Marie Weiss

Retrieval of canopy biophysical variables from bidirectional reflectance Using prior information to solve the ill-posed inverse problem

Estimation of canopy biophysical variables from remote sensing data was investigated using radiative transfer model inversion. Measurement and model uncertainties make the inverse problem ill posed, inducing difficulties and inaccuracies in the search for the solution. This study focuses on the use of prior information to reduce the uncertainties associated to the estimation of canopy biophysical variables in the radiative transfer model inversion process. For this purpose, lookup table (LUT), quasi-Newton algorithm (QNT), and neural network (NNT) inversion techniques were adapted to account for prior information. Results were evaluated over simulated reflectance data sets that allow a detailed analysis of the effect of measurement and model uncertainties. Results demonstrate that the use of prior information significantly improves canopy biophysical variables estimation. LUT and QNT are sensitive to model uncertainties. Conversely, NNT techniques are generally less accurate. However, in our conditions, its accuracy is little dependent significantly on modeling or measurement error. We also observed that bias in the reflectance measurements due to miscalibration did not impact very much the accuracy of biophysical estimation.

MODIS leaf area index products: from validation to algorithm improvement

Global products of vegetation green Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) are being operationally produced from Terra and Aqua Moderate Resolution Imaging Spectroradiometers (MODIS) at 1-km resolution and eight-day frequency. This paper summarizes the experience of several collaborating investigators on validation of MODIS LAI products and demonstrates the close connection between product validation and algorithm refinement activities. The validation of moderate resolution LAI products includes three steps: 1) field sampling representative of LAI spatial distribution and dynamic range within each major land cover type at the validation site; 2) development of a transfer function between field LAI measurements and high resolution satellite data to generate a reference LAI map over an extended area; and 3) comparison of MODIS LAI with aggregated reference LAI map at patch (multipixel) scale in view of geo-location and pixel shift uncertainties. The MODIS LAI validation experiences, summarized here, suggest three key factors that influence the accuracy of LAI retrievals: 1) uncertainties in input land cover data, 2) uncertainties in input surface reflectances, and 3) uncertainties from the model used to build the look-up tables accompanying the algorithm. This strategy of validation efforts guiding algorithm refinements has led to progressively more accurate LAI products from the MODIS sensors aboard NASAs Terra and Aqua platforms

Evaluation of canopy biophysical variable retrieval performances from the accumulation of large swath satellite data

Abstract The objective of this study was to compare the retrieval performances of several biophysical variables from the accumulation of large swath satellite data, the VEGETATION/SPOT4 sensor being taken as an example. This included leaf area index (LAI), fraction of photosynthetically active radiation ( fAPAR ) and chlorophyll content integrated over the canopy ( C ab ·LAI ), gap fraction in any direction [ P 0 (θ)], or in particular directions (nadir [ P 0 (0)], sun direction [ P 0 (θ s )], or 58° [ P 0 (58°)] for which the gap fraction is theoretically independent of the LAI ). A database of top of canopy BRDF (bidirectional reflectance distribution function) of homogeneous canopies was built using simulations by the SAIL , PROSPECT , and SOILSPECT radiative transfer models for a large range of input variables ( LAI , mean leaf inclination angle, hot spot parameter, leaves and soil optical properties, date and latitude of observations) considering the accumulation of observations during an orbit cycle of 26 days. Walthalls BRDF model was used to estimate nadir (ρ 0 ) and hemispherical reflectance (ρ h ). Results showed that ρ 0 and ρ h were estimated with a good accuracy (RMSE=0.02) even when few observations within a sequence were available due to cloud masking. The ρ 0 and ρ h estimates in the blue (445 nm), the red (645 nm), near-infrared (835 nm), and middle infrared (1665 nm) were then used as inputs to neural networks calibrated for estimation of the canopy biophysical variables using part of the data base. Performances evaluated over the rest of the database showed that variables such as nadir gap fraction (≅ P 0 (58°)≅ P 0 (θ s )≅ fAPAR ) were accurately estimated by neural networks (relative RMSE LAI ( ≅LAI·Cab ) was less satisfactory since the level of reflectance saturates for high values of LAI (relative RMSE

Evaluation of the representativeness of networks of sites for the global validation and intercomparison of land biophysical products: proposition of the CEOS-BELMANIP

