Performances of neural networks for deriving LAI estimates from existing CYCLOPES and MODIS products

This paper evaluates the performances of a neural network approach to estimate LAI from CYCLOPES and MODIS nadir normalized reflectance and LAI products. A data base was generated from these products over the BELMANIP sites during the 2001-2003 period. Data were aggregated at 3 km × 3 km, resampled at 1/16 days temporal frequency and filtered to reject outliers. VEGETATION and MODIS reflectances show very consistent values in the red, near infrared and short wave infrared bands. Neural networks were trained over part of this data base for each of the 6 MODIS biome classes to retrieve both MODIS and CYCLOPES LAI products.Results show very good performances of neural networks to estimate the original LAI products with an overall root mean square error (RMSE) around 0.5 for MODIS LAI from both MODIS and CYCLOPES normalized reflectances and a RMSE ranging between 0.12 (CYCLOPES reflectances) and 0.29 (MODIS reflectances) for CYCLOPES LAI. A drop of 15% of performance was found by training MODIS biome dependant algorithm by a single network over all the classes at the same time. More detailed analyses show that CYCLOPES and MODIS LAI values are very consistent for grasses and crops. Conversely, other biomes including shrubs, savanna, needleleaf and broadleaf forests show significant discrepancies, mainly due to differences between LAI definitions used between CYCLOPES (closer to effective LAI) and MODIS (closer to true LAI). However, products derived from the original CYCLOPES LAI products show a better agreement with both effective and true LAI ground measurements values. MODIS LAI products show more instability, partly because of the slightly shorter temporal resolution as compared to CYCLOPES.These results confirm the interest and versatility of neural networks for operational algorithms. This approach could be extended to other products or sensors, and may constitute a step forward for the fusion of data from several sensors, hence contributing to develop ‘virtual constellations’.     

Analysis of leaf area index products from combination of MODIS Terra and Aqua data

A prototype product suite, containing the Terra 8-day, Aqua 8-day, Terra-Aqua combined 8- and 4-day products, was generated as part of testing for the next version (Collection 5) of the MODerate resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI) products. These products were analyzed for consistency between Terra and Aqua retrievals over the following data subsets in North America: single 8-day composite over the whole continent and annual time series over three selected MODIS tiles (1200 × 1200 km). The potential for combining retrievals from the two sensors to derive improved products by reducing the impact of environmental conditions and temporal compositing period was also explored. The results suggest no significant discrepancies between large area (from continent to MODIS tile) averages of the Terra and Aqua 8-day LAI and surface reflectances products. The differences over smaller regions, however, can be large due to the random nature of residual atmospheric effects. High quality retrievals from the radiative transfer based algorithm can be expected in 90-95% of the pixels with mostly herbaceous cover and about 50-75% of the pixels with woody vegetation during the growing season. The quality of retrievals during the growing season is mostly restricted by aerosol contamination of the MODIS data. The Terra-Aqua combined 8-day product helps to minimize this effect and increases the number of high quality retrievals by 10-20% over woody vegetation. The combined 8-day product does not improve the number of high quality retrievals during the winter period because the extent of snow contamination of Terra and Aqua observations is similar. Likewise, cloud contamination in the single-sensor and combined products is also similar. The LAI magnitudes, seasonal profiles and retrieval quality in the combined 4-day product are comparable to those in the single-sensor 8-day products. Thus, the combined 4-day product doubles the temporal resolution of the seasonal cycle, which facilitates phenology monitoring in application studies during vegetation transition periods. Both Terra and Aqua LAI products show anomalous seasonality in boreal needle leaf forests, due to limitations of the radiative transfer algorithm to model seasonal variations of MODIS surface reflectance data with respect to solar zenith angle. Finally, this study suggests that further improvement of the MODIS LAI products is mainly restricted by the accuracy of the MODIS observations.     

