FPAR Products Research Articles

MODIS fPAR products do not reflect in-situ conditions in a tropical dry forest based on wavelet and cross-wavelet transforms

The fraction of Photosynthetically Active Radiation (fPAR) plays a pivotal role in determining the carbon flux in ecosystems. Although the MODIS fPAR product has demonstrated effectiveness in the Northern Hemisphere, its validity still needs to be verified in the context of Tropical Dry Forests (TDFs), which constitute 40% of all tropical forests. This study utilized a Wireless Sensor Network (WSN) to generate an in-situ Green fPAR dataset at the Santa Rosa National Park Environmental Monitoring Supersite, aiming to validate MODIS fPAR products from 2013 to 2017. This study employs a 2-flux fPAR estimation approach for the in-situ dataset, followed by Savitzky–Golay derivative-based smoothing, univariate-wavelet transforms, and cross-wavelet analysis to compare phenological variables between the in-situ and MODIS fPAR datasets. Our findings reveal a significant temporal disparity between the MODIS fPAR products and ground-based data, with MODIS consistently lagging in detecting the onset of green-up or senescence in TDFs by 18–55 days. However, the annual and inter-seasonal patterns were statistically significant (p < 0.05) and replicated in the MODIS and in-situ datasets. Notably, these patterns deviate during extreme water conditions (droughts and hurricanes), with MODIS underestimating the effects of drought and failing to represent hurricane impact. Furthermore, MODIS fPAR products do not effectively capture small-scale fPAR variations and intra-seasonal differences. Therefore, this study underscores the limited accuracy of MODIS fPAR observations in the context of TDFs. Consequently, caution is warranted when relying on MODIS fPAR products to monitor rapid phenological changes in Tropical Dry Forests.

Alpine grassland fPAR change over the Northern Tibetan Plateau from 2002 to 2011

In this study, two different methods including Digital Camera and Reference Panel (DCRP) and traditional in situ fPAR observation for measuring the in situ point fPAR of very short alpine grass vegetation were compared, and the Moderate Resolution Imaging Spectroradiometer (MODIS) fPAR products were evaluated and validated by in situ point data on the alpine grassland over the Northern Tibetan Plateau, which is sensitive to climate change and vulnerable to anthropogenic activities. Results showed that the MODIS alpine grassland fPAR product, examined by using DCRP, and traditional in situ fPAR observation had a significant relationship at the spatial and temporal scales. The decadal MODIS fPAR trend analysis showed that, average growing season fPAR increased by 1.2 × 10−4 per year and in total increased 0.86% from 2002 to 2011 in alpine grassland, when most of the fPAR increments occurred in southeast and center of the Northern Tibetan Plateau, the alpine grassland tended to recover from degradation slightly. However, climatic factors have influenced the various alpine grassland vegetation fPAR over a period of 10 years; precipitation significantly affected the alpine meadow fPAR in the eastern region, whereas temperature considerably influenced the alpine desert steppe fPAR in the west region. These findings suggest that the regional heterogeneity in alpine grassland fPAR results from various environmental factors, except for vegetation characteristics, such as canopy structure and leaf area.

Comparison of ground and satellite-based methods for estimating stand-level fPAR in a boreal forest

The fraction of absorbed Photosynthetically Active Radiation (fPAR, 400–700nm) can be retrieved from satellite measurements and is used to estimate and monitor ecosystem productivity and vegetation phenology. Although fPAR depends primarily on the Leaf Area Index (LAI), it is also influenced by the incoming radiation field and thus varies diurnally and seasonally. The overall aim of this study was to compare different ground and satellite-based fPAR estimates. The field data consisted of nearly 1000 plots located in the southern boreal forest zone in Finland. For all plots, data on canopy transmittance and forest inventory variables were available. We compared two different approaches to estimate fPAR and studied which satellite-based vegetation indices are best correlated with the instantaneous ground reference fPAR. Finally, a canopy fPAR model was used to assess some underlying assumptions of the satellite-based fPAR products: the similarity between instantaneous (at the time of satellite overpass) and daily-integrated fPAR. Results showed that allometric estimates of LAI and Beer’s law are not sufficient to estimate fPAR in a boreal forest, because canopy openness strongly influences the relationship between canopy LAI and fPAR. We found that vegetation indices calculated from Landsat were only moderately correlated (maximum R2=0.34) with the ground-based fPAR. Our study showed that the absolute difference between fPAR at the moment of the satellite overpass and daily integrated fPAR exceeds the 0.05 fPAR-units limit set by Global Climate Observing Systems −network in northern latitudes during the peak growing season. Thus, at high latitudes covered by boreal forests, the satellite-based fPAR systematically underestimates the daily integrated black sky fPAR.

