Huaguo Huang

An Extended 3-D Radiosity–Graphics Combined Model for Studying Thermal-Emission Directionality of Crop Canopy

Radiosity-graphics combined model (RGM) has been proposed to calculate the radiation regime and bidirectional reflectance distribution function of complex 3D scene, which is limited in visible and near-infrared wavelength (0.3-3 mum) region. In this paper, RGM is extended to thermal region (named as TRGM) based on thermal-radiosity theory and thermal-emission directionality of vegetation canopy. The TRGM has been implemented on Microsoft Windows platform, and a parameterization scheme for crop canopies is introduced in this paper. It is then evaluated by comparing with two row-crop directional thermal emission models and one thermal radiative-transfer model. Field experiment data has been used to validate the TRGM for row structural wheat and maize canopies. The root mean square error of directional brightness temperature (DBT) is smaller than 1.0degC for the wheat canopy and 0.5degC for the maize canopy while the canopy DBTs vary more than 4degC. Model sensitivity analyses have also been conducted to illustrate influences of component temperature distribution, component emissivity, incident atmospheric radiation, and canopy structure on the crop canopy DBT.

A realistic structure model for large-scale surface leaving radiance simulation of forest canopy and accuracy assessment

The radiosity-graphics model (RGM) is an important branch of computer simulation modelling for the vegetation bidirectional reflectance distribution function (BRDF). As the radiosity method is based on a global solving technique, the RGM can only deal with limited numbers of polygons, and has only been used for small-scale flat terrain scenes. However, the land surface is generally rugged, so it is necessary to extend the RGM to simulate the surface leaving radiance of the forest canopy at a large scale with complex topography. The methodology adopted in this paper is: (1) virtual forest scene generation combined with a digital elevation model; (2) scene division method, shadowing effect correction and multiple scattering calculation; (3) merging the simulated sub-scene bidirectional reflectance factors (BRFs) to get the whole-scene BRF. The paper compares this new method with other models by choosing a large-scale conifer forest scene with a GAUSS terrain from RAMI3 (http://rami-benchmark.jrc.it). Multi-angle imaging spectroradiometer (MISR) data are used to validate the extended RGM in a Picea crassifolia forest area at a satellite pixel scale in the field campaign in Gansu Province, China. The root mean square error and correlation coefficient between the simulated BRF and the MISR BRF are 0.018 and 0.98, respectively. The uncertainty and error sources of the large-scale RGM model are thoroughly analysed.

Thermal Emission Hot-Spot Effect of Crop Canopies—Part I: Simulation

This paper is the first part of a three-part article series. Simulations of directional brightness temperature over both simple canopies with triangular leaves and the row-planted wheat and corn were used to analyze the thermal emission hot-spot effect on crop canopies. Two models, Cupid and TRGM, were successively used to simulate the thermal hot-spot signatures under conditions which cannot be easily captured in reality. The investigation includes the planting row structure, the leaf area index (LAI), the leaf angle distribution (LAD), the component temperature distribution as well as variations in the microclimate. The results show that there are typically three types of directional emission shapes in the solar principle plane: the bowl, dome and bell shape. Regardless of the shape, the hot spot is significant and can be accurately fitted (R2 = 0.98 and RMSE = 0.04°C) with a function of the phase angle (ξ), the hot-spot amplitude (ΔTHS) and the half width of the hot spot (ξ0)> which can be quantified with the half width in the RED band. The planting row structure can reduce the ΔTHS by a maximum amount (about 1.2°C) when compared with an unstructured horizontal canopy. The ΔTHS is linearly related to the component temperature differences between sunlit and shadowed parts. The linear equation can be used to predict the component temperature differences from ΔTHS. The accuracy is very good for the horizontal canopies with triangular leaves (RMSE <; 0.4°C and R2 > 0.99), and acceptable for the virtual wheat and corn canopies (RMSE <; 1.8°C and R2 > 0.81).

The complicate observations and multi-parameter land information constructions on allied telemetry experiment (COMPLICATE)

The Complicate Observations and Multi-Parameter Land Information Constructions on Allied Telemetry Experiment (COMPLICATE) comprises a network of remote sensing experiments designed to enhance the dynamic analysis and modeling of remotely sensed information for complex land surfaces. Two types of experimental campaigns were established under the framework of COMPLICATE. The first was designed for continuous and elaborate experiments. The experimental strategy helps enhance our understanding of the radiative and scattering mechanisms of soil and vegetation and modeling of remotely sensed information for complex land surfaces. To validate the methodologies and models for dynamic analyses of remote sensing for complex land surfaces, the second campaign consisted of simultaneous satellite-borne, airborne, and ground-based experiments. During field campaigns, several continuous and intensive observations were obtained. Measurements were undertaken to answer key scientific issues, as follows: 1) Determine the characteristics of spatial heterogeneity and the radiative and scattering mechanisms of remote sensing on complex land surfaces. 2) Determine the mechanisms of spatial and temporal scale extensions for remote sensing on complex land surfaces. 3) Determine synergist inversion mechanisms for soil and vegetation parameters using multi-mode remote sensing on complex land surfaces. Here, we introduce the background, the objectives, the experimental designs, the observations and measurements, and the overall advances of COMPLICATE. As a result of the implementation of COMLICATE and for the next several years, we expect to contribute to quantitative remote sensing science and Earth observation techniques.

