Ronghai Hu

A portable Multi-Angle Observation System

This paper presents a portable Multi-Angle Observation System (MAOS) to quickly collect bi-directional reflectance factor (BRF) and directional thermal radiance of land surface along with the spectroradiometer and thermal radiometer. The new system is able to make more than 13 zenith measurements in six minutes at an arbitrary azimuth direction, with the angle-controlling accuracy better than 2°. More observations are sampled in the hot-spot direction. All operations of the MAOS and data-processing are automatically controlled by the computer. Field campaign of winter wheat canopy shows that the MAOS had captured the angular variations of the BRF.

Scale Effect in Indirect Measurement of Leaf Area Index

Scale effect, which is caused by a combination of model nonlinearity and surface heterogeneity, has been of interest to the remote sensing community for decades. However, there is no current analysis of scale effect in the ground-based indirect measurement of leaf area index (LAI), where model nonlinearity and surface heterogeneity also exist. This paper examines the scale effect on the indirect measurement of LAI. We built multiscale data sets based on realistic scenes and field measurements. We then implemented five representative methods of indirect LAI measurement at scales (segment lengths) that range from meters to hundreds of meters. The results show varying degrees of deviation and fluctuation that exist in all five methods when the segment length is shorter than 20 m. The retrieved LAI from either Beers law or the gap-size distribution method shows a decreasing trend with increasing segment lengths. The length at which the LAI values begin to stabilize is about a full period of row in row crops and 100 m in broadleaf or coniferous forests. The impacts of segment length on the finite-length averaging method, the combination of gap-size distribution and finite-length methods, and the path-length distribution method are relatively small. These three methods stabilize at the segment scale longer than 20 m in all scenes. We also find that computing the average LAI of all of the short segment lengths, which is commonly done, is not as good as merging these short segments into a longer one and computing the LAI value of the merged one.

Extracting Leaf Area Index by Sunlit Foliage Component from Downward-Looking Digital Photography under Clear-Sky Conditions

The development of near-surface remote sensing requires the accurate extraction of leaf area index (LAI) from networked digital cameras under all illumination conditions. The widely used directional gap fraction model is more suitable for overcast conditions due to the difficulty to discriminate the shaded foliage from the shadowed parts of images acquired on sunny days. In this study, a new LAI extraction method by the sunlit foliage component from downward-looking digital photography under clear-sky conditions is proposed. In this method, the sunlit foliage component was extracted by an automated image classification algorithm named LAB2, the clumping index was estimated by a path length distribution-based method, the LAD and G function were quantified by leveled digital images and, eventually, the LAI was obtained by introducing a geometric-optical (GO) model which can quantify the sunlit foliage proportion. The proposed method was evaluated at the YJP site, Canada, by the 3D realistic structural scene constructed based on the field measurements. Results suggest that the LAB2 algorithm makes it possible for the automated image processing and the accurate sunlit foliage extraction with the minimum overall accuracy of 91.4%. The widely-used finite-length method tends to underestimate the clumping index, while the path length distribution-based method can reduce the relative error (RE) from 7.8% to 6.6%. Using the directional gap fraction model under sunny conditions can lead to an underestimation of LAI by (1.61; 55.9%), which was significantly outside the accuracy requirement (0.5; 20%) by the Global Climate Observation System (GCOS). The proposed LAI extraction method has an RMSE of 0.35 and an RE of 11.4% under sunny conditions, which can meet the accuracy requirement of the GCOS. This method relaxes the required diffuse illumination conditions for the digital photography, and can be applied to extract LAI from downward-looking webcam images, which is expected for the regional to continental scale monitoring of vegetation dynamics and validation of satellite remote sensing products.

Scaling of FAPAR from the Field to the Satellite

The fraction of absorbed photosynthetically active radiation (FAPAR) is a critical biophysical parameter in eco-environmental studies. Scaling of FAPAR from the field observation to the satellite pixel is essential for validating remote sensing FAPAR product and for further modeling applications. However, compared to spatial mismatches, few studies have considered temporal mismatches between in-situ and satellite observations in the scaling. This paper proposed a general methodology for scaling FAPAR from the field to the satellite pixel considering the temporal variation. Firstly, a temporal normalization method was proposed to normalize the in-situ data measured at different times to the time of satellite overpass. The method was derived from the integration of an atmospheric radiative transfer model (6S) and a FAPAR analytical model (FAPAR-P), which can characterize the diurnal variations of FAPAR comprehensively. Secondly, the logistic model, which derives smooth and consistent temporal profile for vegetation growth, was used to interpolate the in-situ data to match the dates of satellite acquisitions. Thirdly, fine-resolution FAPAR products at different dates were estimated from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data using the temporally corrected in-situ data. Finally, fine-resolution FAPAR were taken as reference datasets and aggregated to coarse resolution, which were further compared to the Moderate Resolution Imaging Spectroradiometer (MODIS) FAPAR product. The methodology is validated for scaling FAPAR from the field to the satellite pixel temporally and spatially. The MODIS FAPAR manifested a good consistency with the aggregated FAPAR with R2 of 0.922 and the root mean squared error of 0.054.

