Search

The fraction of green vegetation is a widely-used indicator of vegetation abundance at regional and/or global scales. The pixel mixture model, especially the dimidiate pixel model (DPM, also referred to as two-endmember model) based on the normalized difference vegetation index (NDVI), plays an important role in the accurate estimation of fractional vegetation cover (FVC) via remote sensing. The two components in the traditional DPM are vegetation and soil (both sunlit and shaded). However, to date, the influence of shaded vegetation and shaded soil has not been fully considered in the NDVI-based DPM. Herein we analyze the necessity and feasibility of processing shaded components separately. The shaded soil was found to largely affect the canopy NDVI and can be combined with the vegetation (both sunlit and shaded) as one of the two components in DPM due to the high NDVI of shaded soil under a small percentage of diffuse sky radiation (< 10 % of the total hemispherical radiation in red band in this study). This finding partially explains why the canopy NDVI is oversensitive to background. The DPM was then improved with the solar and view angles to account for the fraction of shaded soil. We performed simulation and field measurements to validate the proposed models to varying factors including the vegetation structure, soil background, solar and view geometry, and slope gradient. The improved DPMs outperformed the traditional DPM (i.e. , where no effect of shaded soil is considered) when estimating the NDVI and FVC of the mixed pixel. The FVC estimated with traditional DPM results in the RMSE from 0.14 to 0.31, and that with the improved DPMs range from 0.04 to 0.13. The decrease of uncertainty by using the improved DPMs was generally over 50 % when compared to the output from a traditional DPM. The proposed DPM maintains the advantage of an easy-of-use two-component mixture model yet is more accurate than traditional ones and thus expected to improve the FVC estimation from satellite data. • Shaded components are added in the NDVI-based dimidiate pixel model (DPM). • Shaded soil largely affects the canopy NDVI yet shaded vegetation doesn't. • Newly proposed DPMs are derived with solar and view angles to consider shadows. • New DPMs reduce 50 % uncertainty while maintaining ease of use as traditional DPM. • Partially overcome NDVI's oversensitivity to background and improve accuracy of FVC. [ABSTRACT FROM AUTHOR]

The vegetation index-based (VI-based) mixture model is widely used to derive green fractional vegetation cover ( FVC ) from remotely sensed data. Two critical parameters of the model are the vegetation index values of fully-vegetated and bare soil pixels (denoted V x and V n hereafter). These are commonly empirically set according to spatial and/or temporal statistics. The uncertainty and difficulty of accurately determining V x and V n in many ecosystems limits the accuracy of resultant FVC estimates and hence reduces the utility of VI-based mixture model for FVC estimation. Here, an improved method called MultiVI is developed to quantitatively estimate V x and V n from angular VI acquired at two viewing angles. The directional VI is calculated from the MODIS Bidirectional Reflectance Distribution Function (BRDF)/Albedo product (MCD43A1) data. The results of simulated evaluation with 10% added noise show that the root mean square deviation (RMSD) of FVC is approximately 0.1 (the valid FVC range is [0, 1]). Direct evaluation against 34 globally-distributed FVC measurements from VAlidation of Land European Remote sensing Instruments (VALERI) sites during 2000 to 2014 demonstrated that the accuracy of MultiVI FVC (R 2  = 0.866, RMSD = 0.092) exceeds than from SPOT/VEGETATION bioGEOphysical product version 1 (GEOV1) FVC (R 2  = 0.795, RMSD = 0.159). MultiVI FVC also exhibits higher correlation to the VALERI reference FVC than does the MODIS fraction of photosynthetically active radiation product (MCD15A2H; R 2 is 0.696). A key advantage of the MultiVI method is obvious in areas where fully-vegetated and/or bare soil pixels do not exist in moderate-coarse spatial resolution imagery when compared to the conventional VI-based mixture modelling. The MultiVI method can be flexibly implemented over regional or global scales to monitor FVC , with maps of V x and V n generated as two important byproducts. [ABSTRACT FROM AUTHOR]

