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The concept of light use efficiency (Epsilon) and the concept of fraction of photosynthetically active ration (PAR) absorbed for vegetation photosynthesis (PSN), i.e., fAPAR (sub PSN), have been widely utilized to estimate vegetation gross primary productivity (GPP). It has been demonstrated that the photochemical reflectance index (PRI) is empirically related to e. An experimental US Department of Agriculture (USDA) cornfield in Maryland was selected as our study field. We explored the potential of integrating fAPAR(sub chl) (defined as the fraction of PAR absorbed by chlorophyll) and nadir PRI (PRI(sub nadir)) to predict cornfield daily GPP. We acquired nadir or near-nadir EO-1/Hyperion satellite images that covered the cornfield and took nadir in-situ field spectral measurements. Those data were used to derive the PRI(sub nadir) and fAPAR (sub chl). The fAPAR (sub chl) is retrieved with the advanced radiative transfer model PROSAIL2 and the Metropolis approach, a type of Markov Chain Monte Carlo (MCMC) estimation procedure. We define chlorophyll light use efficiency Epsilon (sub chl) as the ratio of vegetation GPP as measured by eddy covariance techniques to PAR absorbed by chlorophyll (Epsilon(sub chl) = GPP/APAR (sub chl). Daily Epsilon (sub chl) retrieved with the EO-1 Hyperion images was regressed with a linear equation of PRI (sub nadir) Epsilon (sub chl) = Alpha × PRI (sub nadir) + Beta). The satellite Epsilon(sub chl- PRI (sub nadir) linear relationship for the cornfield was implemented to develop an integrated daily GPP model [GPP = (Alpha × PRI(sub nadir) + Beta) × fAPAR (sub chl) × PAR], which was evaluated with fAPAR (sub chl) and PRI (sub nadir) retrieved from field measurements. Daily GPP estimated with this fAPAR (sub chl-) PRI (nadir) integration model was strongly correlated with the observed tower in-situ daily GPP (R(sup 2) = 0.93); with a root mean square error (RMSE) of 1.71 g C mol−(sup -1) PPFD and coefficient of variation (CV) of 16.57%. Both seasonal Epsilon (sub chl) and PRI (sub nadir) were strongly correlated with fAPAR (sub chl ) retrieved from field measurements, which indicates that chlorophyll content strongly affects seasonal epsilon (sub chl) and PRI (sub nadir). We demonstrate the potential capacity to monitor GPP with space-based visible through shortwave infrared (VSWIR) imaging spectrometers such as NASA's soon to be decommissioned EO- 1/Hyperion and the future Hyperspectral Infrared Imager (HyspIRI).

Satellite remote sensing estimates of gross primary production (GPP) have routinely been made using spectral vegetation indices (VIs) over the past two decades. The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), the green band Wide Dynamic Range Vegetation Index (WDRVIgreen), and the green band Chlorophyll Index (CIgreen) have been employed to estimate GPP under the assumption that GPP is proportional to the product of VI and photosynthetically active radiation (PAR) (where VI is one of four VIs: NDVI, EVI, WDRVIgreen, or CIgreen). However, the empirical regressions between VI*PAR and GPP measured locally at flux towers do not pass through the origin (i.e., the zero X–Y value for regressions). Therefore they are somewhat difficult to interpret and apply. This study investigates (1) what are the scaling factors and offsets (i.e., regression slopes and intercepts) between the fraction of PAR absorbed by chlorophyll of a canopy (fAPARchl) and the VIs and (2) whether the scaled VIs developed in (1) can eliminate the deficiency and improve the accuracy of GPP estimates. Three AmeriFlux maize and soybean fields were selected for this study, two of which are irrigated and one is rainfed. The four VIs and fAPARchl of the fields were computed with the MODerate resolution Imaging Spectroradiometer (MODIS) satellite images. The GPP estimation performance for the scaled VIs was compared to results obtained with the original VIs and evaluated with standard statistics: the coefficient of determination (R2), the root mean square error (RMSE), and the coefficient of variation (CV). Overall, the scaled EVI obtained the best performance. The performance of the scaled NDVI, EVI and WDRVIgreen was improved across sites, crop types and soil/background wetness conditions. The scaled CIgreen did not improve results, compared to the original CIgreen. The scaled green band indices (WDRVIgreen, CIgreen) did not exhibit superior performance to either the scaled EVI or NDVI in estimating crop daily GPP at these agricultural fields. The scaled VIs are more physiologically meaningful than original un-scaled VIs, but scaling factors and offsets may vary across crop types and surface conditions.

Photosynthesis (PSN) is a pigment level process in which antenna pigments (predominately chlorophylls) in chloroplasts absorb photosynthetically active radiation (PAR) for the photochemical process. PAR absorbed by foliar non-photosynthetic components is not used for PSN. The fraction of PAR absorbed (fAPAR) by a canopy/vegetation (i.e., fAPAR(sub canopy)) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) images, referred to as MOD15A2 FPAR, has been used to compute absorbed PAR (APAR) for PSN (APARPSN) which is utilized to produce the standard MODIS gross primary production (GPP) product, referred to as MOD17A2 GPP. In this study, the fraction of PAR absorbed by chlorophyll throughout the canopy (fAPAR(sub chl)) was retrieved from MODIS images for three AmeriFlux crop fields in Nebraska. There are few studies in the literature that compare the performance of MOD15A2 FPAR versus fAPAR(sub chl) in GPP estimation. In our study MOD15A2 FPAR and the retrieved fAPAR(sub chl) were compared with field fAPAR(sub canopy) and the fraction of PAR absorbed by green leaves of the vegetation (fAPAR(sub green)). MOD15A2 FPAR overestimated field fAPAR(sub canopy) in spring and in fall, and underestimated field fAPAR(sub canopy) in midsummer whereas fAPAR(sub chl) correctly captured the seasonal phenology. The retrieved fAPAR(sub chl) agreed well with field fAPAR(sub green) at early crop growth stage in June, and was less than field fAPAR(sub green) in late July, August and September. GPP estimates with fAPAR(sub chl) and with MOD15A2 FPAR were compared to tower flux GPP. GPP simulated with fAPAR(sub chl) was corroborated with tower flux GPP. Improvements in crop GPP estimation were achieved by replacing MOD15A2 FPAR with fAPAR(sub chl) which also reduced uncertainties of crop GPP estimates by 1.12-2.37 g C m(sup −2) d(sup −1).

