Clumping Index Research Articles

VitiCanopy: A Free Computer App to Estimate Canopy Vigor and Porosity for Grapevine.

Leaf area index (LAI) and plant area index (PAI) are common and important biophysical parameters used to estimate agronomical variables such as canopy growth, light interception and water requirements of plants and trees. LAI can be either measured directly using destructive methods or indirectly using dedicated and expensive instrumentation, both of which require a high level of know-how to operate equipment, handle data and interpret results. Recently, a novel smartphone and tablet PC application, VitiCanopy, has been developed by a group of researchers from the University of Adelaide and the University of Melbourne, to estimate grapevine canopy size (LAI and PAI), canopy porosity, canopy cover and clumping index. VitiCanopy uses the front in-built camera and GPS capabilities of smartphones and tablet PCs to automatically implement image analysis algorithms on upward-looking digital images of canopies and calculates relevant canopy architecture parameters. Results from the use of VitiCanopy on grapevines correlated well with traditional methods to measure/estimate LAI and PAI. Like other indirect methods, VitiCanopy does not distinguish between leaf and non-leaf material but it was demonstrated that the non-leaf material could be extracted from the results, if needed, to increase accuracy. VitiCanopy is an accurate, user-friendly and free alternative to current techniques used by scientists and viticultural practitioners to assess the dynamics of LAI, PAI and canopy architecture in vineyards, and has the potential to be adapted for use on other plants.

Wireless Sensor Network of Typical Land Surface Parameters and Its Preliminary Applications for Coarse-Resolution Remote Sensing Pixel

How to obtain the “truth” of land surface parameter as reference value to validate the remote sensing retrieved parameter in heterogeneous scene and coarse-resolution pixel is one of the most challenging topics in environmental studies. In this paper, a distributed sensor network system named CPP-WSN was established to capture the spatial and temporal variation of land surface parameters at coarse-resolution satellite pixel scale around the Huailai Remote Sensing Station, which locates in the North China Plain. The system consists of three subnetworks that are RadNet, SoilNet, and VegeNet. Time series observations of typical land surface parameters, including UVR, PAR, SWR, LWR, albedo, and land surface temperature (LST) from RadNet, multilayer soil moisture and soil temperature from SoilNet, and fraction of vegetation cover (FVC), clumping index (CI), and leaf area index (LAI) from VegeNet, have been obtained and shared on the web. Compared with traditional single-point measurement, the “true” reference value of coarse pixel is obtained by averaging or representativeness-weighted averaging the multipoint measurements acquired using the sensor network. The preliminary applications, which validate several remote sensing products with CPP-WSN data, demonstrate that a high quality ground “truth” dataset has been available for remote sensing as well as other applications.

Spatial-temporal characteristics of foliage clumping index in China during 2000–2013

The distribution of natural vegetation foliage is often clumped at different scales of three-dimensional structures such as shoots, branches, whorls, tree crowns, and tree groups. Clumping index (CI) quantifies the level of foliage grouping relative to a random distribution in space. As an additional vegetation structural parameter of comparable importance to leaf area index (LAI), CI strongly influences canopy radiation regimes, photosynthesis and land-atmosphere interactions. It is a correction factor to convert the effective LAI which assumes a random distribution of leaves in space to the true LAI, and is also needed to separate the canopy into sunlit and shaded leaves for accurate simulations of process-based ecological and land surface models. However, due to the lack of clumping index available, clumping index values are often assumed to vary only with land cover types in most operational LAI retrieval algorithms and ecological models, which might induce large uncertainties in these model outputs. Therefore, it is highly desirable to map the spatial and temporal variations of the clumping for different land cover types.