Evaluating flood inundation impact on wetland vegetation FPAR of the Macquarie Marshes, Australia

Vegetation is a key measure of changes in wetland hydrology and ecology induced by flood inundation. The fraction of photosynthetically active radiation absorbed by the canopy (FPAR) is an indicator of presence and state of vegetation, which can be estimated from remote sensing data. This study investigates the spatio-temporal variations of FPAR under inundation by integrating remotely sensed images and hydrological data in a GIS environment. Taking the Macquarie Marshes as a study area, it aims to model the behavior of vegetation after an ecologically significant flood using Moderate Resolution Imaging Spectroradiometer (MODIS) FPAR products, so as to reveal the impacts of flood on wetland vegetation. Flow data from four gauging stations at upstream, mid-stream and downstream were analysed. The modified Normalized Difference Water Index (mNDWI) was used to extract inundation extent from MODIS images. Image ratio and lagged cross-correlation methods were employed to disclose the vegetation response to flooding. Vegetation in the Marshes went through considerable structural changes during the identified flood event. FPAR was relatively more stable in the inundated areas than the non-inundated areas. FPAR values in both inundated and non-inundated areas exhibited a similar trend in the “non-flood period”, but a different trend in the “flood period”. Lagged cross-correlation analysis shows diverse vegetation responses to flood inundation between the inundated and non-inundated areas. It proves the water retention of wetland inundation and confirms vegetation in the non-inundated areas more sensitive to soil moisture conditions. This study proposes a feasible method for efficiently studying vegetation dynamics of flood inundation at both spatial and temporal scales. These methodology and results can provide baseline information for eco-hydrological studies of floodplain-wetland ecosystems.

Validation of MODIS and GEOV1 fPAR Products in a Boreal Forest Site in Finland

Remote sensing of the fraction of absorbed Photosynthetically Active Radiation (fPAR) has become a timely option to monitor forest productivity. However, only a few studies have had ground reference fPAR datasets containing both forest canopy and understory fPAR from boreal forests for the validation of satellite products. The aim of this paper was to assess the performance of two currently available satellite-based fPAR products: MODIS fPAR (MOD15A2, C5) and GEOV1 fPAR (g2_BIOPAR_FAPAR), as well as an NDVI-fPAR relationship applied to the MODIS surface reflectance product and a Landsat 8 image, in a boreal forest site in Finland. Our study area covered 16 km2 and field data were collected from 307 forest plots. For all plots, we obtained both forest canopy fPAR and understory fPAR. The ground reference total fPAR agreed better with GEOV1 fPAR than with MODIS fPAR, which showed much more temporal variation during the peak-season than GEOV1 fPAR. At the chosen intercomparison date in peak growing season, MODIS NDVI based fPAR estimates were similar to GEOV1 fPAR, and produced on average 0.01 fPAR units smaller fPAR estimates than ground reference total fPAR. MODIS fPAR and Landsat 8 NDVI based fPAR estimates were similar to forest canopy fPAR.

Estimation of direct, diffuse, and total FPARs from Landsat surface reflectance data and ground‐based estimates over six FLUXNET sites

