Biao Cao

Modeling Directional Brightness Temperature Over Mixed Scenes of Continuous Crop and Road: A Case Study of the Heihe River Basin

A new geometric optical model is proposed in this letter to simulate the directional brightness temperature (DBT) distribution over mixed scenes of continuous crop and road. The DBT distributions of the crop and road zones are separately calculated, and the road zone consists of a road and adjacent crop sides. A road distribution polar map is designed to show all of the roads of different lengths, widths, and orientations in the scene. The airborne multiangle data set of the thermal infrared band that was acquired during the Heihe Watershed Allied Telemetry Experimental Research experiment is used for validation. The results demonstrate that the proposed model can simulate the DBT of a heterogeneous scene (90 × 90 m2) with a root-mean-square error equal to 1.1 K and good trend similarity.

Retrieval of Leaf, Sunlit Soil, and Shaded Soil Component Temperatures Using Airborne Thermal Infrared Multiangle Observations

Land surface component temperatures are important inputs in longwave radiation and evapotranspiration estimation models. Most component temperature inversion approaches focus only on two components, namely, soil and leaves, because space-based multiangle observations are lacking. This approach is inconsistent with ground-based measurements, which suggest that the temperatures of sunlit and shaded soil may significantly differ. This paper explores a three-component temperature inversion scheme that uses airborne multiangle thermal infrared observations to decrease the difference between the retrieved data and the actual subpixel temperature distribution. The FR97 model, which is an analytical directional brightness temperature model that was modified by dividing the soil component into sunlit and shaded portions, is adopted to calculate the matrix of component effective emissivity, which links multiangular observations and component temperatures. The new forward model and the inversion scheme are assessed using simulated data sets from the Scattering by Arbitrarily Inclined Leaves (4SAIL) model. The results indicate that the modified FR97 model provides good precision and that the inversion scheme based on the modified FR97 model is appropriate because of the models simplicity and accuracy and the inversions low sensitivity to noise. The inversion scheme is validated using airborne data collected by the wide-angle infrared dual-mode line/area array scanner over an area planted with maize and ground measurements collected during the Heihe Watershed Allied Telemetry Experimental Research campaign. The results indicate that the root mean square errors of the component temperatures of the leaves, sunlit soil, and shaded soil were 0.72 °C, 1.55 °C, and 2.73 °C, respectively. Because of the modified FR97s straightforward form and acceptable precision, we recommend this new retrieval scheme as an option for retrieving the component temperatures of leaves, sunlit soil, and shaded soil.

Evaluation of Land Surface Temperature Retrieval from FY-3B/VIRR Data in an Arid Area of Northwestern China

This paper uses the refined Generalized Split-Window (GSW) algorithm to derive the land surface temperature (LST) from the data acquired by the Visible and Infrared Radiometer on FengYun 3B (FY-3B/VIRR). The coefficients in the GSW algorithm corresponding to a series of overlapping ranges for the mean emissivity, the atmospheric Water Vapor Content (WVC), and the LST are derived using a statistical regression method from the numerical values simulated with an accurate atmospheric radiative transfer model MODTRAN 4 over a wide range of atmospheric and surface conditions. The GSW algorithm is applied to retrieve LST from FY-3B/VIRR data in an arid area in northwestern China. Three emissivity databases are used to evaluate the accuracy of different emissivity databases for LST retrieval, including the ASTER Global Emissivity Database (ASTER_GED) at a 1-km spatial resolution (AG1km), an average of twelve ASTER emissivity data in the 2012 summer and emissivity spectra extracted from spectral libraries. The LSTs retrieved from the three emissivity databases are evaluated with ground-measured LST at four barren surface sites from June 2012 to December 2013 collected during the HiWATER field campaign. The results indicate that using emissivity

Comparison of Five Slope Correction Methods for Leaf Area Index Estimation From Hemispherical Photography

We compare five slope correction methods developed by Walter et al., Montes et al., Schleppi et al., España et al., and Gonsamo et al. (referred to as WAL, MON, SCH, ESP, and GON, respectively) using artificial fisheye pictures simulated by graphics software and a lookup table (LUT) retrieval method. The LUT is built by simulating the directional gap fraction as a function of leaf area index (LAI) and average leaf inclination angle (ALIA) using the Poisson law. LAI and ALIA estimates correspond to the case of the LUT that provides the lowest root-mean-square error between the observed gap fractions after slope correction and the simulated ones. Three LAI values (1.5, 3.5, and 5.5), four ALIA values (26.8°, 45°, 57.5°, and 63.2°), and three slope angles (0°, 20°, and 50°) constituted 36 samples of random scenes. ESP is recommended because its results are accurate and independent on the leaf angle distribution (LAD), while GON only performs well for spherical LAD. The three other methods present less good performances with underestimation or overestimation of LAI and/or ALIA depending on the LAD, and the recommended order for them is MON, SCH, and WAL.

