Based on two preconditions, the local thermal equilibrium is satisfied and emissivities donot change with temperature, the concept of component effective emissivity of nonisothermal mixed pixel has been put forward an...Based on two preconditions, the local thermal equilibrium is satisfied and emissivities donot 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.展开更多
Leave Area Index (LAI) is one of the most basic parameters to describe the geometric structure of plant canopies. It is also important input data for climatic model and interaction model between Earth surface and atmo...Leave Area Index (LAI) is one of the most basic parameters to describe the geometric structure of plant canopies. It is also important input data for climatic model and interaction model between Earth surface and atmosphere, and some other things. The spatial scaling of retrieved LAI has been widely studied in recent years. Based on the new canopy reflectance model, the mechanism of the scaling effect of con- tinuous canopy Leaf Area Index is studied, and the scaling transform formula among different scales is found. Both the numerical simulation and the field validation show that the scale transform formula is reliable.展开更多
Accurate estimation of crop yields is crucial for ensuring food security. However, crops are distributed so fragmentally in China that mixed pixels account for a large proportion in moderate and coarse resolution remo...Accurate estimation of crop yields is crucial for ensuring food security. However, crops are distributed so fragmentally in China that mixed pixels account for a large proportion in moderate and coarse resolution remote sensing images. As a result, unmixing of mixed pixel becomes a major problem to estimate crop yield by means of remote sensing method. Aimed at mixed pixels, we developed a new method to introduce additional information contained in the spatial scaling transformation equation to the canopy reflectance model. The crop area and LAI can be retrieved simultaneously. On the basis of a precise and simple canopy reflectance model, directional second derivative method was chosen to retrieve LAI from optimal bands of hyper-spectral data; this method can reduce the impact of the canopy non-isotropic features and soil background. To evaluate the performance of the method, Yingke Oasis, Zhangye City, Gansu Province, was chosen as the validation area. This area was covered mainly by maize and wheat. A Hyperion/EO-1 image with the 30 m spatial resolution was acquired on July 15, 2008. Images of 180 m and 1080 m resolutions were generated by linearly interpolating the original Hyperion image to coarser resolutions. Then a multi-scale image serial was obtained. Using the proposed method, we calculated crop area and the average LAI of every 1080 m pixel. A SPOT-5 classification figure serves as the validation data of crop area proportion. Results show that the pattern of crop distribution accords with the classification figure. The errors are restrained mainly to -0.1-0.1, and approximate a Normal Distribution. Meanwhile, 85 LAI values obtained using LAI-2000 Plant Canopy Analyzer, equipped with GPS, were taken as the ground reference. Results show that the standard deviation of the errors is 0.340. The method proposed in the paper is reliable.展开更多
An accurate and operational bidirectional reflectance distribution function （BDRF） canopy model is the basis of quantitative vegetation remote sensing. The canopy reflectance should be approximated as the sum of the...An accurate and operational bidirectional reflectance distribution function （BDRF） canopy model is the basis of quantitative vegetation remote sensing. The canopy reflectance should be approximated as the sum of the single scattering reflectance arising from the sun, pl, and the multiple scattering reflectance arising from the canopy, fin, as their directional characteristics are dramatically different. Based on the existing BRDF model, we obtain a new analytical expression of ρ1 and ρm in this paper, which is suitable for different illumination conditions and different vegetation canopies. According to the geometrical optic model at the leaf scale, the anisotropy of ρ1 can be ascribed to the geometry of the object, sun and the sensor, multiple scale clumping, and the fraction of direct solar radiation and diffuse sky radiation. Then, we parameterize the area ratios of four components： the sunlit foliage, sunlit ground, shadow foliage and shadow ground based on a Poisson distribution, and develop a new approximate analytical single scattering reflectance model. Assuming G=0.5, a recollision probability theory based scattering model is developed which considers the effects of diffuse sky radiation, scattering inside the canopy and rebounds between the canopy and soil. Validation using ground measurements of maize and black spruce forest proves the reliability of the model.展开更多
Row 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 ch...Row 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.展开更多
