Canopy Radiative Transfer Model Research Articles

Discrete return lidar-based prediction of leaf area index in two conifer forests

Leaf area index (LAI) is a key forest structural characteristic that serves as a primary control for exchanges of mass and energy within a vegetated ecosystem. Most previous attempts to estimate LAI from remotely sensed data have relied on empirical relationships between field-measured observations and various spectral vegetation indices (SVIs) derived from optical imagery or the inversion of canopy radiative transfer models. However, as biomass within an ecosystem increases, accurate LAI estimates are difficult to quantify. Here we use lidar data in conjunction with SPOT5-derived spectral vegetation indices (SVIs) to examine the extent to which integration of both lidar and spectral datasets can estimate specific LAI quantities over a broad range of conifer forest stands in the northern Rocky Mountains. Our results show that SPOT5-derived SVIs performed poorly across our study areas, explaining less than 50% of variation in observed LAI, while lidar-only models account for a significant amount of variation across the two study areas located in northern Idaho; the St. Joe Woodlands ( R 2 = 0.86; RMSE = 0.76) and the Nez Perce Reservation ( R 2 = 0.69; RMSE = 0.61). Further, we found that LAI models derived from lidar metrics were only incrementally improved with the inclusion of SPOT 5-derived SVIs; increases in R 2 ranged from 0.02–0.04, though model RMSE values decreased for most models (0–11.76% decrease). Significant lidar-only models tended to utilize a common set of predictor variables such as canopy percentile heights and percentile height differences, percent canopy cover metrics, and covariates that described lidar height distributional parameters. All integrated lidar-SPOT 5 models included textural measures of the visible wavelengths (e.g. green and red reflectance). Due to the limited amount of LAI model improvement when adding SPOT 5 metrics to lidar data, we conclude that lidar data alone can provide superior estimates of LAI for our study areas.

Estimating crop water stress with ETM+ NIR and SWIR data

Monitoring of crop water stress from the reflectance of near infrared (NIR) and shortwave infrared (SWIR) wavelengths in the optical domain provides a promising tool for the use of satellite data in precision agriculture. In this paper, a method to monitor crop water stress is developed using ETM+ NIR and SWIR wavelengths. In particular, we investigate the effect of crop drought on wheat canopy biochemistry especially on the fuel moisture content (FMC). Fuel moisture content-based wheat drought guidelines corresponding to different phenological status are established using field measurements conducted over water controlled and stressed wheat canopies. The relationship between vegetation water stress index (VWSI) developed and wheat drought guidelines are explored using field measured data and leaf canopy radiative transfer models including PROSPECT, Lillesaeter, and SailH. Furthermore, the potential use of the VWSI in crop drought monitoring is evaluated. The results indicate that there are strong correlations between VWSI and FMC. Obvious decline in VWSI is observed with increasing FMC, which corresponds to different water treatments and irrigation activities. VWSI is greater than 0.2 when wheat is exposed to slight stress, and close to 0.4 in areas where the canopy experienced severe water stress. VWSI also demonstrates a strong linear relationship with water deficit index (WDI) ( R 2 = 0.65; P < 0.0001). In conclusion, VWSI is useful in crop drought estimation as a robust index that does not require any other additional information such as meteorological or hydrological data.

Development of the Adjoint Model of a Canopy Radiative Transfer Model for Sensitivity Study and Inversion of Leaf Area Index

Many canopy reflectance models have been developed in the last decades and used for estimating land surface biogeophysical variables, such as leaf area index (LAI), from satellite observations through optimization procedures. In most studies, the derivative information of the canopy reflectance model has not been used effectively, which limits this approach for regional and global applications. The final solutions are often converged to the local minima. To address these issues, the adjoint model of a canopy radiative transfer model is developed in this study through the automatic differentiation technique. The developed adjoint model is used for sensitivity study, and a combination of the adjoint model with the trust region global optimization method is performed to retrieve LAI from the Enhanced Thematic Mapper Plus (ETM+). This study demonstrates that this method can be reliably used for inverting LAI efficiently and is suitable for global applications.

