Xiru Xu

A unified canopy bidirectional reflectance (BRDF) model for row ceops

AbstactRow sowing is a basic crop sowing method in China, and thus an accurate Bidirectional Reflectance Distribution Function (BRDF) model of row crops is the foundation for describing the canopy bidirectional reflectance characteristics and estimating crop ecological parameters. Because of the macroscopically geometric difference, the row crop is usually regarded as a transition between continuous and discrete vegetation in previous studies. Were row treated as the unit for calculating the four components in the Geometric Optical model (GO model), the formula would be too complex and difficult to retrieve. This study focuses on the microscopic structure of row crops. Regarding the row crop as a result of leaves clumped at canopy scale, we apply clumping index to link continuous vegetation and row crops. Meanwhile, the formula of clumping index is deduced theoretically. Then taking leaf as the basic unit, we calculate the four components of the GO model and develop a BRDF model for continuous vegetation, which is gradually extended to the unified BRDF model for row crops. It is of great importance to introduce clumping index into BRDF model. In order to evaluate the performance of the unified BRDF model, the canopy BRDF data collected in field experiment, “Watershed Allied Telemetry Experiment Research (WATER)”, from May 30th to July 1st, 2008 are used as the validation dataset for the simulated values. The results show that the unified model proposed in this paper is able to accurately describe the non-isotropic characteristics of canopy reflectance for row crops. In addition, the model is simple and easy to retrieve. In general, there is no irreconcilable conflict between continuous and discrete vegetation, so understanding their common and individual characteristics is advantageous for simulating canopy BRDF. It is proven that the four components of the GO model is the basic motivational factor for bidirectional reflectance of all vegetation types.

A New FAPAR Analytical Model Based on the Law of Energy Conservation: A Case Study in China

The fraction of absorbed photosynthetically active radiation (FAPAR) characterizes the energy-absorption ability of the vegetation canopy. It is a critical input to many land-surface models such as crop growth models, net primary productivity models, and climate models. There is a great need for FAPAR products derived from remote-sensing data. The objective of this research is to develop a new instantaneous quantitative FAPAR model based on the law of energy conservation and the concept of recollision probability (p). Using the ray-tracing method, the FAPAR-P model separates direct energy absorption by the canopy from energy absorption caused by multiple scattering between the soil and the canopy. Direct sunlight and diffuse skylight are also considered. This model has a clear physical meaning and can be applied to continuous and discrete vegetation. The model was validated by Monte Carlo (MC) simulation and field measurements in the Heihe River basin, China, which proved its reliability for FAPAR calculations.

The concept of effective emissivity of nonisothermal mixed pixel and its test

Based on two preconditions, the local thermal equilibrium is satisfied and emissivities do not change with temperature, the concept of component effective emissivity of nonisothermal mixed pixel has been put forward and then the radiant directionality model of nonisothermal mixed pixel constructed from it. Our study shows that the component effective emissivity is associated with geometric structure, optical properties of target, and viewing angle, but does not depend on the component temepratures. The component temperatures can only change the ratio of component radiance to the total radiance of the mixed-pixel. The total effective emissivity of this pixel is the complement of its directional-hemisphere reflectance. After the simulation of component effective emissivity of the discrete cones and continuous vegetation canopy (winter wheat) by the Monte Carlo method, our model of radiant directionality of nonisothermal mixed pixel have been proved by lab and field measurements.

The spatial scaling effect of the discrete-canopy effective leaf area index retrieved by remote sensing

The leaf area index (LAI) is a critical biophysical variable that describes canopy geometric structures and growth conditions. It is also an important input parameter for climate, energy and carbon cycle models. The scaling effect of the LAI has always been of concern. Considering the effects of the clumping indices on the BRDF models of discrete canopies, an effective LAI is defined. The effective LAI has the same function of describing the leaf density as does the traditional LAI. Therefore, our study was based on the effective LAI. The spatial scaling effect of discrete canopies significantly differed from that of continuous canopies. Based on the directional second-derivative method of effective LAI retrieval, the mechanism responsible for the spatial scaling effect of the discrete-canopy LAI is discussed and a scaling transformation formula for the effective LAI is suggested in this paper. Theoretical analysis shows that the mean values of effective LAIs retrieved from high-resolution pixels were always equal to or larger than the effective LAIs retrieved from corresponding coarse-resolution pixels. Both the conclusions and the scaling transformation formula were validated with airborne hyperspectral remote sensing imagery obtained in Huailai County, Zhangjiakou, Hebei Province, China. The scaling transformation formula agreed well with the effective LAI retrieved from hyperspectral remote sensing imagery.

Algorithm of Leaf Area Index product for HJ-CCD over Heihe River Basin

Middle-resolution Leaf Area Index (LAI) data are of great importance to scientific research relating to atmospheric composition, climate and weather, and the hydrological cycle. This paper introduces a physically based LAI retrieval technique for HJ-CCD at 30-m resolution. The algorithm is based on a canopy BRDF model that characterizes the surface reflectance as a function of a series of parameters. There are three key factors that influence the LAI retrieval processes: 1) the preprocessing to estimate surface reflectance; 2) the quality of the input land cover data; 3) the accuracy of the input parameters. Accounting for these factors, a 30-m LAI product of Heihe River Basin in the whole year of 2012 is created utilizing the data from HJ-CCD. Then field measurements are used to evaluate the quality of the product. Results show that the algorithm has the ability to produce LAI products as expected. Future researches will focus on reducing the uncertainties brought by the input data and parameters and implementing this algorithm at national scale over China.

