies rapidly in space and time primarily due to the earth’s diurnal and annual cycles. Models of the diurnal and annual LST cycle retrieved from satellite data can be reduced to several gap-free parameters that represent the surface’s thermal characteristics and provide a generalized characterization of the LST temporal dynamics. In this letter, we use such an approach to map Europe’s annual and diurnal LST dynamics. In particular, we reduce a five-year time series (2009–2013) of diurnal LST from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) to 48 sets of half-hourly annual cycle parameters (ACPs), namely, the mean annual LST, the yearly amplitude of LST, and the LST phase shift from the spring equinox. The derived data provide a complete representation of how mainland Europe responds to the heating of the sun and the nighttime LST decay and reveal how Europe’s biogeographic regions differ in that respect. We further argue that the SEVIRI ACP can provide an observation-based spatially consistent background for studying and characterizing the thermal behavior of the surface and also a data set to support climate classification at a finer spatial resolution. [ABSTRACT FROM PUBLISHER]

Evapotranspiration (ET) is a critical component of the earth's water budget, a critical modulator of land–atmosphere interactions, and also plays a crucial role in managing the earth's energy balance. In this study, the feasibility of generating spatially continuous daily evaporative fraction (EF) and ET from minimal remotely sensed and meteorological inputs in a trapezoidal framework is demonstrated. A total of four variables, normalized difference vegetation index, land surface temperature (T_s ), gridded daily average temperature (T_a ), and elevation (z) are required to estimate EF. Then, ET can be estimated with the available soil heat flux (G) and net radiation (Rn) data. First, the crucial model variable T_s - T_a is examined how well it characterizes the variation in EF using in-situ data recorded at two eddy correlation flux towers in Southern Great Plains, USA in 2011. Next, the accuracy of satellite-based T_s\ is compared to ground-based T_s. Finally, EF and ET estimates are validated. The results reveal that the model performed satisfactorily in modeling EF and ET variation at winter wheat and deciduous forest during the high evaporative months. Even though the model works best with the observed Moderate Resolution Imaging Spectroradiometer-T_s as opposed to temporally interpolated T_s, results obtained from interpolated T_s are able to close the gaps with reasonable accuracy. Due to the fact that T_s - T_a is not a good indicator of EF outside the growing season when deciduous forest is dormant, potential improvements to the model are proposed to improve accuracy in EF and ET estimates at the expense of adding more variables. [ABSTRACT FROM PUBLISHER]

In 2003, Landsat-7 Enhanced Thematic Mapper Plus encountered a defect in its Scan Line Corrector (SLC) mechanism resulting in the loss of about 22% pixels in each image. Thermal Infrared band is used in applications such as temperature estimation and requires a single source image for estimation of dead pixels. This analysis tests the efficacy of various Radar and Morphological filters in reconstruction of dead pixels and improving the quality of Landsat SLC-off image. Efficacy has been based on visual analysis, histogram, and image quality metrics, namely, histogram error, root mean square error, peak signal to noise ratio, and Structural SIMilarity. Of all the experimented filters, nonlinear filters—Median, Local Region, and Morphological perform better. However, Morphological filter is able to eliminate dead pixels using a 7 * 7 kernel window while Median filter is able to do so with a 29 * 29 window. All four functions—Open, Close, Erode, and Dilate of Morphological filter are able to suppress noise (fill dead pixel gaps) while preserving edges and details resulting in sharp output images. On the basis of image quality metrics, use of Open Morphological filter is suggested. [ABSTRACT FROM PUBLISHER]

Surface soil moisture (SSM) is a key state variable in controlling land surface energy balance and hydrological process. Based on the mechanism behind land surface temperature (LST)–vegetation index (VI) triangle space, an empirical relationship model has been proposed for SSM estimation with LST, NDVI, and surface albedo, and it has been applied in downscaling the coarse resolution microwave soil moisture product. In this paper, three soil moisture observation networks (REMEDHUS, MAQU, and MURRUMBIDGEE) were selected to evaluate the performance of this model at different climate and land cover conditions with in situ soil moisture measurements and Landsat satellite observations. According to the estimation results from different days for each network, it was found that the model was able to capture SSM variation with a satisfied accuracy [overall root-mean-squared error (RMSE) ranging from 0.025 to 0.055 m3/m3], and the R2 can reach 0.9 on some individual days. However, the performance has high daily variability with some poor ones. The reason is partly attributed to the high sensitivity of the coefficients of the model to the variation degrees of the input LST, normalized difference vegetation index (NDVI), and SSM. Meanwhile, the spatial scale differences between the point measurement and satellite footprint observation are another important issue. To improve the model performance, a new relationship model was proposed by introducing the modified normalized difference water index, and the estimation results had a pronounced improvement (overall RMSE ranging from 0.021 to 0.049 m3/m3) compared with the previous model. The application effect of the proposed model showed that the model coefficient calibration accuracy greatly determined the uncertainty level of the estimation results. [ABSTRACT FROM PUBLISHER]