The clumping effect is the main issue causing the heterogeneity in vegetation canopies and the underestimation of leaf area index (LAI) obtained using indirect measurement methods. Significant efforts have been exerted to correct for the clumping effect and derive the true LAI. Recent research has shown that the fractal dimension (FD) is directly related to the clumping effect of foliage, yet practical methods are needed to calculate field estimates. Considering that widely used LAI applications such as digital hemispherical photography (DHP), tracing radiation and architecture of canopies (TRAC), and digital cover photography (DCP) estimate LAI with one-dimensional (1D) gap probability and gap size data, we propose a method to correct for the clumping effect using 1D FD. Resulting formulae describing the relationship between LAI, CI, and 1D FD were based on the box-counting method (BCM) and a binomial distribution model. Sixty-four simulated scenes including four RAdiation transfer Model Intercomparison (RAMI) actual canopies and field measurements from nine plots (four orchard plots and five coniferous forest plots) were used to validate the novel method. Results showed good agreement with reference LAI values for simulated scenes (R2 = 0.96 and RMSE = 0.35). The 1DFD method generated higher LAI estimates compared with the LAI measured using TRAC at the four orchard plots especially at high canopy closure, while its results were more consistent with LAI obtained by litter collection than those of comparable methods at coniferous forest plots (bias from −13.5% to 9.9% for DCP images, from −3.0% to 19.7% for DHP images, and from −3.8% to 17.0% for TRAC transects). Our validation efforts indicate that the method proposed herein corrects for the clumping effect of vegetated canopies more effectively with DCP images, DHP images, and TRAC measurement when compared with traditional indirect optical methods. The 1DFD method is expected to improve indirect measurement accuracy of LAI. • One-dimensional fractal dimension (1D FD) is explored for LAI field measurement. • A formula between 1D FD, LAI, and CI (clumping index) is rigorously deduced. • LAI and CI can be estimated using the formula, 1D FD, and gap fraction. • The new method corrects for clumping effect more properly than classical methods. • The novel method is suitable for many widely used LAI-measurement instruments. [ABSTRACT FROM AUTHOR]

Surface longwave radiation (SLR) plays an important role in the energy budget of the Earth's climate system. Remote sensing provides various data sources to retrieve SLR on a large scale and with high spatial resolution (e.g., 1 km). Multiple retrieval methods of surface downward longwave radiation (SDLR) based on satellite thermal infrared data produce different retrieval results for the same scenario. Therefore, validating these models is necessary to understand their characteristics and limitations. To this end, ground-based measurements were used to provide independent validation of six widely used SDLR models for clear sky conditions over 41 Baseline Surface Radiation Network (BSRN) stations worldwide. The Wang2020 model had the best overall performance (bias of −5.480 W / m 2, root-mean-square errors [RMSE] of 23.226 W / m 2, R 2 of 0.879), and Tang2008 model had similar retrieval capability. The errors of LST had limited influence on the retrieval accuracy of SDLR models. When using the near-surface air temperature, the retrieval accuracy of the Zhou2007 model was significantly improved with a range of ~9.5 W / m 2 for the RMSE. The uncertainty of TCWV had significant effect on all model performances, wherein the Zhou2007 model had stronger error resilience of TCWV. Moreover, MODIS TIR TCWV data provided better performance than NIR TCWV in most situations, and thereby are preferred to use in the SDLR retrieval. Surface altitude had a lesser impact on SDLR retrieval than terrain effects. All models overestimated SDLR for peak stations in mountainous areas, with biases reaching 56.614 W / m 2 and RMSE reaching 63.909 W / m 2. Land cover type also had a significant effect on retrieval accuracy; model performances were poorer in the desert and barren where atmospheric conditions are extremely dry and hot. Remote sensing SDLR data with high accuracy are needed for hydrological, agricultural, and climate change applications. The results of this study provide a reference for the SDLR retrieval accuracy based on clear-sky models. • Six SDLR models were validated using measurements from 41 BSRN stations worldwide. • Comprehensive validation was performed for different influencing factors. • The Wang2020 model had the best overall performance (RMSE of 23.226 W / m 2). • Near-surface air temperature can significantly improve the accuracy of Zhou2007 model. • MODIS TIR TCWV data are preferred in SDLR retrievals with better performance. [ABSTRACT FROM AUTHOR]