Specular reflection in the signal of LAI-2000 plant canopy analyzer

Abstract In a vegetation canopy part of leaves reflect sky radiation specularly to the LAI-2000 sensor. This fraction is smaller in a planophile canopy and higher in an erectophile canopy. As the specularly reflected radiation does not enter the leaf nor it is absorbed by leaf pigments and depending on the incidence angle on the leaf the reflection coefficient may be very high. Depending on the leaf area index (LAI) and leaf angle distribution the scattered sky radiation may cause an overestimation of the gap fraction and consequently an underestimation of LAI up to 20–25% if the LAI measurements with plant canopy analyzer LAI-2000 are carried out under ideal perfectly overcast conditions. Similar biases are present in measurements of gap fraction and LAI with hemispherical photos if these measurement procedures are calibrated against LAI-2000. Considering the scattered sky radiation in optical techniques (LAI-2000, hemispherical photos) of measuring gap fraction in vegetation canopies explains at least part of the observed discrepancies in gap fraction estimated with terrestrial laser scanner and hemispherical photos. The scattered sky radiation in the LAI-2000 signal introduces bias both in the estimated LAI and foliage clumping index.

Evaluation and correction of optically derived leaf area index in different temperate forests

In recent years optical techniques for rapid LAI measurements have been developed, but few studies have been performed to evaluate the accuracy of optical estimation of LAI in mixed deciduous-evergreen forest stands. In this paper, we assessed the accuracy of digital hemispherical photography (DHP) and the LAI-2000 for the estimation of effective LAI (Le) by comparison with litter collection LAI (LAIlit) in four mixed deciduous broadleaf and evergreen needleleaf forests and one deciduous needleleaf forest. We also evaluated the relative contribution of major error sources to the determination of LAI by optical methods, including the woody-to-total area ratio (α), the element clumping index (ΩE) and the needle-to-shoot area ratio (γE). Additionally, incorrect automatic photographic exposure has been considered for DHP. DHP Le underestimated LAIlit by an average of 44-70% in different forests, and the difference between LAIlit and DHP Le after correction for the automatic exposure, α, ΩE and γE ranged from 1% to 21% in five forest stands. In contrast, LAI values from LAI-2000 were more similar to the direct litter collection LAI. The LAI-2000 Le underestimated LAIlit by an average of 13-40% in these forests, while the accuracy of the best estimates of LAI using LAI-2000 methods is over 93% after considering α, ΩE and γE. The error caused by automatic exposure to DHP Le is larger than other factors in all forest stands, and the γE was the main uncertainty to LAI-2000 Le in most forest stands. Moreover, optical LAI (both DHP and LAI-2000) was significantly (P < 0.01) correlated with LAIlit, especially the corrected LAI obtained by the LAI-2000 (R2 = 0.83, RMSE = 1.04). Our results demonstrate that the above factors affect the estimation of LAI by optical methods, thus the species composition of a forest stand should be seriously considered in order to improve the accuracy of LAI by optical methods.

Influences of crown size on the estimation of gap fraction and clumping index

Influences of crown size on the estimation of gap fraction and clumping index

A note on estimating canopy cover from digital cover and hemispherical photography

Fast and accurate estimates of canopy cover are central for a wide range of forestry studies. As direct measurements are impractical, indirect optical methods have often been used in forestry to estimate canopy cover. In this paper the accuracy of canopy cover estimates from two widely used canopy photographic methods, hemispherical photography (DHP) and cover photography (DCP) was evaluated. Canopy cover was approximated in DHP as the complement of gap fraction data at narrow viewing zenith angle range (0°–15°), which was comparable with that of DCP. The methodology was tested using artificial images with known canopy cover; this allowed exploring the influence of actual canopy cover and mean gap size on canopy cover estimation from photography. DCP provided robust estimates of canopy cover, whose accuracy was not influenced by variation in actual canopy cover and mean gap size, based on comparison with artificial images; by contrast, the accuracy of cover estimates from DHP was influenced by both actual canopy cover and mean gap size, because of the lower ability of DHP to detect small gaps within crown. The results were replicated in both DHP and DCP images collected in real forest canopies. Finally, the influence of canopy cover on foliage clumping index and leaf area index was evaluated using a theoretical gap fraction model. The main findings indicate that DCP can overcome the limits of indirect techniques for obtaining unbiased and precise estimates of canopy cover, which are comparable to those obtainable from direct, more labour-intensive techniques, being therefore highly suitable for routine monitoring and inventory purposes.

