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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]

To improve our capacity to map long-term vegetation dynamics in heterogeneous landscapes, this study proposed a new prior knowledge-based spatiotemporal enhancement method, namely, PK-STEM, to fuse MODIS and Landsat FPAR products following the remote sensing trend surface framework. PK-STEM uses historical Landsat FPAR images as prior knowledge and fuses them with new satellite-derived FPAR data. PK-STEM can work in three modes: 1) using only MODIS data; 2) using only Landsat data; and 3) using both MODIS and Landsat data. This study retrieved FPAR from Landsat images using a scaling-based method and tested the performance of PK-STEM in a regional application. For the entire year of 2012, we compared the performance of PK-STEM in different modes and with that of two typical spatiotemporal fusion methods, the enhanced spatial and temporal adaptive reflectance model (ESTARFM) and unmixing-based linear mixing growth model (LMGM). Then, a long time series FPAR data set at 30-m resolution and eight-day intervals was generated for 13 years (2000–2012). Our results show that PK-STEM in mode III is the most robust and accurate (root mean squared error (RMSE) = 0.062; mean $R = 0.851$) among the three modes and more accurate than ESTARFM (mean RMSE = 0.065; mean $R = 0.776$) and LMGM (mean RMSE = 0.074; mean $R = 0.734$). For the 12 years (2000–2011), PK-STEM also achieves high accuracies with mean RMSE = 0.066 and $R = 0.938$. PK-STEM is very flexible with a continual update mechanism and is efficient for long time series applications. [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]

Accurate estimation of the fine-resolution fraction of absorbed photosynthetically active radiation (FPAR) across broad spatial extents and long time periods requires efficient and applicable methods. The existing methods can hardly provide a balance between accuracy, simplicity, and transferability through space and time. Within the remote-sensing trend-surface conceptual framework, this article proposes a scaling-based method to efficiently retrieve FPAR from fine-resolution satellite data using coarse-resolution FPAR products as a reference. The method was particularly developed and applied to Moderate Resolution Imaging Spectroradiometer (MODIS) FPAR product and Landsat imagery. First, necessary prior knowledge related to FPAR retrieval and scaling theories was used to explicitly linearize the complex relationship between MODIS FPAR and Landsat surface reflectance. Second, the explicit linear model for FPAR estimation was trained through one-pair image learning for each date to estimate FPAR from Landsat imagery in real time. Both homogeneous and heterogeneous cases were considered. The method was validated at ten selected worldwide sites from the Validation of Land European Remote Sensing Instruments (VALERI) program and derived an overall root mean squared error (RMSE) of 0.133. A long time series of FPAR data set at the 30-m resolution was generated at the regional scale (approximately 2000 km2) for 13 years (2000–2012). The results were accurate (RMSE = 0.072) and MODIS-consistent, which were significantly better than those of the normalized difference vegetation index (NDVI) downscaling-based and regression tree methods. The scaling-based method provides accurate, MODIS-consistent and spatially consistent FPAR estimates in real time, is highly transferrable through space and time, and allows for future extension of FPAR estimates to the era of the Landsat series satellites. [ABSTRACT FROM AUTHOR]

• A novel colour mixture analysis (CMA) method was proposed for mapping FVC. • Gaussian endmembers were derived from UAV proximal sensing RGB images. • CMA method can minimize the mixed pixel effect in close-range UAV RGB images. • CMA is robust to variations of resolution, vegetation coverage and illumination. Remote sensing via unmanned aerial vehicles (UAVs) is becoming a very important tool for augmenting traditional spaceborne and airborne remote sensing techniques. Commercial RGB cameras are often the payload on UAVs, because they are inexpensive, easy to operate and require little data processing. RGB images are increasingly being used for mapping of fractional vegetation cover (FVC). However, the presence of significantly mixed pixels in close-range RGB images prevents the accurate estimation of FVC. Even where pixel unmixing is applied, limited quantitative spectral information and colour variability within these images could lead to profound errors and uncertainties. This paper proposes a colour mixture analysis (CMA) method based on the Hue-Saturation-Value (HSV) colour space to alleviate the above-mentioned concerns, thereby improving the accuracy and efficiency of FVC estimation from UAV-captured RGB images. First, the a priori colour information of the pure vegetation and background endmembers are extracted from the Hue channel of the UAV proximal sensing images, obviating ground-based image capture and the attendant cost and inconvenience. Second, the relationship between the probability distribution of mixed pixels and that of the two endmembers is estimated. Finally, we estimate FVC from UAV remote sensing images with a maximum a posteriori parameter (MAP) estimator. Two UAV-captured RGB image datasets and a synthetic RGB image dataset were used to test the new method. CMA was compared with three other FVC estimation algorithms, namely, FCLS, HAGFVC and LAB2. The FVC estimates by CMA were found to be highly accurate, with root mean squared errors (RMSE) of less than 0.007 and mean absolute error (MAE) of less than 0.01 for both field datasets. The accuracy was shown to be superior to that of all three algorithms. A comprehensive analysis of the estimation accuracy under various spatial resolutions and vegetation cover levels was conducted using both field and synthetic datasets. Results show that the CMA method can robustly and accurately estimate FVC across the full range of vegetation coverage and various resolutions. Uncertainty and sensitivity analysis of colour variability due to heterogeneity and shadow were also tested. Overall, CMA was shown to be robust to variation in colour and illumination. [ABSTRACT FROM AUTHOR]

Highlights • Latest progress and prospects of indirect LAI measurement. • Comprehensive review of theories, algorithms, instruments, and challenges. • Appealing new means of terrestrial, airborne, and spaceborne laser scanners. • Recent progress on laser scanner, cover photography, and within-crown clumping. • Future directions on scale, LAD, isolated/sparse vegetation, slope, and validation. Abstract Leaf area index (LAI) is a key parameter of vegetation structure in the fields of agriculture, forestry, and ecology. Optical indirect methods based on the Beer-Lambert law are widely adopted in numerous fields given their high efficiency and feasibility for LAI estimation. These methods have undergone considerable progress in the past decades, thereby making them operational in ground-based LAI measurement and even in airborne estimation. However, several challenges remain, given the requirement of increasing accuracy and new applications. Clumping effect correction attained significant progress for continuous canopies with non-randomly disturbed leaves while non-continuous canopies are rarely studied. Convenient and operational measurement of leaf angle distribution and woody components is lacked. Accurate and comprehensive validations are still very difficult due to the limitations of direct measurement. The introduction of active laser scanning technology is a driving force for addressing several challenges, but its three-dimensional information has not been fully explored and utilized. In order to update the general knowledge and identify the possible error source, this study comprehensively reviews the temporal development, theoretical framework, and issues of indirect LAI measurement, followed by current methods, instruments, and platforms. Latest methods and instruments are introduced and compared to traditional ones. Current challenges, recent advances, and future perspectives are discussed to provide recommendations for further research. [ABSTRACT FROM AUTHOR]

