Estimated Leaf Area Index Values Research Articles

Wheat leaf traits monitoring based on machine learning algorithms and high-resolution satellite imagery

The leaf, which is a crucial indicator for evaluating crop status, plays an important role in plants' functions. Determining and monitoring leaf parameters can facilitate the detection and estimation of crop yield, which is essential for food security. Crop monitoring by remote sensing technology is critical to support crop production, especially over large scales. In this study, we developed a methodology to estimate leaf parameters based entirely on vegetation indices (VIs) from remotely sensed imagery in wheat under different management practices. Therefore, the current study aimed to examine the utility of VIs calculated from the sentinel-2 data in estimating the Leaf area index (LAI) and leaf parameters at wheat farms using machine learning algorithms. Leaf parameters included leaf dry weight (LDW), specific leaf area (SLA) and leaf specific weight (SLW), and machine learning algorithms were SVM (support vector machine), ANN (artificial neural network) and DNN (deep neural network). Leaf parameters were measured at several developmental stages of wheat in two contrasting environments in the southern Iran. The results demonstrated that the DNN algorithm could efficiently predict leaf parameters in the southern Iran with an overall precision of >72%, which assessed the potential of employing DNN to achieve the temporal and spatial distribution data of wheat based on the Sentinel-2 imagery. The validation of the DNN model generally showed high accuracy (R = 0.80, RMSE = 1.19, and MAE = 0.98) between observed and estimated LAI values when this model was used. NDVI was also highly sensitive to wheat LDW and SLA parameters, with a good correlation between field measurements and those predicted by the DNN model from sentinel-2 imagery, with the R values of 0.66 and 0.85, respectively. Further, NDVI and PVI (Perpendicular Vegetation Index) were linearly correlated with SLW across both temporal and spatial scales (R = 0.79). Among VIs considered from sentinel-2 imagery to predict wheat leaf parameters, NDVI was more sensitive than other VIs. This research, thus, indicated that using sentinel-2 data within a DNN model could provide a comparatively precise and robust prediction of leaf parameters and yield valuable insights into crop management with high temporal and spatial accuracy.

Fully automated region of interest segmentation pipeline for UAV based RGB images

Unmanned Aerial Vehicles (UAVs) have exhibited its potential for efficient and non-invasive crop data acquisition in high throughput crop phenotyping. In general, for analysis of phenotypic traits, there is a need for extracting the region of interest (RoI) from images captured by UAVs. It involves the generation of orthomosaic, which is a complicated and time-intensive process. In this study, a fully automated AI-based pipeline has been proposed for the RoI segmentation from raw RGB images acquired via UAV. The proposed pipeline achieves a near real-time processing speed compared to the other baseline methods. The key feature of the pipeline is the introduction of Sub-Paths , in which the original UAV flight path is divided into several small paths which facilitates parallel processing. The image quality of the extracted RoI has been examined using blind/referenceless image spatial quality evaluator (BRISQUE) and natural image quality evaluator (NIQE). The performance of the proposed pipeline is exemplified with the Leaf Area Index (LAI) estimation on five datasets containing three different crop types and growth stages. Regression analysis has also been performed on the estimated LAI values. Average R 2 , RMSE, and correlation scores of the estimates are observed to be 0.68, 0.033, and 0.83, respectively. • Fully automated end-to-end AI-based pipeline avoids using orthomosaic. • Processing time for fixed field and fixed overlap is same, significantly faster than using orthomosaic. • Image quality of the extracted ROI has been analysed using BRISQUE and NIQE indices. • A plant trait has been estimated autonomously as a use case of the proposed framework.

Mapping Leaf Area Index of restored mangroves using WorldView-2 imagery in Perancak Estuary, Bali, Indonesia

