Interaction of Isotropic Light with a Compact Plant Leaf*

Allen, William A.; Gausman, H. W.; Richardson, A. J.; Thomas, J. R.

doi:10.1364/josa.59.001376

Cited by 266 publications

(105 citation statements)

References 8 publications

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“…Leaf dry matter is an indicator of cellulose and lignin that absorb shortwave infrared radiation (Baret and Fourty, 1997). The equivalent leaf water exerts an influence on the optical path through the leaf and causes water absorption bands in the wavelength range from 1400 to 2500 nm (Allen et al, 1969). Leaf dry matter and equivalent leaf water were determined gravimetrically and based on image analysis in 2014.…”

Section: Leaf Dry Matter and Equivalent Leaf Watermentioning

confidence: 99%

Seasonal evolution of soil and plant parameters on the agricultural Gebesee test site: a database for the set-up and validation of EO-LDAS and satellite-aided retrieval models

Truckenbrodt

Schmullius

2018

Earth Syst. Sci. Data

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Abstract. Ground reference data are a prerequisite for the calibration, update, and validation of retrieval models facilitating the monitoring of land parameters based on Earth Observation data. Here, we describe the acquisition of a comprehensive ground reference database which was created to test and validate the recently developed Earth Observation Land Data Assimilation System (EO-LDAS) and products derived from remote sensing observations in the visible and infrared range. In situ data were collected for seven crop types (winter barley, winter wheat, spring wheat, durum, winter rape, potato, and sugar beet) cultivated on the agricultural Gebesee test site, central Germany, in 2013 and 2014. The database contains information on hyperspectral surface reflectance factors, the evolution of biophysical and biochemical plant parameters, phenology, surface conditions, atmospheric states, and a set of ground control points. Ground reference data were gathered at an approximately weekly resolution and on different spatial scales to investigate variations within and between acreages. In situ data collected less than 1 day apart from satellite acquisitions (RapidEye, SPOT 5, Landsat-7 and -8) with a cloud coverage ≤ 25 % are available for 10 and 15 days in 2013 and 2014, respectively. The measurements show that the investigated growing seasons were characterized by distinct meteorological conditions causing interannual variations in the parameter evolution. Here, the experimental design of the field campaigns, and methods employed in the determination of all parameters, are described in detail. Insights into the database are provided and potential fields of application are discussed. The data will contribute to a further development of crop monitoring methods based on remote sensing techniques. The database is freely available at PANGAEA (https://doi.org/10.1594/PANGAEA.874251).

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Section: Leaf Dry Matter and Equivalent Leaf Watermentioning

confidence: 99%

Seasonal evolution of soil and plant parameters on the agricultural Gebesee test site: a database for the set-up and validation of EO-LDAS and satellite-aided retrieval models

Truckenbrodt

Schmullius

2018

Earth Syst. Sci. Data

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“…However, this approach is essentially an empirical spectral transformation technique rather than a water-removal technique. Based on the plate model by Allen et al [44], Jacquemoud and Baret [7] proposed a leaf optical properties model: PROSPECT, which now not only can be used to simulate leaf reflectance and transmittance with provided biochemical and structural parameters, but also can be run in a reversed way to estimate the absorption of biophysical contents in leaves. PROSPECT makes it possible to eliminate the influences of water on the measured spectra.…”

Section: Introductionmentioning

confidence: 99%

Limited Effects of Water Absorption on Reducing the Accuracy of Leaf Nitrogen Estimation

Wang

Chen

et al. 2017

Remote Sensing

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Abstract:Nitrogen is an essential nutrient in many terrestrial ecosystems because it affects vegetation's primary production. Due to the variety of nitrogen-containing substances and the differences in their composition across species, statistical approaches are now dominant in remote sensing retrieval of leaf nitrogen content. Many studies remove spectral regions characterized by strong water absorptions before retrieving nitrogen content, because water is believed to mask the absorption features of nitrogen. The objectives of this study are to discuss the necessity of this practice and to explore how water absorption affects leaf nitrogen estimation. Spectral measurements and chemical analyses for Maize, Sawtooth Oak, and Sweetgum leaves were carried out in 2014. The leaf optical properties model PROSPECT5 was used to eliminate the influences of water on the measured reflectance spectra. The inversion accuracy of PROPECT5 for chlorophyll, carotenoid, water, and dry matter of Maize was also discussed. Measured, simulated, and water-removed spectra were used to: (1) find the optimal nitrogen-related spectral index; and (2) regress with the area-based leaf nitrogen concentration (LNC) using the partial least square regression technique (PLSR). Two types of spectral indices were selected in this study: Normalized Difference Spectral Index (NDSI) and Ratio Spectral Index (RSI). Additionally, first-order derivative forms of measured, simulated, and water-removed spectra were devised to search for the optimal spectral indices. Finally, species-specific optimal indices and cross-species optimal indices, as well as their root mean square errors (RMSE) and coefficients of determination (R 2 ), were obtained. The Ending Top Percentile (ETP), an indicator of the performance of cross-species optimal indices, was also calculated. PLSR was combined with leave-one-out cross validation (LOOCV) for each species. The predicted root mean square errors (RMSEP) and predicted R 2 were finally calculated. The results showed that chlorophyll, carotenoid, and water contents could be estimated with R 2 of 0.75, 0.59, and 0.69, respectively, which were acceptable for fresh leaves. The dry matter was retrieved with a relatively lower accuracy because of the fixed absorption coefficients adopted by PROSPECT5. The performances of species-specific optimal indices using water-free spectra were comparable to or worse than the corresponding indices derived with measured or simulated spectra. Compared with measured spectra, ETP did not change much after the effects of water were removed, and the R 2 between cross-species optimal spectral indices and area-based LNC for Sawtooth Oak and Sweetgum decreased while it remained almost the same for Maize, suggesting that the water-removed cross-species optimal indices were inferior to the corresponding optimal indices found without water removal. ETP was larger than 30% for all spectra, demonstrating the non-existence of common optimal NDSI or RSI for the three species. After water removal, the accuracy...

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“…Diffuse reflectance and transmittance of a compact leaf such as corn, a leaf impregnated with water, and an immature cotton leaf immediately after it unfolds (9) can be predicted from a plate theory (4). Generalization of the plate theory (flat plate model) to include the effect of intercellular air spaces (4) leads to the concept of void area index of a leaf.…”

mentioning

confidence: 99%