Estimation of daily downward shortwave radiation (DSR) is of great importance in global energy budget and climatic modeling. The combination of satellite-based instantaneous measurements and temporal extrapolation models is the most feasible way to capture daily radiation variations at large scales. However, previous studies did not pay enough attention to topographic effects and simple temporal extrapolation methods were applied directly to rugged terrains which cover a large amount of the land surface. This paper, divided into two parts, aims at analyzing the topographic uncertainties of existing models and proposing a better method based on a mountain radiative transfer (MRT) model to calculate daily DSR. As the first part, this paper analyze the spatiotemporal variations of DSR influenced by topographic effects and checks the applicability of three temporal extrapolation methods on cloud-free days. Considering that clouds also have a strong influence on solar radiation, cloud-free days are chosen for targeted analysis of topographic effects on DSR. Three indices, the coefficient of variation, entropy-based dispersion coefficient (CH), and sill of semivariogram, are put forward to give a quantitative description of spatial heterogeneity. Our results show that the topography can dramatically strengthen the spatial heterogeneity of DSR. The index, CH, has an advantage for quantifying spatial heterogeneity as it offers a tradeoff between accuracy and efficiency. Spatial heterogeneity distorts the daily variation of DSR. Application of extrapolation methods in rugged terrains leads to overestimation of daily average DSR up to 60 W/m2 and a maximum 200 W/m2 error of instantaneous DSR on cloud-free days. This paper makes a quantitative analysis of topographic effects under different spatiotemporal conditions, which lays the foundation for developing a new extrapolation method. [ABSTRACT FROM AUTHOR]

The airborne laser scanner (ALS) provides great potential for mapping the leaf area index (LAI) at the landscape scale using grid cell statistics, while its application is restricted by the lack of clumping information, which has been an unsolved issue highlighted for a long time. ALS generally provides an effective LAI because its footprint is too large to capture small gaps to apply traditional ground-based clumping correction methods. Here, we present a grid cell method based on path length distribution model to calculate the clumping-corrected LAI using ALS data without the requirement of additional field measurements. We separated the within- and between-crown areas to consider between-crown clumping, and used the path length distribution as estimated by local canopy height distribution to consider 3-D foliage profile and within-crown clumping. The path length distribution model takes advantage of the 3-D information rather than the gap size distribution, thus avoiding the limitation of large ALS footprint. With the 0.4-m-footprint ALS data, the results are generally promising and a multilevel clumping analysis is consistent with landscape flown. The ALS LAIs of different resolutions are consistent, with a difference of less than 5% from 5- to 250-m resolutions. Due to its consistency and simple configuration, the method provides an opportunity to map the clumping-corrected LAI operationally and strengthens the ability of airborne lidar to monitor vegetation change and validate the satellite product. This grid cell method based on path length distribution is worth further testing and application using more recent laser technology. [ABSTRACT FROM AUTHOR]

Surface shortwave (SW) irradiation is the primary driving force of energy exchange in the atmosphere and land interface. The global climate is profoundly influenced by irradiation changes due to the special climatic condition in Antarctica. Remote-sensing retrieval can offer only the instantaneous values in an area, whilst daily cycle and average values are necessary for further studies and applications, including climate change, ecology, and land surface process. When considering the large values of and small diurnal changes of solar zenith angle and cloud coverage, we develop two methods for the temporal extension of remotely sensed downward SW irradiance over Antarctica. The first one is an improved sinusoidal method, and the second one is an interpolation method based on cloud fraction change. The instantaneous irradiance data and cloud products are used in both methods to extend the diurnal cycle, and obtain the daily average value. Data from South Pole and Georg von Neumayer stations are used to validate the estimated value. The coefficient of determination (R2) between the estimated daily averages and the measured values based on the first method is 0.93, and the root mean square error (RMSE) is 32.21 W/m2 (8.52%). As for the traditional sinusoidal method, the R2 and RMSE are 0.68 and 70.32 W/m2 (18.59%), respectively The R2 and RMSE of the second method are 0.96 and 25.27 W/m2 (6.98%), respectively. These values are better than those of the traditional linear interpolation (0.79 and 57.40 W/m2 (15.87%)). [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]

Scale effect, which is caused by a combination of model nonlinearity and surface heterogeneity, has been of interest to the remote sensing community for decades. However, there is no current analysis of scale effect in the ground-based indirect measurement of leaf area index (LAI), where model nonlinearity and surface heterogeneity also exist. This paper examines the scale effect on the indirect measurement of LAI. We built multiscale data sets based on realistic scenes and field measurements. We then implemented five representative methods of indirect LAI measurement at scales (segment lengths) that range from meters to hundreds of meters. The results show varying degrees of deviation and fluctuation that exist in all five methods when the segment length is shorter than 20 m. The retrieved LAI from either Beer's law or the gap-size distribution method shows a decreasing trend with increasing segment lengths. The length at which the LAI values begin to stabilize is about a full period of row in row crops and 100 m in broadleaf or coniferous forests. The impacts of segment length on the finite-length averaging method, the combination of gap-size distribution and finite-length methods, and the path-length distribution method are relatively small. These three methods stabilize at the segment scale longer than 20 m in all scenes. We also find that computing the average LAI of all of the short segment lengths, which is commonly done, is not as good as merging these short segments into a longer one and computing the LAI value of the merged one. [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]

Surface upward longwave radiation (SULR) is one critical component of the surface energy balance and is closely related to the surface temperature fields. The SULR with the moderate spatial resolution and appropriate precision in the Tibetan Plateau, a large and complex cryosphere, can enhance our understanding of the land surface processes, atmospheric circulations and even global climate change. The purpose of this paper is to estimate SULR from top-of-atmosphere (TOA) radiances of Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS). A hybrid method is proposed that combines extensive radiative transfer simulation and the Artificial Neuron Network (ANN) statistical model. For the sake of ensuring the availability of the simulation database, the eligible profiles from MODIS atmospheric profiles product were screened according to five criteria. And then the analyses on the important parameters of the simulation dataset were performed to check the integrality of this dataset. Moreover, the variables' importance was analyzed, and TOA radiances of MODIS channels 29, 31, 32 and 33 (or VIIRS channels M14, M15 and M16) and sensor view zenith angle (VZA) were selected to retrieve SULR. The ANN-based retrieval model was created finally based on this simulation dataset. The evaluation showed good agreements for both MODIS and VIIRS training and testing datasets with R 2 greater than 0.98. The MODIS SULR was validated using two-year ground-measured data at seven sites in the Tibetan Plateau with R 2 of 0.886, root-mean-square error (RMSE) of 26.985 W m − 2 and mean bias error (MBE) of 10.812 W m − 2 as a whole. And the relevance was obvious from the overall trend and distribution though the discrepancy was relatively large when directly comparing SULR from VIIRS and MODIS data. The validation results showed that the ANN model is a good nonlinear model to retrieve SULR with the TOA radiances and VZA. [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]

Accurate spectral calibration of the in-flight sensors is crucial for processing and exploration of remotely sensed data. This paper developed a strategy to make spectral recalibration (i.e., spectral response function, central wavelength, and bandwidth) for in-flight broadband sensor using a device-responsivity-decomposition model with a priori knowledge and an optimization algorithm. Sensitivity analysis indicates that an accurate result requires the targets to be observed under a dry and clear atmospheric condition (column water vapor <2\ \g/cm^2 and visibility > 23 km) and no more than 5 \% error is included in the measured data. The new strategy was used to retrieve the spectral parameters along with radiometric calibration coefficients for a multichannel camera onboard an unmanned aerial vehicle from simultaneously remotely sensed and ground measured data sets over 19 (15 color-scaled and four gray-scaled) man-made surface targets, and the retrieved results were validated with a similar data set over another four man-made targets. It demonstrated that the camera's spectral parameters were accurately retrieved and an error less than 3.5 \W/m^2/\mu\m/sr was brought to the channel radiance. [ABSTRACT FROM AUTHOR]