Retrieval of vegetation equivalent water thickness from reflectance using genetic algorithm (GA)-partial least squares (PLS) regression

This study aimed to investigate the performance of genetic algorithms coupled with partial least squares (GA-PLS) modeling of spectral reflectance in retrieving equivalent water thickness (EWT) at leaf and canopy level. A genetic algorithm was used to identify a subset of spectral bands sensitive to the variation in EWT, and PLS was then applied to relate the identified bands to EWT values. GA-PLS was applied to leaf level reflectance available from LOPEX dataset, and canopy data includes reflectance simulated by a leaf radiative transfer model PROSPECT and a canopy radiative transfer model SAILH and acquired by airborne visible/infrared imaging spectrometer (AVIRIS). The results indicate that GA-PLS has the capability of retrieving EWT from leaf and canopy reflectance, and achieved good estimation accuracy, i.e. low root mean square errors (RMSE) and high squared correlation coefficients ( R 2). For the retrieval at leaf level, the estimation accuracy can be as good as RMSE = 0.0019 g/cm 2 and R 2 = 0.939 or better. For the retrieval at canopy level, the model accuracy is RMSE = 0.0061 g/cm 2 and R 2 = 0.966 or better when PROSPECT-SAILH simulated reflectance was used; when AVIRIS image spectra were used, the model accuracy is RMSE = 0.0094 g/cm 2 and R 2 = 0.8734 for the calibration, and RMSE = 0.0132 g/cm 2 and R 2 = 0.7756 for the validation. These results from GA-PLS modeling support the conclusion that GA-PLS has the potential to be applied to AVIRIS, Hyperion and HyMap imagery for retrieving EWT. The selected bands for the AVIRIS datasets differ from those for the LOPEX and PROSPECT-SAILH simulated datasets, and this inconsistence of the selected bands for different datasets indicates that the GA-PLS method has the advantage of tuning the optimum bands for PLS regression and accommodating the effects of confounding factors.

Retrieving crown leaf area index from an individual tree using ground-based lidar data

Light detection and ranging (lidar) sensors, both at the terrestrial and airborne levels, have recently emerged as useful tools for three-dimensional (3D) reconstruction of vegetated environments. One such terrestrial laser scanner (TLS) is the Intelligent Laser Ranging and Imaging System (ILRIS-3D). The objective of this research was to develop approaches to use ILRIS-3D data to retrieve structural information of an artificial tree in a controlled laboratory experiment. The key crown-level structural parameters investigated in this study were gap fraction, leaf area index (LAI), and clumping index. Measured XYZ point cloud data from a systematically pruned tree were sliced to retrieve laser pulse return density profiles, which subsequently were used to estimate gap fraction, LAI, and clumping index. Gap fraction estimates were cross-validated with traditional methods of histogram thresholding of digital photographs (r2 = 0.95). LAI estimates from lidar data were corrected for the confounding effects of woody material and nonrandom foliage distribution and then compared with direct LAI measurements (r2 = 0.98, RMSE = 0.26). The methods developed in this research provide valuable lessons for application to field-level TLS data for structural parameter retrievals. Successful demonstration of analysis protocols to extract crown-level structural parameters like gap fraction, LAI, and clumping index from TLS data will be important for detailed assessments of 3D canopy radiative transfer modeling and likely will lead to more robust inversion algorithms.

Estimation of leaf and canopy water content in poplar plantations by means of hyperspectral indices and inverse modeling