Fractional Vegetation Cover (FVC) represents the planar fraction of the land-surface covered by green foliage, and its dynamics are important for an enhanced understanding of ecosystems especially how they respond to climate change. The lack of global near-real-time satellite-based products restricts the application of FVC in ecosystem modeling, climate change, and vegetation phenology studies. Earth Polychromatic Imaging Camera (EPIC) onboard Deep Space Climate Observatory (DSCOVR) spacecraft provides daily spectral reflectance of the entire sunlit Earth in the near Hotspot directions. Hotspot observations (i.e., observation in Hotspot direction which has the peak backscattering reflected radiation) with only sunlit vegetation and sunlit soil components are more suitable for FVC estimation with a two-endmember mixture model as such observations exclude contributions from shaded vegetation and soil components. In this study, an algorithm for retrieving quasi-daily FVC from EPIC based on two-endmember mixture and gap fraction models is developed. Analyses of its performance predict that the average Root-Mean-Square Deviations (RMSDs) of retrievals in FVC units is below 0.050 when compared with reference values. The RMSD is 0.043 when compared to field-based Landsat reference FVC, which confirms lower retrieval uncertainty than FVC retrieved from Low-Earth-Orbit (LEO) satellite products such as MODIS, VIIRS, and GEOV2 with RMSDs 0.049– 0.087. The comparison analyses suggest a good consistency between EPIC FVC and FVC products from LEO and geostationary (GEO) satellites sensor, SEVIRI, with RMSD values less than 0.129. EPIC allows for quasi-daily FVC estimation across the global terrestrial surface at 10 km resolution, which is an important development for numerous biophysical applications. • Develop a method for quasi-daily Fractional Vegetation Cover (FVC) estimation. • FVC derived from DSCOVR EPIC at Sun-Earth L1 for global monitoring. • Using hotspot observations to avoid shadow effects and yield more accurate FVC. • EPIC FVC has a good consistency with products from MODIS, VIIRS, GEOV2, and LSA. • EPIC FVC benefits global biophysical applications which need vegetation dynamics. [ABSTRACT FROM AUTHOR]

The hotspot effect, which refers to the increased reflectance in the solar direction, relies on the canopy structure. Numerous bi-directional reflectance distribution function (BRDF) models have been developed with careful attention to the hotspot effect. However, different assumptions may lead to very different shapes of BRDF, especially in the hotspot direction. With the development of high-resolution remote sensing satellites and three-dimensional (3D) mapping technologies, abundant 3D structural vegetation canopy information is available. However, traditional analytical models might not fully use these kinds of information. As a result, most of them are still very limited by their assumptions, such as the shape and spatial distribution of crowns. One BRDF model may do well only for one vegetation type and shows significant errors under other scenarios. We propose a new model named PATH_RT, which can model the hotspot effect at both the leaf and crown levels using the path length distribution (PLD). PLD can be easily obtained with the 3D information of the canopy at given remote sensing geometries. We validated the bi-directional reflectance factor (BRF) predicted by the PATH_RT model with the simulated scenes and field measurements which were produced by a 3D radiative transfer model LESS and UAV observations respectively. Compared to traditional analytical models SAILH, 4SAIL2, GORT, and 5SCALE, the PATH_RT model exhibits the highest accuracy, reducing the average RMSE by 86%, 66%, 52%, and 58% for the hotspot region respectively. PATH_RT accurately depicts the hotspot effects at the canopy and leaf levels and is highly efficient (99%, 75%, 86%, 99%, and 99% less time spent for one repeat run compared to LESS, SAILH, 4SAIL2, GORT, and 5SCALE models, respectively). Considering its high accuracy and efficiency, PATH_RT is expected to improve the accuracy and efficiency of remote sensing inversion. • A BRDF model PATH_RT is developed based on the path length distribution of the canopy. • PATH_RT accounts for the hotspot effect at both the crown and leaf level. • PATH_RT can model the BRDF characteristics of homogeneous or heterogeneous canopies. • PATH_RT is accurate but with fewer assumptions about the canopy structure. • PATH_RT is four times faster than SAILH. [ABSTRACT FROM AUTHOR]

Shortwave radiation (0.3–3.0 μm) is a dominant component of land surface all-wave radiation budget. Reliable estimation of shortwave radiation throughout the globe is particularly important for an in-depth understanding of global changes. Unfortunately, a large number of existing space-based algorithms ignore the effects of topography by simply assuming that the surface is ideally flat. As pointed out by many studies, such neglect toward topographic effects leads to significant errors in derived radiation. This study proposes a shortwave topographic radiation model (SWTRM) by quantifying solar direct radiation shielding, occlusion of sky radiation, reflected radiation from nearby terrain, and invisibility of certain targets. To drive the SWTRM, an artificial neural network (ANN) approach was employed to generate multiple components of the shortwave radiation budget. The new model was run over the Tibetan Plateau region and used MODIS products coupled with digital elevation data (DEM). Topographically corrected shortwave fluxes were derived by coupling SWTRM and ANN outputs. The results show that: (1) the proposed SWTRM works well over mountainous regions; (2) the ANN-based shortwave model provides reasonable retrieval accuracy (RMSE < 60 W/m 2 , bias < 13 W/m 2 for all shortwave radiation components). More importantly, the ANN model can simultaneously provide all radiation components that are indispensable for driving the SWTRM; (3) over mountainous areas, the induced error can exceed 600 W/m 2 for shortwave net flux. Hence, topographic effects cannot be neglected; and (4) topography and solar illumination angle are key modulators in deriving shortwave radiation from space, which control the magnitude and spatial distribution of shortwave radiation over mountainous regions. [ABSTRACT FROM AUTHOR]

