Methods For Leaf Area Index Estimation Research Articles

Leaf area index estimation from the time-series SAR data using the AIEM-MWCM model

ABSTRACTThe leaf area index (LAI) inversion accuracy of the water cloud model (WCM) based on the SAR images is low. The main reason is that the WCM model assumes two independent contributions from plant and soil surfaces, without taking into account their interactions. Based on this, this study proposes an efficient LAI estimation method that combines the advanced integral equation model (AIEM) and modified water cloud model (MWCM). Images collected by GaoFen-3 synthetic aperture radar (GF-3 SAR) in the Xiangfu area in the east of Kaifeng City, Henan Province, are processed with a modified SAR-BM3D method and used as test data of the proposed method. The proposed AIEM-MWCM method that combines the AIEM and MWCM models is employed to perform the remote sensing inversion of winter wheat LAI throughout the growth cycle. The results show that the fitting accuracy of winter wheat LAI in the five growth stages achieved by the proposed AIEM-MWCM method inversion is better than that of the Dubois-MWCM model. The R2 value of the proposed method is higher than 0.8, and its root mean squared error (RMSE) is lower than 0.3.

Estimating the leaf area index of urban individual trees based on actual path length

The leaf area index (LAI) is an essential biophysical variable of trees and a crucial factor affecting the urban environment. Previous studies on LAI measurements mainly focused on continuous forests, which using the cosine of the observed zenith angle for path length correction is incompatible with individual trees, although individual trees are more common in urban areas. Therefore, we modified the Beer-Lambert law for individual trees and developed a new path length correction factor that considers crown shape and actual path length in this study. Based on the new path length correction factor, we proposed a systematic single-tree LAI estimation method using digital cover photography. Comparisons with measurements showed that the root mean square error (RMSE) and Pearson correlation coefficient (r) are 0.35 and 0.97, respectively. A Python scripted module was compiled to support automated processing of this method. Furthermore, we modeled single-tree crown transmissivity based on the new path length correction factor and provided a simple formula to calculate the transmissivity of the spherical crown using some common assumptions. This study offers a theoretical basis for measuring LAI and calculating the crown transmissivity of individual trees.

Dynamic simulation of leaf area index for the soybean canopy based on 3D reconstruction

Leaf area index (LAI) of the soybean canopy was an important indicator to reflect the growth and development of the soybean plant. However, the traditional LAI measurement in the field was expensive, time-consuming, and challenging to achieve high accuracy. Thus, in this study, a calculation method of LAI for the soybean canopy based on 3D reconstruction was proposed, and the dynamic simulation model of canopy LAI was established. First, northeast soybean varieties of Kangxianchong8 and Dongnong252 were taken as the research objects, and a multi-source image synchronous acquisition platform for soybean canopy based on Kinect 2.0 was constructed to obtain the canopy image data from the V3 to R7 growth periods. Second, the 3D structure of the soybean canopy was reconstructed by conditional filtering and statistical filtering. Third, a soybean LAI estimation method was established by canopy analysis. The determination coefficient R2 between the calculated value and the standard value of LAI was greater than 0.99. Finally, a dynamic simulation model of soybean LAI was established based on the Richards model and the genetic parameters of varieties. The results showed that the accuracy of the dynamic simulation model reached above 0.99, which realized the critical technology of rapid detection and dynamic simulation of LAI for the soybean canopy, and provided quantitative dynamic prediction and technical support for scientific regulation of ecology and morphology for the soybean canopy.