This study investigates the applicability of empirical and radiative transfer models to estimate water content at leaf and landscape level. The main goal is to evaluate and compare the accuracy of these two approaches for estimating leaf water content by means of laboratory reflectance/transmittance measurements and for mapping leaf and canopy water content by using airborne Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) data acquired over intensive poplar plantations (Ticino, Italy). At leaf level, we tested the performance of different spectral indices to estimate leaf equivalent water thickness (EWT) and leaf gravimetric water content (GWC) by using inverse ordinary least squares (OLS) regression, and reduced major axis (RMA) regression. The analysis showed that leaf reflectance is related to changes in EWT rather than GWC, with best results obtained by using RMA regression by exploiting the spectral index related to the continuum removed area of the 1200 nm water absorption feature with an explained variance of 61% and prediction error of 6.6%. Moreover, we inverted the PROSPECT leaf radiative transfer model to estimate leaf EWT and GWC and compared the results with those obtained by means of empirical models. The inversion of this model showed that leaf EWT can be successfully estimated with no prior information with mean relative errors of 14% and determination coefficient of 0.65. Inversion of the PROSPECT model showed some difficulties in the simultaneous estimation of leaf EWT and dry matter content, which led to large errors in GWC estimation. At landscape level with MIVIS data, we tested the performance of different spectral indices to estimate canopy water per unit ground area (EWT canopy). We found a relative error of 20% using a continuum removed spectral index around 1200 nm. Furthermore, we used a model simulation to evaluate the possibility of applying empirical models based on appositely developed MIVIS double ratios to estimate mean leaf EWT at landscape level ( EWT ― ). It is shown that combined indices (double ratios) yielded significant results in estimating leaf EWT at landscape level by using MIVIS data (with errors around 2.6%), indicating their potential in reducing the effects of LAI on the recorded signal. The accuracy of the empirical estimation of EWT canopy and EWT ― was finally compared with that obtained from inversion of the PROSPECT + SAILH canopy reflectance model to evaluate the potential of both methods for practical applications. A relative error of 27% was found for EWT canopy and an overestimation of leaf EWT ― with relative errors around 19%. Results arising from this remote sensing application support the robustness of hyperspectral regression indices for estimating water content at both leaf and landscape level, with lower relative errors compared to those obtained from inversion of leaf and 1D canopy radiative transfer models.

The RAMI On-line Model Checker (ROMC): A web-based benchmarking facility for canopy reflectance models

The exploitation of global Earth Observation data hinges increasingly on physically-based radiative transfer (RT) models. These models simulate the interactions of solar radiation within a given medium (e.g., clouds, plant canopies) and are used to generate look-up-tables that are embedded into quantitative retrieval algorithms, such as those delivering the operational surface products for MODIS, MISR and MERIS. An assessment of the quality of canopy RT models thus appears essential if accurate and reliable information is to be derived from them. Until recently such an undertaking was a time consuming and labour intensive process that was made even more challenging by the general lack of absolute reference standards. Several years of benchmarking activities in the frame of the RAdiation transfer Model Intercomparison (RAMI) exercise have now led to the development of the RAMI On-line Model Checker (ROMC). The ROMC is a web-based tool allowing model developers and users to autonomously assess the performance of canopy RT models ( http://romc.jrc.ec.europa.eu/). Access to the ROMC is free and enables users to obtain both statistical and graphical indications as to the performance of their canopy RT model. In addition to providing an overall indication of the skill of a given model to correctly match the reference data, the ROMC allows also for interactive comparison/evaluations of different model versions/submissions of a given user. All ROMC graphs can be downloaded in PostScript format and come with a reference number for easy usage in presentations and publications. It is hoped that the ROMC will prove useful for the RT modeling community as a whole, not only by providing a convenient means to evaluate models outside the triennial phases of RAMI but also to attract participation in future RAMI activities.

A coupled 1-D atmosphere and 3-D canopy radiative transfer model for canopy reflectance, light environment, and photosynthesis simulation in a heterogeneous landscape

Detailed knowledge of light interactions between the atmosphere and vegetation, and within vegetation are of particular interest for terrestrial carbon cycle studies and optical remote sensing. This study describes a model for 3-D canopy radiative transfer that is directly coupled with an atmospheric radiative transfer model (Forest Light Environmental Simulator, FLiES). The model was developed based on the Monte Carlo ray-tracing method using some existing modeling frameworks. To integrate the canopy radiative transfer model with atmosphere, the same numerical method, sampling technique, and variance reduction technique were employed in both the atmospheric and the canopy modules. Farquhar's leaf photosynthesis model was combined to calculate the canopy level photosynthesis from the light environmental parameters obtained by the radiative transfer calculation. In order to document the quality of the coupled model, we first compared the atmospheric radiative transfer module to well known 1-D atmospheric radiative transfer models, and then evaluated the 3-D canopy radiative transfer module against a series of test cases provided by the RAMI On-line Model Checker (ROMC). We used the model to show the impact of atmospheric properties and 3-D canopy structure on the directionality of downward photosynthetically active radiation (PAR) at the top of canopy, the 3-D distribution of absorbed PAR (APAR), and overall canopy photosynthesis. The results indicate the importance to consider angular geometry of incident light at TOC and 3-D canopy structure.