Evaluation of the influence of two operational fraction of absorbed photosynthetically active radiation (FAPAR) products on terrestrial ecosystem productivity modelling

The fraction of photosynthetically active radiation absorbed by vegetation (FAPAR) represents the available light energy for plant productivity and is the key variable influencing photosynthesis, transpiration, and energy balance in most terrestrial vegetation productivity models. With availability of earth observation data from different satellite sensors increasing, a number of FAPAR products are being generated. Several studies have investigated the differences between these products. However, very few studies have investigated how the differences between these products influence the output from ecosystem productivity models that utilise them. This study evaluated the influence of two operational FAPAR products (i.e. the MODIS and CYCLOPES FAPAR products) on the terrestrial vegetation primary productivity predicted by the Carnegie–CASA model across various biomes in the USA. The GPP predicted by the Carnegie–CASA model was compared to GPP measurements from various flux tower sites representing five biomes (i.e. croplands, broadleaf deciduous forests, grassland, needle-leaf evergreen forests, and savanna woodland). With the exception of cropland sites, the two FAPAR products resulted in GPP predictions which were higher than the in situ GPP measurements for the evaluated biomes. However, the CYCLOPES FAPAR product resulted in GPP outputs which were closer (lower RMSE values) to the in situ measurements than the MODIS FAPAR product. The two FAPAR products do not account for the FAPAR absorbed by non-photosynthetic elements of the canopy, which may lead to overestimation of the FAPAR that is actually used in photosynthesis. This could explain the higher GPP values derived using these products when compared to the in situ GPP measurements.     

Estimating time-series leaf area index based on recurrent nonlinear autoregressive neural networks with exogenous inputs

The leaf area index (LAI) is a key parameter in many meteorological, environmental and agricultural models. At present, global LAI products from several sensors have been released. These single sensor-based LAI products are generally discontinuous in time and cannot characterize the status of natural vegetation growth very well. In this study, by fusing Moderate Resolution Imaging Spectroradiometer (MODIS) and Satellite Pour l'Observation de la Terre (SPOT) VEGETATION products, time-series LAIs were used to train recurrent nonlinear autoregressive neural networks with exogenous inputs (NARXNNs) for six typical vegetation types. The exogenous inputs included time-series reflectances in the red, near-infrared and shortwave infrared bands as well as the corresponding sun-viewing angles. These NARXNNs subsequently served to predict the time-series LAI. The validation results show that the predicted LAI of the NARXNN is not only more continuous and stable than the MODIS LAI as a function of time but is also much closer to the ground truth. Thus, the proposed method may be helpful for improving the quality of the MODIS LAI.     

LAI and fAPAR CYCLOPES global products derived from VEGETATION. Part 2: validation and comparison with MODIS collection 4 products

The main objective of this paper is the validation of CYCLOPES version 3.1 LAI and fAPAR products. It is achieved by the comparison with MODIS collection 4 and 4.1 products and ECOCLIMAP LAI climatology over the BELMANIP representative set of sites, and with ground measurements over a limited set of sites. Great attention is paid to the consistency of the comparison: for the spatial dimension, product PSF appears to be the main aspect governing the spatial resolution at which the comparison has to be achieved. For CYCLOPES, a minimal size of the sites should be 3 km × 3 km², while the optimal one is 10 km × 10 km²; regarding the temporal sampling interval and resolution, the problem is much easier to solve when assuming a relatively smooth time course of vegetation characteristics (8-16 days). Great care was also paid to the departure of products from the nominal definition, particularly for LAI where different scales of clumping have to be considered.Results showed that CYCLOPES and MODIS products have generally consistent seasonality, CYCLOPES being however characterized by a smoother temporal evolution as expected. Differences are mainly concentrated on the magnitude of products values, CYCLOPES achieving better performances both for LAI (RMSE = 0.73) and fAPAR (RMSE = 0.10) over the limited number of sites where ground measurements were available. This study also sets a framework to the validation exercise that could be used to evaluate other products or future versions of the same products and contribute to associate quantitative uncertainties as required by the user community.     

