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Downward shortwave radiation (DSR) plays a very important role in land surface radiation budget and land-surface processes modeling. Although there are several radiation products developed based on satellite observations, the coarse spatial resolution greatly limits their applications in regional or local scales. To get high-resolution and accuracy-reliable DSR data, a practical downscaling method for clear-sky condition was proposed by using the scale-invariant relationship of the radiative transfer process to decompose the global radiation into direct and diffuse components at horizontal level and conducting topographic correction finally. Based on this method, the time series of Meteosat Second Generation (MSG) DSR product covering part of Navarre province in the northern Spain was disaggregated into 30-m level with the use of the ALOS World 3D-30m digital elevation model (DEM) data. The downscaled results not only presented high spatial heterogeneity with respect to the changes in surface topography but also showed reasonable values at different times over different days in one year. The in situ validation indicated that the hourly downscaled DSR had quite high correlation with the surface measurements at each day with the coefficient of determination above 0.97 and the root-mean-squared error lower than 35 W/m2. Overall, the evaluation allows concluding on the proposed method that can be a good way to get reliable and high-resolution DSR data from coarse-resolution DSR product under clear-sky condition. [ABSTRACT FROM AUTHOR]

Evaluation of global gridded precipitation datasets typically entails using the in situ or satellite-based data used to derive them, so that out-of-sample testing is usually not possible. Here we detail a methodology that incorporates the physical balance constraints of the surface water and energy budgets to evaluate gridded precipitation estimates, providing the capacity for out-of-sample testing. Performance conclusions are determined by the ability of precipitation products to achieve closure of the linked budgets using adjustments that are within their prescribed uncertainty bounds. We evaluate and compare five global gridded precipitation datasets: IMERG, GPCP, GPCC, REGEN, and MERRA-2. At the spatial level, we show that precipitation is best estimated by GPCC over the high latitudes, by GPCP over the tropics, and by REGEN over North Africa and the Middle East. IMERG and REGEN appear best over Australia and South Asia. Furthermore, our results give insight into the adequacy of prescribed uncertainties of these products and shows that MERRA-2, while being less competent than the other four products in estimating precipitation, has the best representation of uncertainties in its precipitation estimates. The spatial extent of our results is not only limited to grid cells with in situ observations. Therefore, the approach enables a robust evaluation of precipitation estimates and goes some way to addressing the challenge of validation over observation scarce regions. [ABSTRACT FROM AUTHOR]

In this study we derive the environmental lapse rate (ELR) from vertical profiles of temperature in the lower troposphere, applying it to downscale air temperature of the new European Centre For Medium‐Range Weather Forecasts (ECMWF) reanalysis ERA5, which replaces ERA‐Interim (ERAI). We focus over the western U.S. region, a data‐rich area with observations of daily maximum and minimum temperature (Global Historical Climatology Network) and snow depth and soil temperature. Observations indicate an ELR of −4.5 K·km−1 in the region, lower than the commonly used −6.5 K·km−1. ERA5 ELR agrees with the observational estimates, with some overestimation in winter and limitations in the diurnal variability. The elevation correction of ERA5 temperature using different ELR showed the benefits of deriving ELR fields from ERA5 vertical profiles, when compared with a constant ELR. Simulations with the ECMWF land surface model, at 9‐km resolution, driven by ERA5 using different ELR corrections showed the added value of the methodology, but the impact of different ELR corrections is limited. However, the validity of the downscaling method in reducing temperature to station altitude suggests that there is sufficient generality for application at kilometer and subkilometer resolutions. By comparing the estimated representativity errors of observations with reanalysis, the improvements from ERAI to ERA5 are mainly visible in the random component of the error. Large systematic biases remain, which require further attention from the modeling and data assimilation, and limit the potential benefits of ELR corrections. Key Points: Environmental lapse rate derived from atmospheric reanalysis vertical profiles agrees with observational estimatesSurface downscaling outperforms ERA5, but the impact of different ELR corrections to the driving data is reducedSystematic biases in ERA5 near‐surface temperature require further efforts from modeling and data assimilation [ABSTRACT FROM AUTHOR]