Downward shortwave radiation (DSR) is the main energy source for the Earth's system and a dominant component of the radiation budget. Although many algorithms have been proposed for estimating DSR, most models only consider either the radiative effects of terrain or atmosphere, and no research has considered the conditions of clouds on the mountainside. In this paper, a high-resolution Mountain Radiative Transfer model with Clouds (MRTC) is proposed to characterize the cloud-terrain coupling effects on DSR under different surface positions relative to the clouds. To operate the MRTC, a look-up table is employed to obtain initial radiative parameters. The comparison with Monte-Carlo based radiative transfer indicates that MRTC can be applied to conditions of clouds on the mountainside. And the reliability of MRTC under conditions when clouds are above mountains is also validated against in-situ measurements, with an RMSE of 149.9 W / m 2 , a bias of 28.3 W / m 2 , and an R 2 of 0.74. By using more accurate cloud cover data extracted from All-Sky images, the accuracy of MRTC has improved, with the RMSE decreasing to 115.8 W / m 2 , and the R 2 increasing to 0.8. This suggests that the uncertainty in satellite cloud products significantly contributes to errors in DSR estimations. MRTC is expected to more accurately represent cloud-terrain coupling effects on DSR, and improve the accuracy of DSR estimation over rugged terrain under cloudy skies. • A high-resolution mountain radiative transfer model with clouds (MRTC) is proposed. • The coupling effects of clouds and terrain on DSR are fully considered. • The first time the conditions of clouds on the mountainside are handled in DSR estimation. • Validation against a 3D radiative transfer model and in-situ measurements shows good agreement. • Uncertainty in cloud products and the complex 3D cloud structures significantly affect the accuracy of estimation. [ABSTRACT FROM AUTHOR]

Accurate leaf area index (LAI) measurements are important for understanding and evaluating global mass and energy cycle. Foliage clumping serves an important function in traditional indirect LAI measurement methods. The clumping index has been used to modify effective LAI for decades. However, the change in path length within canopies is often the most uncertain factor in indirect LAI estimation using Beer's law. A simple clumping index is incapable of describing the heterogeneity of a canopy and may cause large errors in calculating true LAI values. We proposed a new LAI estimation method by using path length distribution functions in optical measurement. Both simulation and field measurements show that the path length-based method can effectively characterize the LAI values of heterogeneous canopies. Deviation is less than 10% for all the validations. One of the advantages of path length distribution theory is that it can characterize and handle crown shape-induced non-randomness within canopies. Such non-randomness, which may cause underestimation of up to 25%, has not been well addressed by existing algorithms. Path length theory is expected to improve the indirect measurement accuracy of LAI significantly with the use of current optical instruments. [ABSTRACT FROM AUTHOR]

• 3D RT simulations are used to assess the VI performance for estimating forest chlorophyll concentration. • Influences of saturation effect, soil backgrounds, and leaf area index disturbance on the chlorophyll concentration retrieval are evaluated. • Concern about decoupling vegetation canopy structure and spectral information. Remote-sensed vegetation indices (VIs) have emerged as essential tools for retrieving forest chlorophyll concentration. Although VIs are widely used, some concerns regarding VIs for estimating chlorophyll remain to be addressed, such as saturation effect, leaf area index (LAI) disturbance, and soil brightness influence. Currently, a systematic study on such performance evaluation of chlorophyll-related VIs considering these issues is still insufficient. This study coupled two radiative transfer models, the PROSPECT model and the LESS model, to simulate Eucalyptus monocultures with different chlorophyll content and systematically evaluated the 18 broad-band VIs' ability in chlorophyll estimation at different scales. Our results indicate that most VIs designed for chlorophyll estimation were relatively resistant to saturation, except for SIPI and some classical VIs (e.g., NDVI and DVI), which were insensitive to chlorophyll decreases and tended to reach saturation quickly (when leaf chlorophyll content (LCC) exceeded 40 ug/cm2). The relationships between NDVI, SR, DVI, and LCC were easily influenced by soil brightness and LAI. S2REP, MTCI, TGI, TCARI, and EVI were insensitive to soil brightness when estimating LCC. Overall, S2REP was best at quantitatively retrieving chlorophyll and resisting interference from other factors. For practical applications, our study suggests that it is preferable to use S2REP for LCC estimation when the red-edge band is available; otherwise, CVI can be used instead. The judicious utilization of VI can effectively depict chlorophyll levels and improve the understanding of vegetation response to climate change. Our findings provide the necessary information for the selection of specific VIs tailored to specific vegetation parameters. [ABSTRACT FROM AUTHOR]