Nondestructive Measurement Method of Leaf Area Index Using Near-infrared Radiation and Photosynthetically Active Radiation Transmitted through a Leafy Vegetable Canopy

Because the leaf area index (LAI) is an essential parameter for understanding the structure and growth status of plant canopies, nondestructive and continuous estimation methods have been required. Recently, an LAI estimation method using the ratio of near-infrared radiation (NIR; 700–1000 nm) to photosynthetically active radiation (PAR; 400–700 nm) (NIRin/PARin) transmitted through a canopy has been proposed. However, because previous studies on this NIRin/PARin-based LAI estimation method are limited to tall plants (e.g., forest and rice canopies), in this study, we applied this method to a short canopy (i.e., spinach) and investigated its validity. NIRin/PARin and three other traditional indices for indirect LAI estimation—relative PPF density (rPPFD), normalized difference vegetation index (NDVI), and simple ratio (SR)—were measured in 25 canopies with different LAI. NIRin/PARin showed better estimation sensitivity (R2 = 0.88) to the observed LAI than the other three indices, particularly when LAI was greater than 3 m2·m−2. In addition, the LAI estimated from NIRin/PARin measured at 10-min intervals in the entire growth period could capture an increasing trend in the measured LAI throughout the entire growth stage (mean absolute error = 0.87 m2·m−2). Errors in long-term LAI estimations may be caused by the sensor location and insufficient data due to unsuitable weather conditions for measuring NIRin/PARin. The current study demonstrates the merits and limitations of the NIRin/PARin-based LAI estimation method applied to low height canopies, thereby contributing to its practical use in horticultural crops.

An Improved LAI Estimation Method Incorporating with Growth Characteristics of Field-Grown Wheat

Leaf area index (LAI), which is an important vegetation structure parameter, plays a crucial role in evaluating crop growth and yield. Generally, it is difficult to accurately estimate LAI only using vegetation index in remote sensing (RS), especially for unmanned aerial vehicle (UAV) based RS, as its high-resolution advantage has not been fully utilized. This study aims to propose an improved LAI estimation method that comprehensively considers spectral information and structural information provided by the UAV-based RS to improve the LAI estimation accuracy of field-grown wheat. Specifically, this method introduces the canopy height model (CHM) to compensate for the lack of structural information in LAI estimation, and then takes canopy coverage (CC) as a correction parameter to alleviate the LAI overestimation. Finally, the performance of this method is verified on RGB and multispectral images, respectively. The results show that canopy structure, namely CHM and CC, can significantly improve the accuracy of LAI estimation. Compared with the traditional method, the proposed method improves the accuracy by 22.6% on multispectral images (R2 = 0.72, RMSE = 0.556) and by 43.6% on RGB images (R2 = 0.742, RMSE = 0.534). This study provides a simple and practical method for UAV-based LAI estimation, especially for the application of low-cost RGB sensors in precision agriculture and other fields.

Estimation of leaf area index for winter wheat at early stages based on convolutional neural networks

Leaf area index (LAI) is a key growth trait to characterize the winter wheat growth at early stages. However, there needs more study on the low cost and fine-scale estimation of LAI of winter wheat. In this study, a LAI estimation method of winter wheat at the early stages was proposed based on low-cost RGB images and deep learning. The time-series canopy images of winter wheat at early stages were collected for two consecutive growth seasons (growth season 2018 and 2019), based on which the proposed model, as well as the compared models, were built. In the following step, the performances of these models were compared and analyzed. The influences of the input image with different pixel resolutions and the network depth on the model performance were discussed. Moreover, transfer learning was used to test the generalization ability of the proposed model. The results showed that the proposed estimation model could reflect the time-series variation of LAI of winter wheat at early stages. The proposed model with the input image of 128 × 128 pixel resolution achieved the best performance (R2 = 0.82, NRMSE = 24.89%), outperforming the compared models. The generalization test showed that the proposed model had a good generalization ability, achieving accurate LAI estimations for growing season 2019. However, deepen the network by adding extra SAME convolutional layers could not improve the model performance. In conclusion, based on the convolutional neural network (CNN) and low-cost RGB images, the proposed model is fast and accurate in estimating the LAI of winter wheat at early stages. This method can meet the need for LAI estimation of winter wheat at early stages and provide support for growth monitoring and agronomic management of winter wheat at early stages.