Comparison of four global FAPAR datasets over Northern Eurasia for the year 2000

The Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) has been identified as one of several key satellite-derived biophysical datasets. With multiple global FAPAR datasets now available and a lack of in-situ measurements and comparison studies in the far north, this study attempts to provide the reader with an indication of the performance of four global FAPAR datasets (MODIS, CYCLOPES, JRC and GLOBCARBON) over Northern Eurasia in the year 2000 via comparison. Within the year 2000 growing season, both the MODIS and CYCLOPES datasets recorded on average similar but substantially higher values than the JRC and GLOBCARBON datasets. Among three of the four datasets, a high level of agreement in deciduous broadleaf forests and croplands was observed. Largest disagreement occurred among needleleaf forests and grassland/shrubland. Potential reasons for discrepancies among the datasets include different retrieval methods, use of LAI and land cover, snow effects and others. Findings from this study and other published results suggest that overall, JRC best captures FAPAR over northern Eurasia in the year 2000. However, when considering individual landcover types, any one or more of the four products may be suitable. There exists a real need for more in-situ measurements in this region — the lack of such measurements makes evaluation extremely difficult. It appears that areas north of 60° urgently require further investigation.     

Evaluation of the MODIS LAI product using independent lidar-derived LAI: A case study in mixed conifer forest

This study presents an alternative assessment of the MODIS LAI product for a 58,000 ha evergreen needleleaf forest located in the western Rocky Mountain range in northern Idaho by using lidar data to model (R² = 0.86, RMSE = 0.76) and map LAI at higher resolution across a large number of MODIS pixels in their entirety. Moderate resolution (30 m) lidar-based LAI estimates were aggregated to the resolution of the 1-km MODIS LAI product and compared to temporally-coincident MODIS retrievals. Differences in the MODIS and lidar-derived values of LAI were grouped and analyzed by several different factors, including MODIS retrieval algorithm, sun/sensor geometry, and sub-pixel heterogeneity in both vegetation and terrain characteristics. Of particular interest is the disparity in the results when MODIS LAI was analyzed according to algorithm retrieval class. We observed relatively good agreement between lidar-derived and MODIS LAI values for pixels retrieved with the main RT algorithm without saturation for LAI LAI ≤ 4. Moreover, for the entire range of LAI values, considerable overestimation of LAI (relative to lidar-derived LAI) occurred when either the main RT with saturation or back-up algorithm retrievals were used to populate the composite product regardless of sub-pixel vegetation structural complexity or sun/sensor geometry. These results are significant because algorithm retrievals based on the main radiative transfer algorithm with or without saturation are characterized as suitable for validation and subsequent ecosystem modeling, yet the magnitude of difference appears to be specific to retrieval quality class and vegetation structural characteristics.     

Assessment of the MODIS LAI product for Australian ecosystems

The leaf area index (LAI) product from the Moderate Resolution Imaging Spectroradiometer (MODIS) is important for monitoring and modelling global change and terrestrial dynamics at many scales. The algorithm relies on spectral reflectances and a six biome land cover classification. Evaluation of the specific behaviour and performance of the product for regions of the globe such as Australia are needed to assist with product refinement and validation. We made an assessment of Collection 4 of the MODIS LAI product using four approaches: (a) assessment against a continental scale Structural Classification of Australian Vegetation (SCAV); (b) assessment against a continental scale land use classification (LUC); (c) assessment against historical field-based measurement of LAI collected prior to the Terra Mission; and (d) direct comparison of MODIS LAI with coincident field measurements of LAI, mostly from hemispherical photography. The MODIS LAI product produced a wide variety of geographically and structurally specific temporal response profiles between different classes and even for sub-groups within classes of the SCAV. Historical and concurrent field measurements indicated that MODIS LAI was giving reasonable estimates for LAI for most cover types and land use types, but that major overestimation of LAI occurs in some eastern Australian open forests and woodlands. The six biome structural land cover classification showed some significant deviations in class allocation compared to the SCAV particularly where grasslands are allocated to shrubland, savanna woodlands are allocated to shrubland, savanna and broadleaf forest, and open forests are allocated to savanna and broadleaf forest. The land cover and LAI products could benefit from some additional examination of Australian data addressing the structural representation of Eucalypt canopies in the “space of canopy realisation” for savanna and broadleaf forest classes.     