Coupled soil–leaf-canopy and atmosphere radiative transfer modeling to simulate hyperspectral multi-angular surface reflectance and TOA radiance data

Coupling radiative transfer models for the soil background and vegetation canopy layers is facilitated by means of the four-stream flux interaction concept and use of the adding method. Also the coupling to a state-of-the-art atmospheric radiative transfer model like MODTRAN4 can be established in this way, thus enabling the realistic simulation of top-of-atmosphere radiances detected by space-borne remote sensing instruments. Possible applications of coupled modeling vary from mission design to parameter retrieval and data assimilation. This paper introduces a modified Hapke soil BRDF model, a robust version of the PROSPECT leaf model, and a modernized canopy radiative transfer model called 4SAIL2. The latter is a hybrid two-layer version of SAIL accommodating horizontal and vertical heterogeneities, featuring improved modeling of the hot spot effect and output of canopy absorptances. The integrated model is simply called SLC (soil–leaf-canopy) and has been implemented as a speed-optimized Windows DLL which allows efficient use of computer resources even when simulating massive amounts of hyperspectral multi-angular observations. In this paper various examples of possible model output are shown, including simulated satellite image products. First validation results have been obtained from atmospherically corrected hyperspectral multi-angular CHRIS-PROBA data of the Upper Rhine Valley in Germany.

Evaluating remotely sensed live fuel moisture estimations for fire behavior predictions in Georgia, USA

Research has shown that remote sensing techniques can be used for assessing live fuel moisture content (LFMC) from space. The need for dynamic monitoring of the fire risk environment favors the use of fast, site-specific, empirical models for assessing local vegetation moisture status, albeit with some uncertainties. These uncertainties may affect the accuracy of decisions made by fire managers using remote sensing derived LFMC. Consequently, the analysis of these LFMC retrieval uncertainties and their impact on applications, such as fire spread prediction, is needed to ensure the informed use of remote sensing derived LFMC measurements by fire managers. The Okefenokee National Wildlife Refuge, one of the most fire-prone regions in the southeastern United States was chosen as our study area. Our study estimates the uncertainties associated with empirical site specific retrievals using NDWI (Normalized Difference Water Index; ( R 0.86 − R 1.24) / ( R 0.86 + R 1.24)) and NDII (Normalized Difference Infrared Index; ( R 0.86 − R 1.64) / ( R 0.86 + R 1.64)) that are simulated by coupled leaf and canopy radiative transfer models. In order to support the findings from those simulations, a second approach estimates uncertainties using actual MODIS derived indices over Georgia Forestry Commission stations that provide NFDRS model estimates of LFMC. Finally, we used the FARSITE surface fire behavior model to examine the sensitivity of fire spread rates to live fuel moisture content for the NFDRS high pocosin and southern rough fuel models found in Okefenokee. This allowed us to evaluate the effectiveness of satellite based LFMC estimations for use in fire behavior predictions. Sensitivity to LFMC (measured as percentage of moisture weight per unit dry weight of fuel) was analyzed in terms of no-wind no-slope spread rates as well as normalized spread rates. Normalized spread rates, defined as the ratio of spread rate at a particular LFMC to the spread rate at LFMC of 125 under similar conditions, were used in order to make the results adaptable to any wind-slope conditions. Our results show that NDWI has a stronger linear relationship to LFMC than NDII, and can consequently estimate LFMC with lesser uncertainty. Uncertainty analysis shows that 66% of NDWI based LFMC retrievals over non-sparsely vegetated regions are expected to have errors less than 32, while 90% of retrievals should be within an error margin of 56. In pocosin fuel models, under low LFMC conditions (< 100), retrieval errors could lead to normalized spread rate errors of 6.5 which may be equivalent to an error of 47 m/h in no-wind no-slope conditions. For southern rough fuel models, when LFMC < 175, LFMC retrieval errors could amount to normalized spread rate errors of 0.6 or an equivalent error of 9.3 m/h in no-wind no-slope conditions. These spread rate error estimates represent approximately the upper bound of errors resulting from uncertainties in empirical retrievals of LFMC over forested regions.