We present a new algorithm to derive smoke plume height ($\text{H}^{\mathrm {a}}$) using the thermal contrast from the rising mixture of aerosol and emitted gases in the Moderate Resolution Imaging Spectroradiometer (MODIS) 11- $\mu \text{m}$ channel. Validation shows good agreement with the wind-corrected Multi-angle Imaging SpectroRadiometer (MISR)-MISR Interactive Explorer (MINX) values, with about 60% of the MODIS Terra thermal retrievals within 500 m of MISR $\text{H}^{\mathrm {a}}$ and 450 m lower on average. The bias is expected because the thermal technique represents an effective rather than a top plume height from MISR MINX. The comparison of MODIS Aqua retrievals with Cloud-Aerosol LiDAR with Orthogonal Polarization (CALIOP) Cloud-Aerosol LiDAR and Infrared Pathfinder Satellite Observations (CALIPSO) shows similar statistics, with a standard deviation of 458 m for the mean plume height and 216 m lower on average. $\text{H}^{\mathrm {a}}$ is part of the Multi-Angle Implementation of Atmospheric Correction (MAIAC) MODIS Collection 6 suite of products (MCD19), accessible via the Land Product Distributed Active Archive Center (LP DAAC). Aerosol injection height is reported in the daily MAIAC atmospheric product MCD19A2 along with the cloud mask (CM), column water vapor, aerosol optical depth (AOD), AOD uncertainty, and aerosol type, at 1-km resolution on a global sinusoidal grid. Despite some limitations, the vastly increased coverage from MODIS observations makes it a valuable data set complementing the established MISR and CALIOP products. [ABSTRACT FROM AUTHOR]

The performance of the Weather Research and Forecasting (WRF) Model is evaluated in predicting the meteorological conditions over a complex open-pit mining facility in northern Canada in support of more accurate operational reporting of area-fugitive greenhouse gas emission fluxes from such facilities. WRF is studied in a series of sensitivity tests by varying topography, land use, and horizontal and vertical grid spacings to arrive at optimum configurations for reducing modeling biases in comparison with field meteorological observations. Overall, WRF shows a better performance when accounting for the mine topography and modified land use. As a result, the model biases reduce from 1.10 to 0.08 m s−1, from 1.04 to 0.50 m s−1, from 0.98 to 0.32 K, and from 45.7 to 17.3 W m−2, for near-surface wind speed, boundary layer wind speed, near-surface potential temperature, and turbulent sensible heat flux, respectively. Refining the model horizontal and vertical grid spacings results in bias reductions from 3.31 to 0.08 and from 0.80 to −0.11 m s−1 for near-surface and boundary layer wind speeds, respectively. The simulation results also agree with previous observations of meteorological effects on enclosed Earth depressions, characterized by formation of a cool pool of air, reduced wind speeds, and horizontal wind circulations at the bottom of the depression under thermally stable conditions. The results suggest that such configurations for WRF are necessary to arrive at more accurate meteorological predictions over complex open-pit mining terrains with similar features. [ABSTRACT FROM AUTHOR]

During the saline-alkali land improvement process, the soil conductivities throughout the whole land area are usually investigated in advance to grade the soil salinization. As a new soil investigating technique, ground penetrating radar (GPR) has been widely used in geographical and agricultural applications. Nevertheless, there are still challenges in applying GPR to saline-alkali soil conductivity predictions, because it is very difficult to separate the direct path wave (DPW) and the land surface reflecting echoes under near-field condition. To realize fast soil conductivity predicting, a novel waveform correlation analyzing method based on the pulsed GPR technique is proposed in this article. By this method, the saline-alkali soil conductivities can be estimated directly by waveform comparison instead of complex equation solving. Experiments are performed on specific saline-alkali land with different surface morphologies located in the western Jilin province, northeast China. An empirical relationship between GPR echoes and the saline-alkali soil conductivities is established by the measurements in the first survey line. Then, this empirical formula is used to predict the soil conductivities in the second survey line to verify the methodology. Analyzing results show that the estimated conductivities are consistent with the WET (water content, electrical conductivity, and temperature) sensor measurements and the soil sample measuring results. Based on the nondestructive GPR technique, the proposed method can provide a fast and efficient way to estimate the conductivities of the saline-alkali land. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) and its diurnal variation are the critical factors in many aspects of climate study, surface energy balance, and environmental applications. Several satellite-based LST products are available for retrievals from regional to a global scale. However, due to the differences in sensor configurations and retrieval algorithms, these products may not necessarily be consistent. In this letter, the consistency of spatial and temporal skin temperature variations from two infrared satellite platforms has been evaluated over the contiguous United States (CONUS). Comparisons are made between the LST products from the newly launched Geostationary Operation Environmental Satellite R Series (GOES-R) advanced baseline imager (ABI) and the Moderate Resolution Imaging Spectroradiometer (MODIS) on both Aqua and Terra satellites which are polar orbiting. Overall, both products show a general agreement in their diurnal variations with differences mostly under 2 K. However, a temperature-dependent inconsistency has been detected. The MODIS LST product seems to estimate higher temperatures in the summer months while the GOES product estimates higher temperatures during the winter months. Moreover, the maximum observed diurnal differences could reach up to 10 K in mountainous regions. The results suggest that the corresponding temperature differences should be accounted for when LST diurnal variations are compared or generated from satellite observations. [ABSTRACT FROM AUTHOR]

