Inversion of a radiative transfer model for estimation of rice chlorophyll content using support vector machine

2014 ◽  
Author(s):  
Jie Lv ◽  
Zhenguo Yan ◽  
Jingyi Wei
Keyword(s):  
Support Vector Machine ◽  
Radiative Transfer ◽  
Chlorophyll Content ◽  
Radiative Transfer Model ◽  
Support Vector ◽  
Transfer Model

Retrieval of Chlorophyll Content from Leaf Reflectance Spectra Using Support Vector Machine

2014 ◽  
Vol 602-605 ◽  
pp. 2313-2316
Author(s):  
Jie Lv ◽  
Zhen Guo Yan
Keyword(s):  
Support Vector Machine ◽  
Chlorophyll Content ◽  
Hyperspectral Data ◽  
Coefficient Of Determination ◽  
Radiative Transfer Model ◽  
Support Vector ◽  
Transfer Model ◽  
Estimation Model ◽  
Suzhou City ◽  
Health Situation

The chlorophyll content in crop leaf is an indicator of health situation and the crop yield. Hence, it is very important to retrieval of accurate chlorophyll content in paddy rice. This research selected Zhengyi town of Suzhou city as the study area, measurements were acquired during the summer of 2009, in a field campaign in which for 288 rice leaf samples, rice hyperspectral data was measured by ASD FieldSpec3 spectrometer, chlorophyll content was measured by using a SPAD-502 chlorophyll meter. And the parameters of support vector machine were optimized by genetic algorithm, then support vector machine and PROSPECT radiative transfer model were adopted to build estimation model, which used to retrieve the chlorophyll content of rice. The results indicate that: the coefficient of determination for the rice chlorophyll estimation model is 0.8825, and RMSE is 8.7491. Research of this paper provides some reference for quickly and accurately estimating the chlorophyll content in rice.

Non-destructive estimation of potato leaf chlorophyll and protein contents from hyperspectral measurements using the PROSPECT radiative transfer model

2006 ◽  
Vol 86 (1) ◽  
pp. 279-291 ◽  
Author(s):  
E. J. Botha ◽  
B. J. Zebarth ◽  
B. Leblon
Keyword(s):  
Solanum Tuberosum ◽  
Radiative Transfer ◽  
Protein Content ◽  
Chlorophyll Content ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Potato Leaf ◽  
Leaf Reflectance ◽  
Leaf Chlorophyll ◽  
N Status

Optimizing nitrogen (N) fertilization in potato (Solanum tuberosum L.) production by in-season measurements of potato N status may improve fertilizer N-use efficiency. Hyperspectral leaf reflectance and transmittance measurements can be used to assess potato N status by estimating leaf chlorophyll or N contents. This study evaluated the ability of the inverted PROSPECT radiative transfer model to predict leaf chlorophyll and N (as protein) contents. Trials were conducted with Russet Burbank and Shepody potato cultivars under different N fertility rates (0 to 300 kg N ha-1) in 2001 and 2002. Leaf reflectance and transmittance, leaf chlorophyll content, and leaf protein content were measured. Leaf chlorophyll and protein content correlated significantly (r = 0.16*, n = 584), but the relationship was strongly dependent on sampling date (r = 0.55* to 0.92*). Chlorophyll content was predicted with reasonable accuracy by the model, particularly in 2002. The low estimation accuracy in 2001 was probably related to sample variability induced by prolonged drought conditions. Protein content could not be predicted with any degree of accuracy by the model. The relative success of the PROSPECT model to predict chlorophyll content, and the good correlation between leaf chlorophyll and leaf N, suggests that it might be used as a component of a more complex leaf-canopy reflectance model to estimate chlorophyll content from reflectance spectra at the canopy level. Key words: Leaf reflectance, PROSPECT radiative transfer model, Solanum tuberosum

Probing clouds in planets with a simple radiative transfer model: the Jupiter case

2012 ◽  
Vol 33 (6) ◽  
pp. 1611-1624 ◽  
Author(s):  
Iñigo Mendikoa ◽  
Santiago Pérez-Hoyos ◽  
Agustín Sánchez-Lavega
Keyword(s):  
Radiative Transfer ◽  
Radiative Transfer Model ◽  
Transfer Model

scholarly journals Mapping barrier island soil moisture using a radiative transfer model of hyperspectral imagery from an unmanned aerial system

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rehman S. Eon ◽  
Charles M. Bachmann
Keyword(s):  
Soil Moisture ◽  
Radiative Transfer ◽  
Moisture Content ◽  
Layer Thickness ◽  
Soil Moisture Content ◽  
Hyperspectral Imagery ◽  
Barrier Island ◽  
Water Layer ◽  
Radiative Transfer Model ◽  
Transfer Model

AbstractThe advent of remote sensing from unmanned aerial systems (UAS) has opened the door to more affordable and effective methods of imaging and mapping of surface geophysical properties with many important applications in areas such as coastal zone management, ecology, agriculture, and defense. We describe a study to validate and improve soil moisture content retrieval and mapping from hyperspectral imagery collected by a UAS system. Our approach uses a recently developed model known as the multilayer radiative transfer model of soil reflectance (MARMIT). MARMIT partitions contributions due to water and the sediment surface into equivalent but separate layers and describes these layers using an equivalent slab model formalism. The model water layer thickness along with the fraction of wet surface become parameters that must be optimized in a calibration step, with extinction due to water absorption being applied in the model based on equivalent water layer thickness, while transmission and reflection coefficients follow the Fresnel formalism. In this work, we evaluate the model in both field settings, using UAS hyperspectral imagery, and laboratory settings, using hyperspectral spectra obtained with a goniometer. Sediment samples obtained from four different field sites representing disparate environmental settings comprised the laboratory analysis while field validation used hyperspectral UAS imagery and coordinated ground truth obtained on a barrier island shore during field campaigns in 2018 and 2019. Analysis of the most significant wavelengths for retrieval indicate a number of different wavelengths in the short-wave infra-red (SWIR) that provide accurate fits to measured soil moisture content in the laboratory with normalized root mean square error (NRMSE)< 0.145, while independent evaluation from sequestered test data from the hyperspectral UAS imagery obtained during the field campaign obtained an average NRMSE = 0.169 and median NRMSE = 0.152 in a bootstrap analysis.

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