Radiative transfer Vcmax estimation from hyperspectral imagery and SIF retrievals to assess photosynthetic performance in rainfed and irrigated plant phenotyping trials (original) (raw)
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Remote Sensing of Environment, 2015
To date, the utility of ecosystem and Earth system models (EESMs) has been limited by poor spatial and temporal representation of critical input parameters. For example, EESMs often rely on leaf-scale or literature-derived estimates for a key determinant of canopy photosynthesis, the maximum velocity of RuBP carboxylation (V cmax , μmol m −2 s −1 ). Our recent work showed that reflectance spectroscopy could be used to estimate V cmax at the leaf level. Here, we present evidence that imaging spectroscopy data can be used to simultaneously predict V cmax and its sensitivity to temperature (E V ) at the canopy scale. In 2013 and 2014, high-altitude Airborne Visible/Infrared Imaging Spectroscopy (AVIRIS) imagery and contemporaneous ground-based assessments of canopy structure and leaf photosynthesis were acquired across an array of monospecific agroecosystems in central and southern California, USA. A partial least-squares regression (PLSR) modeling approach was employed to characterize the pixel-level variation in canopy V cmax (at a standardized canopy temperature of 30°C) and E V , based on visible and shortwave infrared AVIRIS spectra (414-2447 nm). Our approach yielded parsimonious models with strong predictive capability for V cmax (at 30°C) and E V (R 2 of withheld data = 0.94 and 0.92, respectively), both of which varied substantially in the field (≥1.7 fold) across the sampled crop types. The models were applied to additional AVIRIS imagery to generate maps of V cmax and E V , as well as their uncertainties, for agricultural landscapes in California. The spatial patterns exhibited in the maps were consistent with our in-situ observations. These findings highlight the considerable promise of airborne and, by implication, space-borne imaging spectroscopy, such as the proposed HyspIRI mission, to map spatial and temporal variation in key drivers of photosynthetic metabolism in terrestrial vegetation.
Hyperspectral indices and model simulation for chlorophyll estimation in open-canopy tree crops
Remote Sensing of Environment, 2004
An investigation of the estimation of leaf biochemistry in open tree crop canopies using high-spatial hyperspectral remote sensing imagery is presented. Hyperspectral optical indices related to leaf chlorophyll content were used to test different radiative transfer modelling assumptions in open canopies where crown, soil and shadow components were separately targeted using 1 m spatial resolution ROSIS hyperspectral imagery. Methods for scaling-up of hyperspectral single-ratio indices such as R 750 /R 710 and combined indices such as MCARI, TCARI and OSAVI were studied to investigate the effects of scene components on indices calculated from pure crown pixels and from aggregated soil, shadow and crown reflectance. Methods were tested on 1-m resolution hyperspectral ROSIS datasets acquired over two olive groves in southern Spain during the HySens 2002 campaign conducted by the German Aerospace Center (DLR). Leaf-level biochemical estimation using 1-m ROSIS data when targeting pure olive tree crowns employed PROSPECT-SAILH radiative transfer simulation. At lower spatial resolution, therefore with significant effects of soil and shadow scene components on the aggregated pixels, a canopy model to account for such scene components had to be used for a more appropriate estimation approach for leaf biochemical concentration. The linked models PROSPECT-SAILH-FLIM improved the estimates of chlorophyll concentration from these open tree canopies, demonstrating that crown-derived relationships between hyperspectral indices and biochemical constituents cannot be readily applied to hyperspectral imagery of lower spatial resolutions due to large soil and shadow effects. Predictive equations built on a MCARI/OSAVI scaled-up index through radiative transfer simulation minimized soil background variations in these open canopies, demonstrating superior performance compared to other single-ratio indices previously shown as good indicators of chlorophyll concentration in closed canopies. The MCARI/OSAVI index was demonstrated to be less affected than TCARI/OSAVI by soil background variations when calculated from the pure crown component even at the typically low LAI orchard and grove canopies. D 2004 Elsevier Inc. All rights reserved.
Biogeosciences Discussions, 2016
Nitrogen and water availability are two of staple environmental elements in agroecosystems that can substantially alter canopy structure and physiology then crop growth, yielding large impacts on ecosystem regulating/production provisions. However, to date, explicitly quantifying such impacts remains challenging partially due to lack of adequate methodology to capture spatial dimensions of ecosystem changes associated with nitrogen and water effects. A data assimilation, where close-range remote sensing measurements of vegetation indices derived from a hand-held instrument and an unmanned aerial vehicle (UAV) system are linked to leaf and canopy photosynthetic traits quantified at plot level by portable chamber systems, was applied to capture and interpret inter- and intra-field variations in gross primary productivity (GPP) in lowland rice grown under flooded condition (paddy rice, PD) subject to three available nutrient availability and under rainfed condition (RF) in East-Asian m...