This study examines the impact of parameterization of two variables, light use efficiency (LUE) and the fraction of absorbed photosynthetically active radiation (fPAR or fAPAR), on gross primary production(GPP) modeling. Carbon sequestration by terrestrial plants is a key factor to a comprehensive under-standing of the carbon budget at global scale. In this context, accurate measurements and estimates of GPP will allow us to achieve improved carbon monitoring and to quantitatively assess impacts from cli-mate changes and human activities. Spaceborne remote sensing observations can provide a variety of land surface parameterizations for modeling photosynthetic activities at various spatial and temporal scales. This study utilizes a simple GPP model based on LUE concept and different land surface parameterizations to evaluate the model and monitor GPP. Two maize-soybean rotation fields in Nebraska, USA and the Bartlett Experimental Forest in New Hampshire, USA were selected for study. Tower-based eddy-covariance carbon exchange and PAR measurements were collected from the FLUXNET Synthesis Dataset. For the model parameterization, we utilized different values of LUE and the fPAR derived from various algorithms. We adapted the approach and parameters from the MODIS MOD17 Biome Properties Look-Up Table (BPLUT) to derive LUE. We also used a site-specific analytic approach with tower-based Net Ecosystem Exchange (NEE) and PAR to estimate maximum potential LUE (LUEmax) to derive LUE. For the fPAR parameter, the MODIS MOD15A2 fPAR product was used. We also utilized fAPAR chl, a parameter accounting for the fAPAR linked to the chlorophyll-containing canopy fraction. fAPAR chl was obtained by inversion of a radiative transfer model, which used the MODIS-based reflectances in bands 1-7 produced by Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm. fAPAR chl exhibited seasonal dynamics more similar with the flux tower based GPP than MOD15A2 fPAR, especially in the spring and fall at the agricultural sites. When using the MODIS MOD17-based parameters to estimate LUE, fAPAR chl generated better agreements with GPP (r2= 0.79-0.91) than MOD15A2 fPAR (r2= 0.57-0.84).However, underestimations of GPP were also observed, especially for the crop fields. When applying the site-specific LUE max value to estimate in situ LUE, the magnitude of estimated GPP was closer to in situ GPP; this method produced a slight overestimation for the MOD15A2 fPAR at the Bartlett forest. This study highlights the importance of accurate land surface parameterizations to achieve reliable carbon monitoring capabilities from remote sensing information.

a b s t r a c t Accurate estimation of gross primary production (GPP) is essential for carbon cycle and climate change studies. Three AmeriFlux crop sites of maize and soybean were selected for this study. Two of the sites were irrigated and the other one was rainfed. The normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), the green band chlorophyll index (CIgreen), and the green band wide dynamic range vegetation index (WDRVIgreen) were computed from the moderate resolution imaging spectroradiometer (MODIS) surface reflectance data. We examined the impacts of the MODIS observation footprint and the vegetation bidirectional reflectance distribution function (BRDF) on crop daily GPP esti- mation with the four spectral vegetation indices (VIs - NDVI, EVI, WDRVIgreen and CIgreen) where GPP was predicted with two linear models, with and without offset: GPP = a × VI × PAR and GPP = a × VI × PAR + b. Model performance was evaluated with coefficient of determination (R 2 ), root mean square error (RMSE), and coefficient of variation (CV). The MODIS data were filtered into four categories and four experiments were conducted to assess the impacts. The first experiment included all observations. The second experi- ment only included observations with view zenith angle (VZA) ≤ 35 ◦ to constrain growth of the footprint size,which achieved a better grid cell match with the agricultural fields. The third experiment included only forward scatter observations with VZA ≤ 35 ◦ . The fourth experiment included only backscatter obser- vations with VZA ≤ 35◦. Overall, the EVI yielded the most consistently strong relationships to daily GPP under all examined conditions. The model GPP = a × VI × PAR + b had better performance than the model GPP = a × VI × PAR, and the offset was significant for most cases. Better performance was obtained for the irrigated field than its counterpart rainfed field. Comparison of experiment 2 vs. experiment 1 was used to examine the observation footprint impact whereas comparison of experiment 4 vs. experiment 3 was used to examine the BRDF impact. Changes in R2, RMSE,CV and changes in model coefficients "a" and "b" (experiment 2 vs. experiment 1; and experiment 4 vs. experiment 3) were indicators of the impacts. The second experiment produced better performance than the first experiment, increasing R2 (↑0.13) and

The utilization of remotely sensed observations for light use efficiency (LUE) and tower-based gross primary production (GPP) estimates was studied in a USDA cornfield. Nadir hyperspectral reflectance measurements were acquired at canopy level during a collaborative field campaign conducted in four growing seasons. The Photochemical Reflectance Index (PRI) and solar induced chlorophyll fluorescence (SIF), were derived. SIF retrievals were accomplished in the two telluric atmospheric oxygen absorption features centered at 688 nm (O2-B) and 760 nm (O2-A). The PRI and SIF were examined in conjunction with GPP and LUE determined by flux tower-based measurements. All of these fluxes, environmental variables, and the PRI and SIF exhibited diurnal as well as day-to-day dynamics across the four growing seasons. Consistent with previous studies, the PRI was shown to be related to LUE (r2 = 0.54 with a logarithm fit), but the relationship varied each year. By combining the PRI and SIF in a linear regression model, stronger performances for GPP estimation were obtained. The strongest relationship (r2 = 0.80, RMSE = 0.186 mg CO2/m2/s) was achieved when using the PRI and SIF retrievals at 688 nm. Cross-validation approaches were utilized to demonstrate the robustness and consistency of the performance. This study highlights a GPP retrieval method based entirely on hyperspectral remote sensing observations.

