scholarly journals Tracing Leaf Photosynthetic Parameters Using Hyperspectral Indices in an Alpine Deciduous Forest

2020 ◽  
Vol 12 (7) ◽  
pp. 1124
Author(s):  
Jia Jin ◽  
Bayu Arief Pratama ◽  
Quan Wang
Keyword(s):  
Maximum Rate ◽  
Deciduous Forest ◽  
Vegetation Indices ◽  
Specific Area ◽  
Information Criterion ◽  
Short Wave ◽  
Hyperspectral Data ◽  
Photosynthetic Parameters ◽  
Leaf Sample ◽  
First Order

Leaf photosynthetic parameters are important in understanding the role of photosynthesis in the carbon cycle. Conventional approaches to obtain information on the parameters usually involve long-term field work, even for one leaf sample, and are, thus, only applicable to a small area. The utilization of hyperspectral remote sensing especially of various vegetation indices is a promising approach that has been attracting increasing attention recently. However, most hyperspectral indices are only applicable to a specific area and specific forest stands, depending heavily on the conditions from which the indices are developed. In this study, we tried to develop new hyperspectral indices for tracing the two critical photosynthetic parameters (the maximum rate of carboxylation, Vcmax and the maximum rate of electron transport, Jmax) that are at least generally applicable for alpine deciduous forests, based on original hyperspectral reflectance, first-order derivatives, and apparent absorption spectra. In total, ten types of hyperspectral indices were screened to identify the best indices, and their robustness was determined using the ratio of performance to deviation (RPD) and Akaike’s Information Criterion corrected (AICc). The result revealed that the double differences (DDn) type of indices using the short-wave infrared (SWIR) region based on the first-order derivatives spectra performed best among all indices. The specific DDn type of indices obtained the RPD values of 1.43 (R2 = 0.51) for Vcmax and 1.68 (R2 = 0.64) for Jmax, respectively. These indices have also been tested using the downscaled dataset to examine the possibilities of using hyperspectral data derived from satellite-based information. These findings highlight the possibilities of tracing photosynthetic capacity using hyperspectral indices.

Use of leaf hyperspectral data and different regression models to estimate photosynthetic parameters (Vcmax and Jmax) in three different row crops

2020 ◽  
Author(s):  
Maria Luisa Buchaillot ◽  
David Soba ◽  
Tianchu Shu ◽  
Liu Juan ◽  
José Luis Araus ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Maximum Rate ◽  
Regression Models ◽  
Vegetation Index ◽  
Phenotypic Variability ◽  
Vegetation Indices ◽  
Hyperspectral Data ◽  
Spatial And Temporal Variation ◽  
Photosynthetic Parameters ◽  
Row Crops

<p>By 2050 future global food demand is projected to require a doubling of agricultural output, and climate change will exacerbate this challenge by intensifying the exposure of field crops to abiotic stress conditions, including rising temperature, increased drought, and increased CO<sub>2</sub> concentration ([CO<sub>2</sub>]). One of the keys to improving crop yield under different stresses is studying is photosynthesis. Photosynthetic parameters, such as the maximum rate of carboxylation of RuBP (V<sub>c,max</sub>), and the maximum rate of electron transport driving RuBP regeneration (J<sub>max</sub>) vary in response to climate conditions and have been identified as a target for improvement. However, the techniques used to measure these physiological parameters are very time consuming, ranging from 30 to 70 min per measurement and require specialized personnel. Therefore, breeding or genetic mapping for these traits under these conditions is prohibitively time-consuming. Spatial and temporal variation in plant photosynthesis can be estimated using remote sensing-derived spectral vegetation indices. Spectral estimates of green vegetation biomass and vigor, including vegetation indices such as the Normalized Difference Vegetation Index (NDVI), are widely used to estimate vegetation productivity across spatial and temporal scales but are unable to provide assessments of specific photosynthetic parameters. For that reason, hyperspectral remote sensing shows promise for predicting photosynthetic capacity based on more detailed leaf optical properties. In this study, we developed and assessed estimates of Vcmax and Jmax through four different advanced regression models: PLS, BR, ARDR, and LASSO based on leaf reflectance metrics measured with an ASD FieldSpec4 Hi-RES of different crops under different environmental conditions such as (1) different varieties of soybean under high [CO<sub>2</sub>] and high temperature, (2) different varieties of peanut under drought stress and (3) 20 varieties of cotton diverse origin and grown under field conditions. Both phenotypic variability and varying levels of stress were employed with each crop to ensure adequate ranges of responses. Model sensitivities were assessed for each crop and treatment separately and in combination in order to better understand the strengths and weaknesses of each model in all the different conditions. For the combination of three species, all the models suggest a robust prediction of Vcmax around R<sup>2</sup>:0.67 and the same for the Jmax R<sup>2</sup>: 0.55.</p>

scholarly journals Understanding Vine Hyperspectral Signature through Different Irrigation Plans: A First Step to Monitor Vineyard Water Status