Significant summer land surface warming has been observed in the middle latitudes over East Asia, especially after the mid-1990s, which has evidently affected the East Asian weather and climate. Using multisource observations and reanalysis data during 1979–2013, this study explores the possible reasons for recent land surface warming over this region by considering atmospheric forcing and regional land–atmosphere interaction related to extratropical cyclones (ECs). Results show that there is a close relationship between land surface warming and weakened ECs over East Asia. Recent land surface warming was attributed to local atmospheric forcing and feedback of land–atmosphere interaction associated with weakened ECs. The abnormal large-scale circulation associated with anomalous ECs produced evident dynamic forcing on the land surface. Weakened ECs are usually accompanied by an abnormal high pressure system and anticyclonic circulation around Lake Baikal, which benefit the intensification of anomalous southerly wind in the rear of the anomalous anticyclone, leading to positive temperature advection and temperature increase over East Asia. Meanwhile, the anomalous adiabatic warming caused by abnormal descending motion associated with the anticyclonic anomaly also contributes to local warming. The feedback of local land–atmosphere interaction plays an important role in land surface warming. Weakened ECs increase both incident solar radiation and precipitation. The increased precipitation reduces the soil moisture and in turn weakens the surface evaporation and local cooling effect, resulting in land surface warming. Our findings are helpful for better understanding the mechanisms responsible for recent summer land surface warming over East Asia as well as its climatic effects. [ABSTRACT FROM AUTHOR]

Accurate estimates of terrestrial water and energy cycle components are needed to better understand climate processes and improve models' ability to simulate future change. Various observational estimates are available for the individual budget terms; however, these typically show inconsistencies when combined in a budget. In this work, a Conserving Land–Atmosphere Synthesis Suite (CLASS) of estimates of simultaneously balanced surface water and energy budget components is developed. Individual CLASS variable datasets, where possible, 1) combine a range of existing variable product estimates, and hence overcome the limitations of estimates from a single source; 2) are observationally constrained with in situ measurements; 3) have uncertainty estimates that are consistent with their agreement with in situ observations; and 4) are consistent with each other by being able to solve the water and energy budgets simultaneously. First, available datasets of a budget variable are merged by implementing a weighting method that accounts both for the ability of datasets to match in situ measurements and the error covariance between datasets. Then, the budget terms are adjusted by applying an objective variational data assimilation technique (DAT) that enforces the simultaneous closure of the surface water and energy budgets linked through the equivalence of evapotranspiration and latent heat. Comparing component estimates before and after applying the DAT against in situ measurements of energy fluxes and streamflow showed that modified estimates agree better with in situ observations across various metrics, but also revealed some inconsistencies between water budget terms in June over the higher latitudes. CLASS variable estimates are freely available via https://doi.org/10.25914/5c872258dc183. [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]

In this letter, the National Oceanic and Atmospheric Administration Joint Polar Satellite System enterprise algorithm and the quadratic split-window (SW) algorithm were adapted to high spatial resolution thermal infrared (TIR) data of Chinese Gaofen-5 (GF5) to estimate the land surface temperature (LST) and sea surface temperature (SST), respectively. Lacking official calibration coefficients, GF5 TIR data were cross-calibrated by the well-characterized Visible Infrared Imaging Radiometer Suite (VIIRS) data. The coefficients of two SW algorithms were obtained by linear regression from the simulated data set generated via comprehensive radiative transfer modeling. The performance of the two algorithms was first evaluated by independent simulation data and then cross-validated by Moderate Resolution Imaging Spectroradiometer (MODIS) LST/SST, VIIRS LST/SST, and Advanced Himawari Imager (AHI) SST products. The preliminary results show good agreement between estimated GF5 LSTs/SSTs and referenced LST/SST products, with an average bias (root mean square error) of −0.26 (1.74), −2.48 (3.49), 0.18 (2.43), and −1.47 K (2.86 K) for VLSTO, VNP21, MYD11, and MYD21 LST products, −0.79 (1.55), −0.28(1.58), and −1.71 (2.21) K for VIIRS, MODIS, and AHI SST products. This is the first time that both LST and SST are retrieved from the real GF5 data. This letter provides a practical method to estimate LST and SST from Chinese Gaofen-5. [ABSTRACT FROM AUTHOR]

Many remote sensing soil moisture (SM) products have been developed with global coverage. However, most of them are derived from passive microwave observations with very coarse resolution, greatly constraining the applications at regional scales. To increase the spatial resolution, a downscaling method is developed to downscale the 36-km Soil Moisture Active Passive L3 SM (SMAP SM) product to 1 km using the Moderate Resolution Imaging Spectroradiometer (MODIS) products (8-d land surface temperature, LST, and 16-d normalized difference vegetation index, NDVI). In this method, a linking model is first established between SM and LST and NDVI, and a self-adaptive window method is applied with the use of the geographically weighted regression (GWR) method to obtain an optimal local regression. Then, the uncertainty of the linking model, expressed as the regression residual, is redistributed to fine-resolution pixels to analyze the consistency before and after downscaling. The method was applied to the Iberian Peninsula to produce the 8-d downscaled SM product in 2016. The downscaled SM was validated with the in-situ SM network (REMEDHUS). A good agreement was found between the two data sets, with a correlation coefficient ($R$) of 0.87 and an unbiased root-mean-squared error (ubRMSE) of 0.043 m3/m3 at a network level. At station level, the $R$ is larger than 0.6 for all the REMEDHUS stations, with an ubRMSE smaller than 0.06 m3/m3. The evaluation indicates the good potential of the proposed method in the SM downscaling, which achieves a robust consistency and provides rich spatial information while maintaining good accuracy. [ABSTRACT FROM AUTHOR]