AbstractThe fraction of photosynthetically active radiation (PAR) absorbed by green elements (FPAR) is an essential climate variable in quantifying canopy absorbed PAR (APAR) and gross and net primary production. Current satellite FPAR products typically correspond to black‐sky FPAR under direct illumination only, but the radiation transfer and vegetation absorption processes differ for direct and diffuse PARs. To address this, the present study developed a new approach to estimate direct, diffuse, and total FPARs, separately, from Landsat surface reflectance data. Field‐measured direct and diffuse FPARs were first derived for crops, deciduous broadleaf forests, and evergreen needleleaf forests at six FLUXNET sites. Then, a coupled soil‐leaf‐canopy radiative transfer model (SLC) was used to simulate surface reflectance under direct and diffuse illumination conditions. Direct, diffuse, and total FPARs were estimated by comparing Landsat‐5 Thematic Mapper (TM) data and simulated surface reflectances using a lookup table approach. The differences between the Landsat‐estimated and the field‐measured FPARs are less than 0.05 (10%). The diffuse FPAR is higher than the direct FPAR by up to 19.38%, whereas the total FPAR is larger than the direct FPAR by up to 16.07%. The direct APAR is higher than the diffuse APAR under clear‐sky conditions, but underestimates the total APAR by −277.72 µmol s−1 m−2 on average. The approach described here can be extended to estimate direct, diffuse, and total FPARs from other satellite data and the obtained FPAR variables could be helpful to improve modeling of vegetation processes.

Estimation of leaf area index and the fraction of absorbed photosynthetically active radiation in a boreal forest

The aim of this dissertation is to assess the accuracy of different ground reference methods used to validate satellite based leaf area index (LAI) and the fraction of absorbed photosynthetically active radiation (fPAR) products. LAI and fPAR are strongly linked, although they principally and practically measure different properties: LAI quantifies the areal interphase between soil and atmosphere, whereas fPAR quantifies the energy available for photosynthesis. Until now, the development of remote sensing based methods to estimate LAI and fPAR in a boreal forest has been hindered by the scarcity of ground data, which is required to validate and develop existing algorithms. The aim of the first part of this dissertation was to assess the impacts of different methodological approaches to estimate LAI in boreal forests, and to validate satellite based LAI products. Results showed that the accuracy of ground based LAI estimates is sensitive to both the retrieval methods and sampling scheme used to collect the optical LAI data. The satellite based measurements of LAI demonstrated a large temporal variability in LAI. The second part of the dissertation focused on measuring and modeling fPAR in a boreal forest. A new scheme for measuring and modeling ground reference fPAR based on photon recollision probability was presented in this dissertation. Ground reference fPAR is usually estimated only for the forest canopy layer. This study is among the first ones to validate the new global satellite based fPAR product called GEOV1 using data of both the forest canopy and understory layers from boreal forests. Results showed that satellite based fPAR products may correspond better with the total fPAR, instead of only the forest canopy fPAR as has often been presumed.

Comparison of Gross Primary Productivity Derived from GIMMS NDVI3g, GIMMS, and MODIS in Southeast Asia

Gross primary production (GPP) plays an important role in the net ecosystem exchange of CO2 between the atmosphere and terrestrial ecosystems. It is particularly important to monitor GPP in Southeast Asia because of increasing rates of tropical forest degradation and deforestation in the region in recent decades. The newly available, improved, third generation Normalized Difference Vegetation Index (NDVI3g) from the Global Inventory Modelling and Mapping Studies (GIMMS) group provides a long temporal dataset, from July 1981 to December 2011, for terrestrial carbon cycle and climate response research. However, GIMMS NDVI3g-based GPP estimates are not yet available. We applied the GLOPEM-CEVSA model, which integrates an ecosystem process model and a production efficiency model, to estimate GPP in Southeast Asia based on three independent results of the fraction of photosynthetically active radiation absorbed by vegetation (FPAR) from GIMMS NDVI3g (GPPNDVI3g), GIMMS NDVI1g (GPPNDVI1g), and the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD15A2 FPAR product (GPPMOD15). The GPP results were validated using ground data from eddy flux towers located in different forest biomes, and comparisons were made among the three GPPs as well as the MOD17A2 GPP products (GPPMOD17). Based on validation with flux tower derived GPP estimates the results show that GPPNDVI3g is more accurate than GPPNDVI1g and is comparable in accuracy with GPPMOD15. In addition, GPPNDVI3g and GPPMOD15 have good spatial-temporal consistency. Our results indicate that GIMMS NDVI3g is an effective dataset for regional GPP simulation in Southeast Asia, capable of accurately tracking the variation and trends in long-term terrestrial ecosystem GPP dynamics.