EDOCROS: Early detection of crop water and nutritional stress by remotely sensed indicators
In the framework of ESA Fluorescence campaigns 2010, the EDOCROS (Early Detection Of CROp Stress) campaign, supported by EUFAR (European Facility For Airborne Research project) was aimed to detect early crop stress due to water and nitrogen (N) deficit, by means of advanced hyperspectral remote sensing techniques. CASI 1500 (Itres, Canada), AISA eagle (Specim, Finland) and AHS-160 (Sensytech Inc., USA) imagery were acquired contemporary to an intensive field campaign where vegetation biophysical and ecophysiological measurements were collected. Canopy spectral measurements were also acquired on corn parcels with different N and water treatments by means of a PS and a SWIR Spectral Camera (Specim, Finland) mounted on a cherry picker with a nadir view. In this paper the EDOCROS campaign is presented with particular focus on field data acquisition and analysis. Furthermore, preliminary results of analysis conducted on AISA eagle imagery are presented and discussed. Different vegetation indices were calculated, and relative values of sun-induced chlorophyll fluorescence at 760 nm (F 760 ) were retrieved by the FLD (Frahunofer Line depth) method . The regression analysis between hyperspectral data and field parameters showed that red edge indices combined with soil adjusted indices (TCI/OSAVI and MTCI/MSAVI) were highly correlated with leaf relative chlorophyll content (SPAD values), minimizing the effect of canopy structure (Leaf Area Index -LAI) and soil background. F 760 was highly correlated with canopy relative chlorophyll content (LAI*SPAD), while PRI was highly correlated to light use efficiency measured at leaf level.
A multivariate approach for assessing leaf photo-assimilation performance using the I PL index
Physiologia Plantarum, 2015
The detection of leaf functionality is of pivotal importance for plant scientists from both theoretical and practical point of view. Leaves are the sources of dry matter and food, and they sequester CO 2 as well. Under the perspective of climate change and primary resource scarcity (i.e. water, fertilizers and soil), assessing leaf photo-assimilation in a rapid but comprehensive way can be helpful for understanding plant behavior under different environmental conditions and for managing the agricultural practices properly. Several approaches have been proposed for this goal, however, some of them resulted very efficient but little reliable. On the other hand, the high reliability and exhaustive information of some models used for estimating net photosynthesis are at the expense of time and ease of measurement. The present study employs a multivariate statistical approach to assess a model aiming at estimating leaf photo-assimilation performance, using few and easy-to-measure variables. The model, parameterized for apple and pear and subjected to internal and external cross validation, involves chlorophyll fluorescence, carboxylative activity of ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCo), air and leaf temperature. Results prove that this is a fair-predictive model allowing reliable variable assessment. The dependent variable, called I PL index, was found strongly and linearly correlated to net photosynthesis. I PL and the model behind it seem to be (1) reliable, (2) easy and fast to measure and (3) usable in vivo and in the field for such cases where high amount of data is required (e.g. precision agriculture and phenotyping studies).
International Journal of Applied Earth Observation and Geoinformation - INT J APPL EARTH OBS GEOINF, 2010
The Normalized Area Over reflectance Curve (NAOC) is proposed as a new index for remote sensing estimation of the leaf chlorophyll content of heterogeneous areas with different crops, different canopies and different types of bare soil. This index is based on the calculation of the area over the reflectance curve obtained by high spectral resolution reflectance measurements, determined, from the integral of the red–near-infrared interval, divided by the maximum reflectance in that spectral region. For this, use has been made of the experimental data of the SPARC campaigns, where in situ measurements were made of leaf chlorophyll content, LAI and fCOVER of 9 different crops – thus, yielding 300 different values with broad variability of these biophysical parameters. In addition, Proba/CHRIS hyperspectral images were obtained simultaneously to the ground measurements. By comparing the spectra of each pixel with its experimental leaf chlorophyll value, the NAOC was proven to exhibit a linear correlation to chlorophyll content. Calculating the correlation between these variables in the 600–800 nm interval, the best correlation was obtained by computing the integral of the spectral reflectance curve between 643 and 795 nm, which practically covers the spectral range of maximum chlorophyll absorption (at around 670 nm) and maximum leaf reflectance in the infrared (750–800 nm). Based on a Proba/CHRIS image, a chlorophyll map was generated using NAOC and compared with the land-use (crops classification) map. The method yielded a leaf chlorophyll content map of the study area, comprising a large heterogeneous zone. An analysis was made to determine whether the method also serves to estimate the total chlorophyll content of a canopy, multiplying the leaf chlorophyll content by the LAI. To validate the method, use was made of the data from another campaign ((SEN2FLEX), in which measurements were made of different biophysical parameters of 7 crops, and hyperspectral images were obtained with the CASI imaging radiometer from an aircraft. Applying the method to a CASI image, a map of leaf chlorophyll content was obtained, which on, establishing comparisons with the experimental data allowed us to estimate chlorophyll with a root mean square error of 4.2 μg/cm2, similar or smaller than other methods but with the improvement of applicability to a large set of different crop types.