A generalized implementation of the complementary principle was applied to estimate global land surface evaporation and its spatial distribution. The single parameter in the method was calibrated as a function of aridity index, mainly on the basis of runoff and precipitation data for 524 catchments in different parts of the world. The spatial distribution of annual evaporation from Earth's land surfaces for 2001–13 was then calculated at a spatial resolution of 0.5°, by means of an available global net radiation dataset (commonly referred to as CERES SYN1deg-Day) and a global forcing dataset (referred to as CRU-NCEP v7) for near-surface temperature, humidity, wind speed, and air pressure. The results are shown to agree with reliable previous estimates by more elaborate methods. The global average evaporation for 2001–13 was found to be 472.65 mm a−1 or 36.96 W m−2. The present method should allow not only future updates but also retroactive historical analyses with routine data of net radiation, near-surface air temperature, humidity, wind speed, and precipitation; its main advantage is that the environmental aridity is deduced from atmospheric conditions and requires no knowledge of surface characteristics, such as soil moisture, vegetation, and terrain, which are highly variable and often difficult to quantify at larger spatial scales. Because they are strictly measurement based, the results can serve also as a reality check for different aspects of climate and related models. [ABSTRACT FROM AUTHOR]

Summary: Many plant phenological events are sensitive to temperature, leading to changes in the seasonal cycle of ecosystem function as the climate warms. To evaluate the current and future implications of temperature changes for plant phenology, researchers commonly use a metric of temperature sensitivity, which quantifies the change in phenology per degree change in temperature.Here, we examine the temperature sensitivity of phenology, and highlight conditions under which the widely used days‐per‐degree sensitivity approach is subject to methodological issues that can generate misleading results. We identify several factors, in particular the length of the period over which temperature is integrated, and changes in the statistical characteristics of the integrated temperature, that can affect the estimated apparent sensitivity to temperature.We show how the resulting artifacts can lead to spurious differences in apparent temperature sensitivity and artificial spatial gradients. Such issues are rarely considered in analyses of the temperature sensitivity of phenology.Given the issues identified, we advocate for process‐oriented modelling approaches, informed by observations and with fully characterised uncertainties, as a more robust alternative to the simple days‐per‐degree temperature sensitivity metric. We also suggest approaches to minimise and assess spurious influences in the days‐per‐degree metric. [ABSTRACT FROM AUTHOR]

Land surface emissivity (LSE) is a key parameter for the determination of land surface temperature (LST) from thermal remotely sensed data. A new LSE product has been developed at the National Oceanic and Atmospheric Administration (NOAA), College Park, MD, USA, to enhance the LST product for the Joint Polar Satellite System (JPSS) and the Geostationary Operational Environmental Satellite R-Series (GOES-R) missions as well as to support the forecasting models. A 1-km resolution bare ground emissivity was derived from the historical Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Emissivity Dataset (ASTER-GED) and Moderate Resolution Imaging Spectroradiometer (MODIS) LSE product. It is then spectrally adjusted to the split window (SW) channels of Visible/Infrared Imager Radiometer Suite (VIIRS) onboard JPSS and Advanced Baseline Imager (ABI) from GOES-R. VIIRS daily green vegetation fraction and snow fraction products are subsequently used to account for the dynamic variations. Validation results indicate that the product has a good agreement with in situ observations, with an emissivity difference less than 0.006 at the bare surface sites and a difference less than 0.007 at one cropland site with three growth stages. The intercomparison with NASA VIIRS daily LSE product proves a good agreement with a standard deviation of less than 0.012. To evaluate the LSE performance in LST retrieval, a whole year VIIRS LST is produced and validated over the SURFace RADiation budget observing network (SURFRAD) sites. The results indicate that the LSE works well with LST RMSE of 1.78 (daytime) and 1.58 K (nighttime). The new LSE algorithm has been integrated to NOAA-20 LST with the status of provisional maturity and will be applied on GOES-16/17 LST product in the near future. [ABSTRACT FROM AUTHOR]

