Baodong Xu

Regional Leaf Area Index Retrieval Based on Remote Sensing: The Role of Radiative Transfer Model Selection

Physically-based approaches for estimating Leaf Area Index (LAI) using remote sensing data rely on radiative transfer (RT) models. Currently, many RT models are freely available, but determining the appropriate RT model for LAI retrieval is still problematic. This study aims to evaluate the necessity of RT model selection for LAI retrieval and to propose a retrieval methodology using different RT models for different vegetation types. Both actual experimental observations and RT model simulations were used to conduct the evaluation. Each of them includes needleleaf forests and croplands, which have contrasting structural attributes. The scattering from arbitrarily inclined leaves (SAIL) model and the four-scale model, which are 1D and 3D RT models, respectively, were used to simulate the synthetic test datasets. The experimental test dataset was established through two field campaigns conducted in the Heihe River Basin. The results show that the realistic representation of canopy structure in RT models is very important for LAI retrieval. If an unsuitable RT model is used, then the root mean squared error (RMSE) will increase from 0.43 to 0.60 in croplands and from 0.52 to 0.63 in forests. In addition, an RT model’s potential to retrieve LAI is limited by the availability of a priori information on RT model parameters. 3D RT models require more a priori information, which makes them have poorer generalization capability than 1D models. Therefore, physically-based retrieval algorithms should embed more than one RT model to account for the availability of a priori information and variations in structural attributes among different vegetation types.

An Optimal Sampling Design for Observing and Validating Long-Term Leaf Area Index with Temporal Variations in Spatial Heterogeneities

A sampling strategy to define elementary sampling units (ESUs) for an entire site at the kilometer scale is an important step in the validation process for moderate-resolution leaf area index (LAI) products. Current LAI-sampling strategies are unable to consider the vegetation seasonal changes and are better suited for single-day LAI product validation, whereas the increasingly used wireless sensor network for LAI measurement (LAINet) requires an optimal sampling strategy across both spatial and temporal scales. In this study, we developed an efficient and robust LAI Sampling strategy based on Multi-temporal Prior knowledge (SMP) for long-term, fixed-position LAI observations. The SMP approach employed multi-temporal vegetation index (VI) maps and the vegetation classification map as a priori knowledge. The SMP approach minimized the multi-temporal bias of the VI frequency histogram between the ESUs and the entire site and maximized the nearest-neighbor index to ensure that ESUs were dispersed in the geographical space. The SMP approach was compared with four sampling strategies including random sampling, systematic sampling, sampling based on the land-cover map and a sampling strategy based on vegetation index prior knowledge using the PROSAIL model-based simulation analysis in the Heihe River basin. The results indicate that the ESUs selected using the SMP method spread more evenly in both the multi-temporal feature space and geographical space over the vegetation cycle. By considering the temporal changes in heterogeneity, the average root-mean-square error (RMSE) of the LAI reference maps can be reduced from 0.12 to 0.05, and the relative error can be reduced from 6.1% to 2.2%. The SMP technique was applied to assign the LAINet ESU locations at the Huailai Remote Sensing Experimental Station in Beijing, China, from 4 July to 28 August 2013, to validate three MODIS C5 LAI products. The results suggest that the average R2, RMSE, bias and relative uncertainty for the three MODIS LAI products were 0.60, 0.33, −0.11, and 12.2%, respectively. The MCD15A2 product performed best, exhibiting a RMSE of 0.20, a bias of −0.07 and a relative uncertainty of 7.4%. Future efforts are needed to obtain more long-term validation datasets using the SMP approach on different vegetation types for validating moderate-resolution LAI products in time series.

Improving Leaf Area Index Retrieval Over Heterogeneous Surface by Integrating Textural and Contextual Information: A Case Study in the Heihe River Basin

Spatial heterogeneity of land surface induces scaling bias in leaf area index (LAI) products. In optical remote sensing of vegetation, spatial heterogeneity arises both by textural and contextual effects. A case study made in the middle reach of the Heihe River Basin shows that the scaling bias in LAI retrieval is large up to 26% if the spatial heterogeneity within low-resolution pixels is ignored. To reduce the influence of spatial heterogeneity on LA! products, a correcting method combining both textural and contextual information is adopted, and the scaling bias may decrease to less than 2% in producing resolution-invariant LAI products.