There have been efforts to restore and rehabilitate mangrove forests to compensate for their loss or degradation. Approaches for monitoring these efforts are increasingly developed, including assessment of the dynamic changes in restored mangroves using the biophysical tree parameter called Leaf Area Index (LAI). This study aimed to (1) apply a semi-empirical approach to estimate LAI using WorldView-2 imagery, (2) compare the LAI distributions of three different mangrove forest types (natural, mixed, and planted) at the mangrove restoration site in Perancak Estuary, Bali, Indonesia, and (3) evaluate the applicability of LAI as an indicator for monitoring mangrove growth in restoration areas. To achieve these objectives, a high-spatial-resolution WorldView-2 image data (2 m pixel size) and Normalized Difference Vegetation Index (NDVI) were used. A semi-empirical approach was used to correlate field LAI and NDVI values at the corresponding location. The results showed that the best regression function between the two variables was the third-order polynomial (R2 = 0.98). Overall, the estimated LAI values ranged from 0 to 9.56 (mean = 1.87, σ = 0.95, max. Accuracy = 97.6%) and differed between the three mangrove forest types. The comparison revealed that high LAI was associated with planted mangrove forest type where Rhizophora sp. dominated. It indicates that LAI is a useful indicator for assessing mangrove biophysical conditions and, as such, can be used for monitoring mangrove restoration activities and outcomes.

Downscaling of MODIS leaf area index using landsat vegetation index

Several organizations provide satellite Leaf Area Index (LAI) data regularly, at various scales, at high frequency, but at low spatial resolution. This study attempted to enhance the spatial resolution of the MODIS LAI product to the Landsat resolution level. Four climatically diverse sites in Europe and Africa were selected as study areas. Regression analysis was applied between MODIS Enhanced Vegetation Index (EVI) and LAI data. The regression equations were used as input in a downscaling model, along with Landsat EVI images and land-cover maps. The estimated LAI values showed high correlation with field-measured LAI during the dry period. The model validation gave statistically significant results, with correlation coefficient values ranging from relatively low (0.25–0.32), to moderate (0.48–0.64) and high (0.72–0.94). Limited samples per vegetation type, the diversity of species within the same vegetation type, land-use/land-cover changes and saturated EVI values affected the accuracy of the downscaling model.

New spectral model for estimating leaf area index based on gene expression programming

The accurate evaluation of leaf area index (LAI) in paddy rice is of great importance in predicting the production and assessing food security. Traditional LAI estimation based on the PROSAIL model requires a large number of parameters. To solve this problem, this paper proposes a new estimation model between LAI and spectral reflectance for paddy rice canopies based on gene expression programming. And, we suggest a new spectral index for maximizing the response to LAI and weakening the sensitivity to chlorophyll concentration in the proposed LAI estimation model. Experimental results show that compared with existing indices, the proposed index (VI-R2) maximized the response to green LAI and was less sensitive to chlorophyll content variations, and in the validation with measured LAI values, VI-R2 shows the excellent fitting performance with coefficient of determination (r2) of 0.954. Meanwhile, based on a comparison of the estimated LAI values among existing indices using hyperspectral image, the results indirectly demonstrate that VI-R2 is usable for estimating LAI at the remote sensing pixel scale.

A Data Assimilation Method for Simultaneously Estimating the Multiscale Leaf Area Index From Time-Series Multi-Resolution Satellite Observations

Current global leaf area index (LAI) products are generally produced from single-temporal satellite observations acquired by a single sensor. These LAI products are usually spatiotemporally discontinuous and inaccurate for some vegetation types in many areas, which limit the applications of these LAI products to the understanding of land dynamics. In this paper, a new data assimilation method was proposed to estimate multiscale and temporally continuous LAI values from multi-sensor time-series satellite observations with different spatial resolutions. An ensemble multiscale tree (EnMsT) was used to establish the conversion relationships between different spatial resolution LAI values, and dynamic models of the LAI at different spatial scales were constructed to evolve LAI at the corresponding spatial scales over time. At each time step, a multiscale Kalman filter (MKF) was introduced to fuse the predicted LAI values from the dynamic models at different spatial scales and to construct a forecasted EnMsT. When satellite observations were available, an ensemble multiscale filter (EnMsF) technique was applied to update the LAI values at each node of the EnMsT. The method was applied to estimate temporally continuous multiscale LAI values from the time series of Thematic Mapper (TM) or Enhanced Thematic Mapper Plus (ETM+) surface reflectance data and Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data at several sites with different vegetation types. The estimated multiscale LAI values were compared with the MODIS and GEOV2 LAI products, and the reference LAI values at the corresponding scales aggregated from the high-resolution LAI surface images. The estimated LAI values with the finest spatial resolution were also validated by ground measurements from the selected sites. The results show that the new method is able to simultaneously estimate temporally continuous multiscale LAI values by assimilating satellite observations with different spatial resolutions, and the estimated multiscale LAI values are well consistent with the reference LAI values at the corresponding scales over the selected sites. The root-mean-square error (RMSE) and coefficient of determination of the retrieved LAI values at the finest spatial scale against the ground measurements over the selected sites are 0.539 and 0.788, respectively.