Hyperspectral remote sensing of plant pigments

The dynamics of pigment concentrations are diagnostic of a range of plant physiological properties and processes. This paper appraises the developing technologies and analytical methods for quantifying pigments non-destructively and repeatedly across a range of spatial scales using hyperspectral remote sensing. Progress in deriving predictive relationships between various characteristics and transforms of hyperspectral reflectance data are evaluated and the roles of leaf and canopy radiative transfer models are reviewed. Requirements are identified for more extensive intercomparisons of different approaches and for further work on the strategies for interpreting canopy scale data. The paper examines the prospects for extending research to the wider range of pigments in addition to chlorophyll, testing emerging methods of hyperspectral analysis and exploring the fusion of hyperspectral and LIDAR remote sensing. In spite of these opportunities for further development and the refinement of techniques, current evidence of an expanding range of applications in the ecophysiological, environmental, agricultural, and forestry sciences highlights the growing value of hyperspectral remote sensing of plant pigments.

Estimating vegetation water content with hyperspectral data for different canopy scenarios: Relationships between AVIRIS and MODIS indexes

Three linked leaf and canopy radiative transfer models were used to assess uncertainties in three vegetation architectures for the relationships between canopy water content and Equivalent Water Thickness (EWT). The leaf radiative transfer model PROSPECT was linked to SAILH, rowMCRM, and FLIM canopy reflectance models to generate synthetic spectra for a range of leaf and canopy parameters under closed, row-structured, and forest canopy architectures, respectively. Leaf water content ( C w) and leaf area index (LAI) were used to calculate canopy water content. Leaf and canopy parameters that affect the retrieval of EWT, estimated by the MODTRAN-based fitting technique, were used to investigate their influence on the water content estimates. Results showed a consistent relationship between retrieved EWT and canopy water content for the different simulated architectural scenarios. It was shown that EWT was negatively affected by leaf dry matter and soil background. Retrievals of EWT from hyperspectral Advanced Visible Infrared Imaging Spectrometer (AVIRIS) imagery at three study sites were then used for cross-validation of the Moderate Resolution Imaging Spectrometer (MODIS) data, assessing the behavior of NDVI, EVI, NDWI, and SIWSI as potential indicators of vegetation water content. All four MODIS indexes showed consistent agreement with retrievals of EWT from AVIRIS imagery at the agricultural site and the savanna–shrub site, with EVI having the highest correlation. However, at the conifer forest study site the two water indexes, NDWI and SIWSI, yielded better agreement with retrievals of EWT than NDVI and EVI. The performance of NDVI was inconsistent across sites. This manuscript demonstrates the importance of canopy architecture when estimating EWT by showing that large errors are obtained when EWT estimates derived from absorption feature curve-fitting are applied to different canopy types. These errors are propagated in simple indexes that produce inconsistent results when applied to divergent vegetation conditions.

Radiative transfer, interception and scattering in coniferous forests: models and applications for production ecology and remote sensing