2021 ◽  
Vol 13 (3) ◽  
pp. 536
Author(s):  
Eve Laroche-Pinel ◽  
Mohanad Albughdadi ◽  
Sylvie Duthoit ◽  
Véronique Chéret ◽  
Jacques Rousseau ◽  
...  
Keyword(s):  
Near Infrared ◽  
Water Status ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Short Wave ◽  
Spatial And Temporal Scales ◽  
Spectral Bands ◽  
Main Challenge ◽  
Spectral Scale ◽  
Grape Varieties

The main challenge encountered by Mediterranean winegrowers is water management. Indeed, with climate change, drought events are becoming more intense each year, dragging the yield down. Moreover, the quality of the vineyards is affected and the level of alcohol increases. Remote sensing data are a potential solution to measure water status in vineyards. However, important questions are still open such as which spectral, spatial, and temporal scales are adapted to achieve the latter. This study aims at using hyperspectral measurements to investigate the spectral scale adapted to measure their water status. The final objective is to find out whether it would be possible to monitor the vine water status with the spectral bands available in multispectral satellites such as Sentinel-2. Four Mediterranean vine plots with three grape varieties and different water status management systems are considered for the analysis. Results show the main significant domains related to vine water status (Short Wave Infrared, Near Infrared, and Red-Edge) and the best vegetation indices that combine these domains. These results give some promising perspectives to monitor vine water status.

scholarly journals Atmospheric Compensation of PRISMA Data by Means of a Learning Based Approach

2021 ◽  
Vol 13 (15) ◽  
pp. 2967
Author(s):  
Nicola Acito ◽  
Marco Diani ◽  
Gregorio Procissi ◽  
Giovanni Corsini
Keyword(s):  
Spectral Range ◽  
Near Infrared ◽  
Random Noise ◽  
Synthetic Data ◽  
Regression Function ◽  
Short Wave ◽  
Hyperspectral Data ◽  
Electromagnetic Spectrum ◽  
Great Opportunity ◽  
Atmospheric Compensation

Atmospheric compensation (AC) allows the retrieval of the reflectance from the measured at-sensor radiance and is a fundamental and critical task for the quantitative exploitation of hyperspectral data. Recently, a learning-based (LB) approach, named LBAC, has been proposed for the AC of airborne hyperspectral data in the visible and near-infrared (VNIR) spectral range. LBAC makes use of a parametric regression function whose parameters are learned by a strategy based on synthetic data that accounts for (1) a physics-based model for the radiative transfer, (2) the variability of the surface reflectance spectra, and (3) the effects of random noise and spectral miscalibration errors. In this work we extend LBAC with respect to two different aspects: (1) the platform for data acquisition and (2) the spectral range covered by the sensor. Particularly, we propose the extension of LBAC to spaceborne hyperspectral sensors operating in the VNIR and short-wave infrared (SWIR) portion of the electromagnetic spectrum. We specifically refer to the sensor of the PRISMA (PRecursore IperSpettrale della Missione Applicativa) mission, and the recent Earth Observation mission of the Italian Space Agency that offers a great opportunity to improve the knowledge on the scientific and commercial applications of spaceborne hyperspectral data. In addition, we introduce a curve fitting-based procedure for the estimation of column water vapor content of the atmosphere that directly exploits the reflectance data provided by LBAC. Results obtained on four different PRISMA hyperspectral images are presented and discussed.

scholarly journals Rapid Quantification of Microalgae Growth with Hyperspectral Camera and Vegetation Indices

Plants ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 341
Author(s):  
Pauliina Salmi ◽  
Matti A. Eskelinen ◽  
Matti T. Leppänen ◽  
Ilkka Pölönen
Keyword(s):  
Chlorophyll Fluorescence ◽  
Chlorophyll A ◽  
Near Infrared ◽  
Vegetation Indices ◽  
Hyperspectral Data ◽  
Strong Correlations ◽  
Algae Biomass ◽  
Chlorophyll A Concentration ◽  
Cost Efficient ◽  
Hyperspectral Camera