Assessment of foliage clumping effects on evapotranspiration estimates in forested ecosystems

In forested ecosystems, the aggregation of leaves into different spatial structures at the canopy, branch and shoot scales leads to a non-random spatial distribution of foliar elements. However, effect of foliar aggregation or clumping on the estimation of terrestrial evapotranspiration (ET) is not yet well understood. To evaluate the effect of foliar clumping, the process-based Boreal Ecosystem Productivity Simulator (BEPS) is used to simulate ET at eight flux tower sites in North American forests. BEPS separates a canopy into sunlit and shaded leaf groups in the calculation of canopy-level ET and clumping affects this separation. Three cases are simulated with BEPS, and the modeled ET values are compared with flux tower measurements: Case I serves as a baseline, in which LAI and clumping index at the sites are considered. In this case, BEPS can explain 43–75% the variance of measured ET at these sites. For Case II, the LAI is considered but clumping is ignored; resulting in an overestimation of annual ET at all sites by ∼5% at the most clumped sites. In Case III, when effective LAI is used (i.e. clumping is not considered), the site-averaged mean annual ET is underestimated by 11.5%, with the largest underestimation found at the site with most clumping (CA-DF49; 19.1%). In both Case II and Case III, the more clumped a canopy is, the larger bias is found in the ET estimation (p<0.001). The estimated biases are robust to the errors in key driving variables and model parameters. When LAI is derived from optical measurements on the ground and from satellite platforms without considering the effect of clumping, it can cause substantial underestimation of ET.These results demonstrate the need for considering foliage clumping in process-based ET modeling, with potential biases having large implications for carbon cycle modeling, water budget calculations and understanding and predicting ecosystem responses to future climatic scenarios.

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.

Inter- and intra-annual variations of clumping index derived from the MODIS BRDF product

Clumping index quantifies the level of foliage aggregation, relative to a random distribution, and is a key structural parameter of plant canopies and is widely used in ecological and meteorological models. In this study, the inter- and intra-annual variations in clumping index values, derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF product, are investigated at six forest sites, including conifer forests, a mixed deciduous forest and an oak-savanna system. We find that the clumping index displays large seasonal variation, particularly for the deciduous sites, with the magnitude in clumping index values at each site comparable on an intra-annual basis, and the seasonality of clumping index well captured after noise removal. For broadleaved and mixed forest sites, minimum clumping index values are usually found during the season when leaf area index is at its maximum. The magnitude of MODIS clumping index is validated by ground data collected from 17 sites. Validation shows that the MODIS clumping index can explain 75% of variance in measured values (bias=0.03 and rmse=0.08), although with a narrower amplitude in variation. This study suggests that the MODIS BRDF product has the potential to produce good seasonal trajectories of clumping index values, but with an improved estimation of background reflectance.

The effect of clumping on canopy scattering and its directional properties: a model simulation using spectral invariants

The spectral invariants theory predicts that the bidirectional reflectance factor (BRF) of a vegetation canopy can be expressed in terms of the canopy interceptance (i0), the recollision probability (p), and the directional escape probability (ρ). These spectral invariant parameters together form a novel canopy structural parameter – the directional area scattering factor (DASF). The DASF can be retrieved from remotely sensed hyperspectral imagery and has been found to be useful, e.g. for the separation of tree species. The spectral invariants theory, however, does not provide an interpretation of which specific canopy structural properties are captured by the DASF. In this study, we examined the possible link between the DASF and the canopy clumping index (β). A simple model was designed to simulate the effect of β on canopy first order scattering, which was assumed to govern the directional behaviour of the DASF. The model is based on a modified spectral invariants approach, where the assumption of constant p is relaxed so that the first order recollision probability (p1) and single scattering are calculated separately, and canopy BRF is expressed as the sum of the first and multiple order components. Simulations were performed on model canopies, where radiation penetration is described using a traditional statistical approach but allowing non-random foliage distributions caused by clumping. The results indicated a strong dependency between the modelled DASF and the canopy clumping index.