Determining the land surface temperature (LST) variations in urban surfaces using high spatiotemporal resolution thermal infrared data is necessary for urban microthermal environmental modeling. An unmanned aerial vehicle (UAV) equipped with a thermal infrared imager was used to monitor the high-precision spatiotemporal variation of LSTs for six typical urban surfaces, including water, tree canopy, grass, brick pavement, asphalt pavement, and marble pavement, in the Jiangning district, Nanjing. An estimation of sensible heat release was further conducted to quantitatively describe the fine characteristics of the LSTs of these surfaces. The factors influencing LST were investigated by correlating LSTs with the meteorological factors. The results showed that there were significant differences in LSTs and sensible heat release for six typical urban surfaces in sunny and cloudy days, respectively. On sunny days, the diurnal variations in different surfaces fluctuated substantially, which was not evident on cloudy days. The artificial surfaces always represented as the main source of sensible heat release. Solar radiation and air temperature, among the meteorological factors, were positively correlated with the LSTs of the surfaces. The air humidity was negatively correlated with the LSTs and played a role in cooling the land surface. [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]

Sensor-observed energy from adjacent pixels, known as the adjacency effect, influences land surface reflectivity retrieval accuracy in optical remote sensing. As the spatial resolution of thermal infrared (TIR) images increases, the adjacency effect may influence land surface temperature (LST) retrieval accuracy in TIR remote sensing. However, to our knowledge, few studies have focused on quantifying this adjacency effect on TIR measurements. In this study, a forward adjacency effect radiative transfer model (FAERTM) was developed to quantify the adjacency effect on high-spatial-resolution TIR measurements. The model was verified to be in good agreement with moderate resolution atmospheric transmission (MODTRAN) code, with a discrepancy < 0.15 K. The adjacency effect on target pixel observations was found to be negligible beyond 3 km from the line of sight. Variations in aerosol type only slightly influenced adjacency effect magnitude. However, the adjacency effect quickly increased with increasing image spatial resolution, adjacent pixel temperature, and aerosol density. According to simulation results, the adjacency effect can be > 3 K in some cases. These findings indicate that the adjacency effect should be considered when retrieving LSTs from TIR measurements, at least in some specific conditions. The proposed FAERTM provides a useful model for quantifying and addressing the adjacency effect on TIR measurements. [ABSTRACT FROM AUTHOR]

Current global leaf area index (LAI) products are generally produced from single-temporal satellite observations acquired by a single sensor. These LAI products are usually spatiotemporally discontinuous and inaccurate for some vegetation types in many areas, which limit the applications of these LAI products to the understanding of land dynamics. In this paper, a new data assimilation method was proposed to estimate multiscale and temporally continuous LAI values from multi-sensor time-series satellite observations with different spatial resolutions. An ensemble multiscale tree (EnMsT) was used to establish the conversion relationships between different spatial resolution LAI values, and dynamic models of the LAI at different spatial scales were constructed to evolve LAI at the corresponding spatial scales over time. At each time step, a multiscale Kalman filter (MKF) was introduced to fuse the predicted LAI values from the dynamic models at different spatial scales and to construct a forecasted EnMsT. When satellite observations were available, an ensemble multiscale filter (EnMsF) technique was applied to update the LAI values at each node of the EnMsT. The method was applied to estimate temporally continuous multiscale LAI values from the time series of Thematic Mapper (TM) or Enhanced Thematic Mapper Plus (ETM+) surface reflectance data and Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data at several sites with different vegetation types. The estimated multiscale LAI values were compared with the MODIS and GEOV2 LAI products, and the reference LAI values at the corresponding scales aggregated from the high-resolution LAI surface images. The estimated LAI values with the finest spatial resolution were also validated by ground measurements from the selected sites. The results show that the new method is able to simultaneously estimate temporally continuous multiscale LAI values by assimilating satellite observations with different spatial resolutions, and the estimated multiscale LAI values are well consistent with the reference LAI values at the corresponding scales over the selected sites. The root-mean-square error (RMSE) and coefficient of determination of the retrieved LAI values at the finest spatial scale against the ground measurements over the selected sites are 0.539 and 0.788, respectively. [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]

This study evaluates the potential impact of clouds on ecosystem CO2 and CO2 isotope fluxes (“isofluxes”) in two contrasting ecosystems (a broadleaf deciduous forest and a C4 grassland) in a region for which cloud cover, meteorological, and isotope data are available for driving the isotope-enabled land surface model (ISOLSM). Our model results indicate a large impact of clouds on ecosystem CO2 fluxes and isofluxes. Despite lower irradiance on partly cloudy and cloudy days, predicted forest canopy photosynthesis was substantially higher than on clear, sunny days, and the highest carbon uptake was achieved on the cloudiest day. This effect was driven by a large increase in light-limited shade leaf photosynthesis following an increase in the diffuse fraction of irradiance. Photosynthetic isofluxes, by contrast, were largest on partly cloudy days, as leaf water isotopic composition was only slightly depleted and photosynthesis was enhanced, as compared to adjacent clear-sky days. On the cloudiest day, the forest exhibited intermediate isofluxes: although photosynthesis was highest on this day, leaf-to-atmosphere isofluxes were reduced from a feedback of transpiration on canopy relative humidity and leaf water. Photosynthesis and isofluxes were both reduced in the C4 grass canopy with increasing cloud cover and diffuse fraction as a result of near-constant light limitation of photosynthesis. These results suggest that some of the unexplained variation in global mean δ 18O of CO2 may be driven by large-scale changes in clouds and aerosols and their impacts on diffuse radiation, photosynthesis, and relative humidity.