Abstract: A sensor's footprint determines the target that is observed by the sensor, and influences the angular features of the target's directional brightness temperature (DBT) at the field site. This paper describes a new radiative transfer model (FovMod) to simulate the DBT of the row crop canopy by considering the sensor's footprint in the ground measurements. The FovMod firstly divides the sensor's circular or elliptical footprint into a few small cells, and then estimates the components' fractions (e.g., leaves, sunlit soil and shaded soil) in each cell based on the gap probability theory. The canopy's DBT is finally obtained by weighting the components' brightness temperatures and their fractions using a Gaussian point spreading function (PSF) of the sensor's response. Simulation results indicate that a small footprint causes the distribution of the DBT to be strongly dominated by the row direction and a single component's temperature but little influenced by the solar position. On the contrary, a large footprint smoothes the row-space effect and causes the DBT to distribute as a uniform, continuous canopy. Comparison with a previous parallel model shows that if the diameter of the sensor's circular footprint extends to 1.5–2.0 times as large as the total width of the row crop canopy, the footprint effect is minimized, and the ground measured DBT can, theoretically, be used to evaluate the parallel model with negligible error. Finally, validations with a maize canopy demonstrated that the new model performed more accurately than the parallel model to simulate the DBT. Moreover, the FovMod also provides an opportunity to assess the measurement uncertainty caused by some unexpected changes in the sensor's footprint. [Copyright &y& Elsevier]

Abstract: Shortwave (0.3–3μm) and longwave (3–50μm) surface radiative flux components have been widely used in numerical prediction, meteorology, hydrology, biomass estimation, surface energy circulation and climate change studies, etc. However, during past decades, these components were usually estimated independently using different methods, possibly causing inconsistent estimation biases due to different atmospheric parameters and algorithms, especially for net surface fluxes. Two methods have been proposed in this paper to simultaneously derive surface shortwave (or longwave) radiative flux components based on MODIS products using an artificial neural network (ANN). The validation results show that the maximum root-mean-square error for downward and net shortwave radiative fluxes is less than 45W/m2, about 60W/m2 for direct solar radiation and 25W/m2 for all longwave fluxes, which are comparable or even better than existing algorithms, thus demonstrating their feasibility and efficacy. The ANN-based models are then applied over the Tibetan Plateau region and the characteristics of the surface radiative flux components over such areas are analyzed. [Copyright &y& Elsevier]

Abstract: Clouds are the most important modulator of the amount of solar energy absorbed by the earth–atmosphere system. Traditional one-dimensional (1D) plane-parallel atmospheric radiative transfer models which use the independent pixel approximation (IPA) can only consider two extreme conditions, i.e., either cloud-free or overcast cases. In this paper, two cloud fraction related factors (hemispherical effective cloud fraction and regional cloud fraction) are calculated and incorporated into MODTRAN 4 (one of the most popular radiative transfer packages) to simulate the surface shortwave radiation components and the top-of-atmosphere (TOA) radiance for all possible solar-cloud-viewing geometries. The accuracy of this modified solar radiative transfer model (named as MODTRAN-CF) is consistent with its prototype (MODTRAN 4) which has been widely used and validated in radiative transfer modeling. Some field measurements are used to validate the superiority of MODTRAN-CF. For further understanding and simplifying of this physical model, a global sensitivity analysis (GSA) method is employed to analyze the effect of model parameters on each surface shortwave radiation component. Five parameters including solar zenith angle, surface albedo, hemispherical effective cloud fraction, ground altitude and atmospheric visibility show non-negligible impacts on almost all surface shortwave fluxes, which indicates that these five parameters should be carefully considered in the future modeling of the surface shortwave radiation fluxes. Two cloud optical thickness related parameters (cloud extinction coefficient and cloud thickness) exhibit obvious importance only under cloudy illumination condition especially with optically thin clouds. These findings on the improved model will enhance our knowledge on how to accurately model the surface shortwave radiation fluxes under all sky conditions. [Copyright &y& Elsevier]

Abstract: The angular effects of emissivity are ignored in current land surface temperature (LST) products. As a result, the directionality of these LST products limits their further application in many fields. Accurate correction of the angular problem of LST products requires explicit understanding of the angular effects of emissivity at the pixel scale. Currently, nearly ten years of global emissivity products of MODIS are available. However, the pixel-scale directionality of emissivity has never been analyzed. By performing a statistical analysis of 5-year MODIS emissivity products over most of East Asia, we generated the empirical relationships between the directional emissivity, land cover, and seasonal variations. Two look-up tables (LUTs) of directional emissivity were created for typical land cover types and applied to the generalized split-window algorithm to modify the MODIS LST. The results showed that the angular effect of emissivity could introduce a significant bias of −1–3 K to the 1 km resolution LST. Finally, the spatial scale effects of emissivity were analyzed, and it was found that the temperature differences caused by scale effects fell within +/−0.5 K for most pixels if 5 km emissivity was used in 1 km LST retrieval. Therefore, wide use of the LUTs can be expected. [Copyright &y& Elsevier]

Accurate radiometric and spectral calibrations of hyperspectral remote sensing instruments are essential for optimum data processing and exploitation. Two improved methods for the refinement of the spectral calibration of air- and spaceborne imaging spectrometers are presented in this paper. Both spectral channel position and width can be retrieved by modeling the atmospheric absorption features around 760, 940, 1140, and 2060 nm without making use of external atmospheric or surface parameters. A sensitivity analysis based on synthetic data demonstrated that, for each of the two methods, the root-mean-square errors to be expected were less than 0.18 nm for the retrieval of channel wavelength center and less than 0.8 nm for channel full-width at half-maximum. The application of the proposed methods to a real Hyperion data set showed quite-similar cross-track variations in the spectral calibration for the two methods, although relatively large differences in magnitude were found near the 940- and 1140-nm \H2\O absorption features. The significant improvement of the reflectance spectra derived after the refinement of the instrument spectral calibration confirms the good performance of the proposed methods. [ABSTRACT FROM AUTHOR]

We propose a strategy to construct a priori knowledge in Bidirectional Reflectance Distribution Function (BRDF) model-based Leaf Area Index (LAI) inversion. In this strategy, the physical limitations, a best guess and its uncertainty for each parameter needed to be inverted were obtained from a spectral database. Vegetation index (VI) and growth date were used to provide more information about LAI. The relationship between LAI and VI was obtained by forward simulation using the BRDF model. The empirical model of the changing LAI and the growth date was obtained by statistical analysis of more than 600 field samples from a wheat paddock. A SAIL-reflectance model including the hotspot-effect (SAILH model) was used to generate bidirectional reflectance distribution. Gaussian distributed random noises were added on the reflectance as 'observation'. SAILH model was inverted to validate the effectiveness of this strategy. It was further validated using both of the ground measurements and airborne remote-sensing data. It is found that a priori knowledge is important for successful inversion, and our strategy is expected to yield more reasonable spatial and temporal LAI distribution. [ABSTRACT FROM AUTHOR]