Reprocessing the MODIS Leaf Area Index products for land surface and climate modelling

Land surface and climate modelling requires continuous and consistent Leaf Area Index (LAI). High spatiotemporal resolution and long-time record data are more in demand nowadays and will continue to be in the future. MODIS LAI products meet these requirements to some degree. However, due to the presence of cloud and seasonal snow cover, the instrument problems and the uncertainties of retrieval algorithm, the current MODIS LAI products are spatially and temporally discontinuous and inconsistent, which limits their application in land surface and climate modelling. To improve the MODIS LAI products on a global scale, we considered the characteristics of the MODIS LAI data and made the best use of quality control (QC) information, and developed an integrated two-step method to derive the improved MODIS LAI products effectively and efficiently on a global scale. First, we used the modified temporal spatial filter (mTSF) method taking advantage of background values and QC information at each pixel to do a simple data assimilation for relatively low quality data. Then we applied the post processing-TIMESAT (A software package to analyze time-series of satellite sensor data) Savitzky-Golay (SG) filter to get the final result. We implemented the method to 10 years of the MODIS Collection 5 LAI data. In comparison with the LAI reference maps and the MODIS LAI data, our results showed that the improved MODIS LAI data are closer to the LAI reference maps in magnitude and also more continuous and consistent in both time-series and spatial domains. In addition, simple statistics were used to evaluate the differences between the MODIS LAI and the improved MODIS LAI.     

融合遥感先验信息的叶面积指数反演

借助植被辐射传输模型,利用遥感观测数据估算LAI是一种较为可靠和稳健的反演方法。然而,地表的复杂性、遥感观测的有限性以及自相关性导致遥感数据包含的信息量不足,不能完全支持LAI等地表参数的估算,易造成“病态”反演。在遥感反演过程中引入先验知识能够有效地解决该问题。研究基于遥感数据提取LAI先验信息,并将其用于代价函数的构建,利用PROSAIL辐射传输模型和遗传算法,分别在500 m和250 m尺度反演LAI。将高空间分辨率LAI分别升尺度到500 m和250 m,验证对应尺度LAI结果,评价引入先验信息对于提高LAI反演精度的作用。研究表明,引入先验信息有助于提高不同分辨率下LAI反演精度,且先验信息的质量一定程度上也影响着LAI反演结果。与未加入先验信息的LAI反演结果相比,以MODIS LAI产品作为先验信息反演的500 m尺度LAI结果精度R²由0.55提高至0.65,RMSE由1.29下降至0.38。在250 m尺度,以500 m LAI反演结果作为先验信息反演的叶面积指数,其精度优于以MODIS LAI产品为先验知识的估算结果,验证精度R²增加了0.08,RMSE减少了0.18。研究使用的先验信息主要来自遥感数据本身,没有地面实测数据的参与,在此基础上发展的多分辨率LAI反演方法具有估算大区域尺度LAI的应用潜力。     

融合遥感先验信息的叶面积指数反演

借助植被辐射传输模型，利用遥感观测数据估算LAI是一种较为可靠和稳健的反演方法。然而，地表的复杂性、遥感观测的有限性以及自相关性导致遥感数据包含的信息量不足，不能完全支持LAI等地表参数的估算，易造成“病态”反演。在遥感反演过程中引入先验知识能够有效地解决该问题。研究基于遥感数据提取LAI先验信息，并将其用于代价函数的构建，利用PROSAIL辐射传输模型和遗传算法，分别在500 m和250 m尺度反演LAI。将高空间分辨率LAI分别升尺度到500 m和250 m，验证对应尺度LAI结果，评价引入先验信息对于提高LAI反演精度的作用。研究表明，引入先验信息有助于提高不同分辨率下LAI反演精度，且先验信息的质量一定程度上也影响着LAI反演结果。与未加入先验信息的LAI反演结果相比，以MODIS LAI产品作为先验信息反演的500 m尺度LAI结果精度R²由0.55提高至0.65，RMSE由1.29下降至0.38。在250 m尺度，以500 m LAI反演结果作为先验信息反演的叶面积指数，其精度优于以MODIS LAI产品为先验知识的估算结果，验证精度R²增加了0.08，RMSE减少了0.18。研究使用的先验信息主要来自遥感数据本身，没有地面实测数据的参与，在此基础上发展的多分辨率LAI反演方法具有估算大区域尺度LAI的应用潜力。     