Impacts of light use efficiency and fPAR parameterization on gross primary production modeling

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 understanding 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 climate 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 fAPARchl, a parameter accounting for the fAPAR linked to the chlorophyll-containing canopy fraction. fAPARchl 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. fAPARchl 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, fAPARchl 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 LUEmax 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.

Evaluation of optical remote sensing to estimate actual evapotranspiration and canopy conductance

We compared estimates of actual evapotranspiration (ET) produced with six different vegetation measures derived from the MODerate resolution Imaging Spectroradiometer (MODIS) and three contrasting estimation approaches using measurements from eddy covariance flux towers at 16 FLUXNET sites located over six different land cover types. The aim was to assess optimal approaches in using optical remote sensing to estimate ET. The first two approaches directly regressed various MODIS vegetation indices (VIs) and products such as leaf area index (LAI) and fraction of photosynthetically active radiation (fPAR) with ET and evaporative fraction (EF). In the third approach, the Penman–Monteith (PM) equation was inverted to obtain surface conductance (Gs), for dry plant canopies. The Gs values were then regressed against the MODIS data products and used to parameterize the PM equation for retrievals of ET. Jack-Knife cross-validation was used to evaluate the various regression models against observed ET. The PM-Gs approach provided the lowest root mean square error (RMSE), and highest determination coefficients (R2) across all sites, with an average RMSE=38Wm−2 and R2=0.72. Direct regressions of observed ET against the VIs resulted in an average RMSE=60Wm−2 and R2=0.22, while the EF regressions an average RMSE=42Wm−2 and R2=0.64. The MODIS LAI and fPAR product produced the poorest estimates of ET (RMSE>44Wm−2 and R2<0.6); while the VIs each performed best for some of the land cover types. The enhanced vegetation index (EVI) produced the best ET estimates for evergreen needleleaf forest (RMSE=28.4Wm−2, R2=0.66). The normalized difference vegetation index (NDVI) best estimated ET in grassland (RMSE=23.8Wm−2 and R2=0.68), cropland (RMSE=29.2Wm−2 and R2=0.86) and woody savannas (RMSE=25.4Wm−2 and R2=0.82), while the VI-based crop coefficient (Kc) yielded the best estimates for evergreen and deciduous broadleaf forests (RMSE=27Wm−2 and R2=0.7 in both cases). Using the ensemble-average of ET as estimated using NDVI, EVI and Kc we computed global grids of dry canopy conductance (Gc) from which annual statistics were extracted to characterise different functional types. The resulting Gc values can be used to parameterize land surface models.