Non-Parametric Statistical Approaches for Leaf Area Index Estimation from Sentinel-2 Data: A Multi-Crop Assessment

The leaf area index (LAI) is a key biophysical variable for agroecosystem monitoring, as well as a relevant state variable in crop modelling. For this reason, temporal and spatial determination of LAI are required to improve the understanding of several land surface processes related to vegetation dynamics and crop growth. Despite the large number of retrieved LAI products and the efforts to develop new and updated algorithms for LAI estimation, the available products are not yet capable of capturing site-specific variability, as requested in many agricultural applications. The objective of this study was to evaluate the potential of non-parametric approaches for multi-temporal LAI retrieval by Sentinel-2 multispectral data, in comparison with a VI-based parametric approach. For this purpose, we built a large database combining a multispectral satellite data set and ground LAI measurements collected over two growing seasons (2018 and 2019), including three crops (i.e., winter wheat, maize, and alfalfa) characterized by different growing cycles and canopy structures, and considering different agronomic conditions (i.e., at three farms in three different sites). The accuracy of parametric and non-parametric methods for LAI estimation was assessed by cross-validation (CV) at both the pixel and field levels over mixed-crop (MC) and crop-specific (CS) data sets. Overall, the non-parametric approach showed a higher accuracy of prediction at pixel level than parametric methods, and it was also observed that Gaussian Process Regression (GPR) did not provide any significant difference (p-value > 0.05) between the predicted values of LAI in the MC and CS data sets, regardless of the crop. Indeed, GPR at the field level showed a cross-validated coefficient of determination (R2CV) higher than 0.80 for all three crops.

Comparing methods for estimating leaf area index by multi-angular remote sensing in winter wheat

The reflectance of wheat’s canopy exhibits angular sensitivity, which can influence the accuracy of different methods for its leaf area index (LAI) estimation through multi-angular remote sensing. The primary objective of this study was to assess and compare the ability of various methods for LAI estimation from 13 view zenith angles (VZAs). The four methods included: (1) common hyper-spectral vegetation indices (VIs), (2) optimal two-band combination VIs (i.e., VIs: normalized difference index, simple ratio index, and difference vegetation index), (3) back-propagation neural network (BPNN), and (4) partial least squares regression (PLSR). Our results demonstrated that the red-edge plays a key role in estimating LAI, in that the traditional VIs, optimal two-band VIs, and PLSR including the red-edge band all showed satisfactory performance, with coefficient of determination (R2) > 0.72 in the nadir direction. However, the estimation accuracy of LAI was not positively related with band number, and BPNN gave unsatisfactory results under a larger viewing angle, with R2 ≤ 0.60 for extreme angles. The predictive ability of all four methods declined with an increasing VZA, with reliable LAI estimation near the nadir direction. Importantly, by comparing the four methods, PLSR emerged as superior in both its estimation accuracy and angular insensitivity, with R2 = 0.83 in the nadir direction and ≥ 0.65 for extreme angles. For this reason, we highly recommend it be used with multi-angular remote sensing data, especially in agricultural applications.

Estimating the Seasonal Dynamics of the Leaf Area Index Using Piecewise LAI-VI Relationships Based on Phenophases

