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
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.