Abstract Three-dimensional (3D) radiative transfer modeling of the transport and interaction of radiation through earth surfaces is challenging due to the complexity of the landscapes as well as the intensive computational cost of 3D radiative transfer simulations. To reduce computation time, current models work with schematic landscapes or with small-scale realistic scenes. The computer graphics community provides the most accurate and efficient models (known as renderers) but they were not designed specifically for performing scientific radiative transfer simulations. In this study, we propose LESS, a new 3D radiative transfer modeling framework. LESS employs a weighted forward photon tracing method to simulate multispectral bidirectional reflectance factor (BRF) or flux-related data (e.g., downwelling radiation) and a backward path tracing method to generate sensor images (e.g., fisheye images) or large-scale (e.g. 1 km2) spectral images. The backward path tracing also has been extended to simulate thermal infrared radiation by using an on-the-fly computation of the sunlit and shaded scene components. This framework is achieved through the development of a user-friendly graphic user interface (GUI) and a set of tools to help construct the landscape and set parameters. The accuracy of LESS is evaluated with other models as well as field measurements in terms of directional BRFs and pixel-wise simulated image comparisons, which shows very good agreement. LESS has the potential in simulating datasets of realistically reconstructed landscapes. Such simulated datasets can be used as benchmarks for various applications in remote sensing, forestry investigation and photogrammetry. Highlights • Forward photon tracing and backward path tracing are implemented in LESS. • LESS has been extended to simulate thermal infrared images. • LESS supports simulating large-scale spectral images based on 3D landscapes. • LESS provides an easy-to-use GUI and related tools to construct the 3D scene [ABSTRACT FROM AUTHOR]

Longwave radiation (5–100 μm) is a critical component of the Earth's radiation budget. Most of the existing satellite-based retrieval algorithms are valid only for flat surfaces without accounting for topographic effects. This causes significant errors. Meanwhile, the fixed spatial resolution of remote sensing data makes it difficult to link the satellite-derived longwave radiation to different land models running on various scales. These deficiencies result in an urgent need for topographic modeling and spatial scaling studies of longwave radiation. In this paper, a longwave topographic radiation model (LWTRM) is proposed that quantifies all possible radiation-affecting factors over rugged terrain. For driving the LWTRM, a hybrid method for simultaneously deriving multiple components of longwave radiation from MODIS data is suggested based on artificial neuron networks (ANN) and the radiative transfer simulation. Topographically corrected longwave radiation is then derived by coupling the ANN outputs and LWTRM. Based on this, a general upscaling strategy for longwave radiation is presented. The results demonstrate that: (1) both the proposed LWTRM and the upscaling strategy are rather effective and work well over rugged areas; (2) the ANN-based retrieval method can produce longwave radiation with better accuracy(RMSE < 23 W/m 2 , bias < 9 W/m 2 ). More importantly, it can simultaneously derive multiple components of longwave radiation in a consistent manner; (3) over mountainous areas, the radiation cannot be accurately characterized in terms of either spatial distribution or specific values if topographic effects are neglected, for instance, the induced error can reach up to 100 W/m 2 for the longwave net flux; and (4) the topographic effects cannot be ignored below spatial scale of approximately 5 km in the selected study area. [ABSTRACT FROM AUTHOR]