Evaluation of the JRC-TIP 0.01° products over a mid-latitude deciduous forest site

The Joint Research Centre Two-stream Inversion Package (JRC-TIP) makes use of white sky albedo products—derived from MODIS and MISR observations in the visible and near-infrared domain—to deliver consistent sets of information about the terrestrial environments that gave rise to these data. The baseline version of the JRC-TIP operates at a spatial resolution of 0.01° and yields estimates of the Probability Distribution Functions (PDFs) of the effective canopy Leaf Area Index (LAI), the canopy background albedo, the vegetation scattering properties, as well as, the absorbed, reflected and transmitted fluxes of the vegetation canopy. In this contribution the evaluation efforts of the JRC-TIP products are extended to the deciduous forest site of Hainich (Germany) where multiannual datasets of in-situ estimates of canopy transmission—derived from LAI-2000 observations—are available. As a Fluxnet site, Hainich offers access to camera acquisitions from fixed locations in and above the canopy that are being used in phenological studies. These images qualitatively confirm the seasonal patterns of the effective LAI, canopy transmission and canopy absorption products (in the visible range of the solar spectrum) derived with the JRC-TIP. Making use of the LAI-2000 observations it is found that 3/4 of the JRC-TIP products lie within a ± 0.15 interval around the in-situ estimates of canopy transmission and absorption. The largest discrepancies occur at the end of the senescence phase when the scattering properties of the vegetation (evidenced by the pictures) and the images qualitatively confirm the seasonal patterns of the effective LAI, canopy transmission and canopy absorption products (in the visible range of the solar spectrum) derived with the JRC-TIP. Making use of the LAI-2000 observations it is found that 3/4 of the JRC-TIP products lie within a ± 0.15 interval around the in-situ estimates of canopy transmission and absorption. The largest discrepancies occur at the end of the senescence phase when the scattering properties of the vegetation (evidenced by the pictures) and the effective LAI (also derived from LAI-2000 measurements) are experiencing large simultaneous changes. It was also found that the seasonal pattern of vegetation scattering properties derived from MISR observations in the near-infrared varies together with the Excess Green index computed from the various channels of the camera data acquired at the top of the canopy.     

Monitoring boreal forest leaf area index across a Siberian burn chronosequence: a MODIS validation study

Landscapes containing differing amounts of ecological disturbance provide an excellent opportunity to validate and better understand the emerging Moderate Resolution Imaging Spectrometer (MODIS) vegetation products. Four sites, including 1‐year post‐fire coniferous, 13‐year post‐fire deciduous, 24‐year post‐fire deciduous, and >100 year old post‐fire coniferous forests, were selected to serve as a post‐fire chronosequence in the central Siberian region of Krasnoyarsk (57.3°N, 91.6°E) with which to study the MODIS leaf area index (LAI) and vegetation index (VI) products. The collection 4 MODIS LAI product correctly represented the summer site phenologies, but significantly underestimated the LAI value of the >100 year old coniferous forest during the November to April time period. Landsat 7‐derived enhanced vegetation index (EVI) performed better than normalized difference vegetation index (NDVI) to separate the deciduous and conifer forests, and both indices contained significant correlation with field‐derived LAI values at coniferous forest sites (r ² = 0.61 and r ² = 0.69, respectively). The reduced simple ratio (RSR) markedly improved LAI prediction from satellite measurements (r ² = 0.89) relative to NDVI and EVI. LAI estimates derived from ETM+ images were scaled up to evaluate the 1 km resolution MODIS LAI product; from this analysis MODIS LAI overestimated values in the low LAI deciduous forests (where LAI<5) and underestimated values in the high LAI conifer forests (where LAI>6). Our results indicate that further research on the MODIS LAI product is warranted to better understand and improve remote LAI quantification in disturbed forest landscapes over the course of the year.     