A method for leaf gap fraction estimation based on multispectral digital images from Multispectral Canopy Imager

Gap fraction is a very important parameter to the indirect estimation of the true Leaf Area Index. In this paper, we combined the multispectral digital imageries (RGB color imagery and Near-Infrared imagery), which were obtained from a new device called Multispectral Canopy Imager (MCI), to estimate gap fraction. A new method incorporated with CIE L*a*b* color space has also been proposed to segment the multispectral digital imagery. The preliminary results of the estimated gap fraction have been showed in the conclusions section and been proved to be very well.

Modified gap fraction model of individual trees for estimating leaf area using terrestrial laser scanner

Abstract. Terrestrial laser scanners (TLS) have demonstrated great potential in estimating structural attributes of forest canopy, such as leaf area index (LAI). However, the inversion accuracy of LAI is highly dependent on the measurement configuration of TLS and spatial characteristics of the scanned tree. Therefore, a modified gap fraction model integrating the path length distribution is developed to improve the accuracy of retrieved single-tree leaf area (LA) by considering the shape of a single-tree crown. The sensitivity of TLS measurement configurations on the accuracy of the retrieved LA is also discussed by using the modified gap fraction model based on several groups of simulated and field-measured point clouds. We conclude that (1) the modified gap fraction model has the potential to retrieve LA of an individual tree and (2) scanning distance has the enhanced impact on the accuracy of the retrieved LA than scanning step. A small scanning step for broadleaf trees reduces the scanning time, the storage volume, and postprocessing work in the condition of ensuring the accuracy of the retrieved LA. This work can benefit the design of an optimal survey configuration for the field campaign.

Indirect Measurement of Forest Leaf Area Index Using Path Length Distribution Model and Multispectral Canopy Imager

Spatial heterogeneity within canopies and woody components are two factors that limit the accuracy of indirect leaf area index (LAI) measurements, but they have not been fully considered because of the limitations of commercial instruments. This study combined the path length distribution model and multispectral canopy imager for the first time to improve the accuracy of indirect LAI measurements. Indirect and direct in situ measurements were conducted in broadleaf and coniferous forests. Results show that spatial heterogeneity within canopies underestimates the LAI by 16-25%, whereas woody components overestimate LAI by 14-28% in four forest sites. These two factors exhibit opposing effects, which may be misleading and may thus complicate the quantification and validation of the effect of each factor. Ignoring woody components underestimates the degree of spatial heterogeneity or clumping in forests. Considering both nonrandomness within canopies and woody components is necessary in indirect LAI measurements.

Spectral Recalibration for In-Flight Broadband Sensor Using Man-Made Ground Targets

Accurate spectral calibration of the in-flight sensors is crucial for processing and exploration of remotely sensed data. This paper developed a strategy to make spectral recalibration (i.e., spectral response function, central wavelength, and bandwidth) for in-flight broadband sensor using a device-responsivity-decomposition model with a priori knowledge and an optimization algorithm. Sensitivity analysis indicates that an accurate result requires the targets to be observed under a dry and clear atmospheric condition (column water vapor <; 2 g/cm2 and visibility > 23 km) and no more than 5% error is included in the measured data. The new strategy was used to retrieve the spectral parameters along with radiometric calibration coefficients for a multichannel camera onboard an unmanned aerial vehicle from simultaneously remotely sensed and ground measured data sets over 19 (15 color-scaled and four gray-scaled) man-made surface targets, and the retrieved results were validated with a similar data set over another four man-made targets. It demonstrated that the cameras spectral parameters were accurately retrieved and an error less than 3.5 W/m2/μm/sr was brought to the channel radiance.

Indirect measurement of forest leaf area index using path length model and Multispectral Canopy Imager

Non-randomness within canopies and woody component are two factors limiting the accuracy of indirect leaf area index (LAI) measurement. Here we combine the path length distribution model and Multispectral Canopy Imager (MCI) together for the first time to improve the accuracy. The results show that non-randomness within canopies underestimates 17.1%-28.2% LAI, while woody component overestimates 14.6%-27.8% LAI in four forest sites. Although these two factors were sometimes offset, the degree of non-randomness within canopies and the proportion of woody component vary in different forests. More attention should be paid to the impact of the non-randomness within canopies and the woody component, especially in coniferous forest dominated by tree trunks and branches.

Error analysis for emissivity measurement using FTIR spectrometer

The ground-measured emissivity is always affected by many kinds of noises, which lead the retrieval accuracy to be out of expectation. This paper investigates the influence of three major noises (formula simplification, surface temperature measurement, and temperature emissivity separation algorithm) on the spectral emissivity by using simulation data based on radiative transfer model and field measured data from portable 102F infrared spectrometer. The findings of this paper can provide some suggestions for the further emissivity measurement.