Retrieving and Validating Leaf and Canopy Chlorophyll Content at Moderate Resolution: A Multiscale Analysis with the Sentinel-3 OLCI Sensor

Abstract

ESA’s Eighth Earth Explorer mission “FLuorescence EXplorer” (FLEX) will be dedicated to the global monitoring of the chlorophyll fluorescence emitted by vegetation. In order to properly interpret the measured fluorescence signal, essential vegetation variables need to be retrieved concomitantly. FLEX will fly in tandem formation with Sentinel-3 (S3), which conveys the Ocean and Land Color Instrument (OLCI) that is designed to characterize the atmosphere and the terrestrial vegetation at a spatial resolution of 300 m. In support of FLEX’s preparatory activities, this paper presents a first validation exercise of OLCI vegetation products against in situ data coming from the 2018 FLEXSense campaign. During this campaign, leaf chlorophyll content (LCC) and leaf area index (LAI) measurements were collected over croplands, while HyPlant DUAL images of the area were acquired at a 3 m spatial resolution. A multiscale validation strategy was pursued. First, estimates of these two variables, together with the combined canopy chlorophyll content (CCC = LCC × LAI), were obtained at the HyPlant spatial resolution and were compared against the in situ measurements. Second, the fine-scale retrieval maps from HyPlant were coarsened to the S3 spatial scale as a reference to assess the quality of the OLCI vegetation products. As an intermediary step, vegetation products extracted from Sentinel-2 data were used to compare retrievals at the in-between spatial resolution of 20 m. For all spatial scales, CCC delivered the most accurate estimates with the smallest prediction error obtained at the 300 m resolution (R2 of 0.74 and RMSE = 26.8 μμg cm−2). Results of a scaling analysis suggest that CCC performs well at the different tested spatial resolutions since it presents a linear behavior across scales. LCC, on the other hand, was poorly retrieved at the 300 m scale, showing overestimated values over heterogeneous pixels. The introduction of a new LCC model integrating mixed reflectance spectra in its training enabled to improve by 16% the retrieval accuracy for this variable (RMSE = 10 μμg cm−2 for the new model versus RMSE = 11.9 μμg cm−2 for the former model).

Gaussian processes retrieval of crop traits in Google Earth Engine based on Sentinel-2 top-of-atmosphere data

“…However, coarsening implies aggregation of patchwork landscape, leading to a trend of lower maximal values than at the 20 m resolution. The impact of heterogeneous landscapes on scaling is well understood, e.g., upscaling mechanisms have been more thoroughly studied by Chen (1999) ; Garrigues et al (2006) ; De Grave et al (2021) ; Meier et al (2020) . However, a deeper analysis goes beyond the scope of the present study.…”

Multimedia Image Data Compression Based on Wavelet Analysis

“…So, it can effectively extract information from signals and perform multiscale analysis on functions or signals by scaling and shifting operations. Wavelet transform has solved many problems that Fourier transform could not solve; so, it is known as the "mathematical microscope" and is a landmark progress in the development history of harmonic analysis [8].…”

A Review of Hybrid Approaches for Quantitative Assessment of Crop Traits Using Optical Remote Sensing: Research Trends and Future Directions

“…Other studies [201,221] compared NN and Bayesian network (BN) within a hybrid retrieval framework with LUT-based inversion, aiming to improve the accuracy of estimates. Finally, two studies [220,228] discussed the impact of different spatial resolutions on the vegetation variables and tried to decrease the uncertainties obtained from the model when applied to heterogeneous pixels. Table A6 summarizes the outcomes from the above papers for the CCC using the hybrid nonparametric method.…”