Remote sensing of insect pests in larch forest based on physical model

A physical decision method was proposed here to monitor Larch forest insect pests at early stage. Three remote sensing indicators were defined, which are CWC (canopy water content), TVDI (Temperature/Vegetation Dryness Index) and LAI (Leaf Area Index). The Five-scale model and artificial neural network (ANN) were combined to inverse the three factors from Landsat data. Based on training samples of health or attacked pixels, a decision tree was built to classify pest-infected pixels. Field validation showed that the prediction of forest compartments with insect pest were highly consistent with the ground field data.

Evaluation of Atmospheric Effects on Land-Surface Directional Reflectance With the Coupled RAPID and VLIDORT Models

In order to assess atmospheric effects on the directional reflectance of land surface, we have developed a new approach coupling the 3-D radiosity-based land-surface model [radiosity applicable to porous individual objects (RAPID)] with the atmospheric radiative transfer (RT) model [vector linearized discrete ordinate RT (VLIDORT)]. RAPID is used to generate a lookup table of bidirectional reflectance distribution function (BRDF) elements required by VLIDORT for the surface boundary condition. To test the RAPID–VLIDORT model, we used five natural 3-D scenes along with five aerosol optical depths (AODs). Results for top-of-atmosphere radiances show semiempirical analytical BRDF models are insufficiently accurate to represent bidirectional reflectance factors (BRFs) in hotspot regions and over wide angular variations. The large impact of AOD on BRF hotspot also underlines the importance of precise atmospheric corrections for multiangular remote sensing of the earth’s surface.

Modeling Soil Component Temperature Distribution by Extending CUPID Model

Modeling the soil component temperature distribution is useful to study multi-angular thermal remote sensing. SVAT (soil-plant-atmosphere transfer) model could be a good choice because it can predict canopy temperature distribution. However, most of them, including CUPID model 111. were unable to separate shade soil and sunlit soil. They only gave a single temperature for the soil surface. In this paper, based on the difference of net radiance and evaporation rate between the shade and sunlit soil, an extended model from CUPID was proposed to simultaneously retrieve the shaded temperature and sunlit temperature of soil surface. The comparison showed good agreement between simulated soil temperatures and measured ones.

Validating theoretical simulations of thermal emission hot spot effects on maize canopies

This is a development of some of our previous work on the analysis of thermal emission hot spot effects. In that work, which was a simulation study, a curve-fitting model was proposed to derive the hot spot amplitude and half width. Based on the curve-fitting model, a new algorithm that predicts the component temperature differences from the hot spot amplitude is presented. In this study, the objectives are to evaluate the accuracy of the curve-fitting model and the new prediction algorithm using both airborne measurements based on the wide-angle infrared dual-mode line/area array scanner (WiDAS) system and ground measurements. Based on a prior knowledge of crop structure parameters (e.g. the leaf area index (LAI), leaf angle distribution (LAD) and leaf dimensions), validation results indicate that the curve-fitting model and the prediction algorithm can accurately retrieve the hot spot amplitude and half width (R 2 > 0.90 and root mean square error (RMSE) < 0.15 K), and predict leaf and soil temperature differences from directional signals of both airborne and ground data (bias < 1 K). The error of prior knowledge can significantly affect the prediction accuracy of leaf temperature difference. Also discussed are the limitations of the model and future research and application.

A Study on the Changes of Pinus Massoniana Spatial Pattern by Pine Wood Nematode Invasion Based on Remote Sensing and GIS

In this paper, we combined quantitative ecology theory with Remote Sensing (RS) and Geometric Information System (GIS) technique to study the changes of the Pinus Massoniana spatial patterns before and after the invasion by Bursaphelenchus xylophilus in FuYang and ZhouShan County, Zhejiang province of China, two of the most serious areas affected by the pest. We focused on the information extraction of Pinus massoniana and the spatial pattern model based on remote sensing images. By the analysis of the spatial distribution of the population patterns, we can safely conclude that there is some relationship between the spatial pattern and the invasion level. There are two kinds of patterns for Pine Wood Nematode to spread, continuum and un-continuum diffusion. This result has a good agreement with the field data. So, this approach can help monitor and evaluate the changes in ecological system during the invasion of Bursaphelenchus xylophilus. Keywords—Bursaphelenchus xylophilus; invasion; Remote Sensing;GIS;,Information Extraction;Pinus massoniana; Spatial Pattern

Split-window method for land surface temperature estimation from FY-3A/VIRR data

Remotely sensed land surface temperature (LST)is of great value to the research in the fields of climatology, hydrology, ecology, and biogeochemistry,as well as a wide range of interdisciplinary research areas, since it isan efficient and practical way of acquiringtemperature variability globally and continuously. In the paper, the generalized split-window algorithm proposed by Wan and Dozier (1996)is used to estimate LST from Visible and Infrared Radiometer (VIRR)onboard the second generation of Chinas polar-orbiting meteorological satellite(FY3A).MODTRAN 4.0 and the Thermodynamic Initial Guess Retrieval database 3 (TIGR-3) are used to simulate the data for fitting the algorithms coefficients. The algorithm fitting accuracy is improved by dividing the LST, the average emissivity(e ) and the water vapor content (WVC) into several sub-ranges. Finally, the validation at five locations is performed and the results show thatthe LSTs estimation from FY3A/VIRR dataagree with the ones extracted from the MODIS 1 km LST products very well.