Inversion is an important process in remote sensing. To estimate the accuracy of retrieved parameters before inversion, we defined an Uncertainty and Sensitivity Ratio in the inversion of remote sensing models. It is a ratio of the priori uncertainty and sensitivity of one input parameter in the forward model to the sum of all parameters' uncertainty and sensitivity. USR was shown to reflect the variance of the retrieved parameter by error propagation theory. Subsequently, we took numeric experiments to illustrate the property of USR for typical vegetation cover scenes. Forty-eight multiangular datasets of Bidirectional Reflectance Factors were generated for these scenes by adding random noise to the Light Scattering by Arbitrarily Inclined Leaves model output. Seven parameters were retrieved for each scene. The results suggested that the Mean Square Error of the inversion results is highly correlated with USR. Based on this ratio, we selected two different angular observations in red and near-infrared (NIR) band to get a new Normalized Difference Vegetation Index (NDVI), which could produce less uncertainty to retrieve the Leaf Area Index (LAI) than using the conventional single-angular observations. The new NDVI was tested by retrieving LAI using the simulated multispectral and multiangular datasets. It was found that the inversion results of LAI are more accurate by using this new NDVI than using the traditional one. [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]

As a result of the high albedo of snow or ice and the rugged surface, high altitude mountainsget more reflection between the slopes, which increases the incoming radiation at theground surface. Because of the absence of measurements in snow-covered highmountains, the accurate surface solar radiation (SSR) in this area is not easy toderive. A large-scale remote sensing data and image simulation framework (LESS),which is a ray-tracing based three-dimensional (3-D) radiative transfer model, wasused in this study to have an accurate simulation of SSR in a mountainous area inthe Tibetan Plateau. The direct and diffuse fluxes without topographic effect weresimulated with MODTRAN5 for clear sky, used as the input for the LESS simulation.The simulation result indicates that the radiation increases in mountainous area,even if there is no snow cover. The increased energy origins from two parts: directand diffuse radiation and terrain-reflected radiation. The anomalies of the directand diffuse flux with reference to a horizontal surface is related to the slope andaspect distribution, and varies in a day. The terrain-reflected radiation is positivelycorrelated with the surface albedo, and it can be as large as 360 W/m2 in the areacovered with snow. The domain daily energy increase because of the terrain reflectionis generally less than 40W/m2, which is around 8% of downward SSR on a flatsurface. Three terrain configuration factors using in simplified approaches wereevaluated by the ray tracing simulation. Only one terrain configuration factor has verysimilar result with the LESS in domain-averaged (12km*12km) terrain-reflected flux,the other two factors both underestimate the radiation. However, the simulationwith terrain configuration factor cannot derive a reliable result on local position. [ABSTRACT FROM AUTHOR]

The academician Xiaowen Li devoted much of his life to pursuing fundamental research in remote sensing. A pioneer in the geometric-optical modeling of vegetation canopies, his work is held in high regard by the international remote sensing community. He codeveloped the Li–Strahler geometric-optic model, and this paper was selected by a member of the International Society for Optical Engineering (SPIE) milestone series. As a chief scientist, Xiaowen Li led a scientific team that made outstanding advances in bidirectional reflectance distribution modeling, directional thermal emission modeling, comprehensive experiments, and the understanding of spatial and temporal scale effects in remote sensing information, and of quantitative inversions utilizing remote sensing data. In addition to his broad research activities, he was noted for his humility and his dedication in making science more accessible for the general public. Here, the life and academic contributions of Xiaowen Li to the field of quantitative remote sensing science are briefly reviewed. [ABSTRACT FROM AUTHOR]

Light detection and ranging (LiDAR) is a widely used technology for the acquisition of three-dimensional (3D) information about a wide variety of physical objects and environments. However, before conducting a campaign, a test is typically conducted to assess the potential of the utilized algorithm for information retrieval. It might not be a real campaign but rather a simulation to save time and costs. Here, a multi-platform LiDAR simulation model considering the location, direction, and wavelength of each emitted laser pulse was developed based on the large-scale remote sensing (RS) data and image simulation framework (LESS) model, which is a 3D radiative transfer model for simulating passive optical remote sensing signals using the ray tracing algorithm. The LESS LiDAR simulator took footprint size, returned energy, multiple scattering, and multispectrum LiDAR into account. The waveform and point similarity were assessed with the LiDAR module of the discrete anisotropic radiative transfer (DART) model. Abstract and realistic scenes were designed to assess the simulated LiDAR waveforms and point clouds. A waveform comparison in the abstract scene with the DART LiDAR module showed that the relative error was lower than 1%. In the realistic scene, airborne and terrestrial laser scanning were simulated by LESS and DART LiDAR modules. Their coefficients of determination ranged from 0.9108 to 0.9984 . Their mean was 0.9698 . The number of discrete returns fitted well and the coefficient of determination was 0.9986 . A terrestrial point cloud comparison in the realistic scene showed that the coefficient of determination between the two sets of data could reach 0.9849 . The performance of the LESS LiDAR simulator was also compared with the DART LiDAR module and HELIOS++. The results showed that the LESS LiDAR simulator is over three times faster than the DART LiDAR module and HELIOS++ when simulating terrestrial point clouds in a realistic scene. The proposed LiDAR simulator offers two modes for simulating point clouds: single-ray and multi-ray modes. The findings demonstrate that utilizing a single-ray simulation approach can significantly reduce the simulation time, by over 28 times, without substantially affecting the overall point number or ground pointswhen compared to employing multiple rays for simulations. This new LESS model integrating a LiDAR simulator has great potential in terms of simultaneously simulating LiDAR data and optical images based on the same 3D scene and parameters. As a proof of concept, the normalized difference vegetation index (NDVI) results from multispectral images and the vertical profiles from multispectral LiDAR waveforms were simulated and analyzed. The results showed that the proposed LESS LiDAR simulator can fulfill its design goals. [ABSTRACT FROM AUTHOR]

Light detection and ranging (LiDAR) data can provide 3D structural information of objects and are ideal for extracting individual tree parameters, and individual tree segmentation (ITS) is a vital step for this purpose. Various ITS methods have been emerging from airborne LiDAR scanning (ALS) or unmanned aerial vehicle LiDAR scanning (ULS) data. Here, we propose a new individual tree segmentation method, which couples the classical and efficient watershed algorithm (WS) and the newly developed connection center evolution (CCE) clustering algorithm in pattern recognition. The CCE is first used in ITS and comprehensively optimized by considering tree structure and point cloud characteristics. Firstly, the amount of data is greatly reduced by mean shift voxelization. Then, the optimal clustering scale is automatically determined by the shapes in the projection of three different directions. We select five forest plots in Saihanba, China and 14 public plots in Alpine region, Europe with ULS or ALS point cloud densities from 11 to 3295 pts/m2. Eleven ITS methods were used for comparison. The accuracy of tree top detection and tree height extraction is estimated by five and two metrics, respectively. The results show that the matching rate (Rmatch) of tree tops is up to 0.92, the coefficient of determination (R2) of tree height estimation is up to.94, and the minimum root mean square error (RMSE) is 0.6 m. Our method outperforms the other methods especially in the broadleaf forests plot on slopes, where the five evaluation metrics for tree top detection outperformed the other algorithms by at least 11% on average. Our ITS method is both robust and efficient and has the potential to be used especially in coniferous forests to extract the structural parameters of individual trees for forest management, carbon stock estimation, and habitat mapping. [ABSTRACT FROM AUTHOR]