Analysis of the Land Surface Temperature Scaling Problem: A Case Study of Airborne and Satellite Data over the Heihe Basin

Abstract: This study analyzed the scaling problem of land surface temperature (LST) data retrieved with the Temperature Emissivity Separation (TES) algorithm. We compiled a remotely sensed dataset that included Thermal Airborne Hyperspectral Imager (TASI) and satellite-based Advanced Spaceborne Thermal Emission Reflection (ASTER) data, which were acquired simultaneously. This dataset provided the range of spatial heterogeneities of land surface necessary for the study, which was quantified by the dispersion variance. The LST scaling problem was studied by comparing the remotely sensed LST products in two ways. First, the LST products calculated in the distributed method and the lumped method were compared. Second, the airborne and satellite-based LST products derived from the TES algorithm were compared. Four upscaling methods of LST were used in the process. A scaling correction methodology was developed based on the comparisons. The results showed that the scaling effect could be as large as 0.8 K when the spatial resolution of the TASI LST data was coarse. The scaling effect increases quickly with the spatial resolution until it reaches the characteristic scale of the landscape and is positively correlated with the spatial heterogeneity. The first two upscaling methods denoted as Methods 1–2 can upscale the LST more effectively when compared with the other two scaling methods (Methods 3–4). The scaling effect for the ASTER data is not notable. The comparison between the TASI and

Estimation of Upward Longwave Radiation From Vegetated Surfaces Considering Thermal Directionality

Surface upward longwave radiation (SULR) is an important component of the surface energy balance and is closely related to land surface temperature and emissivity. The estimation of SULR plays an important role in the study of surface energy circulation and climate change. State-of-the-art methods to estimate SULR, including the physical method and the hybrid method, are conducted without considering directional thermal radiation (DTR), which may induce a large error in the estimation, particularly over sparsely vegetated surfaces. In this paper, we modified the physical temperature-emissivity algorithm by combining a directional emissivity model (FRA97) and a kernel-driven DTR model to estimate the SULR of vegetated surfaces while considering the thermal directionality of the land surface. The most suitable kernel-driven model and an angle combination of the DTR were selected from six kernel-driven models and five angular combinations. The sensitivity of the proposed algorithm to the input parameters was also analyzed. The proposed algorithm was then validated with the Wide-angle infrared Dual-mode line/area Array Scanner (WiDAS) data set and longwave radiation data of automatic meteorological stations from the Heihe Watershed Allied Telemetry Experimental Research experiment. The results showed that the five-angle combination with large-angle intervals performs the best. When the leaf area index (LAI) is less than 1.2, the RossThick-LiSparseR model performs the best; when LAI is larger than 1.2, the RossThick-LiDenseR model is the most accurate. The SULR is not sensitive to surface downward longwave radiation and LAI, is slightly sensitive to leaf and soil emissivity at certain LAIs, and is highly sensitive to DTR, which may greatly affect the accuracy of the estimated SULR. The root-mean-square error (RMSE) and the mean bias error (MBE) of the SULR estimated using the WiDAS data and the proposed algorithm are 5.618 and -1.642 W/m2, respectively, thereby improving the estimation accuracy by as much as 7.479 and 10.511 W/m2 at most in terms of RMSE and MBE, respectively, compared with the results calculated without considering the DTR.

A Robust Inversion Algorithm for Surface Leaf and Soil Temperatures Using the Vegetation Clumping Index

The inversion of land surface component temperatures is an essential source of information for mapping heat fluxes and the angular normalization of thermal infrared (TIR) observations. Leaf and soil temperatures can be retrieved using multiple-view-angle TIR observations. In a satellite-scale pixel, the clumping effect of vegetation is usually present, but it is not completely considered during the inversion process. Therefore, we introduced a simple inversion procedure that uses gap frequency with a clumping index (GCI) for leaf and soil temperatures over both crop and forest canopies. Simulated datasets corresponding to turbid vegetation, regularly planted crops and randomly distributed forest were generated using a radiosity model and were used to test the proposed inversion algorithm. The results indicated that the GCI algorithm performed well for both crop and forest canopies, with root mean squared errors of less than 1.0 °C against simulated values. The proposed inversion algorithm was also validated using measured datasets over orchard, maize and wheat canopies. Similar results were achieved, demonstrating that using the clumping index can improve inversion results. In all evaluations, we recommend using the GCI algorithm as a foundation for future satellite-based applications due to its straightforward form and robust performance for both crop and forest canopies using the vegetation clumping index.

Modeling the Temporal Variability of Thermal Emissions From Row-Planted Scenes Using a Radiosity and Energy Budget Method

Land surface temperature (LST) is often needed for using remotely sensed data to study the surface energy budget and hydrological cycle. However, LST is challenging to measure and simulate because of its high sensitivity to atmospheric instability and solar angle, particularly over large-scale heterogeneous scenes. We propose a model that combines radiosity theory and an energy budget method for surface temperatures; we also explore the anisotropic behavior of row-planted crop emissions. The surface thermodynamic equilibrium state is fulfilled via the interaction between the 3-D radiative transfer calculations of the thermal-region radiosity-graphics combined model and the energy balance equation. Despite its shortcomings, such as the time-consuming calculations, the proposed model is feasible according to the results of an intercomparison and validation analysis. The intercomparison shows that the model exhibits similar performance, in terms of surface temperature calculations, to that of the soil-canopy observation, photochemistry and energy balance model (root-mean-square differences) of 0.59 °C and 1.77 °C for the leaf and soil components, respectively. Excellent agreement with the observed directional variation over summer maize canopies is also obtained, with

Convenient Measurement and Modified Model for Broadleaf Permittivity

\text{R}^{2}

Modeling the Distributions of Brightness Temperatures of a Cropland Study Area Using a Model that Combines Fast Radiosity and Energy Budget Methods

values exceeding 0.6 and a mean RMSE of 0.32 °C. Thus, we recommend the new combined model as an option for explaining directional anisotropy due to its potential application to 3-D scenes.