Quantifying the Accuracy of Digital Hemispherical Photography for Leaf Area Index Estimates on Broad-Leaved Tree Species.

Digital hemispherical photography (DHP) has been widely used to estimate leaf area index (LAI) in forestry. Despite the advancement in the processing of hemispherical images with dedicated tools, several steps are still manual and thus easily affected by user’s experience and sensibility. The purpose of this study was to quantify the impact of user’s subjectivity on DHP LAI estimates for broad-leaved woody canopies using the software Can-Eye. Following the ISO 5725 protocol, we quantified the repeatability and reproducibility of the method, thus defining its precision for a wide range of broad-leaved canopies markedly differing for their structure. To get a complete evaluation of the method accuracy, we also quantified its trueness using artificial canopy images with known canopy cover. Moreover, the effect of the segmentation method was analysed. The best results for precision (restrained limits of repeatability and reproducibility) were obtained for high LAI values (>5) with limits corresponding to a variation of 22% in the estimated LAI values. Poorer results were obtained for medium and low LAI values, with a variation of the estimated LAI values that exceeded the 40%. Regardless of the LAI range explored, satisfactory results were achieved for trees in row-structured plantations (limits almost equal to the 30% of the estimated LAI). Satisfactory results were achieved for trueness, regardless of the canopy structure. The paired t-test revealed that the effect of the segmentation method on LAI estimates was significant. Despite a non-negligible user effect, the accuracy metrics for DHP are consistent with those determined for other indirect methods for LAI estimates, confirming the overall reliability of DHP in broad-leaved woody canopies.

Exploring the Potential of WorldView-2 Red-Edge Band-Based Vegetation Indices for Estimation of Mangrove Leaf Area Index with Machine Learning Algorithms

To accurately estimate leaf area index (LAI) in mangrove areas, the selection of appropriate models and predictor variables is critical. However, there is a major challenge in quantifying and mapping LAI using multi-spectral sensors due to the saturation effects of traditional vegetation indices (VIs) for mangrove forests. WorldView-2 (WV2) imagery has proven to be effective to estimate LAI of grasslands and forests, but the sensitivity of its vegetation indices (VIs) has been uncertain for mangrove forests. Furthermore, the single model may exhibit certain randomness and instability in model calibration and estimation accuracy. Therefore, this study aims to explore the sensitivity of WV2 VIs for estimating mangrove LAI by comparing artificial neural network regression (ANNR), support vector regression (SVR) and random forest regression (RFR). The results suggest that the RFR algorithm yields the best results (RMSE = 0.45, 14.55% of the average LAI), followed by ANNR (RMSE = 0.49, 16.04% of the average LAI), and then SVR (RMSE = 0.51, 16.56% of the average LAI) algorithms using 5-fold cross validation (CV) using all VIs. Quantification of the variable importance shows that the VIs derived from the red-edge band consistently remain the most important contributor to LAI estimation. When the red-edge band-derived VIs are removed from the models, estimation accuracies measured in relative RMSE (RMSEr) decrease by 3.79%, 2.70% and 4.47% for ANNR, SVR and RFR models respectively. VIs derived from red-edge band also yield better accuracy compared with other traditional bands of WV2, such as near-infrared-1 and near-infrared-2 band. Furthermore, the estimated LAI values vary significantly across different mangrove species. The study demonstrates the utility of VIs of WV2 imagery and the selected machine-learning algorithms in developing LAI models in mangrove forests. The results indicate that the red-edge band of WV2 imagery can help alleviate the saturation problem and improve the accuracy of LAI estimation in a mangrove area.

LIGHT DIFFUSION IN THE TROPICAL DRY FOREST OF COSTA RICA

Abstract. Leaf Area Index (LAI) has been defined as the total leaf area (one-sided) in relation to the ground. LAI has an impact on tree growth and recruitment through the interception of light, which in turn affects primary productivity. Even though many instruments exist for estimating LAI from ground, they are often laborious and costly to run continuously. Measurements of LAI from the field using traditional sensors (e.g., LAI-2000) require multiple visits to the field under very specific sky conditions, making them unsuitable to operate in inaccessible areas and forests with dense vegetation, as well as areas where persistent sunny conditions are the norm like tropical dry forests. With this context, we proposed a methodology to characterize light diffusion based on NDVI and LAI measurements taken from the field in two successional stages in the tropical dry forest of Santa Rosa National Park in Costa Rica. We estimate a "K" coefficient to characterize light diffusion by the canopy, based on field NDVI measurements derived from optical phenology instruments and MODIS NDVI. From the coefficients determined, we estimated LAI values and compared them with ground measurements of LAI. In both successional stages ground measurements of LAI had no significant difference to the tower-derived LAI and the estimated LAI from MODIS NDVI.