Vegetation cover fraction (fCover) and related quantities are basic yet critical vegetation structure variables in various disciplines and applications. Ground- and aerial-based proximal and remote sensing techniques have been widely adapted across multiple spatial extents. However, the definitions of fCover-related nomenclatures have not yet been fully standardized, leading to confusing terms and making comparing historic measures difficult. With the issues potentially arising from an increasing diversity of fCover and related quantities estimation methods and corresponding uncertainties, there is also a growing need to spread knowledge on the current advances, challenges, and perspectives, especially in the context of no such existing review for ground- and aerial- based estimation. This paper provides the current knowledge mainly concerning passive image-based methods and active light detection and ranging (LiDAR) -based methods. We first harmonized the definitions of fCover and its related quantities (e.g., effective canopy cover, crown cover, stratified vegetation cover, and canopy fraction). Secondly, the typical applications of fCover and related quantities over a range of scales, fields, and ecosystems were summarized. Thirdly yet importantly, we offered a comprehensive review of traditional non-imaging methods, image-based methods (e.g., segmentation, unmixing, and spectral retrieval), point cloud-based methods (e.g., rasterization), and LiDAR return-based methods (e.g., return number index and return intensity retrieval) across different platforms (i.e., ground, unmanned aerial vehicle (UAV) and airplane). Our investigation of fCover and related quantities estimation touches upon various vegetation ecosystems, including agriculture cropland, grassland, wetland, and forest. Finally, the current challenges and future directions were discussed, such as image signal processing under complex heterogeneous surfaces and stratified cover and non-photosynthesis cover retrieval. We, therefore, expect that this review may offer an insight into fCover and related quantities estimation and serve as a reference for remote sensing scientists, agronomists, silviculturists, and ecologists. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is a crucial parameter that reflects land–atmosphere interaction and has thus attracted wide interest from geoscientists. Owing to the rapid development of Earth observation technologies, remotely sensed LST is playing an increasingly essential role in various fields. This review aims to summarize the progress in LST estimation algorithms and accelerate its further applications. Thus, we briefly review the most‐used thermal infrared (TIR) LST estimation algorithms. More importantly, this review provides a comprehensive collection of the widely used TIR‐based LST products and offers important insights into the uncertainties in these products with respect to different land cover conditions via a systematic intercomparison analysis of several representative products. In addition to the discussion on product accuracy, we address problems related to the spatial discontinuity, spatiotemporal incomparability, and short time span of current LST products by introducing the most effective methods. With the aim of overcoming these challenges in available LST products, much progress has been made in developing spatiotemporal seamless LST data, which significantly promotes the successful applications of these products in the field of surface evapotranspiration and soil moisture estimation, agriculture drought monitoring, thermal environment monitoring, thermal anomaly monitoring, and climate change. Overall, this review encompasses the most recent advances in TIR‐based LST and the state‐of‐the‐art of applications of LST products at various spatial and temporal scales, identifies critical further research needs and directions to advance and optimize retrieval methods, and promotes the application of LST to improve the understanding of surface thermal dynamics and exchanges. Plain Language Summary: Land surface temperature (LST) is a crucial geophysical parameter related to surface energy and water balance of the land‐atmosphere system. Satellite remote sensing provides the best way to measure LST and generate various LST products at regional and global scales. In this review, to facilitate the application of LST products in different fields, we first present the physical meaning of satellite‐derived LST. Subsequently, we summarize recent advances in LST retrieval and validation methods, with a special focus on the state‐of‐the‐art product collections, product accuracies and intercomparisons, and main problems in current LST products as well as their possible solutions. Additionally, we also review the major applications of LST products in agricultural drought monitoring, thermal environment monitoring, thermal anomaly monitoring, and climate change. Finally, we offer recommendations or perspectives to promote LST retrieval methods and their applications. This review will aid the user in gaining a thorough comprehensive understanding of satellite‐derived LST products and promoting their appropriate applications. Key Points: State‐of‐the‐art satellite‐derived land surface temperature (LST) product levels, sources, uncertainties, and differences are providedTypical applications of LST products in various fields are summarizedFuture directions for the generation and applications of LST products are recommended [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]

This paper describes two new methods that were used to generate 26 years (1985–2010) of broadband emissivity (BBE) products with spatiotemporal continuity at the global scale from satellite data recorded by the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Very High Resolution Radiometer (AVHRR). On the basis of emissivity libraries, the study began with establishing relationships for converting channel emissivities of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and MODIS to BBEs for the 8–13.5-\mu\m spectral window and then developed two new algorithms from simultaneous ASTER emissivity products to estimate BBEs over vegetated surfaces using the MODIS and AVHRR data. The MODIS-data-based algorithm (MDBA) uses linear equations with MODIS normalized difference vegetation index (NDVI) and seven channels' albedo; the AVHRR-data-based algorithm uses nonlinear equations with AVHRR red and near-infrared reflectances. The proposed algorithms were first validated with ASTER emissivity products. Results indicated that the root-mean-square errors of both the proposed algorithms were less than 0.015 and their biases were less than 0.003. Comparison with MODIS emissivity products from the day/night algorithm showed that the estimated BBEs using the MDBA were generally smaller than the MODIS products. Cross-comparisons were also made between the proposed algorithms and the NDVI threshold method. Finally, strategies for mapping global BBE products from the MODIS and AVHRR data are presented, and some examples are discussed. The global BBE products are planned to be released throughout the network in the near future. [ABSTRACT FROM PUBLISHER]

Accurate and rapid estimation of canopy cover (CC) is crucial for many ecological and environmental models and for forest management. Unmanned aerial vehicle-light detecting and ranging (UAV-LiDAR) systems represent a promising tool for CC estimation due to their high mobility, low cost, and high point density. However, the CC values from UAV-LiDAR point clouds may be underestimated due to the presence of large quantities of within-crown gaps. To alleviate the negative effects of within-crown gaps, we proposed a pit-free CHM-based method for estimating CC, in which a cloth simulation method was used to fill the within-crown gaps. To evaluate the effect of CC values and within-crown gap proportions on the proposed method, the performance of the proposed method was tested on 18 samples with different CC values (40−70%) and 6 samples with different within-crown gap proportions (10−60%). The results showed that the CC accuracy of the proposed method was higher than that of the method without filling within-crown gaps (R2 = 0.99 vs 0.98; RMSE = 1.49% vs 2.2%). The proposed method was insensitive to within-crown gap proportions, although the CC accuracy decreased slightly with the increase in within-crown gap proportions. [ABSTRACT FROM AUTHOR]

The canopy bidirectional reflectance distribution function (BRDF) plays a pivotal role in estimating the biophysical parameters of plants, whereas soil background anisotropy creates challenges for their retrieval. Soil optical properties affect canopy anisotropic characteristics, especially in open-canopy areas. However, the remote sensing of background anisotropy is challenging due to the difficulties of information extraction in complex forest ecosystems and varying illumination conditions. This study develops an efficient photogrammetric technique to extract the background soil bidirectional reflectance factor (BRF) from unmanned aerial vehicle (UAV)-based multiangular images and to verify the need for accurate soil anisotropy information in canopy radiative transfer modeling. Soil BRF profiles were measured over three open-canopy sample plots from multiangular remotely sensed multispectral images collected with a hexacopter. As validation, reference soil BRF profiles were synchronously acquired by a ground-based multiangular imaging system. A high level of consistency between the ground- and UAV-measured soil BRF was observed with an RMSE of less than 0.012. Uncertainty analysis of the measured soil BRF showed that multiple scattering between sunlit soil in large sunflecks and foliage elements contributed less than 5%. Both results demonstrated that soil anisotropy can be accurately extracted from UAV multiangular measurements. To explicitly demonstrate that the use of soil anisotropy can reduce uncertainties in canopy radiative transfer simulations, we simulated the canopy BRF with Lambertian soil and with anisotropic soil using a three-dimensional (3D) radiative transfer model under different soil moisture content (SMC) levels, canopy cover (CC) levels and solar zenith angles (SZAs) with simulated realistic forest scenes. We found that less CC, lower SZAs and less SMC lead to a more significant influence of soil anisotropy on canopy reflectance; e.g., the reflectance bias reaches up to 0.3 in the hotspot direction. This illustrates that neglecting soil anisotropy can cause considerable errors in the modeling of the canopy BRF of open forests (i.e., CC levels of less than 0.5). The proposed technique facilitates the characterization of anisotropic forest background soil, which is important for advancing canopy radiative transfer modeling and validation and for the retrieval of vegetation parameters. [ABSTRACT FROM AUTHOR]