In the last three decades, substantial advancements have been made in understanding the global carbon cycle. Some of these advancements involve using the fraction of absorbed photosynthetically active radiation (fAPAR) by an entire canopy (fAPARcanopy) and/or the Normalized Difference Vegetation Index (NDVI) in modeling studies. In spite of these advancements, large uncertainties still remain. Zhang (Remote Sens. Environ., 2005) [1] tried to mitigate some of these uncertainties with the concept of using fAPAR that is restricted to the foliage chlorophyll (fAPARchl) instead of the entire canopy. In this current study, we calculated fAPARcanopy, fAPARchl, and foliage non-chlorophyll fAPAR (fAPARnon-hl) for the Harvard Forest using a radiative transfer model and multi-temporal Earth Observing One (EO-1) Hyperion satellite images. The canopy-level proportions of foliar chlorophyll and non-chlorophyll absorption were determined at different seasons (spring, summer, autumn) in an effort to demonstrate temporal variations of three plant functional types: deciduous forest, coniferous forest, and grass. Comparisons were made for NDVI versus fAPARcanopy and for the Enhanced Vegetation Index (EVI) versus fAPARchl. In addition, EO-1 Hyperion images were utilized to simulate these new fAPARcanopy, fAPARchl, and fAPARnon-chl products at 60 m as prototypes for the proposed NASA HyspIRI satellite spectrometer. These products should prove useful for future terrestrial carbon cycle and ecosystem studies.

The two-layer Markov chain Analytical Canopy Reflectance Model (ACRM) was linked with in situ hyperspectral leaf optical properties to simulate the Photochemical Reflectance Index (PRI) for a corn crop canopy at three different growth stages. This is an extended study after a successful demonstration of PRI simulations for a cornfield previously conducted at an early vegetative growth stage. Consistent with previous in situ studies, sunlit leaves exhibited lower PRI values than shaded leaves. Since sunlit (shaded) foliage dominates the canopy in the reflectance hotspot (coldspot), the canopy PRI derived from field hyperspectral observations displayed sensitivity to both view zenith angle and relative azimuth angle at all growth stages. Consequently, sunlit and shaded canopy sectors were most differentiated when viewed along the azimuth matching the solar principal plane. These directional PRI responses associated with sunlit/shaded foliage were successfully reproduced by the ACRM. As before, the simulated PRI values from the current study were closer to in situ values when both sunlit and shaded leaves were utilized as model input data in a two-layer mode, instead of a one-layer mode with sunlit leaves only. Model performance as judged by correlation between in situ and simulated values was strongest for the mature corn crop (r = 0.87, RMSE = 0.0048), followed by the early vegetative stage (r = 0.78; RMSE = 0.0051) and the early senescent stage (r = 0.65; RMSE = 0.0104). Since the benefit of including shaded leaves in the scheme varied across different growth stages, a further analysis was conducted to investigate how variable fractions of sunlit/shaded leaves affect the canopy PRI values expected for a cornfield, with implications for 20 remote sensing monitoring options. Simulations of the sunlit to shaded canopy ratio near 50/50 +/- 10 (e.g., 60/40) matching field observations at all growth stages were examined. Our results suggest in the importance of the sunlit/shaded fraction and canopy structure in understanding and interpreting PRI.

Two radiative transfer canopy models, SAIL and the two-layer Markov-Chain Canopy Reflectance Model (MCRM), were coupled with in situ leaf optical properties to simulate canopy-level spectral band ratio vegetation indices with the focus on the photochemical reflectance index in a cornfield. In situ hyperspectral measurements were made at both leaf and canopy levels. Leaf optical properties were obtained from both sunlit and shaded leaves. Canopy reflectance was acquired for eight different relative azimuth angles (psi) at three different view zenith angles (Theta (sub v)), and later used to validate model outputs. Field observations of photochemical reflectance index (PRI) for sunlit leaves exhibited lower values than shaded leaves, indicating higher light stress. Canopy PRI expressed obvious sensitivity to viewing geometry, as a function of both Theta (sub v) and psi . Overall, simulations from MCRM exhibited better agreements with in situ values than SAIL. When using only sunlit leaves as input, the MCRM-simulated PRI values showed satisfactory correlation and RMSE, as compared to in situ values. However, the performance of the MCRM model was significantly improved after defining a lower canopy layer comprised of shaded leaves beneath the upper sunlit leaf layer. Four other widely used band ratio vegetation indices were also studied and compared with the PRI results. MCRM simulations were able to generate satisfactory simulations for these other four indices when using only sunlit leaves as input; but unlike PRI, adding shaded leaves did not improve the performance of MCRM. These results support the hypothesis that the PRI is sensitive to physiological dynamics while the others detect static factors related to canopy structure. Sensitivity analysis was performed on MCRM in order to better understand the effects of structure related parameters on the PRI simulations. Leaf area index (LAI) showed the most significant impact on MCRM-simulated PRI among the parameters studied. This research shows the importance of hyperspectral and narrow band sensor studies, and especially the necessity of including the green wavelengths (e.g., 531 nm) on satellites proposing to monitor carbon dynamics of terrestrial ecosystems.