This study investigates the representativeness of land cover and leaf area index (LAI) sampled by a global network of sites to be used for the evaluation of land biophysical products, such as LAI or fAPAR, derived from current satellite systems. The networks of sites considered include 100 sites where ground measurements of LAI or fAPAR have been performed for the validation of medium resolution satellite land biophysical products, 188 FLUXNET sites and 52 AERONET sites. All the sites retained had less than 25% of water bodies within a 8times8 km 2 window, and were separated by more than 20 km. The ECOCLIMAP global classification was used to quantify the representativeness of the networks. It allowed describing the Earths surface with seven main types and proposed a climatology for monthly LAI values at a spatial resolution around 1 km. The site distribution indicates a large over representation of the northern midlatitudes relative to other regions, and an under-representation of bare surfaces, grass, and evergreen broadleaf forests. These three networks represent all together 295 sites after elimination of sites that were too close. They were thus completed by 76 additional sites to improve the representativeness in latitude, longitude, and surface type. This constitutes the BELMANIP network proposed as a benchmark for intercomparison of land biophysical products. Suitable approaches to conducting intercomparison at the sites are recommended

Coupling canopy functioning and radiative transfer models for remote sensing data assimilation

Abstract Crop functioning models (CFM) are used in many agricultural and environmental applications. Remote sensing data assimilation appears as a good tool to provide more information about canopy state variables in time and space. It permits a reduction in the uncertainties in crop functioning model predictions. This study presents the first step of the assimilation of optical remote sensing data into a crop functioning model. It consists in defining a coupling strategy between well known and validated crop functioning and radiative transfer models (RTM), applied to wheat crops. The radiative transfer model is first adapted to consistently describe wheat, considering of four layers in the canopy that contain different vegetation organs (soil, yellow leaves and senescent stems, green leaves and stems, green and senescent ears). The coupling is then performed through several state variables: leaf area index, leaf chlorophyll content, organ dry matter and relative water content. The relationships between the CFM outputs (agronomic variables) and RTM inputs (biophysical variables) are defined using experimental data sets corresponding to wheat crops under different climatic and stress conditions. The coupling scheme is then tested on the data set provided by the Alpilles–ReSeDA campaign. Results show a good fitting between the simulated reflectance data at top of canopy and the measured ones provided by SPOT images corrected from atmospheric and geometric effects, with a root mean square error lower than 0.05 for all the wavebands.

The CACAO Method for Smoothing, Gap Filling, and Characterizing Seasonal Anomalies in Satellite Time Series

Consistent, continuous, and long time series of global biophysical variables derived from satellite data are required for global change research. A novel climatology fitting approach called CACAO (Consistent Adjustment of the Climatology to Actual Observations) is proposed to reduce noise and fill gaps in time series by scaling and shifting the seasonal climatological patterns to the actual observations. The shift and scale CACAO parameters adjusted for each season allow quantifying shifts in the timing of seasonal phenology and inter-annual variations in magnitude as compared to the average climatology. CACAO was assessed first over simulated daily Leaf Area Index (LAI) time series with varying fractions of missing data and noise. Then, performances were analyzed over actual satellite LAI products derived from AVHRR Long-Term Data Record for the 1981-2000 period over the BELMANIP2 globally representative sample of sites. Comparison with two widely used temporal filtering methods-the asymmetric Gaussian (AG) model and the Savitzky-Golay (SG) filter as implemented in TIMESAT-revealed that CACAO achieved better performances for smoothing AVHRR time series characterized by high level of noise and frequent missing observations. The resulting smoothed time series captures well the vegetation dynamics and shows no gaps as compared to the 50-60% of still missing data after AG or SG reconstructions. Results of simulation experiments as well as confrontation with actual AVHRR time series indicate that the proposed CACAO method is more robust to noise and missing data than AG and SG methods for phenology extraction.