Validation is one of the most important processes used to evaluate whether remotely sensed products can accurately reflect land surface configuration. Leaf Area Index( LAI) is a key parameter that represents vegetatio...Validation is one of the most important processes used to evaluate whether remotely sensed products can accurately reflect land surface configuration. Leaf Area Index( LAI) is a key parameter that represents vegetation canopy structures and growth conditions. Accurate evaluation of LAI products is the basis for applying them to land surface models. In this study,validation methods of coarse resolution MODIS and GLASS LAI products for heterogeneous pixels are established on the basis of the scaling effect and the scaling transformation. Considering spatial heterogeneity and growth difference,we transformed LAI from field measurements into a 1 km resolution scale with the aid of middle resolution images. We used average LAI and apparent LAI separately to validate the algorithms and products of MODIS and GLASS LAI. Two study areas,Hebi City and the Yingke Oasis,were selected for validation. Both MODIS and GLASS LAI products underestimate the true LAI in crop area. However,this result cannot be completely attributed to their algorithms. Instead,the primary reason is the heterogeneity and nonuniformity of the coarse pixels.Underestimation is evident in the Yingke Oasis,where heterogeneity is significant. Given that GLASS LAI product is the fusion of multiple LAI products,the mean value of this product is closer to the real situation,but the dynamic range is narrower than that of MODIS LAI product.展开更多
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 scali...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.展开更多
文摘Based on two preconditions, the local thermal equilibrium is satisfied and emissivities donot 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.
基金Supported by National Basic Research Program of China (Grant No. 2007CB714402)National Natural Science Foundation of China (Grant Nos. 40401036, 40734025 and 40401036)
文摘Leave Area Index (LAI) is one of the most basic parameters to describe the geometric structure of plant canopies. It is also important input data for climatic model and interaction model between Earth surface and atmosphere, and some other things. The spatial scaling of retrieved LAI has been widely studied in recent years. Based on the new canopy reflectance model, the mechanism of the scaling effect of con- tinuous canopy Leaf Area Index is studied, and the scaling transform formula among different scales is found. Both the numerical simulation and the field validation show that the scale transform formula is reliable.
基金supported by National Natural Science Foundation of China (Grant Nos.40871186,40730525,40401036)National High Technology Research and Development Program of China (Grant No.2009AA12Z143)+1 种基金Special Funds for National Basic Research Program of China (Grant No.2007CB714402)"Simultaneous Remote Sensing and Groundbased Experiment in Heihe River Basin and Comprehensive Platform Construction" in Chinese Academy of Sciences’ Action-Plan for West Development (the second phase) (Grant No.KZCX2-XB2-09)
文摘Accurate estimation of crop yields is crucial for ensuring food security. However, crops are distributed so fragmentally in China that mixed pixels account for a large proportion in moderate and coarse resolution remote sensing images. As a result, unmixing of mixed pixel becomes a major problem to estimate crop yield by means of remote sensing method. Aimed at mixed pixels, we developed a new method to introduce additional information contained in the spatial scaling transformation equation to the canopy reflectance model. The crop area and LAI can be retrieved simultaneously. On the basis of a precise and simple canopy reflectance model, directional second derivative method was chosen to retrieve LAI from optimal bands of hyper-spectral data; this method can reduce the impact of the canopy non-isotropic features and soil background. To evaluate the performance of the method, Yingke Oasis, Zhangye City, Gansu Province, was chosen as the validation area. This area was covered mainly by maize and wheat. A Hyperion/EO-1 image with the 30 m spatial resolution was acquired on July 15, 2008. Images of 180 m and 1080 m resolutions were generated by linearly interpolating the original Hyperion image to coarser resolutions. Then a multi-scale image serial was obtained. Using the proposed method, we calculated crop area and the average LAI of every 1080 m pixel. A SPOT-5 classification figure serves as the validation data of crop area proportion. Results show that the pattern of crop distribution accords with the classification figure. The errors are restrained mainly to -0.1-0.1, and approximate a Normal Distribution. Meanwhile, 85 LAI values obtained using LAI-2000 Plant Canopy Analyzer, equipped with GPS, were taken as the ground reference. Results show that the standard deviation of the errors is 0.340. The method proposed in the paper is reliable.