MODIS 광합성유효복사흡수율과 WRF 기상자료를 이용한 북한지역의 총일차생산성 추정

미국항공우주국(NASA)의 MODIS GPP 자료는 육상식생의 총일차생산성(GPP) 모니터링에 중요한 수단을 제공한다. 그러나 GPP 추정의 입력자료로 사용하는 DAO기상자료의 거친 공간해상도(<TEX>$1.25^{\circ}{\times}1^{\circ}$</TEX>)와 FPAR 자료의 구름영향에 의한 신뢰도 저하 등은 지역 수준의 GPP 모니터링에 문제를 야기한다. 이 연구에서는 북한지역을 대상으로 FPAR의 구름영향 제거하고 고해상도(3 km<TEX>${\times}$</TEX>3 km) WRF 기상자료를 사용함으로써 입력자료의 문제를 개선한 GPP를 추정한 후, 2000-2008년 간의 GPP 연간 변동특성 및 지역적 분포특성을 분석하였다. 개선한 GPP는 2000년의 645 <TEX>$gC\;m^{-2}\;y^{-1}$</TEX>에서 2008년의 863 <TEX>$gC\;m^{-2}\;y^{-1}$</TEX>까지 변화하였고, 혼효림지역이 1,076 <TEX>$gC\;m^{-2}\;y^{-1}$</TEX>으로 가장 큰 값을 보였다. NASA GPP (790 <TEX>$gC\;m^{-2}\;y^{-1}$</TEX>)에 비해 FPAR 개선 후 GPP가 증가하였고(861), WRF 자료 이용 시 감소(710), FPAR와 WRF 자료 이용 시 다소 증가(809)하는 양상을 보였다. 개선효과는 북한에서 제일 큰 면적을 차지한 혼효림에서 가장 뚜렷하였다(+10.2%). 한편 WRF 기상자료가 DAO에 비해 지형적 영향을 보다 잘 반영함으로써, 결과적으로 개선한 GPP의 공간이질성이 증가한 것으로 나타났다. 각 피복유형별 연간변동성을 분석한 결과 개선한 GPP는 NASA GPP와 상이한 연간변화를 보였다. 위 결과들을 종합하면, 북한 등의 지역수준의 GPP 모니터링을 목적으로할 경우, 입력자료의 개선에 의한 MODIS GPP를 재산출할 필요가 있다고 보여진다. NASA MODIS GPP provides a useful tool to monitor global terrestrial vegetation productivity. Two major problems of NASA GPP in regional applications are coarse spatial resolution (<TEX>$1.25^{\circ}{\times}1^{\circ}$</TEX>) of DAO meteorological data and cloud contamination of MODIS FPAR product. In this study, we improved the NASA GPP by using enhanced input data of high spatial resolution (3 km<TEX>${\times}$</TEX>3 km) WRF meteorological data and cloud-corrected FPAR over the North Korea. The improved GPP was utilized to investigate characteristics of GPP interannual variation and spatial patterns from 2000 to 2008. The GPP varied from 645 to 863 <TEX>$gC\;m^{-2}\;y^{-1}$</TEX> in 2000 and 2008, respectively. Mixed forest showed the highest GPP (1,076 <TEX>$gC\;m^{-2}\;y^{-1}$</TEX>). Compared to NASA GPP (790 <TEX>$gC\;m^{-2}\;y^{-1}$</TEX>);FPAR enhancement increased GPP (861) but utilization of WRF data decreased GPP (710). Enhancements of both FPAR and meteorological input resulted in GPP increase (809) and the improvement was the greatest for mixed forest regions (+10.2%). The improved GPP showed better spatial heterogeneity reflecting local topography due to high resolution WRF data. It is remarkable that the improved and NASA GPPs showed distinctly different interannual variations with each other. Our study indicates improvement of NASA GPP by enhancing input variables is necessary to monitor region-scale terrestrial vegetation productivity.

Comparison of MODIS fPAR Products with Landsat-5 TM-Derived fPAR over Semiarid Rangelands of Idaho

While validation of the MODIS fPAR product is well behind that of the LAI product, it is recently receiving more attention. In this study, MODIS fPAR and Landsat-5 TM-derived fPAR (TM fPAR) were calculated and quantitatively compared using imagery from 2005 to 2008 for the semiarid rangelands of Idaho, USA. fPAR change maps were calculated between active growth and late-summer senescence periods. Accuracy of the MODIS fPAR and TM fPAR were determined indirectly by incorporating field-based measurements of above-ground forage biomass and percent ground cover from a variety of sites (n = 442).

Temporally smoothed and gap‐filled MODIS land products for carbon modelling: application of the fPAR product

TIMESAT software is used to produce a temporally and spatially Gap‐Filled and Smoothed (GFS) version of the MODIS (Moderate Resolution Imaging Spectro‐radiometer) fPAR (fraction of absorbed photosynthetically active radiation) product (MOD15). We apply this new ƒPAR product within two commonly used carbon and vegetation productivity models, CASA (Carnegie‐Ames‐Stanford Approach) and the MODIS GPP (Gross Primary Production) algorithm (MOD17). The GFS product removes noise present within the original MOD15 fPAR dataset, yet is comparable to the linearly interpolated UMT (University of Montana) fPAR used in the MOD17 algorithm. However, the GSF data provides a realistic fPAR time‐series in relation to magnitude and seasonality associated with radiation in regions where persistent cloud cover is an issue. It is available for North America and the northern part of South America covering the Amazon basin for the MODIS acquisition period (2000–2005).