Linking remote sensing parameters to CO2 assimilation rates at a leaf scale
Journal of Plant Research, 2021
Solar-induced chlorophyll fluorescence (SIF) and photochemical reflectance index (PRI) are expected to be useful for remote sensing of photosynthetic activity at various spatial scales. This review discusses how chlorophyll fluorescence and PRI are related to the CO2 assimilation rate at a leaf scale. Light energy absorbed by photosystem II chlorophylls is allocated to photochemistry, fluorescence, and heat dissipation evaluated as non-photochemical quenching (NPQ). PRI is correlated with NPQ because it reflects the composition of xanthophylls, which are involved in heat dissipation. Assuming that NPQ is uniquely related to the photochemical efficiency (quantum yield of photochemistry), photochemical efficiencies can be assessed from either chlorophyll fluorescence or PRI. However, this assumption may not be held under some conditions such as low temperatures and photoinhibitory environments. Even in such cases, photosynthesis may be estimated more accurately if both chlorophyll flu...
Remote estimation of canopy chlorophyll content in crops
Geophysical Research Letters, 2005
1] Accurate estimation of spatially distributed chlorophyll content (Chl) in crops is of great importance for regional and global studies of carbon balance and responses to fertilizer (e.g., nitrogen) application. In this paper a recently developed conceptual model was applied for remotely estimating Chl in maize and soybean canopies. We tuned the spectral regions to be included in the model, according to the optical characteristics of the crops studied, and showed that the developed technique allowed accurate estimation of total Chl in both crops, explaining more than 92% of Chl variation. This new technique shows great potential for remotely tracking the physiological status of crops, with contrasting canopy architectures, and their responses to environmental changes. Citation: Gitelson,
International Journal of Applied Earth Observation and Geoinformation
Crop canopy water content (CWC) is an essential indicator of the crop's physiological state. While a diverse range of vegetation indices have earlier been developed for the remote estimation of CWC, most of them are defined for specific crop types and areas, making them less universally applicable. We propose two new water content indices applicable to a wide variety of crop types, allowing to derive CWC maps at a large spatial scale. These indices were developed based on PROSAIL simulations and then optimized with an experimental dataset (SPARC03; Barrax, Spain). This dataset consists of water content and other biophysical variables for five common crop types (lucerne, corn, potato, sugar beet and onion) and corresponding top-of-canopy (TOC) reflectance spectra acquired by the hyperspectral HyMap airborne sensor. First, commonly used water content index formulations were analysed and validated for the variety of crops, overall resulting in a R 2 lower than 0.6. In an attempt to move towards more generically applicable indices, the two new CWC indices exploit the principal water absorption features in the near-infrared by using multiple bands sensitive to water content. We propose the Water Absorption Area Index (WAAI) as the difference between the area under the null water content of TOC reflectance (reference line) simulated with PROSAIL and the area under measured TOC reflectance between 911 and 1271 nm. We also propose the Depth Water Index (DWI), a simplified four-band index based on the spectral depths produced by the water absorption at 970 and 1200 nm and two reference bands. Both the WAAI and DWI outperform established indices in predicting CWC when applied to heterogeneous croplands, with a R 2 of 0.8 and 0.7, respectively, using an exponential fit. However, these indices did not perform well for species with a low fractional vegetation cover (< 30%). HyMap CWC maps calculated with both indices are shown for the Barrax region. The results confirmed the potential of using generically applicable indices for calculating CWC over a great variety of crops.
Ground Based Hyperspectral Imaging to Characterize Canopy-Level Photosynthetic Activities
Remote Sensing, 2020
Improving plant photosynthesis provides the best possibility for increasing crop yield potential, which is considered a crucial effort for global food security. Chlorophyll fluorescence is an important indicator for the study of plant photosynthesis. Previous studies have intensively examined the use of spectrometer, airborne, and spaceborne spectral data to retrieve solar induced fluorescence (SIF) for estimating gross primary productivity and carbon fixation. None of the methods, however, had a spatial resolution and a scanning throughput suitable for applications at the canopy and sub-canopy levels, thereby limiting photosynthesis analysis for breeding programs and genetics/genomics studies. The goal of this study was to develop a hyperspectral imaging approach to characterize plant photosynthesis at the canopy level. An experimental field was planted with two cotton cultivars that received two different treatments (control and herbicide treated), with each cultivar-treatment com...