Spectral cameras are traditionally used in remote sensing of microalgae, but increasingly also in laboratory-scale applications, to study and monitor algae biomass in cultures. Practical and cost-efficient protocols for collecting and analyzing hyperspectral data are currently needed. The purpose of this study was to test a commercial, easy-to-use hyperspectral camera to monitor the growth of different algae strains in liquid samples. Indices calculated from wavebands from transmission imaging were compared against algae abundance and wet biomass obtained from an electronic cell counter, chlorophyll a concentration, and chlorophyll fluorescence. A ratio of selected wavebands containing near-infrared and red turned out to be a powerful index because it was simple to calculate and interpret, yet it yielded strong correlations to abundances strain-specifically (0.85 < r < 0.96, p < 0.001). When all the indices formulated as A/B, A/(A + B) or (A − B)/(A + B), where A and B were wavebands of the spectral camera, were scrutinized, good correlations were found amongst them for biomass of each strain (0.66 < r < 0.98, p < 0.001). Comparison of near-infrared/red index to chlorophyll a concentration demonstrated that small-celled strains had higher chlorophyll absorbance compared to strains with larger cells. The comparison of spectral imaging to chlorophyll fluorescence was done for one strain of green algae and yielded strong correlations (near-infrared/red, r = 0.97, p < 0.001). Consequently, we described a simple imaging setup and information extraction based on vegetation indices that could be used to monitor algae cultures.

scholarly journals Retrieving Sun-Induced Chlorophyll Fluorescence from Hyperspectral Data with TanSat Satellite

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4886
Author(s):  
Shilei Li ◽  
Maofang Gao ◽  
Zhao-Liang Li
Keyword(s):  
Chlorophyll Fluorescence ◽  
Fluorescence Spectrum ◽  
Singular Vector ◽  
Low Frequency ◽  
Information Criterion ◽  
Hyperspectral Data ◽  
High Spectral Resolution ◽  
Remotely Sensed Data ◽  
Spectral Window ◽  
Vector Decomposition

A series of algorithms for satellite retrievals of sun-induced chlorophyll fluorescence (SIF) have been developed and applied to different sensors. However, research on SIF retrieval using hyperspectral data is performed in narrow spectral windows, assuming that SIF remains constant. In this paper, based on the singular vector decomposition (SVD) technique, we present an approach for retrieving SIF, which can be applied to remotely sensed data with ultra-high spectral resolution and in a broad spectral window without assuming that the SIF remains constant. The idea is to combine the first singular vector, the pivotal information of the non-fluorescence spectrum, with the low-frequency contribution of the atmosphere, plus a linear combination of the remaining singular vectors to express the non-fluorescence spectrum. Subject to instrument settings, the retrieval was performed within a spectral window of approximately 7 nm that contained only Fraunhofer lines. In our retrieval, hyperspectral data of the O2-A band from the first Chinese carbon dioxide observation satellite (TanSat) was used. The Bayesian Information Criterion (BIC) was introduced to self-adaptively determine the number of free parameters and reduce retrieval noise. SIF retrievals were compared with TanSat SIF and OCO-2 SIF. The results showed good consistency and rationality. A sensitivity analysis was also conducted to verify the performance of this approach. To summarize, the approach would provide more possibilities for retrieving SIF from hyperspectral data.

scholarly journals HEIGHT-DIAMETER MODELS FOR THREE SUBTROPICAL FOREST TYPES IN SOUTHERN BRAZIL

2015 ◽  
Vol 39 (3) ◽  
pp. 205-215 ◽  
Author(s):  
Alexander Christian Vibrans ◽  
Paolo Moser ◽  
Laio Zimermann Oliveira ◽  
João Paulo de Maçaneiro
Keyword(s):  
Deciduous Forest ◽  
Tree Height ◽  
Information Criterion ◽  
Confidence Bands ◽  
Natural Forests ◽  
Forest Types ◽  
Model Quality ◽  
Statistical Parameters ◽  
Santa Catarina ◽  
Ombrophilous Forest

Total tree height (h) is often difficult to measure in natural forests. Regression models based on easily accessed variables like DBH (d) can be an alternative, since their assumptions are validated. The aims of this study are to: (i) calibrate specific and generic h-d models for three forest types (Seasonal Deciduous Forest, DEC; Mixed Ombrophilous Forest, MIX; and Dense Rainforest, DEN) in Santa Catarina state testing the regression assumptions and evaluating model quality; (ii) verify different h-d relationship between forest types. The dataset (1,766 measured tree h and 3,150 estimated h) was collected by Santa Catarina Forest and Floristic Inventory (IFFSC) in 418 systematically located sample plots. Models were calibrated for two datasets, one containing hypsometer measurements, the other h estimations made by field crews. Specific models were calibrated for species with at least 30 sampled trees. Residual normality, randomness and heteroskedasticity were evaluated by analytical methods. Confidence bands were generated by the Working-Hotelling method; z test for means was applied to compare models based on the two databases. The statistical parameters such as corrected Akaike Information Criterion provided evidences that logarithmic models were better adjusted to the data. Both datasets were statistically different for DEN and MIX. Differences in h-d relationships were found between forest types. The use of calibrated h-d models is an alternative for studying the relationships between these variables and to assess vertical structure patterns of forest communities, when h measurements are not feasible, although, for situations that more accurate h values are needed, they will not always provide reliable predictions.