Variational retrieval of leaf area index from MODIS time series data: examples from the Heihe river basin,north-west China

Leaf area index (LAI) products retrieved from observations acquired on one occasion have obvious discontinuity in the time series owing to cloud coverage and other factors, and the accuracy may not meet the needs of many applications. Effectively utilizing data assimilation techniques to retrieve biophysical parameters from the time series of remote-sensing data has attracted special interest. The data assimilation technique is based on a reasonable consideration of dynamic change rules of biophysical parameters and time series observational quantities, thereby improving the quality of retrieved profiles. In this article, a variational assimilation procedure for retrieving LAI from the time series of remote-sensing data is developed. The procedure is based on the formulation of an objective function. A dynamic model is constructed based on the climatology from multi-year Moderate Resolution Imaging Spectroradiometer (MODIS) LAI data to evolve LAI in time, and a radiative transfer model is coupled with the dynamic model to simulate a time series of surface reflectances. A shuffled complex evolution method (developed at the University of Arizona; SCE-UA) optimization algorithm is then used to minimize the objective function and estimate the dynamic model states and the parameters of the coupled model from the MODIS reflectance data with a higher quality in a given time window. The variational assimilation method is applied to the MODIS surface reflectance data for the whole of 2008 at the Heihe river basin to produce regional LAI mapping results. The ground LAI data measured in situ are used to develop algorithms to estimate LAI from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) surface reflectance, and ASTER LAI maps are produced for each ASTER scene using the algorithms developed. Then the ASTER LAI maps are aggregated to compare with the new LAI products. It is found that the variational assimilation method is able to produce temporal continuous LAI products and that accuracy has been improved over the MODIS LAI standard product.     

A new method for cross-calibration of two satellite sensors

Given that many operational satellite sensors are not calibrated, while a handful of research sensors are, cross-calibration between the two types of sensor is a cost-effective means of calibration. A new method of sensor cross-calibration is demonstrated here using the Chinese Multi-channel Visible Infrared Scanning radiometer (MVIRS) and the US Moderate Resolution Imaging Spectrometer (MODIS). MVIRS has six channels, equivalent to the current National Oceanic and Atmospheric Administration's (NOAA) Advanced Very High Resolution Radiometer (AVHRR) and four additional ones for remote sensing of ocean colour and moisture. The MVIRS on-board China's polar-orbiting meteorological satellite (FY-1D) was launched on 15 May 2002 with an earlier overpass time than Terra. The sensor has no on-board calibration assembly. This study attempts to calibrate MVIRS against the well-calibrated MODIS, by taking a series of measures to account for their differences. Clear-sky measurements made from the two sensors in July-October 2002 were first collocated. Using the 6S radiative transfer model, MODIS reflectances measured at the top-of-the atmosphere were converted into surface reflectances. They were corrected to the viewing geometry of the MVIRS using the bidirectional reflectance distribution function (BRDF) measured on the ground. The spectral response functions of the two sensors were employed to account for spectral discrepancies. After these corrections, very close linear correlations were found between radiances estimated from the MODIS and the digital readings from the MVIRS, from which the calibration gains were derived. The gains differ considerably from the pre-launch values and are subject to degradation over time. The calibration accuracy is estimated to be less than 5%, which is compatible to that obtained by the more expensive vicarious calibration approach.     