Assessment and extension of the MODIS FPAR products in temperate forests of the eastern United States

Spatial and temporal variability in the quality of the collection 4 and 5 MODIS FPAR products were examined over a 1.1 million km2 region dominated by temperate forests. The MODIS FPAR products were parsed into different quality levels based on the retrieval method of the FPAR estimates, and the spatial coverage of the different quality MODIS subsets was examined through time and by land cover. The spatial extent of good quality FPAR estimates, derived from the main radiative transfer algorithm without saturation, was found to be quite limited, particularly during the growing season. The majority of the MODIS C4 FPAR retrievals were derived from the back‐up, empirically based algorithm. The C5 products available to date showed greater data quality but still had limitations in forested areas. As an alternative to using the lower‐quality MODIS FPAR data, we explored the utility of estimating FPAR from the more spatially extensive (and higher resolution) MODIS NDVI product using a simple, regionally based linear regression. A strong linear relationship between high quality MODIS NDVI and FPAR products within our study site was found, indicating its utility for estimating FPAR at regional scales where good quality retrievals from the MODIS algorithm are not available and where higher resolution FPAR maps are desired. We discuss these results and their significance for down‐stream MODIS users.

A lidar-based hierarchical approach for assessing MODIS fPAR

The purpose of this study was to estimate the fraction of photosynthetically active radiation absorbed by the canopy (fPAR) from point measurements to airborne lidar for hierarchical scaling up and assessment of the Moderate Resolution Imaging Spectroradiometer (MODIS) fPAR product within a “medium-sized” (7 km × 18 km) watershed. Nine sites across Canada, containing one or more (of 11) distinct species types and age classes at varying stages of regeneration and seasonal phenology were examined using a combination of discrete pulse airborne scanning Light Detection And Ranging (lidar) and coincident analog and digital hemispherical photography (HP). Estimates of fPAR were first compared using three methods: PAR radiation sensors, HP, and airborne lidar. HP provided reasonable estimates of fPAR when compared with radiation sensors. A simplified fractional canopy cover ratio from lidar based on the number of within canopy returns to the total number of returns was then compared with fPAR estimated from HP at 486 geographically registered measurement locations. The return ratio fractional cover method from lidar compared well with HP-derived fPAR (coefficient of determination = 0.72, RMSE = 0.11), despite varying the lidar survey configurations, canopy structural characteristics, seasonal phenologies, and possible slight inaccuracies in location using handheld GPS at some sites. Lidar-derived fractional cover estimates of fPAR were ∼ 10% larger than those obtained using HP (after removing wood components), indicating that lidar likely provides a more realistic estimate of fPAR than HP when compared with radiation sensors. Finally, fPAR derived from lidar fractional cover was modelled at 1 m resolution and averaged over 99 1 km areas for comparison with MODIS fPAR. The following study is one of the first to scale between plot measurements and MODIS pixels using airborne lidar.

Generating vegetation leaf area index earth system data record from multiple sensors. Part 1: Theory

The generation of multi-decade long Earth System Data Records (ESDRs) of Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) from remote sensing measurements of multiple sensors is key to monitoring long-term changes in vegetation due to natural and anthropogenic influences. Challenges in developing such ESDRs include problems in remote sensing science (modeling of variability in global vegetation, scaling, atmospheric correction) and sensor hardware (differences in spatial resolution, spectral bands, calibration, and information content). In this paper, we develop a physically based approach for deriving LAI and FPAR products from the Advanced Very High Resolution Radiometer (AVHRR) data that are of comparable quality to the Moderate resolution Imaging Spectroradiometer (MODIS) LAI and FPAR products, thus realizing the objective of producing a long (multi-decadal) time series of these products. The approach is based on the radiative transfer theory of canopy spectral invariants which facilitates parameterization of the canopy spectral bidirectional reflectance factor (BRF). The methodology permits decoupling of the structural and radiometric components and obeys the energy conservation law. The approach is applicable to any optical sensor, however, it requires selection of sensor-specific values of configurable parameters, namely, the single scattering albedo and data uncertainty. According to the theory of spectral invariants, the single scattering albedo is a function of the spatial scale, and thus, accounts for the variation in BRF with sensor spatial resolution. Likewise, the single scattering albedo accounts for the variation in spectral BRF with sensor bandwidths. The second adjustable parameter is data uncertainty, which accounts for varying information content of the remote sensing measurements, i.e., Normalized Difference Vegetation Index (NDVI, low information content), vs. spectral BRF (higher information content). Implementation of this approach indicates good consistency in LAI values retrieved from NDVI (AVHRR-mode) and spectral BRF (MODIS-mode). Specific details of the implementation and evaluation of the derived products are detailed in the second part of this two-paper series.