Using internally consistent albedo, aerosol, cloud, and surface data from the Multiangle Imaging Spectroradiometer (MISR) instrument onboard the Terra satellite, top-of-atmosphere (TOA) spectral albedo change (dα) in the presence of aerosols over land is estimated and its dependence on aerosol and surface properties is analyzed. Linear regressions between spectral TOA albedo and aerosol optical depth (AOD) for different surface types are examined to derive the aerosol-free TOA albedo. MISR surface BiHemispherical Reflectance (BHR) values are used to differentiate surface types. We find relatively high correlations between spectral TOA albedo and AOD for BHR-stratified data in 2° × 2° grid cells. The global mean values of cloud-free dα over land for June–September 2007 are estimated to be 0.018 ± 0.003 (blue), 0.010 ± 0.003 (green), 0.007 ± 0.003 (red), and 0.008 ± 0.006 (near-infrared). Individual regions show large variations from these values. Global patterns of dα are determined mainly by AOD and aerosol radiative efficiency. Large positive values of dα are observed over regions with high aerosol loading and large single-scattering albedo, where the aerosol scattering effect is dominant. The presence of light-absorbing aerosols reduces aerosol radiative efficiency and dα. Surface reflectance influences both aerosol scattering and absorbing effects. Generally, the aerosol radiative efficiency decreases with increasing BHR. We also examined dα-AOD correlations over different vegetation types. We find the smallest dα values are over needleleaf forests and shrublands, whereas the largest values are over cropland and barren regions. The aerosol radiative efficiencies are lowest over needleleaf forests and barren regions and highest over grasslands and croplands.

A statistical–dynamical study was performed on the role of hydrometeorological interactions in the midlatitudes and the semiarid Tropics. For this, observations from two field experiments, the First International Satellite Land Surface Climatology Project Field Experiment (FIFE) and the Hydrological Atmospheric Pilot Experiment (HAPEX)– Sahel, representative of the midlatitudes and the semiarid tropical conditions, and simulated results from a land surface model, Simplified Simple Biosphere (SSiB) model were statistically analyzed for direct and interaction effects. The study objectives were to test the hypothesis that there are significant differences in the land surface processes in the semiarid tropical and midlatitudinal regimes and to identify the nature of the differences in the evapotranspiration exchanges for the two biogeographical domains. Results suggest there are similarities in the direct responses but the interactions or the indirect feedback pathways could be very different. The arid tropical regimes are dominated through vegetative pathways (via variables such leaf area index, stomatal resistance, and vegetal cover); the midlatitudes show soil wetness (moisture)–related feedback. In addition, for the midlatitudinal case, the vegetation and the soil surface acted in unison, leading to more interactive exchanges between the vegetation and the soil surface. The water-stressed semiarid tropical surface, on the other hand, showed response either directly between the vegetation and the atmosphere or between the soil and the atmosphere with very little interaction between the vegetation and the soil variables. Thus, the semiarid Tropics would require explicit bare ground and vegetation fluxes consideration, whereas the effective (combined vegetation and soil fluxes) surface representation used in various models may be more valid for the midlatitudinal case. This result also implied that with higher resource (water) availability the surface invested more in the surrounding environment. On the other hand, with poor resource availability (such as water stress in the tropical site), the surface components retain individual resources without sharing.

Different surface covers such as soil, rock, plant and etc, lead to be created different climate conditions in small scales by impacting on the adjacent atmospheric elements. The greatest difference tangibly in land surfaces and air temperatures were observed in arid and desert regions on hot days, which can have significant microclimate and ecological effects on environment. In this study, 8 different land surface cover including, green plant cover, dead plant cover, arable soil, cement surface, non-arable soil, pavement, asphalt and dark metal surface were measured using two types of mercury and digital thermometers to compare between the measured temperature at the Yazd meteorological station and the temperature of different land surfaces, in June and July, 2016. Then, the land surface temperatures were compared to each other and also compared with the air temperature measured at the Yazd meteorological station. Based on the results, the highest measured temperature reaches to above 70 0C in asphalt cover and the lowest surface temperature of 47.4 0C were recorded in plant surface. According to the result of Kolmogorov Smirnov test, there are a significant difference between the different land surfaces and the air temperatures (R2= 0.86) at 5% level during the measured period. Generally, asphalt and plant surfaces showed the highest (230C) and the lowest (50C) thermal differences with air temperature. Concluding, different land surfaces show different thermal patterns.