Traditional Light Detection and Rangings (LiDARs) can quickly collect high-accuracy of three-dimensional (3D) point cloud data at a designated wavelength (i.e., cannot obtain hyperspectral data), while the passive hyperspectral imager can collect rich spectral data of ground objects, but are lack of 3D spatial data. This paper presents one innovative study on the design of airborne-oriented supercontinuum laser hyperspectral (SCLaHS) LiDAR with 50 bands covering 400 nm to 900 nm at a spectral resolution of 10 nm and ground sampling distance (GSD) of 0.5 m. The major innovations include (1) development of the high-power narrow-pulse supercontinuum laser source covering 400 nm to 900 nm with 50 bands using multi-core microstructure fibre, all-polarization maintaining fibre and ultra-long cavity structure, (2) a miniaturized aberration correction holographic concave grating spectroscopic and streak tube technique are developed for 50 bands laser echoes detection at high spectral-spatial-temporal resolution and dynamic airborne platform, and (3) the algorithm theoretic basis for SCLaHS LiDAR point cloud data 3D geodetic coordination calculation, including in-flight airborne calibration algorithm. The initial experimental results demonstrated that the designed SCLaHS LiDAR is doable, and a prototype of the (SCLaHS) LiDAR intends to be implemented. [ABSTRACT FROM AUTHOR]

Leaf angle distribution (LAD) is an important attribute of forest canopy architecture and affects the solar radiation regime within the canopy. Terrestrial laser scanning (TLS) has been increasingly used in LAD estimation. The point clouds data suffer from the occlusion effect, which leads to incomplete scanning and depends on measurement strategies such as the number of scans and scanner location. Evaluating these factors is important to understand how to improve LAD, which is still lacking. Here, we introduce an easy way of estimating the LAD using open source software. Importantly, the influence of the occlusion effect on the LAD was evaluated by combining the proposed complete point clouds (CPCs) with the simulated data of 3D tree models of Aspen, Pin Oak and White Oak. We analyzed the effects of the point density, the number of scans and the scanner height on the LAD and G-function. Results show that: (1) the CPC can be used to evaluate the TLS-based normal vector reconstruction accuracy without an occlusion effect; (2) the accuracy is slightly affected by the normal vector reconstruction method and is greatly affected by the point density and the occlusion effect. The higher the point density (with a number of points per unit leaf area of 0.2 cm−2 to 27 cm−2 tested), the better the result is; (3) the performance is more sensitive to the scanner location than the number of scans. Increasing the scanner height improves LAD estimation, which has not been seriously considered in previous studies. It is worth noting that relatively tall trees suffer from a more severe occlusion effect, which deserves further attention in further study. [ABSTRACT FROM AUTHOR]

Occlusion effect, an inherent problem of terrestrial laser scanning (TLS) measurements, limits the potential of TLS data in tree attribute estimation. Multiple scans seek to mitigate this effect to provide enhanced scan completeness. However, the numbers and locations of the scans (i.e., the scan design) are usually determined via a subjective assessment of the tree density, spatial patterns of trees, and attributes to be derived. These could cause suboptimal scan completeness and limit tree attribute estimation. This study proposed an iterative-mode scan design to minimize the occlusion effect. First, we introduced a PoTo index based on visibility analysis to evaluate how many trees can be scanned from a location and to select effective candidates for the optimal TLS location. Second, we introduced a cumulative degree of ring closure (CDRC) to quantify the scan completeness for each candidate and determine the optimal TLS location. The TLS data sets of virtual forests with field-measured and synthetic plot parameter settings were simulated according to iterative- and regular-mode designs by using a Heidelberg light detection and ranging (LiDAR) Operations Simulator (HELIOS). The results demonstrated that an iterative-mode design can improve the scan completeness of trees compared to the regular-mode design. The tree attribute (diameter at breast height (DBH), tree height, stem curve, and crown volume) estimates of the iterative-mode design were less erroneous than those of the regular-mode design (e.g., the root-mean-square error (RMSE) could decrease the stem curve estimation by 38% and the crown volume estimation by 15%). This study suggests that the iterative-mode design can obtain an improved quality of the TLS data, especially for dense stands. [ABSTRACT FROM AUTHOR]

The 3-D information collected from sample plots is significant for forest inventories. Terrestrial laser scanning (TLS) has been demonstrated to be an effective device in data acquisition of forest plots. Although TLS is able to achieve precise measurements, multiple scans are usually necessary to collect more detailed data, which generally requires more time in scan preparation and field data acquisition. In contrast, mobile laser scanning (MLS) is being increasingly utilized in mapping due to its mobility. However, the geometrical peculiarity of forests introduces challenges. In this article, a test backpack-based MLS system, i.e., backpack laser scanning (BLS), is designed for forest plot mapping without a global navigation satellite system/inertial measurement unit (GNSS-IMU) system. To achieve accurate matching, this article proposes to combine the line and point features for calculating transformation, in which the line feature is derived from trunk skeletons. Then, a scan-to-map matching strategy is proposed for correcting positional drift. Finally, this article evaluates the effectiveness and the mapping accuracy of the proposed method in forest sample plots. The experimental results indicate that the proposed method achieves accurate forest plot mapping using the BLS; meanwhile, compared to the existing methods, the proposed method utilizes the geometric attributes of the trees and reaches a lower mapping error, in which the mean errors and the root square mean errors for the horizontal/vertical direction in plots are less than 3 cm. [ABSTRACT FROM AUTHOR]

Advanced Very High Resolution Radiometer (AVHRR) sensors provide a valuable data source for generating long-term global land surface temperature (LST). However, changes in the observation time that are caused by satellite orbit drift restrict their wide application. Here, a generalized split-window (GSW) algorithm was implemented to retrieve the LST from the time series AVHRR data. Afterwards, a novel orbit drift correction (ODC) algorithm, which was based on the diurnal temperature cycle (DTC) model and Bayesian optimization algorithm, was also proposed for normalizing the estimated LST to the same local time. This ODC algorithm is pixel-based and it only needs one observation every day. The resulting LSTs from the six-year National Oceanic and Atmospheric Administration (NOAA)-14 satellite data were validated while using Surface Radiation Budget Network (SURFRAD) in-situ measurements. The average accuracies for LST retrieval varied from −0.4 K to 2.0 K over six stations and they also depended on the viewing zenith angle and season. The simulated data illustrate that the proposed ODC method can improve the LST estimate at a similar magnitude to the accuracy of the LST retrieval, i.e., the root-mean-square errors (RMSEs) of the corrected LSTs were 1.3 K, 2.2 K, and 3.1 K for the LST with a retrieval RMSE of 1 K, 2 K, and 3 K, respectively. This method was less sensitive to the fractional vegetation cover (FVC), including the FVC retrieval error, size, and degree of change within a neighboring area, which suggested that it could be easily updated by applying other LST expression models. In addition, ground validation also showed an encouraging correction effect. The RMSE variations of LST estimation that were introduced by ODC were within ±0.5 K, and the correlation coefficients between the corrected LST errors and original LST errors could approach 0.91. [ABSTRACT FROM AUTHOR]