Canopy radiative transfer (RT) modeling is critical for the quantitative retrieval of vegetation biophysical parameters and has been under intensive research over the decades. RT models of discontinuous canopies, such as three-dimensional (3D) RT models, posed challenges for the early one-dimensional (1D) hypothesis. Although 3D RT models have higher accuracy, theoretically, they suffer from two problems: detailed scene parameters and complex computational steps. To overcome these problems, the stochastic radiative transfer (SRT) theory, which is known to have the accuracy of 3D RT while being as simple as 1D RT, has been adapted from atmospheric research to the study of vegetation canopies. While the SRT model has been adopted into the operational production of vegetation parameters, its accuracy needs further improvement because of the insufficient consideration of hotspot effects. Additionally, the evaluation and validation of SRT process are still preliminary, which hinders its further development and application. To provide the community with missing information and a scientific basis for subsequent model improvement, we modified, evaluated, and validated the SRT model in this study. First, we proposed the new version of SRT model to better achieve the coupling of SRT process and hotspot effect by dividing the previous SRT into four subproblems. Then, we evaluated the performance of the modified SRT by comparing multiple intermediate variables in the SRT process with 3D computer simulations, and analyzed the model sensitivity to key input parameters as well as the spatial distribution and conservation of radiation energy. Our findings reconfirmed that the SRT theory can well describe the radiation regime of discontinuous canopies with balanced efficiency and accuracy. Moreover, the newly proposed coupling scheme of hotspot effect further improves the model performance in the hotspot regions. Finally, the unmanned aerial vehicle (UAV) observations served as a reference to validate the modeled canopy reflectance, which shows a high concordance. These results provide a detailed theoretical basis for applications and further improvements of the SRT model. • Stochastic radiative transfer (SRT) model was modified in terms of hotspot effects. • A comprehensive evaluation of the SRT process was achieved. • The 3D RT model based on computer simulations was used to evaluate the SRT model. • The SRT model was validated using UAV measurements. • Provided a detailed basis for the retrieval of vegetation biophysical parameters. [ABSTRACT FROM AUTHOR]

Thermal anisotropy is an important phenomenon in thermal infrared remote sensing as it restricts the retrieval accuracy of surface longwave radiation (SLR). Topography is an essential controlling factor for the directionality of SLR for high-relief regions (e.g., mountain regions) where there is land surface heterogeneity and non-isothermal properties at pixel scales. However, satellite sensors can only receive radiance from a specific surface object at a small number of simultaneous viewing angles, which makes the quantitative modeling of thermal anisotropy challenging. Therefore, we developed the topographic longwave radiation model (TLRM) to describe the directionality of SLR components taking into account the variability of both subpixel topography and thermal anisotropy in high-relief regions. The reliability of TLRM was validated using the Discrete Anisotropic Radiative Transfer (DART) model over two typical geomorphic areas: a valley scene and a peak scene. The preliminary validation shows good agreement in terms of surface upward longwave radiance, which confirms the potential of TLRM for capturing the anisotropic patterns of land surfaces. The radiance values simulated by the DART model were first revised for the spectral mismatch. Then, they are used to correct residual deviation for TLRM using linear regressions. The root mean square error (RMSE) and coefficient of determination (R 2 ) were 0.830 W/(m 2 ∙ sr) and 0.746 for the valley scene, respectively, and 0.239 W/(m 2 ∙ sr) and 0.711 for the peak scene, respectively. Compared with TLRM, models that do not consider terrain effects generate significant discrepancies in high relief SLR components. The differences in downward longwave radiation can reach −60 W/ m 2 in valleys without considering terrain effects. Based on the reference of hemispherical upward longwave radiation, surface upward longwave radiation estimated by the direct estimation method had a bias of 11.41 W/ m 2 and standard deviation (STD) of 7.30 W/ m 2, while the directional upward longwave radiation had a bias of 5.99 W/m2 and STD of 4.08 W/ m 2, showing lower estimation variation. The discrepancy between surface net longwave radiation (NLR) and terrain-corrected NLR ranged between 50 and −130 W/ m 2 with clear negative biases mainly occurring in valleys. With higher spatial resolutions of remotely sensed imagery, the influence of complex terrain on land surface radiative flux has become more significant. This parameterization scheme is expected to better represent the topographic effects on SLR, enhance understanding of thermal anisotropy in non-isothermal mixed pixel areas of high relief, and improve the inversion accuracy of SLR. • Terrain effects on retrieval of surface longwave radiation (SLR) are significant. • Large scale SLR can be estimated by the topographic longwave radiation model (TLRM). • TLRM integrates terrain, effective emissivity and broadband thermal modeling. • Subgrid radiation parameterization calculates SLR at multiple spatial scales. • TLRM has the capability to capture anisotropy patterns of SLR. [ABSTRACT FROM AUTHOR]