This work develops methods to account for shoot structure in models of coniferous canopy radiative transfer. Shoot structure, as it varies along the light gradient inside canopy, affects the efficiency of light interception per unit needle area, foliage biomass, or foliage nitrogen. The clumping of needles in the shoot volume also causes a notable amount of multiple scattering of light within coniferous shoots. The effect of shoot structure on light interception is treated in the context of canopy level photosynthesis and resource use models, and the phenomenon of within-shoot multiple scattering in the context of physical canopy reflectance models for remote sensing purposes. Light interception. A method for estimating the amount of PAR (Photosynthetically Active Radiation) intercepted by a conifer shoot is presented. The method combines modelling of the directional distribution of radiation above canopy, fish-eye photographs taken at shoot locations to measure canopy gap fraction, and geometrical measurements of shoot orientation and structure. Data on light availability, shoot and needle structure and nitrogen content has been collected from canopies of Pacific silver fir (Abies amabilis (Dougl.) Forbes) and Norway spruce (Picea abies (L.) Karst.). Shoot structure acclimated to light gradient inside canopy so that more shaded shoots have better light interception efficiency. Light interception efficiency of shoots varied about two-fold per needle area, about four-fold per needle dry mass, and about five-fold per nitrogen content. Comparison of fertilized and control stands of Norway spruce indicated that light interception efficiency is not greatly affected by fertilization. Light scattering. Structure of coniferous shoots gives rise to multiple scattering of light between the needles of the shoot. Using geometric models of shoots, multiple scattering was studied by photon tracing simulations. Based on simulation results, the dependence of the scattering coefficient of shoot from the scattering coefficient of needles is shown to follow a simple one-parameter model. The single parameter, termed the recollision probability, describes the level of clumping of the needles in the shoot, is wavelength independent, and can be connected to previously used clumping indices. By using the recollision probability to correct for the within-shoot multiple scattering, canopy radiative transfer models which have used leaves as basic elements can use shoots as basic elements, and thus be applied for coniferous forests. Preliminary testing of this approach seems to explain, at least partially, why coniferous forests appear darker than broadleaved forests in satellite data.

Efficient retrieval of vegetation leaf area index and canopy clumping factor from satellite data to support pollutant deposition assessments

Canopy leaf area index (LAI) is an important structural parameter of the vegetation controlling pollutant uptake by terrestrial ecosystems. This paper presents a computationally efficient algorithm for retrieval of vegetation LAI and canopy clumping factor from satellite data using observed Simple Ratios (SR) of near-infrared to red reflectance. The method employs numerical inversion of a physics-based analytical canopy radiative transfer model that simulates the bi-directional reflectance distribution function (BRDF). The algorithm is independent of ecosystem type. The method is applied to 1-km resolution AVHRR satellite images to retrieve a geo-referenced data set of monthly LAI values for the conterminous USA. Satellite-based LAI estimates are compared against independent ground LAI measurements over a range of ecosystem types. Verification results suggest that the new algorithm represents a viable approach to LAI retrieval at continental scale, and can facilitate spatially explicit studies of regional pollutant deposition and trace gas exchange.

A method to convert AVHRR Normalized Difference Vegetation Index time series to a standard viewing and illumination geometry

The bi-directional reflectance distribution function (BRDF) alters the seasonal and inter-annual variations exhibited in Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) data and this hampers the detection and, consequently, the interpretation of temporal variations in land-surface vegetation. The magnitude and sign of bi-directional effects in commonly used AVHRR data sets depend on land-surface properties, atmospheric composition and the type of atmospheric correction that is applied to the data. We develop an approach to estimate BRDF effects in AVHRR NDVI time series using the Moderate Resolution Imaging Spectrometer (MODIS) BRDF kernels and subsequently adjust NDVI time series to a standard illumination and viewing geometry. The approach is tested on NDVI time series that are simulated for representative AVHRR viewing and illumination geometry. These time series are simulated with a canopy radiative transfer model coupled to an atmospheric radiative transfer model for four different land cover types—tropical forest, boreal forest, temperate forest and grassland – and five different atmospheric conditions – turbid and clear top-of-atmosphere, turbid and clear top-of-atmosphere with a correction for ozone absorption and Rayleigh scattering applied (Pathfinder AVHRR Land data) and ground-observations (fully corrected for atmospheric effects). The simulations indicate that the timing of key phenological stages, such as start and end of growing season and time of maximum greenness, is affected by BRDF effects. Moreover, BRDF effects vary with latitude and season and increase over the time of operation of subsequent NOAA satellites because of orbital drift. Application of the MODIS kernels on simulated NVDI data results in a 50% to 85% reduction of BRDF effects. When applied to the global 18-year global Normalized Difference Vegetation Index (NDVI) Pathfinder data we find BRDF effects similar in magnitude to those in the simulations. Our analysis of the global data shows that BRDF effects are especially large in high latitudes; here we find that in at least 20% of the data BRDF errors are too large for accurate detection of seasonal and interannual variability. These large BRDF errors tend to compensate, however, when averaged over latitude.