Validation of MODIS F/sub PAR/ products in boreal forests of Alaska

Moderate Resolution Imaging Spectroradiometer (MODIS) fraction of photosynthetically active radiation absorbed by vegetation (FPAR ) products covering the boreal forest of interior Alaska were analyzed and compared with field measurements of canopy light harvesting over a multiyear period (2001 to 2004), as well as to high-resolution F PAR maps derived from IKONOS and Landsat ETM+ imagery. The spatial variability of FPAR within the MODIS products was examined by incorporating the field measurements and aggregating the high-resolution FPAR maps to the MODIS scale. Characterization of the temporal accuracy of the MODIS FPAR products was conducted through comparisons with continuously operating canopy light interception measurements. The MODIS product tended to overestimate FPAR relative to both ground-based measurements and Landsat-derived estimates of FPAR, particularly in the more sparsely vegetated burn scars on which we focus related research, but adequately captured seasonal variability associated with vegetation phenology. A combination of canopy closure and ground cover vegetation was identified as the source of most of the discrepancies between the MODIS estimated and field measured FPAR values. Neither the field measurements nor the high resolution image FPAR maps based on the field measurements characterized the light environment of the ground cover vegetation (i.e., <10 cm height); thus, absolute validation of the MODIS products was incomplete - despite the extensive spatial and temporal characterization of FPAR dynamics in the study region. We discuss these results, explore some other possible sources for observed differences between the MODIS, field, and high-resolution FPAR maps, and consider possible ways to address these issues in future work

Performance of the MISR LAI and FPAR algorithm: a case study in Africa

The Multi-angle Imaging SpectroRadiometer (MISR) instrument is designed to provide global imagery at nine discrete viewing angles and four visible/near-infrared spectral bands. The MISR standard products include green leaf area index (LAI) of vegetation and fraction of photosynthetically active radiation absorbed by vegetation (FPAR). These parameters are being routinely processed from MISR data at the Langley Atmospheric Sciences Data Center (ASDC) since October 2002. This paper describes the research basis for transitioning the MISR LAI/FPAR product from beta to provisional status. The quality and spatial coverage of MISR land surface reflectances that are input to the algorithm determine the quality and spatial coverage of the LAI and FPAR products. Therefore, considerable efforts have been expended to analyze the performance of the algorithm as a function of uncertainties of MISR surface reflectances and to establish the convergence property of the MISR LAI/FPAR algorithm, namely, that the reliability and accuracy of the retrievals increase with increased input information content and accuracy. An additional objective of the MISR LAI/FPAR algorithm is classification of global vegetation into biome types—information that is usually an input to remote sensing algorithms that use single-angle observations. An upper limit of uncertainties of MISR surface reflectances that allows discrimination between biomes, minimizes the impact of biome misidentification on LAI retrievals, and maximizes the spatial coverage of retrievals was estimated. Algorithm performance evaluated on a limited set of MISR data from Africa suggests valid LAI retrievals and correct biome identification in about 20% of the pixels, on an average, given the current level of uncertainties in the MISR surface reflectance data. The other 80% of the LAI values are retrieved using incorrect information about the type of biome. However, the use of multi-angle data minimizes the impact of biome misidentification on LAI retrievals; that is, with a probability of about 70%, uncertainties in LAI retrievals due to biome misclassification do not exceed uncertainties in the observations. We also discuss in depth the parameters that characterize LAI/FPAR product quality—such as quality assessment (QA) that is available to the users along with the product. The analysis of the MISR LAI/FPAR product presented here demonstrates the physical basis of the radiative transfer algorithm used in the retrievals and, importantly, that the reliability and accuracy of the retrievals increase with increased input information content and accuracy. Further improvements in the quality of MISR surface reflectances are therefore expected to lead to LAI and FPAR retrievals of increasing quality.