Craters contain important information on geological history and have been widely used for dating absolute age and reconstructing impact history. The impact process results in a lot of ejected fragments and these fragments may form secondary craters. Studies on distinguishing primary craters from secondary craters are helpful in improving the accuracy of crater dating. However, previous studies about distinguishing primary craters from secondary craters were either conducted by manual identification or used approaches mainly concerning crater spatial distribution, which are time-consuming or have low accuracy. This paper presents a machine learning approach to distinguish primary craters from secondary craters. First, samples used for training and testing were identified and unified. The whole dataset contained 1032 primary craters and 4041 secondary craters. Then, considering the differences between primary and secondary craters, features mainly related to crater shape, depth, and density were calculated. Finally, a random forest classifier was trained and tested. This approach showed a favorable performance. The accuracy and F1-score for fivefold cross-validation were 0.939 and 0.839, respectively. The proposed machine learning approach enables an automated method of distinguishing primary craters from secondary craters, which results in better performance. [ABSTRACT FROM AUTHOR]

Soil-Adjusted Vegetation Index (SAVI) is found to be undesirable to estimate Leaf Area Index (LAI) with heterogeneous canopy structure in low vegetation cover. In this article, three new vegetation indices (VIs), such as Normalized Hotspot-Signature Vegetation Index 2 (NHVI2), Hotspot-Signature Soil-Adjusted Vegetation Index (HSVI), and Hotspot-Signature 2-Band Enhanced Vegetation Index (HEVI2), are proposed for a better quantitative estimation of LAI and soil-noise resistance than with SAVI. To obtain these new indices, the angular index called Normalized Difference between Hotspot and Darkspot (NDHD) is introduced which represents the distribution of foliage in vegetation canopy. The validity of new VIs is statistically verified using simulated data and field measurements. The Discrete Anisotropic Radiative Transfer (DART) model is used to simulate both the homogeneous and heterogeneous canopy for analyzing vegetation isolines behaviors, soil-noise resistance, and LAI estimation. In situ measurements of LAI and bidirectional reflectance factor from the Boreal Ecosystem-Atmosphere Study (BOREAS) are also used to test the robustness of the new VIs for the estimation of LAI. By considering the distribution of the foliage, the accuracy of LAI estimation of SAVI for heterogeneous canopy improved almost 16% using exponential regression analysis. With the improvement of multiangular remote-sensing and Bidirectional Reflectance Distribution Function (BRDF) models in the future, hotspot-signature VIs have the potential to provide a more accurate LAI estimation for heterogeneous canopy in strong soil-noise interference area. [ABSTRACT FROM AUTHOR]

Precise structural information collected from plots is significant in the management of and decision-making regarding forest resources. Currently, laser scanning is widely used in forestry inventories to acquire three-dimensional (3D) structural information. There are three main data-acquisition modes in ground-based forest measurements: single-scan terrestrial laser scanning (TLS), multi-scan TLS and multi-single-scan TLS. Nevertheless, each of these modes causes specific difficulties for forest measurements. Due to occlusion effects, the single-scan TLS mode provides scans for only one side of the tree. The multi-scan TLS mode overcomes occlusion problems, however, at the cost of longer acquisition times, more human labor and more effort in data preprocessing. The multi-single-scan TLS mode decreases the workload and occlusion effects but lacks the complete 3D reconstruction of forests. These problems in TLS methods are largely avoided with mobile laser scanning (MLS); however, the geometrical peculiarity of forests (e.g., similarity between tree shapes, placements, and occlusion) complicates the motion estimation and reduces mapping accuracy. Therefore, this paper proposes a novel method combining single-scan TLS and MLS for forest 3D data acquisition. We use single-scan TLS data as a reference, onto which we register MLS point clouds, so they fill in the omission of the single-scan TLS data. To register MLS point clouds on the reference, we extract virtual feature points that are sampling the centerlines of tree stems and propose a new optimization-based registration framework. In contrast to previous MLS-based studies, the proposed method sufficiently exploits the natural geometric characteristics of trees. We demonstrate the effectiveness, robustness, and accuracy of the proposed method on three datasets, from which we extract structural information. The experimental results show that the omission of tree stem data caused by one scan can be compensated for by the MLS data, and the time of the field measurement is much less than that of the multi-scan TLS mode. In addition, single-scan TLS data provide strong global constraints for MLS-based forest mapping, which allows low mapping errors to be achieved, e.g., less than 2.0 cm mean errors in both the horizontal and vertical directions. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various articles within the issue on topics including solar radiation, forest biomass and air-quality monitoring.

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]

Highlights • Path length distribution model is implemented on TLS data for LAI estimation. • Alpha hull is used for 3D crown modeling and calculation of path length. • The proposed TLS-extracted LAI compared well with the hemispherical photography. • Two simulated plots were built to provide more objective validation. Abstract Terrestrial Laser Scanning (TLS) is an active technology that can acquire the finest characteristics of canopy structure and plays an increasing role in estimating Leaf Area Index (LAI) in forest canopies. However, 3D information is not directly used in conventional TLS-based methods using the gap fraction theory. In addition, quantifying clumping effect within canopies is still a difficult task. In this paper, we presented a method to reduce clumping effect and estimate LAI using TLS data. Our recently proposed path length distribution model was applied to TLS data. Instead of converting 3D points to 2D image, the path length distribution can be extracted using the TLS-recorded 3D data and the crown models built with the alpha shapes algorithm. Two simulated scenes and one actual forest plot were utilized for validation. The results of the proposed method agree well with both the true LAI (in the simulated scenes) and the extracted PAI by the digital hemispherical photography (in the actual plot). This LAI estimation method using TLS and the path length distribution model provides a novel way for ground-based LAI measurements and shows its great potential. [ABSTRACT FROM AUTHOR]

Accurate estimates of fractional vegetation cover (FVC) using remotely sensed images collected using unmanned aerial vehicles (UAVs) offer considerable potential for field measurement. However, most existing methods, which were originally designed to extract FVC from ground-based remotely sensed images (acquired at a few meters above the ground level), cannot be directly used to process aerial images because of the presence of large quantities of mixed pixels. To alleviate the negative effects of mixed pixels, we proposed a new method for decomposing the Gaussian mixture model and estimating FVC, namely, the half-Gaussian fitting method for FVC estimation (HAGFVC). In this method, the histograms of pure vegetation pixels and pure background pixels are firstly fit using two half-Gaussian distributions in the Commission Internationale d’Eclairage (CIE) L*a*b* color space. Then, a threshold is determined based on the parameters of Gaussian distribution to generate a more accurate FVC estimate. We acquired low-altitude remote-sensing (LARS) images in three vegetative growth stages at different flight altitudes over a cornfield. The HAGFVC method successfully fitted the half-Gaussian distributions and obtained stable thresholds for FVC estimation. The results indicate that the HAGFVC method can be used to effectively and accurately derive FVC images, with a small mean bias error (MBE) and with root mean square error (RMSE) of less than 0.04 in all cases. Comparatively, other methods we tested performed poorly (RMSE of up to 0.36) because of the abundance of mixed pixels in LARS images, especially at high altitudes above ground level (AGL) or in the case of moderate vegetation coverage. The results demonstrate the importance of developing image-processing methods that specially account for mixed pixels for LARS images. Simulations indicated that the theoretical accuracy (no errors in fitting the half-Gaussian distributions) of the HAGFVC method reflected an RMSE of less than 0.07. Additionally, this method provides a useful approach to efficiently estimating FVC by using LARS images over large areas. [ABSTRACT FROM AUTHOR]