The light-use efficiency (LUE) of a mature Canadian Douglas-fir forest (DF49) was studied using high-resolution in situ temporal, spatial, and spectral measurements in conjunction with fluxes acquired from an instrumented tower. We examined the photochemical reflectance index (PRI), a spectral index responsive to high light conditions that alters reflectance at 531 nm, in combination with several alternative reference bands at 551, 570, and 488 nm. These indices were derived from directional reflectance spectra acquired by a hyperspectral radiometer system mounted on the DF49 tower, viewing the canopy through almost 360° rotations multiple times an hour daily throughout the 2006 growing season. From canopy structure information, three foliage sectors within the canopy were delineated according to instantaneous illumination conditions (sunlit, shaded, and mixed shaded–sunlit). On sunny days, the PRI indices for the sunlit foliage sector captured high light-induced stress responses, expressed as significant...

Objective:The objective of this study was to map vegetation composition across a 24 000 ha watershed. Location: The study was conducted on the western slope of the Sierra Nevada mountain range of California, USA. Methods: Automated image segmentation was used to delineate image objects representing vegetation patches of similar physiognomy and structure. Image objects were classified us ing a decision tree and data sources extracted from individual species distribution models, Landsat spectral data, and life form cover estimates derived from aerial image-based texture variables. Results: A total of 12 plant communities were mapped with an overall accuracy of 75% and a κ-value of 0.69. Species distribution model inputs improved map accuracy by approximately 15% over maps derived solely from image data. Automated mapping of existing vegetation distributions, based solely on predictive distribution model results, proved to be more accurate than mapping based on Landsat data, and equivalent in accuracy to mapping based on all image data sources. Conclusions: Results highlight the importance of terrain, edaphic, and bioclimatic variables when mapping vegetation communities in complex terrain. Mapping errors stemmed from the lack of spectral discernability between vegetation classes, and the inability to account for the confounding effects of land use history and disturbance within a static distribution modeling framework.

Hyperspectral water retrievals from AVIRIS data, equivalent water thickness (EWT), were compared to in situ leaf water content and LAI measurements at a semiarid site in southeastern Arizona. Retrievals of EWT showed good correlation with field canopy water content measurements. Statistical analysis suggested that EWT was significantly different among seven community types, from savanna to agriculture. Four band–ratio indexes (NDVI, EVI, NDWI, and NDII) were derived from MODIS showing strong spatial agreement between maps of AVIRIS EWT and MODIS indexes, and good statistical agreement for the range of habitats at the site. Temporal patterns of these four indexes in all vegetation communities except creosote bush and agriculture showed distinct seasonal patterns that responded to the timing and amount of precipitation. Moreover, these time series captured different ecological responses among the different vegetation communities.

An inversion of linked radiative transfer models (RTM) through artificial neural networks (ANN) was applied to MODIS data to retrieve vegetation canopy water content (CWC). The estimates were calibrated and validated using water retrievals from AVIRIS data from study sites located around the United States that included a wide range of environmental conditions. The ANN algorithm showed good performance across different vegetation types, with high correlations and consistent determination coefficients. The approach outperformed a multiple linear regression approach used to independently retrieve the same variable. The calibrated algorithm was then applied at the MODIS 500 m scale to follow changes in CWC for the year 2005 across the continental United States, subdivided into three vegetation types (grassland, shrubland, and forest). The ANN estimates of CWC correlated well with rainfall, indicating a strong ecological response. The high correlations suggest that the inversion of RTM through an ANN provide a realistic basis for multi-temporal assessments of CWC over wide areas for continental and global studies.

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 (R2). For the retrieval at leaf level, the estimation accuracy can be as good as RMSE = 0.0019 g/cm2 and R2 = 0.939 or better. For the retrieval at canopy level, the model accuracy is RMSE = 0.0061 g/cm2 and R2 = 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/cm2 and R2 = 0.8734 for the calibration, and RMSE = 0.0132 g/cm2 and R2 = 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.

The ordinary kriging method, a geostatistical interpolation technique, was applied for developing contour maps of design storm depth in northern Taiwan using intensity–duration–frequency (IDF) data. Results of variogram modelling on design storm depths indicate that the design storms can be categorized into two distinct storm types: (i) storms of short duration and high spatial variation and (ii) storms of long duration and less spatial variation. For storms of the first category, the influence range of rainfall depth decreases when the recurrence interval increases, owing to the increasing degree of their spatial independence. However, for storms of the second category, the influence range of rainfall depth does not change significantly and has an average of approximately 72 km. For very extreme events, such as events of short duration and long recurrence interval, we do not recommend usage of the established design storm contours, because most of the interstation distances exceed the influence ranges. Our study concludes that the influence range of the design storm depth is dependent on the design duration and recurrence interval and is a key factor in developing design storm contours. Copyright  2003 John Wiley & Sons, Ltd.

2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany, 22-27 July 2012, Two bio-indicators, the Photochemical Reflectance Index (PRI) and solar-induced red and far-red Chlorophyll Fluorescence (SIF), were derived from directional hyperspectral observations and studied in a cornfield on two contrasting days in the growing season. Both red and far-red SIF exhibited higher values on the day when the canopy in the early senescent stage, but only the far-red SIF showed sensitivity to viewing geometry. Consequently, the red/far-red SIF ratio varied greatly among azimuth positions while the largest values were obtained for the “hotspot” at both growth stages. This ratio was lower (~0.88 ± 0.4) in early July than in August when the ratio approached equivalence (near ~1). In concert, the PRI exhibited stronger responses to both zenith and azimuth angles and different values on the two growth stages. The potential of using these indices to monitor photosynthetic activities needs further investigation.