Leaf Area Index Retrieval Combining HJ1/CCD and Landsat8/OLI Data in the Heihe River Basin, China

The primary restriction on high resolution remote sensing data is the limit observation frequency. Using a network of multiple sensors is an efficient approach to increase the observations in a specific period. This study explores a leaf area index (LAI) inversion method based on a 30 m multi-sensor dataset generated from HJ1/CCD and Landsat8/OLI, from June to August 2013 in the middle reach of the Heihe River Basin, China. The characteristics of the multi-sensor dataset, including the percentage of valid observations, the distribution of observation angles and the variation between different sensor observations, were analyzed. To reduce the possible discrepancy between different satellite sensors on LAI inversion, a quality control system for the observations was designed. LAI is retrieved from the high quality of single-sensor observations based on a look-up table constructed by a unified model. The averaged LAI inversion over a 10-day period is set as the synthetic LAI value. The percentage of valid LAI inversions increases significantly from 6.4% to 49.7% for single-sensors to 75.9% for multi-sensors. LAI retrieved from the multi-sensor dataset show good agreement with the field measurements. The correlation coefficient (R2) is 0.90, and the average root mean square error (RMSE) is 0.42. The network of multiple sensors with 30 m spatial resolution can generate LAI products with reasonable accuracy and meaningful temporal resolution.

A Sampling Strategy for Remotely Sensed LAI Product Validation Over Heterogeneous Land Surfaces

The development of efficient and systematic groundbased spatial sampling strategies is critical for the validation of medium-resolution satellite-derived leaf area index (LAI) products, particularly over heterogeneous land surfaces. In this paper, a new sampling strategy based on high-resolution vegetation index prior knowledge (SSVIP) is proposed to generate accurate LAI reference maps over heterogeneous areas. To capture the variability across a site, the SSVIP is designed to 1) stratify the nonhomogeneous area into zones with minimum within-class variance; 2) assign the number of samples to each zone using Neyman optimal allocation; and 3) determine the spatial distribution of samples with a nearest neighbor index. The efficiency of the proposed method was examined using different vegetation types and pixel heterogeneities. The results indicate that the SSVIP approach can properly divide a heterogeneous area into different vegetation cover zones. Whereas the LAI reference maps generated by SSVIP attain the target accuracy of 0.1 LAI units in cropland and broadleaf forest sites, the current sampling strategy based on vegetation type has a root mean square error (RMSE) of 0.14 for the same number of samples. SSVIP was compared with the current sampling strategy at 24 VALERI sites, and the results suggested that samples selected by SSVIP were more representative in the feature space and geographical space, which further indicated the reasonable validation over heterogeneous land surfaces.

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.

A Radiative Transfer Model for Heterogeneous Agro-Forestry Scenarios

Landscape heterogeneity is a common natural phenomenon but is seldom considered in current radiative transfer (RT) models for predicting the surface reflectance. This paper developed an analytical RT model for heterogeneous Agro-Forestry scenarios (RTAF) by dividing the scenario into nonboundary regions (NRs) and boundary regions (BRs). The scattering contribution of the NRs can be estimated from the scattering-by-arbitrarily-inclined-leaves-with-the-hot-spot-effect model as homogeneous canopies, whereas that of the BRs is calculated based on the bidirectional gap probability by considering the interactions and mutual shadowing effects among different patches. The multiangular airborne observations and discrete-anisotropic-RT model simulations were used to validate and evaluate the RTAF model over an agro-forestry scenario in the Heihe River Basin, China. The results suggest that the RTAF model can accurately simulate the hemispherical-directional reflectance factors (HDRFs) of the heterogeneous scenarios in the red and near-infrared (NIR) bands. The boundary effect can significantly influence the angular distribution of the HDRFs and consequently enlarge the HDRF variations between the backward and forward directions. Compared with the widely used dominant cover type (DCT) and spectral linear mixture (SLM) models, the RTAF model reduced the maximum relative error from 25.7% (SLM) and 23.0% (DCT) to 9.8% in the red band and from 19.6% (DCT) and 13.7% (SLM) to 8.7% in the NIR band. The RTAF model provides a promising way to improve the retrieval of biophysical parameters (e.g., leaf area index) from remote sensing data over heterogeneous agro-forestry scenarios.