Estimating light absorption by chlorophyll, leaf and canopy in a deciduous broadleaf forest using MODIS data and a radiative transfer model

In this paper, we present a theoretical and modeling framework to estimate the fractions of photosynthetically active radiation (PAR) absorbed by vegetation canopy (FAPAR canopy), leaf (FAPAR leaf ), and chlorophyll (FAPAR chl), respectively. FAPAR canopy is an important biophysical variable and has been used to estimate gross and net primary production. However, only PAR absorbed by chlorophyll is used for photosynthesis, and therefore there is a need to quantify FAPAR chl. We modified and coupled a leaf radiative transfer model (PROSPECT) and a canopy radiative transfer model (SAIL-2), and incorporated a Markov Chain Monte Carlo (MCMC) method (the Metropolis algorithm) for model inversion, which provides probability distributions of the retrieved variables. Our two-step procedure is: (1) to retrieve biophysical and biochemical variables using coupled PROSPECT + SAIL-2 model (PROSAIL-2), combined with multiple daily images (five spectral bands) from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor; and (2) to calculate FAPAR canopy, FAPAR leaf and FAPAR chl with the estimated model variables from the first step. We evaluated our approach for a temperate forest area in the Northeastern US, using MODIS data from 2001 to 2003. The inverted PROSAIL-2 fit the observed MODIS reflectance data well for the five MODIS spectral bands. The estimated leaf area index (LAI) values are within the range of field measured data. Significant differences between FAPAR canopy and FAPAR chl are found for this test case. Our study demonstrates the potential for using a model such as PROSAIL-2, combined with an inverse approach, for quantifying FAPAR chl, FAPAR leaf, FAPAR canopy, biophysical variables, and biochemical variables for deciduous broadleaf forests at leaf- and canopy-levels over time.

Evaluation of Digital Photography from Model Aircraft for Remote Sensing of Crop Biomass and Nitrogen Status

Remote sensing is a key technology for precision agriculture to assess actual crop conditions. Commercial, high-spatial-resolution imagery from aircraft and satellites are expensive so the costs may outweigh the benefits of the information. Hobbyists have been acquiring aerial photography from radio-controlled model aircraft; we evaluated these very-low-cost, very high-resolution digital photography for use in estimating nutrient status of corn and crop biomass of corn, alfalfa, and soybeans. Based on conclusions from previous work, we optimized an aerobatic model aircraft for acquiring pictures using a consumer-oriented digital camera. Colored tarpaulins were used to calibrate the images; there were large differences in digital number (DN) for the same reflectance because of differences in the exposure settings selected by the digital camera. To account for differences in exposure a Normalized Green–Red Difference Index [(NGRDI = (Green DN − Red DN)/(Green DN + Red DN)] was used; this index was linearly related to the normalized difference of the green and red reflectances, respectively. For soybeans, alfalfa and corn, dry biomass from zero to 120 g m−2 was linearly correlated to NGRDI, but for biomass greater than 150 g m−2 in corn and soybean, NGRDI did not increase further. In a fertilization experiment with corn, NGRDI did not show differences in nitrogen status, even though areas of low nitrogen status were clearly visible on late-season digital photographs. Simulations from the SAIL (Scattering of Arbitrarily Inclined Leaves) canopy radiative transfer model verified that NGRDI would be sensitive to biomass before canopy closure and that variations in leaf chlorophyll concentration would not be detectable. There are many advantages of model aircraft platforms for precision agriculture; currently, the imagery is best visually interpreted. Automated analysis of within-field variability requires more work on sensors that can be used with model aircraft platforms.

Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture

A growing number of studies have focused on evaluating spectral indices in terms of their sensitivity to vegetation biophysical parameters, as well as to external factors affecting canopy reflectance. In this context, leaf and canopy radiative transfer models are valuable for modeling and understanding the behavior of such indices. In the present work, PROSPECT and SAILH models have been used to simulate a wide range of crop canopy reflectances in an attempt to study the sensitivity of a set of vegetation indices to green leaf area index (LAI), and to modify some of them in order to enhance their responsivity to LAI variations. The aim of the paper was to present a method for minimizing the effect of leaf chlorophyll content on the prediction of green LAI, and to develop new algorithms that adequately predict the LAI of crop canopies. Analyses based on both simulated and real hyperspectral data were carried out to compare performances of existing vegetation indices (Normalized Difference Vegetation Index [NDVI], Renormalized Difference Vegetation Index [RDVI], Modified Simple Ratio [MSR], Soil-Adjusted Vegetation Index [SAVI], Soil and Atmospherically Resistant Vegetation Index [SARVI], MSAVI, Triangular Vegetation Index [TVI], and Modified Chlorophyll Absorption Ratio Index [MCARI]) and to design new ones (MTVI1, MCARI1, MTVI2, and MCARI2) that are both less sensitive to chlorophyll content variations and linearly related to green LAI. Thorough analyses showed that the above existing vegetation indices were either sensitive to chlorophyll concentration changes or affected by saturation at high LAI levels. Conversely, two of the spectral indices developed as a part of this study, a modified triangular vegetation index (MTVI2) and a modified chlorophyll absorption ratio index (MCARI2), proved to be the best predictors of green LAI. Related predictive algorithms were tested on CASI (Compact Airborne Spectrographic Imager) hyperspectral images and, then, validated using ground truth measurements. The latter were collected simultaneously with image acquisition for different crop types (soybean, corn, and wheat), at different growth stages, and under various fertilization treatments. Prediction power analysis of proposed algorithms based on MCARI2 and MTVI2 resulted in agreements between modeled and ground measurement of non-destructive LAI, with coefficients of determination ( r 2) being 0.98 for soybean, 0.89 for corn, and 0.74 for wheat. The corresponding RMSE for LAI were estimated at 0.28, 0.46, and 0.85, respectively.

Airborne measurement of hot spot reflectance signatures

The so-called hot spot is a fine directional signature of the surface reflectance observed for phase angles close to zero. In this paper, we analyze and discuss several such signatures acquired from the airborne POLDER and HyMap instruments during the DAISEX'99 campaign over agricultural crops. The observed signatures are very similar to those acquired from space at a resolution of several tens of kilometers [J. Geophys. Res. 107 (2002)], which provides further evidence that the hot spot is a scale-free feature. The hot spots can be fitted by a two-parameter function (amplitude and width) of the phase angle derived from canopy radiative transfer modeling. The model predicts that the amplitude is directly related to the leaf reflectance, while the width is a function of the canopy structure. The retrieved leaf reflectance values over a wide spectral range are in very good agreement with noncoincident laboratory measurement. The retrieved half widths are on the order of 1° with no significant variability between the analyzed targets. There is an apparent spectral variability in the half width, a feature in contradiction with the modeling, although the variations are not larger than the uncertainties.

Retrieving leaf area index using a genetic algorithm with a canopy radiative transfer model

Leaf area index (LAI) is an important structural property of vegetation canopy and is also one of the basic quantities driving the algorithms used in regional and global biogeochemical, ecological and meteorological applications. LAI can be estimated from remotely sensed data through the vegetation indices (VI) and the inversion of a canopy radiative transfer (RT) model. In recent years, applications of the genetic algorithms (GA) to a variety of optimization problems in remote sensing have been successfully demonstrated. In this study, we estimated LAI by integrating a canopy RT model and the GA optimization technique. This method was used to retrieve LAI from field measured reflectance as well as from atmospherically corrected Landsat ETM+ data. Four different ETM+ band combinations were tested to evaluate their effectiveness. The impacts of using the number of the genes were also examined. The results were very promising compared with field measured LAI data, and the best results were obtained with three genes in which the R 2 is 0.776 and the root-mean-square error (RMSE) 1.064.