Early Diagnosis of Vegetation Health From High-Resolution Hyperspectral and Thermal Imagery: Lessons Learned From Empirical Relationships and Radiative Transfer Modelling

[Purpose of Review] We provide a comprehensive review of the empirical and modelling approaches used to quantify the radiation–vegetation interactions related to vegetation temperature, leaf optical properties linked to pigment absorption and chlorophyll fluorescence emission, and of their capability to monitor vegetation health. Part 1 provides an overview of the main physiological indicators (PIs) applied in remote sensing to detect alterations in plant functioning linked to vegetation diseases and decline processes. Part 2 reviews the recent advances in the development of quantitative methods to assess PI through hyperspectral and thermal images.[Recent Findings] In recent years, the availability of high-resolution hyperspectral and thermal images has increased due to the extraordinary progress made in sensor technology, including the miniaturization of advanced cameras designed for unmanned aerial vehicle (UAV) systems and lightweight aircrafts. This technological revolution has contributed to the wider use of hyperspectral imaging sensors by the scientific community and industry; it has led to better modelling and understanding of the sensitivity of different ranges of the electromagnetic spectrum to detect biophysical alterations used as early warning indicators of vegetation health.[Summary] The review deals with the capability of PIs such as vegetation temperature, chlorophyll fluorescence, photosynthetic energy downregulation and photosynthetic pigments detected through remote sensing to monitor the early responses of plants to different stressors. Various methods for the detection of PI alterations have recently been proposed and validated to monitor vegetation health. The greatest challenges for the remote sensing community today are (i) the availability of high spatial, spectral and temporal resolution image data; (ii) the empirical validation of radiation–vegetation interactions; (iii) the upscaling of physiological alterations from the leaf to the canopy, mainly in complex heterogeneous vegetation landscapes; and (iv) the temporal dynamics of the PIs and the interaction between physiological changes.The authors received funding provided by the FluorFLIGHT (GGR801) Marie Curie Fellowship, the QUERCUSAT and ESPECTRAMED projects (Spanish Ministry of Economy and Competitiveness), the Academy of Finland (grants 266152, 317387) and the European Research Council Synergy grant ERC-2013-SyG-610028 IMBALANCE-P.Peer reviewe

Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream will open up a vast array of opportunities to quantify a diversity of biochemical and structural vegetation properties. The processing requirements for such large data streams require reliable retrieval techniques enabling the spatiotemporally explicit quantification of biophysical variables. With the aim of preparing for this new era of Earth observation, this review summarizes the state-of-the-art retrieval methods that have been applied in experimental imaging spectroscopy studies inferring all kinds of vegetation biophysical variables. Identified retrieval methods are categorized into: (1) parametric regression, including vegetation indices, shape indices and spectral transformations; (2) nonparametric regression, including linear and nonlinear machine learning regression algorithms; (3) physically based, including inversion of radiative transfer models (RTMs) using numerical optimization and look-up table approaches; and (4) hybrid regression methods, which combine RTM simulations with machine learning regression methods. For each of these categories, an overview of widely applied methods with application to mapping vegetation properties is given. In view of processing imaging spectroscopy data, a critical aspect involves the challenge of dealing with spectral multicollinearity. The ability to provide robust estimates, retrieval uncertainties and acceptable retrieval processing speed are other important aspects in view of operational processing. Recommendations towards new-generation spectroscopy-based processing chains for operational production of biophysical variables are given

A broad-band leaf chlorophyll vegetation index at the canopy scale

An assessment of the sensitivity at the canopy scale to leaf chlorophyll concentration of the broad-band chlorophyll vegetation index (CVI) is carried out for a wide range of soils and crops conditions and for different sun zenith angles by the analysis of a large synthetic dataset obtained by using in the direct mode the coupled PROSPECT ? SAILH leaf and canopy reflectance model. An optimized version (OCVI) of the CVI is proposed. A single correction factor is incorporated in the OCVI algorithm to take into account the different spectral behaviors due to crop and soil types, sensor spectral resolution and scene sun zenith angle. An estimate of the value of the correction factor and of the minimum leaf area index (LAI) value of applicability are given for each considered condition. The results of the analysis of the synthetic dataset indicated that the broad-band CVI index could be used as a leaf chlorophyll estimator for planophile crops in most soil conditions. Results indicated as well that, in principle, a single correction factor incorporated in the OCVI could take into account the different spectral behaviors due to crop and soil types, sensor spectral resolution and scene sun zenith angle

Beyond NDVI: Extraction of biophysical variables from remote sensing imagery

This chapter provides an overview of methods used for the extraction of biophysical vegetation variables from remote sensing imagery. It starts with the description of the main spectral regions in the optical window of the electromagnetic spectrum based on typical spectral signatures of land surfaces. Subsequently, the merit and problems of using radiative transfer models to describe the relationship between spectral measurements and biophysical and chemical variables of vegetation are described. Next, the use of statistical methods by means of vegetation indices for the same purpose gets attention. An overview of different types of indices is given without having the ambition in being exhaustive. Subsequently, an overview is provided of the biogeophysical vegetation variables that can directly be estimated from optical remote sensing observations, with emphasis on using vegetation indices. These vegetation variables are: (1) chlorophyll and nitrogen, (2) vegetation cover fraction and fAPAR, (3) leaf area index, and (4) canopy water. Finally, an outlook for a major research direction in the near future in this context is provided