Impact of a satellite-derived leaf area index monthly climatology in a global numerical weather prediction model

The leaf area index (LAI), defined as the one-sided green leaf area per unit ground area, is used in many numerical weather prediction (NWP) models as an indicator of the vegetation development state, which is of paramount importance to characterize land evaporation, photosynthesis, and carbon-uptake processes. LAI is often simply represented by lookup tables, dependent on the vegetation type and seasons. However, global LAI datasets derived from remote sensing observations have more recently become available. These products are based on sensors such as the Advanced Very High Resolution Radiometer (AVHRR) or the Moderate Resolution Imaging Spectroradiometer (MODIS), onboard polar orbiting satellites that can cover the entire globe within typically 3 days and with a spatial resolution of the order of 1 km.

We examine the meteorological impact of satellite-derived LAI products on near-surface air temperature and humidity, which comes both from the stomatal transpiration of leaves and from the intercepted water on the surface of leaves, re-evaporating into the atmosphere.

Two distinct monthly LAI climatology datasets derived respectively from AVHRR and MODIS sensors are tested. A set of forecasts and data assimilation experiments with the integrated forecasting system of the European Centre for Medium-range Weather Forecasts is performed with the monthly LAI climatology datasets as opposed to a vegetation-dependent constant LAI. The monthly LAI is shown to improve the forecasts of near-surface (screen-level) air temperature and relative humidity through its effect on evapotranspiration, with the largest impact obtained over needleleaf forests, crops, and grassland. At longer time-scales, the introduction of the monthly LAI is shown to have a positive impact on the model climate particularly during the boreal spring, where the LAI climatology has a large seasonal cycle.     

Evaluation of Collections 4 and 5 of the MODIS Gross Primary Productivity product and algorithm improvement at a tropical savanna site in northern Australia

In this study, we assessed the accuracy of the MODIS (Moderate Resolution Imaging Spectroradiometer) GPP (gross primary productivity) Collections 4.5, 4.8 and 5 along with Leaf Area Index (LAI), fraction of absorbed Photosynthetically Active Radiation (fPAR), light use efficiency (LUE) and meteorological variables that are used to estimate GPP for a northern Australian savanna site. Results of this study indicated that the MODIS products captured the seasonal variation in GPP, LAI and fPAR well. Using the index of agreement (IOA), it was found that Collections 4.5 and 4.8 (IOA 0.89 respectively) agreed reasonably well with flux tower measurements between 2001 and 2006. It was also found that MODIS Collection 4.5 predicted the dry season GPP well (Relative Predictive Error (RPE) 4.17%, IOA 0.72 and Root Mean Square Error (RMSE) of 1.05 g C m^− 2 day^− 1), whilst Collection 4.8 performed better in capturing wet season dynamics (RPE 1.11%, IOA 0.80 and RMSE of 0.91 g C m^− 2 day^− 1). Although the wet season magnitude of GPP was predicted well by Collection 4.8, an examination of the inputs to the GPP algorithm revealed that MODIS fPAR was too high, but this was compensated by PAR and LUE that was too low. Although LAI and fPAR estimated by Collection 5 were more accurate, GPP for this Collection resulted in a much lower value (RPE 25%) due to errors in other factors. Recalculation of MODIS GPP using site specific input parameters indicated that MODIS fPAR was the main reason for the differences between MODIS and tower derived GPP followed by LUE and meteorological inputs. GPP calculated using all site specific values agreed very well with tower data on an annual basis (IOA 0.94, RPE 6.06% and RMSE 0.83 g C m^− 2 day^− 1) but the early initiation of the growing season calculated by the MODIS algorithm was improved when the vapor pressure deficit (VPD) function was replaced with a soil water deficit function. The results of this study however, reinforce previous findings in water limited regions, like Australia, and incorporation of soil moisture in a LUE model is needed to accurately estimate the productivity.     

