Retrieving spectral and biophysical parameters of land vegetation by the Earth Observation Land Data Assimilation System

In this thesis, a new methodology for retrieval of land spectral and biophysical parameters from optical remote sensing data has been designed and used. The result of the work was a physically based methodology for Fraction of Photosynthetically Active Radiation (FAPAR) and Leaf Area Index (LAI) retrievals, simulation of hyper-spectral information and estimation of associated uncertainties. The presented methodology is based on the generic Earth Observation-Land Data Assimilation System (EO-LDAS). In the course of the work it was found that EO-LDAS can be used for daily estimation of FAPAR and associated uncertainties without any in-situ information and when the number of available observations is low. The results were in line with the field measurements with r2 varying from 0.84 to 0.92 and Root Mean Square Error (RMSE) from 0.11 to 0.16. This was the highest rate among compared products (Two Stream Inversion Package - JRC-TIP, Medium Resolution Imaging Spectrometer - MERIS FR and Moderate Resolution Imaging Spectro-radiometer - MODIS MCD15). It was shown, that using MISR information, EO-LDAS temporal regularization and generic dynamic prior, it was possible to stabilize results of the retrieval and to obtain better results than MERIS FAPAR or JRC-TIP MISR. In addition, inclusion of generic static and dynamic prior information, decreases posterior uncertainties and can increase accuracies compared to in-situ data. The results showed that proper estimation of LAI and soil parameters were sufficient to simulate a hyper-spectral signal between 400 and 1000 nm with acceptable precision: best RMSE is equal to 0.03 for real data and less than 0.008 for synthetic data. This implies that in case of the given experimental set-up, LAI and soil parameters are the major mechanisms controlling spectral variations in the visible and near infrared regions

Estimation of FAPAR over Croplands Using MISR Data and the Earth Observation Land Data Assimilation System (EO-LDAS)

The Fraction of Absorbed Photosynthetically-Active Radiation (FAPAR) is an important parameter in climate and carbon cycle studies. In this paper, we use the Earth Observation Land Data Assimilation System (EO-LDAS) framework to retrieve FAPAR from observations of directional surface reflectance measurements from the Multi-angle Imaging SpectroRadiometer(MISR) instrument. The procedure works by interpreting the reflectance data via the semi-discrete Radiative Transfer (RT) model, supported by a prior parameter distribution and a dynamic regularisation model and resulting in an inference of land surface parameters, such as effective Leaf Area Index (LAI), leaf chlorophyll concentration and fraction of senescent leaves, with full uncertainty quantification. The method is demonstrated over three agricultural FLUXNET sites, and the EO-LDAS results are compared with eight years of in situ measurements of FAPAR and LAI, resulting in a total of 24 site years. We additionally compare three other widely-used EO FAPAR products, namely the MEdium Resolution Imaging Spectrometer (MERIS) Full Resolution, the MISR High Resolution (HR) Joint Research Centre Two-stream Inversion Package (JRC-TIP) and MODIS MCD15 FAPAR products. The EO-LDAS MISR FAPAR retrievals show a high correlation with the ground measurements ( r 2 > 0.8), as well as the lowest average R M S E (0.14), in line with the MODIS product. As the EO-LDAS solution is effectively interpolated, if only measurements that are coincident with MISR observations are considered, the correlation increases ( r 2 > 0.85); the R M S E is lower by 4–5%; and the bias is 2% and 7%. The EO-LDAS MISR LAI estimates show a strong correlation with ground-based LAI (average r 2 = 0.76), but an underestimate of LAI for optically-thick canopies due to saturation (average R M S E = 2.23). These results suggest that the EO-LDAS approach is successful in retrieving both FAPAR and other land surface parameters. A large part of this success is based on the use of a dynamic regularisation model that counteracts the poor temporal sampling from the MISR instrument

BACI State Surafce Vector Technical Note

Changes of Earth surface can have different nature and can influence different domains of electromagnetic spectrum. The main requirement for BACI project WP2 was to provide frequent time series of remote sensing information in different domains of electromagnetic spectrum covering largest possible regions. It is important to have data which allows detect change as precise as possible without attribution which is not a priority of WP2. WP2 combines layers of optical, thermal infrared and microwave data providing comprehensive set of information. This technical note cover the technical basics for the State Surface Vector dataset produced on the BACI project to meet these requirements