Synchronous retrieval of land surface temperature and emissivity

This is an old topic for more than ten years to retrieve land surface temperature (LST) from satellite data, but it has not been solved yet. At first, people tried to transplant traditional split window method of sea surface temperature (SST) to the retrieval of LST, but it was found that the emissivities of land surface (εi) must be involved in atmospheric correction. Then many different formulas appeared with assumption of emissivities known. In fact, emissivities of land surface with pixel size cannot be known beforehand because of various reasons, so in recent years the focus of attention has been transferred to retrieving emissivities (εi) and LST at the same time. Therefore, we have to solve missing equations problem. For this some people try to introduce middle infrared information, but new problems will be brought in which means that it is very difficult to describe middle infrared BRDF of targets with high accuracy and the scattering of atmospheric aerosol cannot be ignored. Therefore a different way is offered to solve this problem only using two thermo-infrared bands data based on three assumptions, constant emissivities in two measurements, and the same atmospheric parameters for neighbouring pixels and the difference of emissivity (Δε) of two channels can be known beforehand. Results of digital simulations show that it is possible to retrieve LST with its root mean square (RMS) of errors less than 1 K and RMS of relative error of ground radiance at 7% if the error of atmospheric temperature at ± 2°C and the relative error of atmospheric water vapor at ± 10% can be satisfied. Results have been confirmed by initial field test.

Scale transformation of Leaf Area Index product retrieved from multiresolution remotely sensed data: analysis and case studies

Climate and land–atmosphere models rely on accurate land-surface parameters, such as Leaf Area Index (LAI). It is crucial that the estimation of LAI represents actual ground truth. Yet it is known that the LAI values retrieved from remote sensing images suffer from scaling effects. The values retrieved from coarse resolution images are generally smaller. Scale transformations aim to derive accurate leaf area index values at a specific scale from values at other scales. In this paper, we study the scaling effect and the scale transformation algorithm of LAI in regions with different vegetation distribution characteristics, and analyse the factors that can affect the scale transformation algorithm, so that the LAI values derived from a low resolution dataset match the average LAI values of higher resolution images. Using our hybrid reflectance model and the scale transformation algorithm for continuous vegetation, we have successfully calculated the LAI values at different scales, from reflectance images of 2.5 m and 10 m spatial resolution SPOT-5 data as well as 250 m and 500 m spatial resolution MODIS data. The scaling algorithm was validated in two geographic regions and the results agreed well with the actual values. This scale transformation algorithm will allow researchers to extend the size of their study regions and eliminate the impact of remote sensing image resolution.

Study on the law of radiant directionality of row crops

The style of crops planting is frequently in row-structure, the row-structure style may result in big difference among the sunlit, shaded soil surface and foliage temperatures and cause pixel component to vary in azimuth orientation, these further lead to the change of radiant directionlity of row crops in the zenith and azimuth orientations. Since the row crops are often tackled as isotropic in the azimuth orientation based on continuous vegetation assumption, big errors will be brought about. In order to eliminate the errors, it is necessary to study the law of radiant directionality of the row crops. In this paper, Monte Carlo method has been employed to simulate the angular effects on radiation caused by row architecture parameters. The simulated results show that the parameters, for example, row height, row width, row interval between the neighbor rows and the leaf area index have significant influences on the radiant directionality, but the azimuth orientation ranks the first among the parameters.

Estimating Savanna Clumping Index Using Hemispherical Photographs Integrated with High Resolution Remote Sensing Images

In contrast to herbaceous canopies and forests, savannas are grassland ecosystems with sparsely distributed individual trees, so the canopy is spatially heterogeneous and open, whereas the woody cover in savannas, e.g., tree cover, adversely affects ecosystem structures and functions. Studies have shown that the dynamics of canopy structure are related to available water, climate, and human activities in the form of porosity, leaf area index (LAI), and clumping index (CI). Therefore, it is important to identify the biophysical parameters of savanna ecosystems, and undertake practical actions for savanna conservation and management. The canopy openness presents a challenge for evaluating canopy LAI and other biophysical parameters, as most remotely sensed methods were developed for homogeneous and closed canopies. Clumping index is a key variable that can represent the clumping effect from spatial distribution patterns of components within a canopy. However, it is a difficult task to measure the clumping index of the moderate resolution savanna pixels directly using optical instruments, such as the Tracing Radiation and Architecture of Canopies, LAI-2000 Canopy Analyzer, or digital hemispherical photography. This paper proposed a new method using hemispherical photographs combined with high resolution remote sensing images to estimate the clumping index of savanna canopies. The effects of single tree LAI, crown density, and herbaceous layer on the clumping index of savanna pixels were also evaluated. The proposed method effectively calculated the clumping index of moderate resolution pixels. The clumping indices of two study regions located in Ejina Banner and Weichang were compared with the clumping index product over China’s landmass.

Estimating Crop Albedo in the Application of a Physical Model Based on the Law of Energy Conservation and Spectral Invariants

Albedo characterizes the radiometric interface of land surfaces, especially vegetation, and the atmosphere. Albedo is a critical input to many models, such as crop growth models, hydrological models and climate models. For the extensive attention to crop monitoring, a physical albedo model for crops is developed based on the law of energy conservation and spectral invariants, which is derived from a prior forest albedo model. The model inputs have been efficiently and physically parameterized, including the dependency of albedo on the solar zenith/azimuth angle, the fraction of diffuse skylight in the incident radiance, the canopy structure, the leaf reflectance/transmittance and the soil reflectance characteristics. Both the anisotropy of soil reflectance and the clumping effect of crop leaves at the canopy scale are considered, which contribute to the improvement of the model accuracy. The comparison between the model results and Monte Carlo simulation results indicates that the canopy albedo has high accuracy with an RMSE < 0.005. The validation using ground measurements has also demonstrated the reliability of the model and that it can reflect the interaction mechanism between radiation and the canopy-soil system.