Accurate assessment of vegetation canopy optical properties plays a critical role in monitoring natural and managed ecosystems under environmental changes. In this context, radiative transfer (RT) models simulating vegetation canopy reflectance have been demonstrated to be a powerful tool for understanding and estimating spectral bio-indicators. In this study, two narrow band spectroradiometers were utilized to acquire observations over corn canopies for two summers. These in situ spectral data were then used to validate a two-layer Markov chain-based canopy reflectance model for simulating the Photochemical Reflectance Index (PRI), which has been widely used in recent vegetation photosynthetic light use efficiency (LUE) studies. The in situ PRI derived from narrow band hyperspectral reflectance exhibited clear responses to: 1) viewing geometry which affects the asset of light environment; and 2) seasonal variation corresponding to the growth stage. The RT model (ACRM) successfully simulated the responses to the variable viewing geometry. The best simulations were obtained when the model was set to run in the two layer mode using the sunlit leaves as the upper layer and shaded leaves as the lower layer. Simulated PRI values yielded much better correlations to in situ observations when the cornfield was dominated by green foliage during the early growth, vegetative and reproductive stages (r = 0.78 to 0.86) than in the later senescent stage (r = 0.65). Further sensitivity analyses were conducted to show the important influences of leaf area index (LAI) and the sunlit/shaded ratio on PRI observations.

The NASA EO-1 Hyperion observations were utilized to derive at-sensor Top-of-atmosphere (TOA) and ATREMcorrected surface reflectance over three study sites of different land use types. Direct comparisons between TOA and ATREM reflectance showed the most disagreement in the visible spectral region and regions that were affected by water absorption features. Nevertheless, as little as 3% overall differences were found for an arid dessert scene. Furthermore, example applications using Hyperion at-sensor TOA reflectance were investigated. Selected band ratio vegetation indices calculated from both TOA and surface reflectance were correlated (r∼0.6 to 0.94) but differed in magnitude. For instance, NDVI calculated from at-sensor TOA reflectance consistently showed lower values. A potential quick-look product using these indices to model relative vegetation stress was demonstrated in this study.

To improve satellite-derived estimates of terrestrial plant production and exchange of CO 2 , water, and energy with the atmosphere, scientists need to consider ecosystem composition, structure, function, and health. This can be accomplished through the fusion of Light Detection And Ranging (LiDAR) data, which can provide 3D information about the vertical and horizontal distribution of vegetation [1,2,3,4,6,7,9]; and hyperspectral remote sensing, which can inform us about variations in biophysical variables (e.g., photosynthetic pigments) and responses to environmental stressors (e.g., heat, moisture loss) [3,5,8,10]. Satellite observations from upcoming Decadal Survey missions [11] will provide NASA with the unique opportunity to fuse LiDAR data from ICESat-II, DESDynI, and LIST with hyperspectral and thermal imagery from HyspIRI and GEO-CAPE. This synergy will augment and enhance the individual science objectives of decadal survey missions, and will allow scientists the opportunity to develop 3D models of plant canopies that better describe global cycling of carbon, water and energy. Multiple NASA's Earth Science Focus Areas are served by this science, including carbon cycle and ecosystems; water and energy cycle; and climate variability and change (i.e., ecosystem responses and feedbacks to climate change). One of the major obstacles to the development of data fusion algorithms is the availability of accurately co-registered data of similar grain size [1]. This is often the case when instruments are flown on different platforms and at different times during a field campaign. We believe that “instrument fusion” is a prerequisite to “data fusion”, and we have developed a system the integrates a full-waveform LiDAR, narrow band hyperspectral imager, and broad band thermal imager in a single, compact and portable instrument package that could be readily deployed on a number of observation platforms. FUSION will provide accurate co aligned datasets that are needed for: (i) calibration and validation of satellite-derived land products; (ii) development of data fusion algorithms; and (iii) combine observations from multiple sensors to characterize ecosystem composition, structure, function, and health.

This work was aimed at investigating the effectiveness of genetic algorithms (GA) - partial least square (PLS) regression in estimation of canopy equivalent water thickness (EWT) from MODIS image data of the SMEX'04 site. Calibration of MODIS spectral variables (single bands, band ratios and curvatures) against the AVIRIS-derived EWT at a degraded resolution resulted in a high correlation (R2=0.77) and an equivalent well correlation was obtained for validation on the MODIS pixels not used in the calibration. The spectral variables used in the GA-PLS model include MODIS band 2 (841–875 nm), band 5 (1230– 1250 nm), and band curvatures at MODIS band 4 (545–565 nm), band 1 (620–670 nm) and band 2. A caveat for applying these spectral variables to estimate EWT in other areas is that their applicability may depend on local environmental conditions.