On improving the accuracy of digital hemispherical photography measurements of seasonal leaf area index variation in deciduous broadleaf forests

Optical methods have been widely used to estimate seasonal changes of the leaf area index (LAI) in forest stands because they are convenient and effective; however, their accuracy in deciduous broadleaf forests has rarely been evaluated. We estimate the seasonal changes in the LAI by combining periodic observations of leaf area variation with litter collection (LAIdir) in deciduous broadleaf forests and use these estimates to evaluate the accuracy of optical LAI measurements made using digital hemispherical photography (DHP). We also propose a method to correct DHP-derived LAI (LAIDHP) values for seasonal changes in major factors that influence the determination of LAI, including the woody to total area ratio (α), the element clumping index (ΩE, using three different methods), and the photographic exposure setting (E). Before these corrections were made, LAIDHP underestimates LAIdir by 14%–55% from 21 May to 1 October but overestimates it by 78% on 12 May and by 226% on 11 October. Although pronounced differences are observed between LAIdir and LAIDHP, they are significantly correlated (R2 = 0.85, RMSE = 0.32, P &lt; 0.001). After considering seasonal changes in α, ΩE, and E, the accuracy of LAIDHP improves markedly, with a mean difference between the corrected LAIDHP and LAIdir of &lt;17% in all periods. The results suggest that the proposed scheme for correcting LAIDHP is useful and effective for estimating seasonal LAI variation in deciduous broadleaf forests.

On the angular dependency of canopy gap fractions in pine, spruce and birch stands

The angular profiles of canopy gap fraction curves are influenced by canopy structure, and are commonly expected to vary with stand- and crown-level variables such as tree pattern, crown shape and leaf orientation. In this study, measurements of canopy structure, gap fractions and effective LAI in 986 plots of Scots pine, Norway spruce and Silver birch stands in Finland were used to assess how similar the angular canopy gap fraction profiles are for common boreal tree species. The profiles were characterized with help of the shape function ψ(θ), defined as the normalized value of the canopy light extinction coefficient at zenith angle (θ). Variation in ψ(θ) would be induced not only by a non-spherical leaf orientation, but also by differences in the directional clumping indices, such as could result from species-specific differences in crown shape. Our results showed that there is wide variation in the shape of ψ in the individual plots of the three different species. The species-specific mean curves ψ(θ), however, showed relatively small variation with θ, except for a sudden drop at large zenith angles, and the shape of the curves was similar for the different tree species. Results indicate that differences in crown shape of the study species do not significantly affect the angular profiles of canopy gap fraction.

Canopy clumping appraisal using terrestrial and airborne laser scanning

Accurate spatial information of canopy clumping degree (Ω) contributes to better understanding of the light regime within the canopy and the physiological processes associated with it. This paper evaluates the potential of terrestrial (TLS) and airborne laser scanning (ALS) to estimate Ω in different vegetation types after converting the point cloud into a 3-dimensional (3D) voxel-based model. Three methods are presented based on the spatial distribution of the returns (Standardized Morisita's Index — SMI); the gap distribution (Pielou's coefficient of segregation — PCS) and the gap size distribution (Chen & Cihlar's clumping index — CCI). Compared to Ω values derived from hemispherical photographs (HPs), the CCI method outperformed PCS and SMI for both instruments, with a correlation value of 0.93 (vs. 0.79 — PCS and 0.65 — SMI) for oak trees using TLS; 0.83 (vs. 0.78 — PCS and 0.73 — SMI) for a shrub chaparral using ALS data; and 0.84 (vs. 0.81 — PCS and 0.50 — SMI) for a mixed Mediterranean forest using ALS data. Voxel size was an important parameter to estimate Ω showing statistically significant differences for the different resolutions tested. Voxel size had an opposite effect on SMI than that on PCS and CCI, with SMI providing better results for coarser voxel sizes, and PCS and CCI yielding higher accuracies for finer voxels. In the case of the TLS, the influence of the zenith angle was also evaluated by means of a Kruskal–Wallis test. CCI and PCS did not show significant differences among the zenith angles tested, but SMI did. The radius of the plot used to analyze ALS data significantly affected the correlations with HP, with the best results found at 13, 7 and 15m for mixed Mediterranean forest and at 11, 10 and 5m for shrubs for CCI, PCS and SMI, respectively. The methods presented have the potential to be operationally applied to other areas using TLS and ALS data, since they are not based on an empirical fit but on the analysis of the gap size in the canopy and the distribution of returns after voxelization.

