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Clumping Index (CI) is an important vegetation structure parameter, which describes the grouping of leaves relative to the random distribution. Multi-angle data of the POLarization and Directionality of Earth Reflectance (POLDER) sensor (about 6×7 km) and the MODerate resolution Imaging Spectradiometer (MODIS) (500 m) are two main sources for global CI products. To better understand the variability inherent in CIs of such different spatial resolutions and optimize the used of CI products, extensive POLDER CIs and corresponding MODIS CIs were retrieved and compared in this study. Field measurements were conduct in one selected POLDER pixel in Hebei, China. Our results showed that POLDER and MODIS CIs presented relative good consistency (R2=0.65, RMSE=0.07, bias=0.003), and the correlation coefficient can reach 0.98 at the class level. Both POLDER CI (0.67) and MODIS CI (0.62±0.06) showed good consistency with field CIs (0.64±0.10) and POLDER CI was more likely to overestimate than MODIS CI.

Foliage Clumping Index (CI) is an important structural parameter within vegetation canopy, and current satellite-borne CI products mainly retrieved by using the linear relationship between the CI and the normalized difference between hotspot and dark spot (NDHD), while there is no directly model to calculate the CI of mixed forest. The objective of this paper is to propose a new method to calculate the mixed forest CI (MFCI) and access the ability to response the spatial heterogeneity of mixed forest pixels. The results show that: (1) The accuracy of MFCI is significantly higher than that of existing MODIS CI products, the average error can be reduced by 6.3% (2) the total sensitivity of MFCI to spatial heterogeneity is high(>0.6), and with the highest sensitivity to bare soil.

Clumping index (CI) characterizes the clumping degree of vegetation canopy foliage relative to the random distribution, which is an important vegetation structure parameter. Digital Hemispherical Photography (DHP) is widely used in ground CI measurement and one of the key steps using this method is to determine the sampling resolution. This study presents a set of sampling way including 30 sampling methods with 17 levels of sampling resolutions. They were applied to four vegetation types and different growth stages of crops to explore the variation of CI with the decrease of sampling resolution of 17 levels. The results show that with the decrease of sampling resolution at 17 levels, the average increase of CI in the four vegetation types was 26%, 29%, 14% and 35%, and in different growth stages of soybean crop, the increase of CI was different which could be up to 60% at the maximum increase.

Bidirectional Reflectance Distribution Function (BRDF) is an important component in quantitative remote sensing. In this study, we explored the relationship of sampling distribution and reconstructed BRDF curve for snow surface. In order to get enough observations, we utilize the field-measured data to analyze the different BRDF under different sampling pattern. This work can be very meaningful because it is hard to acquire sufficient measurements in all directions especially for snow in the real life.