Corn is the most widely produced grain in the United States with 87 million acres planted in 2009 accounting for more than 90 percent of total value and production of feed grains. Half of United States' corn production is used in livestock feed with the remainder processed into a multitude of food and industrial products including starch, sweeteners, corn oil, beverage and industrial alcohol, and fuel ethanol. With increased focus on renewable energy, an unusual link between corn and oil commodities has been created increasing the demand for the grain in ethanol production. As a result, monitoring crop performance is vital for yield forecasting and developing timely remediation strategies to optimize crop performance. Several factors including water availability, nitrogen (N) supply, soil organic mater, disease, and supply of other nutrients, have a significant impact on crop growth and grain yields. Imaging spectroscopy can provide timely, spatially explicit information for managing agricultural ecosystems. The HyspIRI mission called for by the NRC Decadal Survey [11] identifies the need for a near term space-borne hyperspectral imaging spectrometer to globally map early signs of ecosystem change through altered physiology. The primary instrument on the proposed NASA HyspIRI mission is a hyperspectral (10 nm FWHM) mapper with a 60 m ground resolution and a 19 day global revisit, which will enable imaging spectroscopy with high temporal repeat to capture the impact of environmental perturbations on ecosystem productivity. Recent advances in airborne hyperspectral imaging systems [i.e., AVIRIS [7], AISA EAGLE & Hawk (Specim, Oulu, Finland)] along with Earth Observing One (EO-1) Hyperion satellite data have made it possible to obtain high resolution spatial and full range visible (VIS) to short wave infrared (SWIR) spectral information that can be further employed to explore vegetation productivity and change in both agricultural and surrounding ecosystems to further define algorithms and products applicable to the HyspIRI mission. From hyperspectral data, numerous statistical and spectroscopic approaches have been developed that use features in vegetation spectral curves to gain insight to biophysical parameters, including: biomass, pigments, tissue water content, and the amount of lignin, cellulose, and foliar N [1, 3, 5, 6, 9, 10, 12, 13, 14]. In the case of optically dense vegetation, the spectral derivative has been shown to be indicative of the abundance and activity of the absorbers in the leaves [2, 4]. Further, linear unmixing and spectral angle matching techniques take advantage of the high dimensionality of hyperspectral data and can be used alone or in conjunction with other vegetation indices for ecosystem assessment [8]. Here we will further investigate these spectroscopic techniques to enhance corn yield forecasting capabilities.

SPIE Optical Engineering + Applications, 2009, San Diego, California, United States, Patterns of change in vegetation growth and condition are one of the primary indicators of the present and future ecological status of the globe. Nitrogen (N) is involved in photochemical processes and is one of the primary resources regulating plant growth. As a result, biological carbon (C) sequestration is driven by N availability. Large scale monitoring of photosynthetic processes are currently possible only with remote sensing systems that rely heavily on passive reflectance (R) information. Unlike R, fluorescence (F) emitted from chlorophyll is directly related to photochemical reactions and has been extensively used for the elucidation of the photosynthetic pathways. Recent advances in passive fluorescence instrumentation have made the remote acquisition of solar-induced fluorescence possible. The goal of this effort is to evaluate existing reflectance and emerging fluorescence methodologies for determining vegetation parameters related to photosynthetic function and carbon sequestration dynamics in plants. Field corn N treatment levels of 280, 140, 70, and 0 kg N / ha were sampled from an intensive test site for a multi-disciplinary project, Optimizing Production Inputs for Economic and Environmental Enhancement (OPE). Aircraft, near-ground, and leaf-level measurements were used to compare and contrast treatment effects within this experiment site assessed with both reflectance and fluorescence approaches. A number of spectral indices including the R derivative index D₇₃₀/D₇₀₅, the normalized difference of R₇₅₀ vs. R₇₀₅, and simple ratio R₈₀₀/R₇₅₀ differentiated three of the four N fertilization rates and yielded high correlations to three important carbon parameters: C:N, light use efficiency, and grain yield. These results advocate the application of hyperspectral sensors for remotely monitoring carbon cycle dynamics in terrestrial ecosystems.

Proceedings Volume 7457, Imaging Spectrometry XIV; 745705 (2009) Event: SPIE Optical Engineering + Applications, 2009, San Diego, California, United States, This manuscript details the development and validation of a unique forward thinking instrument and methodology for monitoring terrestrial carbon dynamics through synthesis of existing hyperspectal sensing and Light Detection and Ranging (LIDAR) technologies. This technology demonstration is directly applicable to linking target mission concepts identified as scientific priorities in the National Research Council (NRC, 2007) Earth Science Decadal Survey; namely, DESDynI and HyspIRI. The primary components of the Hyperspec-LIDAR system are the ruggedized imaging spectrometer and a small footprint LIDAR system. The system is mounted on a heavy duty motorized pan-tilt unit programmed to support both push-broom style hyperspectral imaging and 3-D canopy LIDAR structural profiling. The integrated Hyperspec-LIDAR sensor system yields a hypserspectral data cube with up to 800 bands covering the spectral range of 400 to 1000 nm and a 3-D scanning LIDAR system accurately measuring the vertical distribution of intercepted surfaces within a range of 150 m with an accuracy of 15 mm. Preliminary field tests of the Hyperspec-LIDAR sensor system were conducted at a mature deciduous mixed forest tower site located at the Smithsonian Environmental Research Center in Edgewater, MD. The goal of this research is to produce integrated science and data products from ground observations that will support satellite-based hybrid spectral/structural profile linked through appropriate models to monitor Net Ecosystem Exchange and related parameters such as ecosystem Light Use Efficiency.

IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium, 07-11 July 2008, Boston, MA, USA, This study evaluates the ability of reflectance (R) and chlorophyll fluorescence (ChlF) parameters to express the differences in foliar properties associated with varying light environment. In the summer of 2007 a tall crane was used to acquire foliar reflectance measurements and collect samples from sunlit and shaded tree crowns, from the upper and lower canopy of a mature tulip poplar (Lirodendron Tulipifera L.) forest. Leaf-level photosynthesis, R, ChlF spectra and kinetics and bio-physical parameters were measured on excised samples. The differences in the light environment greatly affected the spectral and biophysical data, especially the photosynthetic parameters (Amax, LUE, photochemical quenching, Qp, Qn, Qp/Qn). For sunlit foliage, ChlF kinetics (Fo, Fm, Fs, and Fv/Fm) were significantly lower than shaded foliage. Our analysis indicated that some of the tested spectral bio-indicators (e.g. PRI1, RE2, G035, Dmax) were strongly associated with LUE and differed significantly depending on the light environment. ChlF as compared to R indices followed more closely the trends in LUE, which emphasizes the application of ChlF for the timely detection of changes in vegetation physiology and carbon dynamics.