LIGHT DIFFUSION IN THE TROPICAL DRY FOREST OF COSTA RICA

Leaf Area Index (LAI) has been defined as the total leaf area (one-sided) in relation to the ground. LAI has an impact on tree growth and recruitment through the interception of light, which in turn affects primary productivity. Even though many instruments exist for estimating LAI from ground, they are often laborious and costly to run continuously. Measurements of LAI from the field using traditional sensors (e.g., LAI-2000) require multiple visits to the field under very specific sky conditions, making them unsuitable to operate in inaccessible areas and forests with dense vegetation, as well as areas where persistent sunny conditions are the norm like tropical dry forests. With this context, we proposed a methodology to characterize light diffusion based on NDVI and LAI measurements taken from the field in two successional stages in the tropical dry forest of Santa Rosa National Park in Costa Rica. We estimate a "K" coefficient to characterize light diffusion by the canopy, based on field NDVI measurements derived from optical phenology instruments and MODIS NDVI. From the coefficients determined, we estimated LAI values and compared them with ground measurements of LAI. In both successional stages ground measurements of LAI had no significant difference to the tower-derived LAI and the estimated LAI from MODIS NDVI.

Estimation of Leaf Area Index (LAI) in corn and soybeans using multi-polarization C- and L-band radar data

Abstract The feasibility of using optical remote sensing for crop monitoring has been widely studied over the past three decades. Considering the advantages offered with longer microwave wavelengths, most importantly an ability to collect data under cloudy conditions, the exploitation of Synthetic Aperture Radar (SAR) for monitoring crop condition is of great interest. In this study, multi-polarization RADARSAT-2 (C-band) and Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) (L-band) were used to investigate the potential of SAR to estimate Leaf Area Index (LAI), a strong indicator of crop productivity. A new LAI estimation approach based on coupling of two existing models has been developed. The coupled model was calibrated and validated for two economically important crops, corn and soybeans, using the data collected during the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12) over a site in the Canadian Prairies. The validation of the model generally showed high correlation coefficients (R) between ground measured and estimated LAI values, when dual like-cross polarizations were used (either HH–HV or VV–HV). At C-band, correlations were similar for both dual polarizations with corn R values of 0.83 and 0.81 for HH–HV and VV–HV, respectively; and with soybean R values of 0.80 for both HH–HV and VV–HV. L-band was less sensitive to corn LAI with R values ranging from 0.79 for HH–HV to 0.59 for VV–HV. Our method was not able to accurately estimate LAI for soybeans using UAVSAR. Errors of estimation (both root mean square error and mean average error) established that C-band for corn and soybeans canopies and L-band for corn canopies can be used to estimate LAI at accuracies similar to those reported in studies using satellite optical sensors. These optical satellite-based LAI measurements are currently used to correct estimates of yield and biomass from crop growth models. This study is important in that it demonstrates the capability of SAR to also estimate LAI but with an all-weather acquisition capability, offering an important advantage for operational crop growth monitoring.

A Framework for Consistent Estimation of Leaf Area Index, Fraction of Absorbed Photosynthetically Active Radiation, and Surface Albedo from MODIS Time-Series Data