Abstract Urban leaf area measurement is crucial to properly determining the effect of urban trees on micro-climate regulation, heat island effect, building cooling, air quality improvement, and ozone formation. Previous works on the leaf area measurement have mainly focused on the stand level, although the presence of individual trees is more common than forests in urban areas. The only feasible ways for an operational non-destructive leaf area measurement, namely, optical indirect methods, are mostly limited in urban areas because light path is constantly intercepted by surrounding buildings or other objects. A terrestrial laser scanner (TLS), which can extract an individual tree by using its unique distance information, provides a possibility for indirectly measuring the leaf area index (LAI) in urban areas. However, indirect LAI measurement theory, which uses the cosine of an observation zenith angle for path-length correction, is incompatible for an individual tree because the representative projected area of LAI changes while the observation zenith angle changes, thus making the results incomparable and ambiguous. Therefore, we modified a path length distribution model for the leaf area measurement of an individual tree by replacing the traditional cosine path length correction for a continuous canopy with real path length distribution. We reconstructed the tree crown envelope from a TLS point cloud and calculated a real path length distribution through laser pulse-envelope intersections. Consequently, leaf area density was separated from the path length distribution model for leaf area calculation. Comparisons with reference measurement for an individual tree showed that the TLS-derived leaf area using the path length distribution is insensitive to the scanning resolution and agrees well with an allometric measurement with an overestimation from 5 m2 to 18 m2 (3–10%, respectively). Results from different stations are globally consistent, and using a weighted mean for different stations by sample numbers further improves the universality and efficiency of the proposed method. Further automation of the proposed method can facilitate a rapid and operational leaf area extraction of an individual tree for urban climate modeling. [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]

Ground filtering is an essential procedure in almost all LiDAR applications. However, most existing ground filtering algorithms require different amounts of user input to manually set up initial parameters, such as terrain relief amplitude and average slope, which is subjective, time consuming, and prone to errors. Here, we propose a simple terrain relief index derived from raw airborne LiDAR data to automatically tune the slope-related parameters of ground filtering algorithms. The terrain relief index is a ratio between the height difference of the entire point cloud and the maximum above ground level of non-ground points. The latter variable can be estimated with the maximum local height difference of raw LiDAR data through gridding. We validated our method using the benchmark airborne LiDAR datasets provided by the International Society for Photogrammetry and Remote Sensing. The results showed a high correlation ( r  = 0.876) between the terrain relief index and the referential terrain relief amplitude. The degree of correlation was greater across larger areas ( r  = 0.926) than small areas ( r  = 0.861) regardless of the type of land cover (e.g., city or forest). The terrain relief index was introduced into two existing filtering algorithms: Cloth Simulation Filtering (CSF) and Progressive Morphological (PM) Filter, by relating the terrain relief index to the cloth rigidness of the CSF and the slope threshold of the PM filter. To compare the results, the two algorithms were implemented both with manually tuned parameters and with the parameters derived from the terrain relief index. The results showed that there was only a slight discrepancy in average Total Error (0.1%) between them in the CSF, which means that the terrain relief index can automatically determine the cloth rigidness without noticeable loss of accuracy. The average difference between the slope threshold provided by the terrain relief index and the manually tuned optimal slope threshold was 0.142 rad (8.136°) for the PM filter, which is acceptable relative to manually parameter setting without any prior knowledge. The terrain relief index can estimate the terrain relief amplitude from raw airborne LiDAR data, and the parameter settings suggested in this paper for the filtering algorithm can improve automation. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR) absorbed by vegetation have been successfully generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) data since early 2000. As the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument onboard, the Suomi National Polar-orbiting Partnership (SNPP) has inherited the scientific role of MODIS, and the development of a continuous, consistent, and well-characterized VIIRS LAI/FPAR data set is critical to continue the MODIS time series. In this paper, we build the radiative transfer-based VIIRS-specific lookup tables by achieving minimal difference with the MODIS data set and maximal spatial coverage of retrievals from the main algorithm. The theory of spectral invariants provides the configurable physical parameters, i.e., single scattering albedos (SSAs) that are optimized for VIIRS-specific characteristics. The effort finds a set of smaller red-band SSA and larger near-infrared-band SSA for VIIRS compared with the MODIS heritage. The VIIRS LAI/FPAR is evaluated through comparisons with one year of MODIS product in terms of both spatial and temporal patterns. Further validation efforts are still necessary to ensure the product quality. Current results, however, imbue confidence in the VIIRS data set and suggest that the efforts described here meet the goal of achieving the operationally consistent multisensor LAI/FPAR data sets. Moreover, the strategies of parametric adjustment and LAI/FPAR evaluation applied to SNPP-VIIRS can also be employed to the subsequent Joint Polar Satellite System VIIRS or other instruments. [ABSTRACT FROM AUTHOR]

Accurate and efficient in situ measurement methods of leaf area index (LAI) and leaf angle distribution (LAD) are needed to estimate the fluxes of water and energy in agricultural settings. However, available methods: to estimate these two parameters, especially LAD, are limited. In this study, we propose a field measurement method using multi-angular digital images to estimate LAI and LAD simultaneously from the area proportions of: (i) sunlit soil; (ii) sunlit leaves; (iii) shaded soil; and (iv) shaded leaves. A new expression of the fraction of sunlit leaves is developed based on the radiative transfer theory. Coupling the measured and modeled fractions with an optimization scheme, LAI and the LAD parameters are derived from inverting a fractional model of sunlit and shaded leaves and soil. Through four tests using simulated scenes and in situ measurements for row crops, it is determined that our method performs well. The absolute error of LAI estimation is less than 0.1 when LAI is low (i.e., <1.2), and the absolute deviations of LAI estimates are approximately 0.5 when the reference LAI is 3.5. The estimation errors of LAI and the G function (a representative of LAD which quantifies the projection of unit foliage area) for in situ measurements are respectively less than 0.2 and 0.06 in general. In addition, the accuracy of estimation is even higher when leaves are simulated as randomly distributed disks or observations from multiple azimuth planes are used. One of the most interesting features of this method is its ability to estimate reasonable LAD directly from the fractions of sunlit and shaded leaves, even when LAI is high (i.e., >3), so little background soil is seen. The sensitivity and uncertainty analysis is consistent with the estimation errors. Theoretically, the application of this method is not limited to row crops or to field measurement, as the derived formulae of sunlit and shaded components can be used for other types of vegetation by introducing the clumping index and can be used in the modeling of canopy vegetation parameters (e.g., canopy reflectance). [ABSTRACT FROM AUTHOR]