In situ directional reflectance observations were taken on trees of three species and were used to validate the Four-Scale Linear Model for AnIsotropic Reflectance (FLAIR). FLAIR-simulated spectra were satisfactory in general, especially for the shaded and shaded/sunlit mixed portions of the canopy but were species dependent. Good agreements with field observations were found when utilizing these simulations to derive band ratio indexes, Normalized Difference Vegetation Index (NDVI) and Photochemical Reflectance Index (PRI). Sensitivity analysis were performed to investigate the importance of three canopy biophysical parameters, and the leaf to shoot ratio showed the most impact on simulation results. The results in this study showed the potential to simulate directional reflectances with FLAIR on canopies with complicated structure.

This study examines the ability of narrow band vegetation indexes to detect spectral changes associated with stress and relate them to light use efficiency (LUE) over the course of a day as well as through the growing season. In a corn field in Beltsville, MD, carbon flux measurements were made at a flux tower using eddy covariance techniques. At this site on six days during the 2007 growing season hyperspectral reflectance measurements were collected at hourly intervals. LUE was determined at the time of the reflectance measurements from carbon fluxes measured at the flux tower. The measured LUE values were compared with a number of spectral indices that have been shown to be related to a number of leaf characteristics including: the status of the xanthophyll cycle; solar induced fluorescence; leaf water content; and concentrations of pigments such as chlorophyll, carotenoids, and anthocyanins. We found that several of these indices provide good estimates of both diurnal and seasonal LUE variability.

Methods to accurately estimate the biophysical and biochemical properties of vegetation are a major research objective of remote sensing. We assess the capability of the MODIS satellite sensor to measure canopy water content and evaluate its relationship to ecosystem exchange (NEE) for an evergreen forest canopy. A time-series of three vegetation indexes were derived from MODIS data, the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI), and the Normalized Difference Infrared Index (NDII), which were compared to physically based estimates of equivalent water thickness (EWT) from the airborne AVIRIS hyperspectral instrument over a temperate conifer forest in southwestern Washington. After cross-calibration of the imagery, water indexes derived from MODIS showed good agreement with AVIRIS EWT, while the NDVI was insensitive to water content variation. Three years of NEE data from eddy covariance measurements at the Wind River AmeriFlux tower were compared with the time series of MODIS indexes, which show seasonal water content has similar trajectory with NEE. In contrast, the MODIS NDVI time series did not yield a good relationship with NEE. This study demonstrates the potential to use MODIS water indexes for spatial and temporal NEE estimation at regional and global scales in appropriate ecosystems., Portions of this study were supported by National Aeronautics and Space Administration (NASA) under grant # NNG04GQ42G "Global Estimation of Canopy Water Content" and Contract # NAG5-9360 "New EOS Data Products to Improve Biogeochemical Estimates” and by the Office of Science (BER), U.S. Department of Energy, through the Western Regional Center of the National Institute for Global Environmental Change under Cooperative Agreements No. DE-FC03-90ER61010 and DE-FC02-03ER63613.

En: Remote Sensing and Modeling of Ecosystems for Sustainability III . -- Proceedings of the International Society for Optical Engineering (SPIE), 14 August 2006 , San Diego, California, USA, We assessed the capability of AVIRIS and MODIS to estimate canopy water content. Hyperspectral water retrievals with AVIRIS data, EWT, were compared to in situ leaf water content and LAI measurements at a semi-arid site in southeastern Arizona. Retrievals of EWT showed good correlation with field canopy water content measurements. Statistical analysis also suggested that EWT was significant among seven different vegetation communities. Four MODIS indexes derived from band ratios using the reflectance product and were compared to retrievals of EWT with AVIRIS at both the semi-arid site and a temperate conifer forest. Good statistical agreements were found between AVIRIS EWT and all four MODIS indexes at the semi-arid site in savanna shrub communities. Slightly poorer correlations were found at the forest site where water indexes had better correlation to AVIRIS EWT than vegetation indexes. Temporal patterns of the four indexes in all semi-arid vegetation communities except creosote bush and agriculture show distinct seasonal variation and responded to precipitation at the savanna site. Three years of net ecosystem exchange (NEE) data from eddy covariance measurements at the forest site were compared to the time series of MODIS indexes. MODIS water indexes showed similar seasonal patterns to NEE that were strongest during the period of net carbon sequestration. In contrast, the time series of MODIS vegetation indexes did not yield a good relationship to NEE, Portions of this study were supported by National Aeronautics and Space Administration (NASA) under grant # NNG04GQ42G "Global Estimation of Canopy Water Content" and Contract # NAG5-9360 "New EOS Data Products to Improve Biogeochemical Estimates” and by the Office of Science (BER), U.S. Department of Energy, through the Western Regional Center of the National Institute for Global Environmental Change under Cooperative Agreements No. DE-FC03-90ER61010 and DE-FC02-03ER63613.