The interferometric imaging radar altimeter (InIRA) on-board Chinese Tiangong-2 space laboratory is a Ku-band interferometric radar whose major aim is to measure the wide-swath ocean topography. Unlike traditional interferometric synthetic aperture radar (InSAR) aiming at measuring digital elevation model of land surface, Tiangong-2 InIRA adopts small incidence angles and short baseline to get strong and high coherency reflections from ocean surface. The interferometric phase noise is a notable source of errors which limits the accuracy of the ocean height measurements. Compared with traditional InSAR, the height error of Tiangong-2 InIRA shows different distribution pattern due to the absolutely small but relatively large variation incidence angles. This study gives a filtering method for interferometric phase generated with small incidence angles and short baseline. Non-uniform windows are selected according to the relation between the coherence, imaging geometry, and the height variation. Results on Tiangong-2 InIRA data are analysed by comparing the non-uniform filtering method with traditional multi-looking method.

Changes in atmospheric water vapor mainly occur in the atmospheric boundary layer. However, due to many factors, such as orography and ground thermal dynamic conditions, the change trends and transformation law of atmospheric water vapor contents above different surfaces are still unclear. In this work, a Doppler weather radar with high spatial-temporal resolution was used to monitor the variations and transformations of water vapor contents over different land surfaces for two years. The results show that the atmospheric water vapor content shows a very good positive correlation with elevation at altitudes between 600 m and 1200 m, while different land surfaces have delicate impacts on atmospheric water vapor contents, such as extreme values appearing above impervious urban surfaces, uniform distributions appearing over water body and vegetated surfaces being wet but avoiding extreme conditions. Compared with previous studies, the results and conclusions of this study are mainly derived from accurate direct observations based on high-resolution radar. Identifying the distribution and transformation of water vapor over different surfaces can enhance our understanding of the movement and variation of atmospheric water vapor over complex terrain and different land surfaces, and improve the planning and construction capacity of different surfaces, such that humankind can mitigate the severe disasters caused by drastic changes in atmospheric water vapor.

Many models used to represent the boundary condition for the separation of atmospheric scattering from the surface reflection in polarized remote sensing measurements, assuming that the polarized surface reflectance is spectrally neutral. To test this hypothesis, referred to the spectral invariance hypothesis, JPL’s Ground-based Multiangle SpectroPolarimetric Imager (GroundMSPI) was used to measure the polarized bidirectional reflectance factors (pBRFs) of different types of outdoor surfaces. GroundMSPI measures the linear polarization Stokes parameters ($I$ , $Q$ , and $U$) at three wavebands centered at 470, 660, and 865 nm. Images of pBRF were acquired at the three wavelengths and the best-fitting slopes of pairwise spectral regressions were determined. The spectral invariance hypothesis predicts that these slopes should be unity. All region types, excluding grass, meet this criterion within 8% for all three wavelengths. Grass measurements show a large mean deviation of 31.1% from the expected slope when regressing 865-nm data against other wavelengths due to the differences in the spectral absorbance of chlorophyll. Angle of linear polarization (AoLP) analysis of cotton crops is presented as a method to isolate cases where the scattered light is dominated by single reflection within the plants from cases where multiple scattering plays a larger role. Spectral invariance is observed in those cases where specular reflection from the top surfaces of the leaves is primarily responsible for the measured polarization. [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]

The land parameter retrieval model (LPRM) has been widely used for the retrieval of microwave-based global surface soil moisture (SSM). In the original LPRM algorithm, soil surface effective temperature (SSET) was retrieved from the Ka-band (36.5 GHz) brightness temperature (BT) and then used as input data for the estimation of SSM. In this study, SSET retrieved from the Advanced Microwave Scanning Radiometer of the Earth Observing System Radiometer (AMSR-E) Ka-band BT was validated against MODIS land surface temperature (LST) data, and the results show that when the fraction of water surface (FWS) within an AMSR-E pixel increases in the range from 0–0.01 to 0.15–0.4, the root-mean-square error (RMSE) increases drastically from 1.98 to 11.42 Kelvin (K). When validation was conducted on SSET data retrieved with the K-band (18.7 and 23.8 GHz) BT, the RMSEs were maintained below 2.0 K for FWS ranging from 0 to 0.4. Therefore, in the original LPRM, we substituted the Ka-band BT with the K-band BT to produce more accurate estimations of SSET and hence improved SSM retrievals. Our results show that for FWS ranges of 0–0.01, 0.01–0.05, 0.05–0.15, and 0.15–0.4, the correlation coefficients ($R$ -values) between SSM retrieved with the improved LPRM and Global Land Data Assimilation System (GLDAS) measurements are 0.78, 0.75, 0.77, and 0.46, respectively, compared with the corresponding $R$ -values of 0.78, 0.65, 0.56, and 0.34 for SSM retrieved with the original LPRM. This demonstrates that against the original LPRM, our improved LPRM can produce more accurate SSM retrievals under water–land mixed pixels. [ABSTRACT FROM AUTHOR]