Uncertainty Analysis in the Creation of a Fine-Resolution Leaf Area Index (LAI) Reference Map for Validation of Moderate Resolution LAI Products

The validation process for a moderate resolution leaf area index (LAI) product (i.e., MODIS) involves the creation of a high spatial resolution LAI reference map (Lai-RM), which when scaled to the moderate LAI resolution (i.e., &gt; 1 km) allows for comparison and analysis with this LAI product. This research addresses two major sources of uncertainty in the creation of the LAI-RM: (1) the uncertainty associated with the indirect in situ optical measurements of southeastern United States needle-leaf LAI and (2) the uncertainty in the process of classifying land cover (LC). Uncertainty within the loblolly pine (Pinus taeda) in situ data collection was highest for the assessment of the plant area index (PAI), Le (27.2%), and the woody-to-total ratio, α, (30.6%). The needle-to-shoot ratio, λE, and the element clumping index, ΩE, contributed 14.9% and 9.3%, respectively, to the uncertainty in the calculation of LAI. Combining LC differences (3.4%) with the uncertainty within the loblolly pine component resulted in doubling the LAI-RM variability (σ = 0.50 to σ = 0.97) at the 1 km2 validation site located in Appomattox, Virginia, USA.

Intercomparison of clumping index estimates from POLDER, MODIS, and MISR satellite data over reference sites

Clumping index is the measure of foliage grouping relative to a random distribution of leaves in space. It is a key structural parameter of plant canopies that influences canopy radiation regimes and controls canopy photosynthesis and other land–atmosphere interactions. The Normalized Difference between Hotspot and Darkspot (NDHD) index has been previously used to retrieve global clumping index maps from POLarization and Directionality of the Earth’s Reflectances (POLDER) data at ∼6km resolution and the Bidirectional Reflectance Distribution Function (BRDF) product from Moderate Resolution Imaging Spectroradiometer (MODIS) at 500m resolution. Most recently the algorithm was also applied with Multi-angle Imaging SpectroRadiometer (MISR) data at 275m resolution over selected areas. In this study for the first time we characterized and compared the three products over a set of sites representing diverse biomes and different canopy structures. The products were also directly validated with both in-situ vertical profiles and available seasonal trajectories of clumping index over several sites. We demonstrated that the vertical distribution of foliage and especially the effect of understory need to be taken into account while validating foliage clumping products from remote sensing products with values measured in the field. Satellite measurements responded to the structural effects near the top of canopies, while ground measurements may be biased by the lower vegetation layers. Additionally, caution should be taken regarding the misclassification in land cover maps as their errors can propagate into the foliage clumping maps. Our results indicate that MODIS data and MISR data, with 275m in particular, can provide good quality clumping index estimates at spatial scales pertinent for modeling local carbon and energy fluxes.

Estimation of foliage clumping from the LAI-2000 Plant Canopy Analyzer: effect of view caps

Foliage clumping can be estimated from logarithm averaging method in LAI-2000. The spatial scaling of clumping effects considered by the instrument is dependent on the sensor’s azimuthal view. Accurate estimates of foliage clumping index (Ω) are required to improve the retrieval of leaf area index (L) from optical instruments like LAI-2000/2200 Plant Canopy Analyzer (PCA) and digital hemispherical photography (DHP). The logarithm averaging method is often used to approximate L because clumping effects are considered at scales larger than the sensor’s field of view. However, the spatial scaling considered for logarithm averaging typically differs between PCA and DHP, resulting in different estimates of foliage clumping. Based on simulation, we demonstrated that applying restricting azimuth view caps (e.g., 45° or 10°) allows reliable estimation of Ω and more accurate estimation of L from PCA. Simulated Ω and L values were comparable to those measured using the PCA, DHP and litter traps. Linear averaging of the gap fractions across readings at a plot or site yields a concurrent estimate of effective leaf area index (L e), thus enabling the calculation of L e, L, and Ω from a single instrument fitted with view caps. Users need to be aware that the method they use for averaging gap fractions determines whether they are measuring L e or L, and PCA users need to be aware that they are applying increasingly large corrections for foliage clumping as they use more restrictive view caps, a fact that they can use to their advantage to improve estimates of L.