基金supported by the National Natural Science Foundation of China(Grant Nos.41271346,41571329&41230747)the Major State Basic Research Development Program of China(Grant No.2013CB733402)
文摘An accurate and operational bidirectional reflectance distribution function （BDRF） canopy model is the basis of quantitative vegetation remote sensing. The canopy reflectance should be approximated as the sum of the single scattering reflectance arising from the sun, pl, and the multiple scattering reflectance arising from the canopy, fin, as their directional characteristics are dramatically different. Based on the existing BRDF model, we obtain a new analytical expression of ρ1 and ρm in this paper, which is suitable for different illumination conditions and different vegetation canopies. According to the geometrical optic model at the leaf scale, the anisotropy of ρ1 can be ascribed to the geometry of the object, sun and the sensor, multiple scale clumping, and the fraction of direct solar radiation and diffuse sky radiation. Then, we parameterize the area ratios of four components： the sunlit foliage, sunlit ground, shadow foliage and shadow ground based on a Poisson distribution, and develop a new approximate analytical single scattering reflectance model. Assuming G=0.5, a recollision probability theory based scattering model is developed which considers the effects of diffuse sky radiation, scattering inside the canopy and rebounds between the canopy and soil. Validation using ground measurements of maize and black spruce forest proves the reliability of the model.
基金supported by National Natural Science Foundation of China (Grant Nos. 91025006, 40730525, 40871186 and 40801125)Special Funds for National High Technology Research and Development Program of China (Grant Nos. 2009AA12Z143 and 2009A122103)+1 种基金Major State Basic Research Project (973) (Grant No. 2007CB714402)"Simultaneous Remote Sensing and Ground-based Experiment in Heihe River Basin and Comprehensive Platform Construction" in the Chinese Academy of Sciences’ Action-Plan for West Development (the second phase) (Grant No. KZCX2-XB2-09)
文摘Row 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.
基金National High Technology Research and Development Program of China(863 Program)(No.2009AA122103,2012AA12A304)National Natural Science Foundation of China(No.91025006,91325105,41271346)National Basic Research Program of China(973 Program)(No.2013CB733402)
文摘Validation is one of the most important processes used to evaluate whether remotely sensed products can accurately reflect land surface configuration. Leaf Area Index( LAI) is a key parameter that represents vegetation canopy structures and growth conditions. Accurate evaluation of LAI products is the basis for applying them to land surface models. In this study,validation methods of coarse resolution MODIS and GLASS LAI products for heterogeneous pixels are established on the basis of the scaling effect and the scaling transformation. Considering spatial heterogeneity and growth difference,we transformed LAI from field measurements into a 1 km resolution scale with the aid of middle resolution images. We used average LAI and apparent LAI separately to validate the algorithms and products of MODIS and GLASS LAI. Two study areas,Hebi City and the Yingke Oasis,were selected for validation. Both MODIS and GLASS LAI products underestimate the true LAI in crop area. However,this result cannot be completely attributed to their algorithms. Instead,the primary reason is the heterogeneity and nonuniformity of the coarse pixels.Underestimation is evident in the Yingke Oasis,where heterogeneity is significant. Given that GLASS LAI product is the fusion of multiple LAI products,the mean value of this product is closer to the real situation,but the dynamic range is narrower than that of MODIS LAI product.
基金supported by the National Natural Science Foundation of China(Grant Nos.91025006,40871186,40730525)National Basic Research Program of China(Grant No.2007CB714402)National High Technology Research and Development Program of China(Grant Nos.2009AA12Z143,2009AA122103)
文摘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.