Currently available land-surface parameter products are generated using parameter-specific algorithms from various satellite data and contain several inconsistencies. This paper developed a new data assimilation framework for consistent estimation of multiple land-surface parameters from time-series MODerate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data. If the reflectance data showed snow-free areas, an ensemble Kalman filter (EnKF) technique was used to estimate leaf area index (LAI) for a two-layer canopy reflectance model (ACRM) by combining predictions from a phenology model and the MODIS surface reflectance data. The estimated LAI values were then input into the ACRM to calculate the surface albedo and the fraction of absorbed photosynthetically active radiation (FAPAR). For snow-covered areas, the surface albedo was calculated as the underlying vegetation canopy albedo plus the weighted distance between the underlying vegetation canopy albedo and the albedo over deep snow. The LAI/FAPAR and surface albedo values estimated using this framework were compared with MODIS collection 5 eight-day 1-km LAI/FAPAR products (MOD15A2) and 500-m surface albedo product (MCD43A3), and GEOV1 LAI/FAPAR products at 1/112° spatial resolution and a ten-day frequency, respectively, and validated by ground measurement data from several sites with different vegetation types. The results demonstrate that this new data assimilation framework can estimate temporally complete land-surface parameter profiles from MODIS time-series reflectance data even if some of the reflectance data are contaminated by residual cloud or are missing and that the retrieved LAI, FAPAR, and surface albedo values are physically consistent. The root mean square errors of the retrieved LAI, FAPAR, and surface albedo against ground measurements are 0.5791, 0.0453, and 0.0190, respectively.

MODIS NBAR Time Series Modeling With Two Statistical Methods and Application to Leaf Area Index Recursive Estimation

The inconsistent data quality of remote sensing observation, which is largely a result of atmospheric conditions, presents problems in the application of these data. Pixel reflectance in remote sensing observation varies with the type of land cover and the observation time. For land cover types that cycle yearly, such as vegetation, the variations in surface reflectance usually have dynamic periodic characteristics. In this study, we modeled the temporal feature of Moderate-Resolution Imaging Spectroradiometer (MODIS) Nadir BRDF-adjusted reflectance (NBAR) time series data of typical forest and cropland areas using two statistical methods: season-trend and seasonal autoregressive (AR) integrated moving average (SARIMA). The fitting values of these models were used in the recursive estimation of leaf area index (LAI) time series based on a nonlinear AR exogenous (NARX) neural network. This suppressed interferences from observational data noise and missing values. The results from 6 years (2008–2013) of MODIS NBAR modeling indicate that the two statistical methods are effective to model the NBAR time series of the vegetation surface; the season-trend model can extract both seasonal and trend components of long time series, and the SARIMA model has a good fitting capacity for general time series data. The NARX neural network performs well with the improved NBAR time series input, and the estimated LAI time series is more continuous than the MODIS LAI. Comparison with field data reveals the reliability of the estimated LAI values.

Coupled estimation of surface heat fluxes and vegetation dynamics from remotely sensed land surface temperature and fraction of photosynthetically active radiation

AbstractRemotely sensed Land Surface Temperature (LST) and Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) are assimilated, respectively, into the Surface Energy Balance (SEB) equation and a Vegetation Dynamics Model (VDM) in order to estimate surface fluxes and vegetation dynamics. The problem is posed in terms of three unknown and dimensionless parameters: (1) neutral bulk heat transfer coefficient, which scales the sum of turbulent heat fluxes, (2) soil and canopy evaporative fractions that characterize partitioning among the turbulent heat fluxes over soil and vegetation, and (3) specific leaf area, which captures seasonal phenology and vegetation dynamics. The model is applied over the Gourma site in Mali, the northern region of the West African Monsoon (WAM) domain. The application of the model over the Gourma site shows that spaceborne LST observations can be used to constrain the SEB equation and obtain its key two unknown parameters (i.e., neutral bulk heat transfer coefficient and evaporative fraction). We demonstrate that the spatial patterns of estimated neutral bulk heat transfer coefficient and evaporative fraction resemble, respectively, those of independently observed vegetation index and soil moisture. The framework also yields estimates of surface energy balance components. The daily sensible, latent, and ground heat flux estimates at the Agoufou site that is located in the south of the Gourma region have, respectively, a root‐mean‐square error (RMSE) of 53.6, 34.4, and 45.1 Wm−2. The daily sensible heat flux estimates at the Bamba site, which is located in the north of the Gourma domain, have a RMSE of 42.6 Wm−2. The results also show that remotely sensed FPAR observations can constrain the VDM and retrieve its main unknown parameter (specific leaf area) over large‐scale domains without costly in situ measurements. The results indicate that the estimated specific leaf area values vary reasonably with the expected influential environmental variables such as precipitation, air temperature, and solar radiation. Assimilating FPAR observations into the VDM can also provide an estimate of Leaf Area Index (LAI) dynamics. The estimated LAI values are comparable in magnitude, spatial pattern and temporal evolution with satellite retrievals.

