Estimation of vegetation traits with kernel NDVI

Abstract

Vegetation indices computed from spectral signatures are vastly used for monitoring the terrestrial biosphere. Indices are convenient proxies for canopy structure, and leaf pigment content, and consequently to estimate the photosynthetic activity of vegetation. Owing to its simplicity, the celebrated Normalized Difference Vegetation Index (NDVI) has been used as a proxy for greenness and canopy structure. Unfortunately, NDVI can only capture linear relationships of the near infrared (NIR) - red difference with the parameter of interest. To account for higher-order relations between the spectral channels, kernel NDVI (kNDVI) was proposed in (Camps-Valls et al., 2021). In this work, we give useful prescriptions for its proper use and show its good performance in a wider set of applications. We discuss the good characteristics of the index like boundedness, low error propagation. Furthermore, we give empirical evidence of performance in estimating in-situ vegetation parameters (leaf area index (LAI), gross primary productivity (GPP), leaf, and canopy chlorophyll content, green and total LAI and fraction of absorbed photosynthetically active radiation (fAPAR)) as well as the estimation of latent heat at flux tower level. We confirm the generally good performance of the index (correlation coefficient of kNDVI and canopy chlorophyll content is 0.919 and 0.933 for maize over two sites, as well as the correlation coefficient between kNDVI and carotenoid, is 0.816, 0.520 and 0.579 for three forest sites) and highlight its convenience in monitoring terrestrial ecosystems. To foster wider adoption of the new family of the index, we provide source code in 6 programming languages as well as efficient implementations in the Google Earth Engine (GEE) platform at https://github.com/IPL-UV/kNDVI.