The interferometric imaging radar altimeter (InIRA) on-board Chinese Tiangong-2 space laboratory is a Ku-band interferometric radar whose major aim is to measure the wide-swath ocean topography. Unlike traditional interferometric synthetic aperture radar (InSAR) aiming at measuring digital elevation model of land surface, Tiangong-2 InIRA adopts small incidence angles and short baseline to get strong and high coherency reflections from ocean surface. The interferometric phase noise is a notable source of errors which limits the accuracy of the ocean height measurements. Compared with traditional InSAR, the height error of Tiangong-2 InIRA shows different distribution pattern due to the absolutely small but relatively large variation incidence angles. This study gives a filtering method for interferometric phase generated with small incidence angles and short baseline. Non-uniform windows are selected according to the relation between the coherence, imaging geometry, and the height variation. Results on Tiangong-2 InIRA data are analysed by comparing the non-uniform filtering method with traditional multi-looking method. [ABSTRACT FROM AUTHOR]

This letter presents an experiment to explore the nonisothermal effects on temperature and emissivity separation (TES). The innovation of this experiment lies in its design, which highlights the contrast between isothermal and nonisothermal conditions in emissivity measurements. We artificially created a sharply contrasting nonisothermal soil surface using liquid nitrogen cooling and solar heating. The iterative spectrally smooth TES (ISSTES) algorithm was used to process the experimental data. The analyzed results of the experimental data show that the nonisothermal conditions have a significant effect on TES. The bias of retrieved emissivity increases with the component temperature difference as well as with wavelength. The bias around the split window band can reach up to 1% when the difference of the component temperature is 40K. Considering that 1% error in emissivity can cause approximately 1K error of retrieved land surface temperature (LST), the nonisothermal effects on emissivity cannot be ignored. We hope that this experiment will arouse attention of the nonisothermal effects on TES and call for more efforts to be devoted to this issue in the future. [ABSTRACT FROM AUTHOR]

This paper presents a study on long-term surface solar radiation (SSR) changes over China under clear- and all-sky conditions and analyzes the causes of the "dimming" and "brightening." To eliminate the nonclimatic signals in the historical records, the daily SSR dataset was first homogenized using quantile-matching (QM) adjustment. The results reveal rapid dimming before 2000 not only under all-sky conditions, but also under clear-sky conditions, at a decline rate of −9.7 ± 0.4 W m−2 decade−1 (1958–99). This is slightly stronger than that under all-sky conditions at −7.4 ± 0.4 W m−2 decade−1, since the clear-sky dimming stopped 15 years later. A rapid "wettening" of about 40-Pa surface water vapor pressure (SWVP) from 1985 to 2000 was found over China. It contributed 2.2% to the SSR decline under clear-sky conditions during the whole dimming period (1958–99). Therefore, water vapor cannot be the main cause of the long-term dimming in China. After a stable decade (1999–2008), an intensive brightening appeared under the clear-sky conditions at a rate of 10.6 ± 2.0 W m−2 decade−1, whereas a much weaker brightening (−0.8 ± 3.1 W m−2 decade−1) has been observed under all-sky conditions between 2008 and 2016. The remarkable divergence between clear- and all-sky trends in recent decades indicates that the clouds played two opposite roles in the SSR changes during the past 30 years, by compensating for the declining SSR under the cloud-free conditions in 1985–99 and by counteracting the increasing SSR under cloud-free conditions in 2008–16. Aerosols remain as the main cause of dimming and brightening over China in the last 60 years, although the clouds counteract the effects of aerosols after 2000. [ABSTRACT FROM AUTHOR]

In this paper, the spatio-temporal variations of the urban heat island (UHI) in Hangzhou are analyzed using multisource remote sensing data. The annual human settlement index (HSI) was derived from the annual MODIS NDVI and DMSP-OLS datasets from 2000 through 2013. The spatio-temporal patterns of HSI as well as the longitudinal variations of land surface temperature (LST) versus HSI were analyzed. The surface urban heat island intensity (SUHII) was derived by fitting a linear relationship between LST and HSI and it denotes the UHI effect. The spatial variation of SUHII is consistent with the distribution of HSI. According to the district division of Hangzhou, the SUHII of northeast Hangzhou is higher than that of southwest Hangzhou, and the UHI enhancement in the most developed districts of Hangzhou city, Yuhang and Xiaoshan, is remarkable. These results suggest that Hangzhou has a strong UHI effect and that the UHI has local regional characteristics. The UHI effect enhancement is mainly due to the expansion of the urban area, urban anthropogenic activities, and heat emission of the built-up area. According to the changes in SUHII for the period from 2000 to 2013, the UHI effect is intensifying in Hangzhou and measures for reducing the SUHII needs to be taken. To our knowledge, this is the first time that the spatio-temporal variation of the UHI in Hangzhou has been quantitatively analyzed using long time series of multisource remote sensing data, which can help understand the evolution of UHI and guide urban planning and development. [ABSTRACT FROM AUTHOR]