On Line Validation Exercise (OLIVE): A Web Based Service for the Validation of Medium Resolution Land Products. Application to FAPAR Products

The OLIVE (On Line Interactive Validation Exercise) platform is dedicated to the validation of global biophysical products such as LAI (Leaf Area Index) and FAPAR (Fraction of Absorbed Photosynthetically Active Radiation). It was developed under the framework of the CEOS (Committee on Earth Observation Satellites) Land Product Validation (LPV) sub-group. OLIVE has three main objectives: (i) to provide a consistent and centralized information on the definition of the biophysical variables, as well as a description of the main available products and their performances (ii) to provide transparency and traceability by an online validation procedure compliant with the CEOS LPV and QA4EO (Quality Assurance for Earth Observation) recommendations (iii) and finally, to provide a tool to benchmark new products, update product validation results and host new ground measurement sites for accuracy assessment. The functionalities and algorithms of OLIVE are described to provide full transparency of its procedures to the community. The validation process and typical results are illustrated for three FAPAR products: GEOV1 (VEGETATION sensor), MGVIo (MERIS sensor) and MODIS collection 5 FPAR. OLIVE is available on the European Space Agency CAL/VAL portal), including full documentation, validation exercise results, and product extracts.

Hemispherical reflectance and albedo estimates from the accumulation of across‐track sun‐synchronous satellite data

The estimation of the hemispherical reflectance and the instantaneous albedo of canopies from top of canopy satellite reflectance data was investigated. The study was designed to approximate the specifications of generic sensors aboard satellites like NOAA, VEGETATION, MERIS, MISR, MODIS, and PRISM. These sensors acquire reflectance data in two to six wave bands distributed along the visible, near-infrared, and middle infrared domains. Five great biomes (grassland, sparse vegetation, tropical forest, boreal forest, and bare soil) were approximated, simulating the corresponding top of canopy reflectances as observed from the satellites using well-known leaf, soil, and canopy radiative transfer models, including the effect of cloud cover that limits the actual data acquisition scheme. Albedo was accurately derived from the hemispherical reflectance observed in only a few wave bands. When using six wave bands, albedo was estimated within 1% relative accuracy. The MRPV bidirectional reflectance distribution function (BRDF) model was tested to derive the hemispherical reflectance from the top of canopy bidirectional data as sampled by the generic sensors during a 32 day orbit cycle. Results showed that this is the main source of error, with a relative accuracy around 6%. This showed the importance of the directional sampling scheme and possible improvements that may be made to the model and the way it is fitted to the observed data. The algorithm developed produced a relative accuracy less than 7% for the albedo estimation, when using the six wave bands and a ±45° across-track directional scanning capacity. The results were discussed with particular emphasis on the optimization of sensors and algorithms dedicated to albedo estimation and to the use of hemispherical reflectance as a potential normalized geophysical product for monitoring vegetation.

Comparison of three radiative transfer model inversion techniques to estimate canopy biophysical variables from remote sensing data

The objective of this study was to compare three model inversion techniques (neural networks (NNT), look up tables (LUT), iterative optimization (OPT)) for the estimation of four main biophysical variables: LAI, C/sub ab/, f/sub cover/ and f/sub PAR/. The canopy radiative transfer model used is SAIL coupled with PROSPECT leaf model. Results show that OPT performs better when applied to datasets consistent with the radiative transfer model assumptions used in the inversion approach. Conversely, NNT shows significant improvement when applied to different datasets. The authors observed also large differences in the retrieval performances between the biophysical variables investigated, f/sub PAR/ and f/sub cover/ being better estimated than LAI and C/sub ab/.

Mapping short-wave albedo of agricultural surfaces using airborne PolDER data

This study focuses on albedo mapping over agricultural surfaces using multidirectional and multispectral remote sensing data. These data were acquired using the airborne PolDER sensor during the Remote Sensing Data Assimilation (ReSeDA) experiment. The data set allowed to perform a validation over the growth cycles of several crops. Problems induced by mixed pixels were reduced since the ground spatial resolution was 20 m. First, linear kernel-driven bidirectional reflectance distribution function (BRDF) models were used to retrieve the BRDF and then to compute the hemispherical reflectance in the PolDER channels. We tested the four most classical models: Li-Ross, MRPV, Roujean, and Walthall. They presented similar interpolation performances, whereas the quality of the hemispherical reflectance estimates was also driven by the extrapolation performances. Second, the albedo was computed as a linear combination of the waveband hemispherical reflectances. We used several sets of coefficients proposed in the literature for different sensors. The validation of the albedo maps against field measurements showed that it was possible to achieve a relative accuracy about 9% when using an appropriate coefficient set.