Exploring the Best Hyperspectral Features for LAI Estimation Using Partial Least Squares Regression

The use of spectral features to estimate leaf area index (LAI) is generally considered a challenging task for hyperspectral data. In this study, the hyperspectral reflectance of winter wheat was selected to optimize the selection of spectral features and to evaluate their performance in modeling LAI at various growth stages during 2008 and 2009. We extracted hyperspectral features using different techniques, including reflectance spectra and first derivative spectra, absorption and reflectance position and vegetation indices. In order to find the best subset of features with the best predictive accuracy, partial least squares regression (PLSR) and variable importance in projection (VIP) were applied to estimated LAI values. The results indicated that the red edge–NIR spectral region (680 nm–1300 nm) was the most sensitive to LAI. Most features in this region exhibited a high correlation with LAI and had higher VIP values, especially the first derivative waveband at 750 nm (r = 0.900, VIP = 1.144). Adding a large number of features would not significantly improve the accuracy of the PLSR model. The PLSR model based on the fourteen features with the highest VIP values predicted LAI with a mean bootstrapped R2 value of 0.880 and a mean RMSE of 0.943 on the validation dataset and produced an estimated LAI result better than that, including the entire 54-feature dataset with a mean R2 of 0.875 and a mean RMSE of 0.965. The results of this study thus suggest that the use of only a few of the best features by VIP values is sufficient for LAI estimation.

A comparison of three methods for estimating the LAI of black locust (Robinia pseudoacacia L.) plantations on the Loess Plateau, China

Optical remote sensing is the most widely used method for obtaining leaf area index (LAI) information. However, there is a need for improved processing techniques to increase the accuracy of LAI estimates obtained in this way. This article describes the use of high-resolution optical data from the Quickbird satellite for LAI estimation in the semi-arid region of the Loess Plateau, China. Three different image processing techniques were evaluated: processing based on spectral vegetation indices (SVIs), texture parameters, and combinations of SVIs with textural analyses. Simple linear and nonlinear regression models were developed to describe the relationship between image parameters obtained using these approaches and 52 field measurements of LAI. SVI-based approaches did not yield reliable LAI estimates, accounting for at best 68% of the observed variation in LAI. Texture-based methods were somewhat better, explaining up to 72% of the observed variation. A combination of the two approaches yielded an even better adjusted r2 value of 0.84. This demonstrates that the accuracy of estimated LAI values based on remote-sensing data can be significantly increased by considering a combination of SVIs and texture parameters.

Hyperspectral characteristics and growth monitoring of rice (Oryza sativa) under asymmetric warming

Climate warming shows great diurnal variation, with a higher warming rate at night-time, and consequently causes significant impacts on rice growth and development. Soil and plant analyser development (SPAD) values and leaf area index (LAI) are important parameters for indicating rice growth potential. The non-destructive, quick assessment of SPAD values and LAI is necessary for growth diagnosis in rice production. The objectives of this study were to determine the effects of asymmetric warming on rice growth and development, to define the relationships of SPAD values and LAI to ground-based canopy hyperspectral reflectance and derivative parameters, and to establish quantitative models for real-time monitoring of rice growth using sensitive spectral parameters under asymmetric warming. Two field warming experiments were performed in Nanjing in Jiangsu Province, China, to investigate the effects of asymmetric warming (all-day warming, AW; daytime warming from 7:00 to 19:00, DW; and night-time warming from 19:00 to 7:00, NW) on the growth and development of the Huaidao 5 rice cultivar, using a free air temperature increase (FATI) apparatus. The daily mean rice canopy temperatures in the AW, DW, and NW treatment plots were 2.0°C, 1.1°C, and 1.3°C higher, respectively, than those in the control (CK) plots. Canopy hyperspectral reflectance, SPAD, and LAI were measured under warming treatments during different growth stages. The results showed that rice SPAD and LAI decreased under warming treatments, with significant differences among certain growth stages compared with CK. The SPAD and LAI dynamics during growth stages exhibited single-peak patterns. Sensitive spectral bands occurred mostly within the visible light and near-infrared regions. An integrated linear regression equation relating SPAD to first-order derivative 514 (FD514) and ratio vegetation index (810,560) (RVI (810,560)) accurately described the dynamic pattern of SPAD changes, the coefficients of determination (R2) of 0.53 and 0.59, with the standard errors (SEs) of 0.85 and 1.13. The hyperspectral parameters first-order derivative 506 (FD506) and blue edge area (SDb) exhibited a linear relationship with LAI, with R2 values of 0.79 and 0.80 and SE values of 0.69 and 0.23, respectively. When independent data were used to test the derived equations, the R2 values between the measured and estimated SPAD from FD514 and RVI (810,560) were 0.72 and 0.60, with root mean square errors (RMSEs) of 4.34 and 4.44 and relative errors (REs) of 11.00% and 12.00%, respectively. For spectral parameters FD506 and SDb, the R2 values between the measured and estimated LAI values were 0.75 and 0.75, with RMSEs of 0.57 and 0.48 and REs of 17.00% and 4.00%, respectively. The strong fit between the measured and estimated values indicated that the present models based on hyperspectral reflectance could be used for reliable SPAD and LAI estimation under asymmetric warming.