The leaf area index (LAI) is not only an important parameter used to describe the geometry of vegetation canopy but also a key input variable for ecological models. One of the most commonly used methods for LAI estimation is to establish an empirical relationship between the LAI and the vegetation index (VI). However, the LAI-VI relationships had high seasonal variability, and they differed among phenophases and VIs. In this study, the LAI-VI relationships in different phenophases and for different VIs (i.e., the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and near-infrared reflectance of vegetation (NIRv)) were investigated based on 82 site-years of LAI observed data and the Moderate Resolution Imaging Spectroradiometer (MODIS) VI products. Significant LAI-VI relationships were observed during the vegetation growing and declining periods. There were weak LAI-VI relationships (p > 0.05) during the flourishing period. The accuracies for the LAIs estimated with the piecewise LAI-VI relationships based on different phenophases were significantly higher than those estimated based on a single LAI-VI relationship for the entire vegetation active period. The average root mean square error (RMSE) ± standard deviation (SD) value for the LAIs estimated with the piecewise LAI-VI relationships was 0.38 ± 0.13 (based on the NDVI), 0.41 ± 0.13 (based on the EVI) and 0.41 ± 0.14 (based on the NIRv), respectively. In comparison, it was 0.46 ± 0.13 (based on the NDVI), 0.55 ± 0.15 (based on the EVI) and 0.55 ± 0.15 (based on the NIRv) for those estimated with a single LAI-VI relationship. The performance of the three VIs in estimating the LAI also varied among phenophases. During the growing period, the mean RMSE ± SD value for the estimated LAIs was 0.30 ± 0.11 (LAI-NDVI relationships), 0.37 ± 0.11 (LAI-EVI relationships) and 0.36 ± 0.13 (LAI-NIRv relationships), respectively, indicating the NDVI produced significantly better LAI estimations than those from the other two VIs. In contrast, the EVI produced slightly better LAI estimations than those from the other two VIs during the declining period (p > 0.05), and the mean RMSE ± SD value for the estimated LAIs was 0.45 ± 0.16 (LAI-NDVI relationships), 0.43 ± 0.23 (LAI-EVI relationships) and 0.45 ± 0.25 (LAI-NIRv relationships), respectively. Hence, the piecewise LAI-VI relationships based on different phenophases were recommended for the estimations of the LAI instead of a single LAI-VI relationship for the entire vegetation active period. Furthermore, the optimal VI in each phenophase should be selected for the estimations of the LAI according to the characteristics of vegetation growth.

Leaf area index estimated by direct, semi-direct, and indirect methods in European beech and sycamore maple stands

Leaf area index (LAI) is one of the most important characteristics of forest stands that affects the fundamentals of tree physiological processes, biomass production, and mechanical stability. The LAI results obtained by the semi-direct and indirect methods (the needle technique and an LAI-2000 PCA) in three European beech (Fagus sylvatica L.) stands and one sycamore maple (Acer pseudoplatanus L.) stand were compared with LAI estimated by litter traps during the 2013 growing season. Seasonal LAI was estimated using an LAI-2000 PCA which showed similar trends among the stands and strongly corresponded to phenological phases of deciduous stands in Europe, with the fastest rate of leaf area increment occurring during the first month following bud break. During the growing season, maximum stand LAI value was on June 19th and reached 4.5–5.1, and 4.0 in the beech and maple stands, respectively. The needle technique significantly underestimated (p .05) LAI on average by 15.1% and 5.8% in the beech and maple stands, respectively. All methods for LAI estimation at the stand level could be applicable in deciduous forest stands (beech, maple) with similar site and stand characteristics. However, calibration by direct method is necessary to obtain the required precision.

Estimation of forest leaf area index using terrestrial laser scanning data and path length distribution model in open-canopy forests

Terrestrial Laser Scanning (TLS) is an active technology that can acquire the finest characteristics of canopy structure and plays an increasing role in estimating Leaf Area Index (LAI) in forest canopies. However, 3D information is not directly used in conventional TLS-based methods using the gap fraction theory. In addition, quantifying clumping effect within canopies is still a difficult task. In this paper, we presented a method to reduce clumping effect and estimate LAI using TLS data. Our recently proposed path length distribution model was applied to TLS data. Instead of converting 3D points to 2D image, the path length distribution can be extracted using the TLS-recorded 3D data and the crown models built with the alpha shapes algorithm. Two simulated scenes and one actual forest plot were utilized for validation. The results of the proposed method agree well with both the true LAI (in the simulated scenes) and the extracted PAI by the digital hemispherical photography (in the actual plot). This LAI estimation method using TLS and the path length distribution model provides a novel way for ground-based LAI measurements and shows its great potential.

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.

