Methodologies and Uncertainties in the Use of the Terrestrial Chlorophyll Index for the Sentinel-3 Mission

Francesco Vuolo,Jadunandan Dash,Ewa Kwiatkowska,Dulce Lajas,Paul J. Curran

doi:10.3390/rs4051112

Abstract

A methodology is described for the validation of Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) data over heterogeneous land surfaces in an agricultural region in Southern Italy. The approach involves the use inverse canopy reflectance modeling techniques to derive maps of canopy chlorophyll content (CCC) and leaf area index (LAI) at fine spatial resolution. Indirect field measurements are used for validation of the fine spatial resolution data. Subsequently, these maps are aggregated based on a regular grid at 1 km spatial resolution to validate MERIS Level 2 MTCI (300 m). RapidEye satellite sensor data with a pixel size of 6.5 m are used for this purpose. Based on a set of independent ground measurements, fine spatial resolution maps achieved an R2 = 0.78 and RMSE = 0.39 for CCC and R2 = 0.76 and RMSE = 0.64 for LAI. The relationship between MERIS L2 MTCI and CCC [g∙m−2] achieved a coefficient of determination of 0.74 and it resulted to be extremely statistically significant (p-value < 0.001). Additionally, a relative validation of two other satellite products at medium resolution spatial scale, namely MERIS leaf area index (LAI) and Moderate Resolution Imaging Spectrometer (MODIS) LAI was performed by comparison with the fine spatial resolution LAI map. Results indicated a better accuracy in LAI estimation of MERIS (RMSE = 0.33) compared to MODIS (RMSE = 0.81) data.

Highlights

Medium spatial resolution Earth observation (EO) data are used routinely to monitor land surface characteristics from regional to global scales
The accuracy with which leaf area index (LAI) and canopy chlorophyll content (CCC) can be mapped was quantified using the coefficient of determination (R2), the root-mean-square-error (RMSE) and the relative RMSE (RMSE/mean value of field estimates) between satellite sensor and ground based estimations
Estimations of LAI using the PROSPECT+SAILH model (PROSAIL) LUT model inversion approach resulted in an accuracy of R2 = 0.76

Summary

Introduction

Medium spatial resolution Earth observation (EO) data are used routinely to monitor land surface characteristics from regional to global scales. Spectrometer (MODIS), CNES’s Vegetation and ESA’s Medium Resolution Imaging Spectrometer (MERIS) have been used to obtain systematic estimates of terrestrial biophysical variables such as Fraction of Absorbed Photosynthetically Active Radiation (fAPAR); leaf area index (LAI) [1,2]; canopy chlorophyll content (CCC) [3,4] and vegetation phenology [5]. Estimates of these variables play an important role in ecosystem modeling and broader environmental studies and contribute to our understanding of biogeochemical fluxes and global climate. It has been used successfully for the detection of vegetation stress, photosynthetic capacity and productivity [7,8]

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Results

Conclusion