A comparison of NDVI and MTVI2 for estimating LAI using CHRIS imagery: a case study in wheat

Leaf area index (LAI) is a key variable in crop growth models. The derivation of reliable LAI maps from satellite imagery would provide a means of spatially extrapolating these models. During the 2004 and 2005 growing seasons, hyperspectral data from the Compact High Resolution Imaging Spectrometer (CHRIS) sensor were acquired over a wheat crop in southern Alberta. The ability to obtain reliable LAI estimates from CHRIS data was investigated using a preexisting relationship between LAI and the modified transformed vegetation index (MTVI2). The performance of the recently develop MTVI2 in estimating LAI was compared to that of the more commonly used normalized difference vegetation index (NDVI). Both narrow and broad bands simulated from the CHRIS data were used in calculating the NDVI. All vegetation indices provided good relationships with LAI (R2 = 0.70–0.90). The errors in the estimated LAI values varied with vegetation index and were dependent on growth stage. The MTVI2 performed better than the NDVI at full canopy closure. Confirming previous reports in the literature, the NDVI tended to saturate at LAI values of 37–4, which resulted in an underestimation of LAI at full canopy closure. There was no benefit to using narrow spectral bands in the NDVI. Estimation of LAI using the MTVI2 was underestimated late in the season during the seed-filling period.

Slope correction for LAI estimation from gap fraction measurements

Digital hemispherical photography poses specific problems when deriving leaf area index (LAI) over sloping terrain. This study proposes a method to correct from the slope effect. It is based on simple geometrical considerations to account for the path length variation within the canopy for cameras pointing vertically. Simulations over sloping terrain show that gap fraction increases up-slope while decreasing down-slope. As a consequence of this balance between up- and down-slope effects, effective LAI estimates derived from inversion of the Poisson model are marginally affected for low to medium slopes (<25°) and LAI (LAI < 2). However, for larger slopes and LAI values, estimated LAI values may be strongly underestimated. The proposed correction was evaluated over four forested sites located over sloping terrain. Results indicate that in these conditions (LAI between 0.6 up to 3.0, clumped canopies with relatively erectophile leaf distribution), the effect of the slope (between 25° and 36°) was moderate as compared to other potential sources of problems when deriving LAI from gap fraction measurements, including clumping, leaf angle inclination and spatial sampling.

LEAF-AREA INDEX AND LIGHT ATTENUATION IN RAPIDLY EXPANDING SHRUB THICKETS

There is increasing interest in the changes in ecosystem services that accompany the conversion of grasslands to shrub-dominated communities. Shrub structure and associated effects on the light environment may be especially important in affecting productivity and diversity. Leaf-area index (LAI) and understory light levels of Morella cerifera shrub thickets were assessed on Hog Island, Virginia, USA, at four sites along a soil chronosequence. LAI was estimated from annual leaf litter, with allometric models relating stem diameter to leaf area, with a portable integrating radiometer (LI-COR LAI-2000), and from photosynthetically active radiation (PAR) using the Beer-Lambert law. For the two youngest thickets, LAI estimates from leaf litter (approximately 10.0) approached levels often associated with tropical rain forest. Allometric models estimated LAI values at 9.8 and 12.5 for the same thickets. High LAI in thickets also results in high light attenuation. Light levels within thickets were as low as 0.7% of above-canopy PAR in the youngest thicket. These data suggest that M. cerifera shrub thickets have a very high potential for annual net primary production. Furthermore, extreme modification of the light environment, coupled with heavy shrub litter fall, may exclude potential competitors during thicket establishment and rapidly alter community structure and ecosystem function.