Development of an app for estimating leaf area index using a smartphone. Trueness and precision determination and comparison with other indirect methods

Leaf area index (LAI) is a crucial variable in agronomic and environmental studies, because of its importance for estimating the amount of radiation intercepted by the canopy and the crop water requirements. Direct methods for LAI estimation are destructive, labor and time consuming, and hardly applicable in case of forest ecosystems. This led to the development of different indirect methods, based on models for light transmission into the canopy and implemented into dedicated commercial instruments (e.g., LAI-2000 and different models of ceptometers). However, these instruments are usually expensive and characterized by a low portability, and could require long and expensive maintenance services in case of damages.In this study, we present an app for smartphone implementing two methods for LAI estimation, based on the use of sensors and processing power normally present in most of the modern mobile phones. The first method (App-L) is based on the estimation of the gap fraction at 57.5° (to acquire values that are almost independent of leaf inclination) from luminance estimated above and below the canopy. The second method (App-G) estimates the gap fraction via automatic processing of images acquired below the canopy. The performances of the two methods implemented in the app were evaluated using data collected in a scatter-seeded rice field in northern Italy, and compared with those of the LAI-2000 and AccuPAR ceptometer, by determining the methods’ accuracy (trueness and precision, the latter represented by repeatability and reproducibility) and linearity. The performances of App-G (mean repeatability limit=0.80m2m−2; mean reproducibility limit=0.82m2m−2; RMSE=1.04m2m−2) were similar to those shown by LAI-2000 and AccuPAR, whereas App-L achieved the best trueness value (RMSE=0.37m2m−2), although it resulted the less precise, requiring a large number of replicates to provide reliable estimations. Despite the satisfactory performances, the app proposed should be considered just as an alternative to the available commercial instruments, useful in contexts characterized by low economic resources or when the highest portability is needed.

Leaf area index estimation with MODIS reflectance time series and model inversion during full rotations of Eucalyptus plantations

The leaf area index (LAI) of fast-growing Eucalyptus plantations is highly dynamic both seasonally and inter-annually, and is spatially variable depending on pedo-climatic conditions. LAI is very important in determining the carbon and water balance of a stand, but is difficult to measure during a complete stand rotation and at large scales. Remote-sensing methods allowing the retrieval of LAI time series with accuracy and precision are therefore necessary. Here, we tested two methods for LAI estimation from MODIS 250m resolution red and near-infrared (NIR) reflectance time series. The first method involved the inversion of a coupled model of leaf reflectance and transmittance (PROSPECT4), soil reflectance (SOILSPECT) and canopy radiative transfer (4SAIL2). Model parameters other than the LAI were either fixed to measured constant values, or allowed to vary seasonally and/or with stand age according to trends observed in field measurements. The LAI was assumed to vary throughout the rotation following a series of alternately increasing and decreasing sigmoid curves. The parameters of each sigmoid curve that allowed the best fit of simulated canopy reflectance to MODIS red and NIR reflectance data were obtained by minimization techniques. The second method was based on a linear relationship between the LAI and values of the GEneralized Soil Adjusted Vegetation Index (GESAVI), which was calibrated using destructive LAI measurements made at two seasons, on Eucalyptus stands of different ages and productivity levels. The ability of each approach to reproduce field-measured LAI values was assessed, and uncertainty on results and parameter sensitivities were examined. Both methods offered a good fit between measured and estimated LAI ( R 2 = 0.80 and R 2 = 0.62 for model inversion and GESAVI-based methods, respectively), but the GESAVI-based method overestimated the LAI at young ages.

Estimation of LAI in the forested watershed using ASTER data based on Price’s model in summer and winter

Leaf area index (LAI) is an important parameter to identify the water balance in forested watershed as a biological factor influencing directly on the evapotranspiration in the forest area. The purpose of this study was to estimate the LAI in a small forested watershed in summer and winter by applying the Terra/Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data to the LAI estimation method. In this study, the estimation was based on the absorption and scattering processes of the solar radiation in the vegetation canopy and the spectral reflectance characteristics of soil vegetation. First, we estimated LAI based on Price’s model by application of ASTER data on the forested watershed located in the Tenzan Mountains of Saga, Japan. To validate the results of LAI estimation, secondly, we compared them to the measured LAI obtained by a plant canopy analyzer (LAI-2000) on the observation area inside the target region. This study showed that the LAI estimation method was a feasible and accurate method as indicated by the high relationship (r = 0.97) between LAI derived from ASTER data and LAI measured by LAI-2000. This paper is the first report on LAI estimation using Terra/ASTER data based on Price’s model and field investigation. This LAI estimation method is a reliable and applicable method.