Evaluating Spatial Representativeness of Station Observations for Remotely Sensed Leaf Area Index Products

Continuous leaf area index (LAI) observations from global ground stations are an important reference dataset for the validation of remotely sensed LAI products. In this study, a pragmatic approach is presented for evaluating the spatial representativeness of station-observed LAI dataset in the product pixel grid. Three evaluation indicators, including dominant vegetation type percent (DVTP), relative absolute error (RAE) and coefficient of sill (CS), were established to quantify different levels of spatial representativeness. The DVTP was used to evaluate whether the station-observed vegetation type was the dominant one in the pixel grid, and the RAE and CS were applied to quantify the point-to-area consistency for a given station observation and the spatial heterogeneity caused by the different density of vegetation within the pixel, respectively. The proposed approach was applied to 25 stations from the Chinese Ecosystem Research Network, and results show significant differences of representativeness errors at different levels. The spatial representativeness for different stations varied seasonally with different vegetation growth stages due to temporal changes in heterogeneity, but the spatial representativeness remained consistent at interannual timeframes due to the relatively stable vegetation structure and pattern between adjacent years. A large error can occur in MOD15A2 product validation when the representativeness level of station LAI observations is low. This approach can effectively distinguish various levels of spatial representativeness for the station-observed LAI dataset at the pixel grid scale, which can consequently improve the reliability of LAI product validation by selecting LAI observations with a high degree of representativeness.

A Cost-Constrained Sampling Strategy in Support of LAI Product Validation in Mountainous Areas

Increasing attention is being paid on leaf area index (LAI) retrieval in mountainous areas. Mountainous areas present extreme topographic variability, and are characterized by more spatial heterogeneity and inaccessibility compared with flat terrain. It is difficult to collect representative ground-truth measurements, and the validation of LAI in mountainous areas is still problematic. A cost-constrained sampling strategy (CSS) in support of LAI validation was presented in this study. To account for the influence of rugged terrain on implementation cost, a cost-objective function was incorporated to traditional conditioned Latin hypercube (CLH) sampling strategy. A case study in Hailuogou, Sichuan province, China was used to assess the efficiency of CSS. Normalized difference vegetation index (NDVI), land cover type, and slope were selected as auxiliary variables to present the variability of LAI in the study area. Results show that CSS can satisfactorily capture the variability across the site extent, while minimizing field efforts. One appealing feature of CSS is that the compromise between representativeness and implementation cost can be regulated according to actual surface heterogeneity and budget constraints, and this makes CSS flexible. Although the proposed method was only validated for the auxiliary variables rather than the LAI measurements, it serves as a starting point for establishing the locations of field plots and facilitates the preparation of field campaigns in mountainous areas.

A methodology to estimate representativeness of LAI station observation for validation: a case study with Chinese Ecosystem Research Network (CERN) in situ data

Leaf Area Index (LAI) is known as a key vegetation biophysical variable. To effectively use remote sensing LAI products in various disciplines, it is critical to understand the accuracy of them. The common method for the validation of LAI products is firstly establish the empirical relationship between the field data and high-resolution imagery, to derive LAI maps, then aggregate high-resolution LAI maps to match moderate-resolution LAI products. This method is just suited for the small region, and its frequencies of measurement are limited. Therefore, the continuous observing LAI datasets from ground station network are important for the validation of multi-temporal LAI products. However, due to the scale mismatch between the point observation in the ground station and the pixel observation, the direct comparison will bring the scale error. Thus it is needed to evaluate the representativeness of ground station measurement within pixel scale of products for the reasonable validation. In this paper, a case study with Chinese Ecosystem Research Network (CERN) in situ data was taken to introduce a methodology to estimate representativeness of LAI station observation for validating LAI products. We first analyzed the indicators to evaluate the observation representativeness, and then graded the station measurement data. Finally, the LAI measurement data which can represent the pixel scale was used to validate the MODIS, GLASS and GEOV1 LAI products. The result shows that the best agreement is reached between the GLASS and GEOV1, while the lowest uncertainty is achieved by GEOV1 followed by GLASS and MODIS. We conclude that the ground station measurement data can validate multi-temporal LAI products objectively based on the evaluation indicators of station observation representativeness, which can also improve the reliability for the validation of remote sensing products.