BACI State Surafce Vector Technical Note

Changes of Earth surface can have different nature and can influence different domains of electromagnetic spectrum. The main requirement for BACI project WP2 was to provide frequent time series of remote sensing information in different domains of electromagnetic spectrum covering largest possible regions. It is important to have data which allows detect change as precise as possible without attribution which is not a priority of WP2. WP2 combines layers of optical, thermal infrared and microwave data providing comprehensive set of information. This technical note cover the technical basics for the State Surface Vector dataset produced on the BACI project to meet these requirements

Estimation of FAPAR over croplands using MISR data and the Earth Observation Land Data Assimilation System (EO-LDAS)

The fraction of photosynthetically active radiation (FAPAR) is an important parameter in climate and carbon cycle studies. In this paper, we use the Earth Observation Land Data Assimilation System (EO-LDAS) framework to retrieve FAPAR from observations of directional surface reflectance measurements from the Multi-angle Imaging SpectroRadiometer (MISR) instrument. The procedure works by interpreting the reflectance data via the semi-discrete radiative transfer (RT) model, supported by a prior parameter distribution and a dynamic regularisation model, and resulting in an inference of land surface parameters, such as effective leaf area index (LAI), leaf chlorophyll concentration and fraction of senescent leaves, with full uncertainty quantification. The method is demonstrated over three agricultural FLUXNET sites, and the EO-LDAS results are compared with 8 years of in situ measurements of FAPAR and LAI, resulting in a total of 24 site years. We additionally compare three other widely used EO FAPAR products, namely the MEdium Resolution Imaging Spectrometer (MERIS) Full Resolution, theMISR High Resolution (HR) Joint Research Centre Two-stream Inversion Package (JRC-TIP) and MODIS MCD15 FAPAR products. The EO-LDAS MISR FAPAR retrievals show a high correlation with the ground measurements (r2>0.8), as well as the lowest average RMSE (0.14), in line with the MODIS product. As the EO-LDAS solution is effectively interpolated, if only measurements that are coincident with MISR observations are considered, the correlation increases (r2>0.85), the RMSE is lower by 4-5%, and the bias is 2 and 7%. The EO-LDAS MISR LAI estimates show a strong correlation with ground based LAI (average r2=0.76), but an underestimate of LAI for optically thick canopies due to saturation (average RMSE=2.23). These results suggest that the EO-LDAS approach is successful in retrieving both FAPAR and other land surface parameters. A large part of this success is based on the use of a dynamic regularisation model that counteracts the poor temporal sampling from the MISR instrument.JRC.D.6-Knowledge for Sustainable Development and Food Securit

Simulating arbitrary hyperspectral bandsets from multispectral observations via a generic Earth Observation-Land Data Assimilation System (EO-LDAS)

This paper presents results of using multi-sensor and multi-angular constraints in the generic Earth Observation-Land Data Assimilation System (EO-LDAS) for reproducing arbitrary bandsets of hyperspectral reflectance at the top-of-canopy (TOC) level by merging observations from multispectral sensors with different spectral characteristics. This is demonstrated by combining Multi-angle Imaging Spectroradiometer (MISR) and Landsat Enhanced Thematic Mapper Plus (ETM+) data to simulate the Compact High Resolution Imaging Spectrometer CHRIS/PROBA hyperspectral signal over an agricultural test site, in Barrax, Spain. However, the method can be more generally applied to any combination of spectral data, providing a tool for merging EO data to any arbitrary hyperspectral bandset. Comparisons are presented using both synthetic and observed MISR and Landsat data, and retrieving surface biophysical properties. We find that when using simulated MISR and Landsat data, the CHRIS/PROBA hyperspectral signal is reproduced with RMSE 0.0001–0.04. LAI is retrieved with r2 from 0.97 to 0.99 and RMSE of from 0.21 to 0.38. The results based on observed MISR and Landsat data have lower performances, with RMSE for the reproduced CHRIS/PROBA hyperspectral signal varying from 0.007 to 0.2. LAI is retrievedwith r2 from 0.7 to 0.9 and RMSE from 0.7 to 1.4. We found that for the data considered here the main spectral variations in the visible and near infrared regions can be described by a limited number of parameters (3–4) that can be estimated from multispectral information. Results show that the method can be used to simulate arbitrary bandsets, which will be of importance to any application which requires combining new and existing streams of new EO data in the optical domain, particularly intercalibration of EO satellites in order to get continuous time series of surface reflectance, across programmes and sensors of different designs.JRC.D.6-Knowledge for Sustainable Development and Food Securit