Indirect measurement of leaf area index on the basis of path length distribution

Accurate leaf area index (LAI) measurements are important for understanding and evaluating global mass and energy cycle. Foliage clumping serves an important function in traditional indirect LAI measurement methods. The clumping index has been used to modify effective LAI for decades. However, the change in path length within canopies is often the most uncertain factor in indirect LAI estimation using Beer's law. A simple clumping index is incapable of describing the heterogeneity of a canopy and may cause large errors in calculating true LAI values. We proposed a new LAI estimation method by using path length distribution functions in optical measurement. Both simulation and field measurements show that the path length-based method can effectively characterize the LAI values of heterogeneous canopies. Deviation is less than 10% for all the validations. One of the advantages of path length distribution theory is that it can characterize and handle crown shape-induced non-randomness within canopies. Such non-randomness, which may cause underestimation of up to 25%, has not been well addressed by existing algorithms. Path length theory is expected to improve the indirect measurement accuracy of LAI significantly with the use of current optical instruments.

Estimation of leaf area index in understory deciduous trees using digital photography

Fast and accurate estimates of understory leaf area are essential to a wide range of ecological applications. Indirect methods have mainly been used to estimate leaf area of overstory but their application in understory remains largely unexplored. In this study we described a combination of digital photographic methods to obtain rapid, reliable and non-destructive estimate of leaf area index of understory deciduous trees. Nadir photography was used to estimate foliage cover, vertical gap fraction and foliage clumping index. Leveled photography was used to characterize the leaf angle distribution of the examined tree species. Leaf area index estimates obtained combining the two photographic methods were compared with direct measurements obtained from harvesting (L).We applied these methods in Quercus cerris, Carpinus betulus and Fagus sylvatica stands. Foliage cover estimates derived from two nadir image classification methods were significantly correlated with leaf area index measurements obtained from harvesting. The leveled digital photographic method, previously tested in tall trees and field crops, provided reliable leaf angle measurements in understory tree species. Digital photography provided good indirect estimates of L. We conclude that digital photography is suitable for routine estimate and monitoring of understory leaf area, on account of its fast and cost-effective procedure.

Seasonal variation of leaf area index (LAI) over paddy rice fields in NE China: Intercomparison of destructive sampling, LAI-2200, digital hemispherical photography (DHP), and AccuPAR methods

Continuous field leaf area index (LAI) measurement has become increasingly important for the validation of remote sensing LAI products. A seasonal field campaign was carried out to take continuous LAI measurements over paddy rice fields in NE China in 2012. Three indirect optical methods, LAI-2200, digital hemispherical photography (DHP), and AccuPAR, were compared with a destructive sampling method conducted concurrently. Corrections for the clumping effect were applied to the effective plant area indices (PAIeff) estimated from the indirect optical measurements.Both LAI-2200 and DHP produce consistent PAIeff estimates over the season (R2=0.76, RMSE=0.97). The clumping index (CI) values obtained from DHP generally decrease with plant growth and range between 0.63 and 0.74 during the peak growing period from day of year (DOY) 191−230. The CI values retrieved from DHP photos generally decrease with increasing view angles. The optical PAI and LAI values estimated from LAI-2200 and DHP correspond very well with the destructive values before DOY 230 (R2=0.75, RMSE=1.15 for PAI and R2=0.78, RMSE=0.74 for LAI), and the relative errors are less than 10% and 5%, respectively, for the two instruments. Omitting ring 5 for LAI-2200 generates very accurate PAI and LAI estimations during the peak season. Nevertheless, AccuPAR underestimates the PAIeff, PAI, and LAI values obtained from other methods (up to 30%). After DOY 231, the capability to detect PAI decreases significantly for both destructive and optical methods due to the leaf senescence and the DHP classification difficulty. In general, rice PAI could be accurately estimated with LAI-2200 and DHP before senescence if the clumping effect could be properly taken into account. The seasonal continuous LAI measurements obtained from this study are valuable for the validation of remote sensing LAI products.