Clumping index (CI) field measurements based on the logarithmic gap fraction averaging (LX) method are widely used. However, some challenges regarding this method have been recognized; e.g., CI overestimation or underestimation occurs in the sampling units where there is no measurement gap, which creates major uncertainties in CI field measurements. To address this issue, we proposed an improved eight-connected LX method that replaces null gap units with the arithmetic mean of the gaps in the eight connected neighbouring units, considering the neighbouring connections of the natural foliage extension. To validate this method, we designed two controlled experimental schemes based on simulated digital hemispheric photography (DHP) images through the LargE-Scale Remote Sensing Data and Image Simulation Framework (LESS) model considering the leaf area index (LAI) and leaf angle distribution (LAD), respectively, together with collected field measurements. The results showed that our method could almost prevent overestimation and improve underestimated CIs by nearly 20%. In addition, CIs of our method had the smallest error compared to the “true” CIs (error<0.1). In conclusion, our method can significantly improve the data quality of the simulation results relative to the existing methods and present potentials in the CI measurements of upcoming field campaigns.

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.

The surface anisotropy, usually described as bidirectional reflectance distribution function (BRDF), plays a key role in the quantitative remote sensing. Numerous BRDF studies are focus on sensors onboard polar-orbiting satellites such as POLDER and MODIS based on multi-angle sensors or accumulative observations through multiple days. Notably, sensors onboard geostationary satellites can also obtain multi-angle reflectances benefited from their high revisit frequencies, while by which only a few BRDF studies have been completed. In this study, we aim to evaluate the BRDF information collected from time-series directional observations of the Advanced Himawari Imager (AHI) onboard geostationary satellite Himawari-8. The multi-angle reflectances of 6 × 7 km POLDER and 0.05° AHI at mixed forest area during a whole month were collected. Generally, AHI has a good weight of Determination (WoD) of 0.03, as well as small fit-RMSEs of 0.0055 and 0.0235 in the red and NIR bands based on the kernel-driven Ross-Li BRDF model, which shows promising potential to provide BRDF information with a good quality.

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.

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.

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.