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

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 R 2 = 0.78 and RMSE = 0.39 for CCC and R 2 = 0.76 and RMSE = 0.64 for LAI. The relationship between MERIS L2 MTCI and CCC [g·machieved 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

Non-destructive estimation of foliar chlorophyll and carotenoid contents: Focus on informative spectral bands

“…This index has been applied successfully for many applications. As heritage of MERIS, this MTCI is the basis of one of the Level 2B main terrestrial products of Sentinel-2 and Sentinel-3, called the OLCI Terrestrial Chlorophyll Index (OTCI) (Vuolo et al, 2012). Therefore, this index will also get explicit attention when three techniques presented in this paper would be tested at canopy level.…”

Assimilation of Sentinel-2 Leaf Area Index Data into a Physically-Based Crop Growth Model for Yield Estimation

“…The LAI sampling was always carried out early in the morning or later in the afternoon under diffuse light conditions. Previous studies showed that the best results for LAI were measured in diffuse light conditions to avoid direct sunlight and, therefore, minimizing the underestimation of LAI [38,39]. diffuse light conditions to avoid direct sunlight and, therefore, minimizing the underestimation of LAI [38,39].…”

“…Previous studies showed that the best results for LAI were measured in diffuse light conditions to avoid direct sunlight and, therefore, minimizing the underestimation of LAI [38,39]. diffuse light conditions to avoid direct sunlight and, therefore, minimizing the underestimation of LAI [38,39]. Step 1 is the selection of the field; in Step 2 the GPS point, representative of the center of the ESU, is taken; and in Step 3 the measurements are performed obtaining an LAI value as a mean value of the 24 measurements below the canopy (six for each square).…”

Remote sensing of canopy nitrogen at regional scale in Mediterranean forests using the spaceborne MERIS Terrestrial Chlorophyll Index

“…Imaging spectrometry has proven efficient in improving N sensing capabilities at the local scale. Imaging spectrometry images are acquired from either airborne or spaceborne sensors and are analysed with different methods, including partial least squares regression (PLS), continuum removal, spectral unmixing or vegetation indices (Smith et al, 2003;Ollinger et al, 2008;Huber et al, 2008;Martin et al, 2008;Schlerf et al, 2010;Wang et al, 2016). Among other techniques, ratios or normalised differences of reflectance bands in the red and NIR regions of the spectrum, the so-called vegetation indices (VIs) (Glenn et al, 2008), are one of the most straightforward methods for canopy N detection.…”