LAI and chlorophyll estimation for a heterogeneous grassland using hyperspectral measurements

By Roshanak Darvishzadeh, Andrew K. Skidmore, Martin Schlerf, Clement Atzberger, Fabio Corsi, Moses Azong Cho. 2008

424 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Ecology, Plant Science

View Paper: https://www.semanticscholar.org/paper/6e607dd5b4208d023f084fc9dd51bfcb2ff26d0c

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In Analytical Chemistry

In Ecology

In Plant Science

Citations in the corpus

Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and -3 (2012, methodology): “For Chl , the most relevant band emerged in the NIR centered at 940 nm while the bands centered around 620 nm and 761 nm were of second importance.”
LAI assessment of wheat and potato crops by VENμS and Sentinel-2 bands (2011, methodology): “Leaf Area Index (LAI) was defined by Watson (1947) as the total one-sided area of leaf tissue per unit ground surface area, giving a dimensionless value, typically ranging from 0, for bare ground, to more than 7 for dense vegetation (Darvishzadeh et al., 2008).”
Wheat Yield Forecasting for the Tisza River Catchment Using Landsat 8 NDVI and SAVI Time Series and Reported Crop Statistics (2021, background): “RMSE is a commonly used uncertainty metric for absolute predictions of errors and NRMSE is useful for comparisons between seasons in case of variable yield ranges [36].”
Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties - A review (2015, methodology): “At the same time, chlorophyll is to some extent correlated with plant structural variables such as LAI and fractional vegetation cover, imposing additional spectral variations in the NIR and SWIR (Darvishzadeh et al., 2008b; Verrelst et al., 2010; Ollinger, 2011).”
Evaluation of Sentinel-2 Red-Edge Bands for Empirical Estimation of Green LAI and Chlorophyll Content (2011, methodology): “Although Ch distribution in the plant is not necessarily uniform, taking into account that LAI represents the portion of green leaves per ground area, the product chlorophyll by LAI (Ch*LAI) provides an indication of the total chlorophyll content per unit ground area in the canopy [64,65].”
Integrating remotely sensed leaf area index and leaf nitrogen accumulation with RiceGrow model based on particle swarm optimization algorithm for rice grain yield assessment (2014, methodology): “In addition, data for both of these parameters could be retrieved by RS successfully.(34,35) Sowing date, seeding rate, and nitrogen rate, which are generally difficult to obtain accurately at the regional scale, were chosen as the assimilating parameters of the RiceGrow model.”
Comparison of various modelling approaches for water deficit stress monitoring in rice crop through hyperspectral remote sensing (2019, methodology): “PLSR is very popular and has been extensively used in Remote Sensing (Asner and Martin, 2008; Darvishzadeh et al., 2008; Li et al., 2007; Ramoelo et al., 2011).”
Water-removed spectra increase the retrieval accuracy when estimating savanna grass nitrogen and phosphorus concentrations (2011, methodology): “The leave-one-out cross validation, as defined by the lowest root mean square error (RMSE), was used to determine the optimal number of factors or latent variables to be used for model development (Cho et al. 2007; Viscarra Rossel 2008; Darvishzadeh et al. 2008a).”
Retrieval of leaf water content spanning the visible to thermal infrared spectra (2014, methodology): “The lowest RMSECV (RMSE-leave-one-out cross validation) was adopted as criterion to select the optimal number of components for model development (Darvishzadeh et al., 2008).”
Comparative analysis of three chemometric techniques for the spectroradiometric assessment of canopy chlorophyll content in winter wheat (2010, background): “…a large range of spectral indicators (e.g. vegetation ndices, red-edge indices), responding strongly to the main vegetaion biophysical variables such as LAI or leaf pigmentation (Broge nd Mortensen, 2002; Rondeaux, 1995; Jago et al., 1999; Filella and enuelas, 1994; Darvishzadeh et al., 2009).”
Proximal Remote Sensing Buggies and Potential Applications for Field-Based Phenotyping (2014, methodology): “These include the use of 2D correlation plots [66], partial least squares regression [67,68], principal components analysis [65], support vector machines (SVM) [69], neural networks [70] and other machine learning approaches for stress detection.”
A Review of Imaging Techniques for Plant Phenotyping (2014, methodology): “These appropriate mathematical treatments and algorithms include 2D correlation plots [136], partial least squares regression [137,138], principal components analysis [139], support vector machines [140], neural networks [141] and other machine learning approaches.”