scholarly journals Determination of the Order of a Markov Chain for Daily Rainfall Data of North East India: "Application AIC criterion"

2018 ◽  
Vol 10 (1) ◽  
pp. 80-87
Author(s):  
Surobhi Deka
Keyword(s):  
Markov Chain ◽  
Markov Chains ◽  
Akaike Information Criterion ◽  
Markov Chain Model ◽  
Information Criterion ◽  
Chain Model ◽  
Adequate Model ◽  
First Order ◽  
North East India ◽  
North East

The paper aims at demonstrating the application of the Akaike information criterion to determine the order of two state Markov chain for studying the pattern of occurrence of wet and dry days during the rainy season (April to September) in North-East India. For each station, each day is classified as dry day if the amount of rainfall is less than 3 mm and wet day if the amount of rainfall is greater than or equal to 3 mm. We apply Markov chain of order up to three to the sequences of wet and dry days observed at seven distantly located stations in North East region of India. The Markov chain model of appropriate order for analyzing wet and dry days is determined. This is done using the Akaike Information Criterion (AIC) by checking the minimum of AIC estimate. Markov chain of order one is found to be superior to the majority of the stations in comparison to the other order Markov chains. More precisely, first order Markov chain model is an adequate model for the stations North Bank, Tocklai, Silcoorie, Mohanbari and Guwahati. Further, it is observed that second order and third order Markov chains are competing with first order in the stations Cherrapunji and Imphal, respectively. A fore-knowledge of rainfall pattern is of immense help not only to farmers, but also to the authorities concerned with planning of irrigation schemes. The outcomes are useful for taking decisions well in advance for transplanting of rice as well as for other input management and farm activities during different stages of the crop growing season.

scholarly journals Estimation of Carbon Fluxes from Eddy Covariance Data and Satellite-Derived Vegetation Indices in a Karst Grassland (Podgorski Kras, Slovenia)

2019 ◽  
Vol 11 (6) ◽  
pp. 649 ◽  
Author(s):  
Koffi Noumonvi ◽  
Mitja Ferlan ◽  
Klemen Eler ◽  
Giorgio Alberti ◽  
Alessandro Peressotti ◽  
...  
Keyword(s):  
Linear Relationship ◽  
Eddy Covariance ◽  
Vegetation Index ◽  
Gross Primary Production ◽  
Vegetation Indices ◽  
Information Criterion ◽  
Growing Season ◽  
Coefficient Of Determination ◽  
Regression Equations ◽  
Eddy Covariance Method

The Eddy Covariance method (EC) is widely used for measuring carbon (C) and energy fluxes at high frequency between the atmosphere and the ecosystem, but has some methodological limitations and a spatial restriction to an area, called a footprint. Remotely sensed information is usually used in combination with eddy covariance data in order to estimate C fluxes over larger areas. In fact, spectral vegetation indices derived from available satellite data can be combined with EC measurements to estimate C fluxes outside of the tower footprint. Following this approach, the present study aimed to model C fluxes for a karst grassland in Slovenia. Three types of model were considered: (1) a linear relationship between Net Ecosystem Exchange (NEE) or Gross Primary Production (GPP) and each vegetation index; (2) a linear relationship between GPP and the product of a vegetation index with PAR (Photosynthetically Active Radiation); and (3) a simplified LUE (Light Use-Efficiency) model assuming a constant LUE. We compared the performance of several vegetation indices derived from two remote platforms (Landsat and Proba-V) as predictors of NEE and GPP, based on three accuracy metrics, the coefficient of determination (R2), the Root Mean Square Error (RMSE) and the Akaike Information Criterion (AIC). Two types of aggregation of flux data were explored: midday average and daily average fluxes. The vapor pressure deficit (VPD) was used to separate the growing season into two phases, a wet and a dry phase, which were considered separately in the modelling process, in addition to the growing season as a whole. The results showed that NDVI is the best predictor of GPP and NEE during the wet phase, whereas water-related vegetation indices, namely LSWI and MNDWI, were the best predictors during the dry phase, both for midday and daily aggregates. Model 1 (linear relationship) was found to be the best in many cases. The best regression equations obtained were used to map GPP and NEE for the whole study area. Digital maps obtained can practically contribute, in a cost-effective way to the management of karst grasslands.

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