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, rowstructured, and forest canopy architectures, respectively. Leaf water content (Cw) 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., Portions of this study were supported by National Aeronautics and Space Administration (NASA) under Grant No. NNG0 4GQ42G “Global Estimation of Canopy Water Content” and Contract No. NAG5-9360 “New EOS Data Products to Improve Biogeochemical Estimates” and support from the Cotton Foundation, #5002-008-00, “Evaluation of Advanced Hyperspectral Remote Sensing for Detection of Cotton Water Stress and Irrigation Refinement”. Financial support was provided to P.J Zarco- Tejada under the “Ramón y Cajal” and “Averroes” Programs from Spain are acknowledged. Financial support to D. Riaño under the “Ramón y Cajal” Program of Ministry of Science and Technology from Spain is acknowledged.

In water limited environments, the density and water content of plant canopies are highly correlated to available soil moisture. Specific absorption bands for liquid water are identifiable and the variation in their depths can be related to canopy water content using high spectral resolution (hyperspectral) imagery. The spectral absorption feature centered at approximately 980 nm has been widely utilized for estimating equivalent water thickness, a measure of the volume of canopy water if it is equally distributed over the area of the pixel. Although it is affected by canopy structure, it is highly correlated with plant water content, and is independent of reflectance changes due to photosynthetic pigments. This study relates the depth of the 980 nm water band absorption, measured by the continuum removal (CR) technique, to crop water stress, and compares these results to other vegetation and plant stress indicators, NDVI and NDWI.

Climate change is heavily impacted by changing vegetation cover and productivity with large scale monitoring of vegetation only possible with remote sensing techniques. The goal of this effort was to evaluate existing reflectance (R) spectroscopic methods for determining vegetation parameters related to photosynthetic function and carbon (C) dynamics in plants. Since nitrogen (N) is a key constituent of photosynthetic pigments and C fixing enzymes, biological C sequestration is regulated in part by N availability. Spectral R information was obtained from field corn grown at four N application rates (0, 70, 140, 280 kg N/ha). A hierarchy of spectral observations were obtained: leaf and canopy with a spectral radiometer; aircraft with the AISA sensor; and satellite with EO-1 Hyperion. A number of spectral R indices were calculated from these hyperspectral observations and compared to geo-located biophysical measures of plant growth and physiological condition. Top performing indices included the R derivative index D730/D705 and the normalized difference of R750 vs. R705 (ND705), both of which differentiated three of the four N fertilization rates at multiple observation levels and yielded high correlations to these carbon parameters: light use efficiency (LUE); C:N ratio; and crop grain yield. These results advocate the use of hyperspectral sensors for remotely monitoring carbon cycle dynamics in managed terrestrial ecosystems.

The carbon science community must rely on satellite remote sensing to obtain global estimates of photosynthetic activity, typically expressed as net primary production (NPP), gross primary production (GPP) or light use efficiency (LUE). The photochemical reflectance index (PRI), calculated as a normalized difference reflectance index using a physiologically active green band (~531 nm) and another physiologically insensitive green reference band (~570 nm), denoted as PRI(570), has been confirmed in many studies as being strongly related to LUE. Here, we examined the potential of utilizing PRI(570) observations under different illumination conditions for canopy LUE estimation of a forest. In order to evaluate this, directional hyperspectral reflectance measurements were collected continuously throughout the daytime periods using an automated spectroradiometer in conjunction with tower-based eddy covariance fluxes and environmental measurements at a coastal conifer forest in British Columbia, Canada throughout the 2006 growing season. A parameter calculated as the PRI(570) difference (dPRI(570)) between shaded versus sunlit canopy foliage sectors showed a strong correlation to tower-based LUE. The seasonal pattern for this correlation produced a dramatic change from high negative (r ~ ?0.80) values in the springtime and early fall to high positive values (r ~ 0.80) during the summer months, which could represent the seasonality of physiological characteristics and environmental factors. Although the PRI(570) successfully tracked canopy LUE, one or both of its green bands (~531 and 570 nm) used to calculate the PRI are unavailable on most existing and planned near-term satellites. Therefore, we examined the potential to use 24 other spectral indexes for LUE monitoring that might be correlated to PRI, and thereby a substitute for it. We also continued our previous investigations into the influence of illumination conditions on the observed PRI(570) and other indexes. Among the 24 indexes examined, three PRI indexes using different reference bands (488, 551 and 705 nm) showed high correlations to the traditional PRI(570), especially PRI(551) and PRI(705). This indicates three additional PRI variations for LUE monitoring if the traditional reference band at 570 nm is not available but the 531 nm band is available. Five other indexes also yielded high correlations to PRI(570): Dmax and DM705, two indexes calculated from derivative reflectance spectra; a simple ratio of reflectance values at 685 nm and 655 nm (SR685_655); and a double-peak optical index (DPI). The diurnal and seasonal dynamics of these eight indexes and PRI(570) were explored. All of these indexes except DPI expressed linear dependence on available sunlight and more strongly expressed diurnal dynamics in April than in August during summer drought. The differences for shaded versus sunlit canopy foliage sectors were also calculated for the eight indexes, and their correlations to canopy LUE across the season were examined. The performances were similar for the most successful and seasonally stable indexes: dPRI(551), dPRI(705) and dPRI(570). The other five indexes showed good correlation to LUE in some but not all the months, and the months with high correlations varied among them.

Invasive plant species are causing severe environmental and ecological impacts. This study utilized airborne hyperspectral image data and digital image processing techniques to map one of the most aggressive weeds, kudzu (Pueraria montana), in western Georgia. Minimum Noise Fraction (MNF) transform followed by Spectral Angle Mapper (SAM) produced the best map results among several other procedures. Validation with field data show that this procedure delivered user's accuracy of 83.02% for kudzu-invaded plots and 95.90% for non-invaded plots, with Producer's accuracy of 73.26% and 82.47%, respectively. Further analysis using a GIS-based CART analysis indicates the importance of elevation in limiting the spatial distribution of kudzu.