Binyan Yan

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.

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.

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.

The identification of indicator grass species of grassland degradation based on the field spectral characteristics

Grassland is an essential part of terrestrial ecosystems. It has a significant impact on the carbon cycle, as well as on climate and on regional economies. Till now, vegetation indices are the most popular remote sensed detecting method of grassland degradation. Although vegetation indices are useful for estimating the biomass, but detecting changes of vegetation indices are not always effective, as grassland vegetation with different characteristics may still produce similar vegetation index values. The development of hyperspectral sensors provides a new approach to solve this problem. The Hulunbeier grassland was chosen as a study object. Reflectance spectra of leaves and pure canopies of some dominant grassland species, as well as reflectance spectra of mixed grass community were measured. Using spectral feature parameterization methods such as spectral slope, spectral derivative, spectral integration, and spectral index, the spectral feather of leaves and pure canopies had been extracted. So the typical grassland vegetation species can be distinguished. Then the spectra of mixed grass community were unmixed using linear mixing models, and the proportion of all the components had been calculated. The field validation proved spectral feature parameterization and pixel unmixing methods in this research are effective.

A model for instantaneous FAPAR retrieval: Theory and validation

The Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is a critical input parameter to many climate and ecological models. Its calculation accuracy from remote sensing images directly influences the estimation of net primary productivity (NPP) and carbon cycle. This paper presents a hybrid model combining the characteristics of geometric optic model and radiation transfer model. It considers the illuminated and shadow area of the canopy and soil, as well as the multiple scattering between the canopy and soil. The Monte Carlo simulations of canopy FAPAR are also conducted and the results are compared with model results. In addition, we did the simulation of FAPAR daily change by the model and MC method, and compare the results with field measured daily data of FAPAR. All the results prove the model effective.

Retrieve soil moisture from mixed-pixels based on scale transformation using hyperspectral data

Soil moisture is a key parameter for drought monitoring. Crops distribute so fragmentally in China that mixed pixels account for a large proportion in moderate and coarse resolution remote sensing images. The soil moisture retrieved from vegetation-soil mixed pixels is a very important problem for drought monitoring and ecological study. Focusing on vegetation-soil mixed pixels, a new method for retrieving soil moisture from hyperspectral data is provided based on scale transformation method. Yingke Oasis, Zhangye, Gansu province was selected as validation area. A Hyperion/EO-1 data acquired on Jul.15, 2008 was pre-processed and linearly interpolated to 180m and 1080m resolution images. Then a multi-scale image series was obtained. Using the above method, the soil moisture of pixels whose space resolution is 1080m were calculated. The retrieved results were verified by synchronized ground observation data. The results show that the proposed method is reliable.

A physical model for LAI retrieval: directional second derivative of canopy spectra

Hyperspectral and multi-angular data provides an opportunity to accurately retrieve LAI and other biophysical parameters for vegetation monitoring. Based on a vegetation canopy BRDF model, the Directional Second Derivative (DSD) method has been obtained. In order to further evaluate the performance of the method, canopy reflectance spectra at different LAI values and different view angles are simulated using PROSAIL models first. Then LAI are retrieved from the spectra using DSD, SD (Second Derivative) and BRDF model inversion method. Results show that DSD is the most accurate and stable method compared with the other two. It also indicates that the optimal direction for LAI retrieval is near the hot spot. Finally, the single-angular Hyperion/EO-1 and multi-angular CHIRS/PROBA images are used to demonstrate the application of DSD method. The retrieved values are validated by ground measurements. The results are in good agreement with numerical simulations.