Validation of MERIS LAI and FAPAR products for winter wheat-sown test fields in North-East Bulgaria

Progress in deriving land surface biophysical parameters in a spatially explicit manner using remotely sensed data has greatly enhanced our ability to model ecosystem processes and monitor crop development. A multitude of satellite sensors and algorithms have been used to generate ready-to-use maps of various biophysical parameters. Validation of these products for different vegetation types is needed to assess their reliability and consistency. While most of the current satellite biophysical products have spatial resolution of one kilometre, a recent effort utilizing data from the Medium Resolution Imaging Spectrometer (MERIS) provided leaf area index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), and other canopy parameters in a resolution as fine as 300 m over the European continent. This resolution would be more appropriate for application at the regional scale, particularly for crop monitoring. This higher-resolution MERIS product has been evaluated in a limited number of studies to date. This article aims to validate LAI and FAPAR from the MERIS 10-day composite BioPar BP-10 product over winter wheat fields in northeast Bulgaria. The ground measurements of LAI and FAPAR were up-scaled and 30 m resolution reference raster layers were created using empirical relationships with Landsat TM (RMSE = 0.06 and RMSE = 0.22 for FAPAR and LAI, respectively). MERIS FAPAR and LAI were found to have significant correlation with FAPAR and LAI from the reference raster layers (R² = 0.84 and R² = 0.78, respectively). When MERIS Green LAI was calculated (incorporating the fraction of vegetation and brown vegetation cover from the BioPar BP-10 product), better correspondence with LAI values from the reference raster layer was achieved, with RMSE and bias reduced by 30–35%. The results from this study confirm the findings of previous validations showing that MERIS Green LAI tends to overestimate LAI values lower than 1. As a conclusion of the study, the BioPar BP-10 product was found to provide reliable estimates of FAPAR and acceptably accurate estimates of LAI for winter wheat crops in North-East Bulgaria.     

Comparisons of land cover and LAI estimates derived from ETM+ and MODIS for four sites in North America: a quality assessment of 2000/2001 provisional MODIS products

The MODIS land science team produces a number of standard products, including land cover and leaf area index (LAI). Critical to the success of MODIS and other sensor products is an independent evaluation of product quality. In that context, we describe a study using field data and Landsat ETM+ to map land cover and LAI at four 49-km² sites in North America containing agricultural cropland (AGRO), prairie grassland (KONZ), boreal needleleaf forest, and temperate mixed forest. The purpose was to: (1) develop accurate maps of land cover, based on the MODIS IGBP (International Geosphere-Biosphere Programme) land cover classification scheme; (2) derive continuous surfaces of LAI that capture the mean and variability of the LAI field measurements; and (3) conduct initial MODIS validation exercises to assess the quality of early (i.e., provisional) MODIS products. ETM+ land cover maps varied in overall accuracy from 81% to 95%. The boreal forest was the most spatially complex, had the greatest number of classes, and the lowest accuracy. The intensive agricultural cropland had the simplest spatial structure, the least number of classes, and the highest overall accuracy. At each site, mapped LAI patterns generally followed patterns of land cover across the site. Predicted versus observed LAI indicated a high degree of correspondence between field-based measures and ETM+ predictions of LAI. Direct comparisons of ETM+ land cover maps with Collection 3 MODIS cover maps revealed several important distinctions and similarities. One obvious difference was associated with image/map resolution. ETM+ captured much of the spatial complexity of land cover at the sites. In contrast, the relatively coarse resolution of MODIS did not allow for that level of spatial detail. Over the extent of all sites, the greatest difference was an overprediction by MODIS of evergreen needleleaf forest cover at the boreal forest site, which consisted largely of open shrubland, woody savanna, and savanna. At the agricultural, temperate mixed forest, and prairie grassland sites, ETM+ and MODIS cover estimates were similar. Collection 3 MODIS-based LAI estimates were considerably higher (up to 4 m² m⁻²) than those based on ETM+ LAI at each site. There are numerous probable reasons for this, the most important being the algorithms' sensitivity to MODIS reflectance calibration, its use of a prelaunch AVHRR-based land cover map, and its apparent reliance on mainly red and near-IR reflectance. Samples of Collection 4 LAI products were examined and found to consist of significantly improved LAI predictions for KONZ, and to some extent for AGRO, but not for the other two sites. In this study, we demonstrate that MODIS reflectance data are highly correlated with LAI across three study sites, with relationships increasing in strength from 500 to 1000 m spatial resolution, when shortwave-infrared bands are included.