scholarly journals Spectral Diffractive Lenses for Measuring a Modified Red Edge Simple Ratio Index and a Water Band Index

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7694
Author(s):  
Veronika Blank ◽  
Roman Skidanov ◽  
Leonid Doskolovich ◽  
Nikolay Kazanskiy
Keyword(s):  
Vegetation Index ◽  
Vegetation Indices ◽  
Diffractive Lenses ◽  
Red Edge ◽  
Water Band ◽  
Direct Laser ◽  
Band Index ◽  
Simple Ratio ◽  
Imaging Sensor ◽  
Linear Scanning

We propose a novel type of spectral diffractive lenses that operate in the ±1-st diffraction orders. Such spectral lenses generate a sharp image of the wavelengths of interest in the +1-st and –1-st diffraction orders. The spectral lenses are convenient to use for obtaining remotely sensed vegetation index images instead of full-fledged hyperspectral images. We discuss the design and fabrication of spectral diffractive lenses for measuring vegetation indices, which include a Modified Red Edge Simple Ratio Index and a Water Band Index. We report synthesizing diffractive lenses with a microrelief thickness of 4 µm using the direct laser writing in a photoresist. The use of the fabricated spectral lenses in a prototype scheme of an imaging sensor for index measurements is discussed. Distributions of the aforesaid spectral indices are obtained by the linear scanning of vegetation specimens. Using a linear scanning of vegetation samples, distributions of the above-said water band index were experimentally measured.

scholarly journals Retrieving the Diurnal FPAR of a Maize Canopy from the Jointing Stage to the Tasseling Stage with Vegetation Indices under Different Water Stresses and Light Conditions

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3965 ◽  
Author(s):  
Liang Zhao ◽  
Zhigang Liu ◽  
Shan Xu ◽  
Xue He ◽  
Zhuoya Ni ◽  
...  
Keyword(s):  
Regression Models ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Quadratic Function ◽  
High Temporal Resolution ◽  
Area Index ◽  
Red Edge ◽  
Jointing Stage ◽  
Simple Ratio

The fraction of absorbed photosynthetically active radiation (FPAR) is a key variable in the model of vegetation productivity. Vegetation indices (VIs) that were derived from instantaneous remote-sensing data have been successfully used to estimate the FPAR of a day or a longer period. However, it has not yet been verified whether continuous VIs can be used to accurately estimate the diurnal dynamics of a vegetation canopy FPAR, which may fluctuate dramatically within a day. In this study, we measured the high temporal resolution spectral data (480 to 850 nm) and FPAR data of a maize canopy from the jointing stage to the tasseling stage under different irrigation and illumination conditions using two automatic observation systems. To estimate the FPAR, we developed regression models based on a quadratic function using 13 kinds of VIs. The results show the following: (1) Under nondrought conditions, although the illumination condition (sunny or cloudy) influenced the trend of the canopy diurnal FPAR, it had only a slight effect on the model accuracies of the FPAR-VIs. The maximum coefficients of determination (R2) of the FPAR-VIs models generated for the sunny nondrought data, the cloudy nondrought data, and all of the nondrought data were 0.895, 0.88, and 0.828, respectively. The VIs—including normalized difference vegetation index (NDVI), green NDVI (GNDVI), red-edge simple ratio (SR705), modified simple ratio 2 (mSR2), red-edge normalized difference vegetation index (NDVI705), and enhanced vegetation index (EVI)—that were related to the canopy structure had higher estimation accuracies (R2 > 0.8) than the other VIs that were related to the soil adjustment, chlorophyll, and physiology. The estimation accuracies of the GNDVI and some red-edge VIs (including NDVI705, SR705, and mSR2) were higher than the estimation accuracy of the NDVI. (2) Under drought stress, the FPAR decreased significantly because of leaf wilting and the effective leaf area index decrease around noon. When we included drought data in the model, accuracies were reduced dramatically and the R2 value of the best model was only 0.59. When we built the regression models based only on drought data, the EVI, which can weaken the influence of soil, had the best estimate accuracy (R2 = 0.68).

scholarly journals ESTIMATION OF MANGROVE FOREST ABOVEGROUND BIOMASS USING MULTISPECTRAL BANDS, VEGETATION INDICES AND BIOPHYSICAL VARIABLES DERIVED FROM OPTICAL SATELLITE IMAGERIES: RAPIDEYE, PLANETSCOPE AND SENTINEL-2

2018 ◽  
Vol IV-3 ◽  
pp. 29-36 ◽  
Author(s):  
Alvin Balidoy Baloloy ◽  
Ariel Conferido Blanco ◽  
Christian Gumbao Candido ◽  
Reginal Jay Labadisos Argamosa ◽  
John Bart Lovern Caboboy Dumalag ◽  
...  
Keyword(s):  
Linear Regression ◽  
Mangrove Forest ◽  
Field Data ◽  
Vegetation Index ◽  
Prediction Models ◽  
Vegetation Indices ◽  
Satellite Systems ◽  
Red Edge ◽  
Simple Ratio ◽  
Sentinel 2

Aboveground biomass estimation (AGB) is essential in determining the environmental and economic values of mangrove forests. Biomass prediction models can be developed through integration of remote sensing, field data and statistical models. This study aims to assess and compare the biomass predictor potential of multispectral bands, vegetation indices and biophysical variables that can be derived from three optical satellite systems: the Sentinel-2 with 10&amp;thinsp;m, 20&amp;thinsp;m and 60&amp;thinsp;m resolution; RapidEye with 5m resolution and PlanetScope with 3m ground resolution. Field data for biomass were collected from a <i>Rhizophoraceae</i>-dominated mangrove forest in Masinloc, Zambales, Philippines where 30 test plots (1.2&amp;thinsp;ha) and 5 validation plots (0.2&amp;thinsp;ha) were established. Prior to the generation of indices, images from the three satellite systems were pre-processed using atmospheric correction tools in SNAP (Sentinel-2), ENVI (RapidEye) and python (PlanetScope). The major predictor bands tested are Blue, Green and Red, which are present in the three systems; and Red-edge band from Sentinel-2 and Rapideye. The tested vegetation index predictors are Normalized Differenced Vegetation Index (NDVI), Soil-adjusted Vegetation Index (SAVI), Green-NDVI (GNDVI), Simple Ratio (SR), and Red-edge Simple Ratio (SRre). The study generated prediction models through conventional linear regression and multivariate regression. Higher coefficient of determination (r<sup>2</sup>) values were obtained using multispectral band predictors for Sentinel-2 (r<sup>2</sup>&amp;thinsp;=&amp;thinsp;0.89) and Planetscope (r<sup>2</sup>&amp;thinsp;=&amp;thinsp;0.80); and vegetation indices for RapidEye (r<sup>2</sup>&amp;thinsp;=&amp;thinsp;0.92). Multivariate Adaptive Regression Spline (MARS) models performed better than the linear regression models with r<sup>2</sup> ranging from 0.62 to 0.92. Based on the r<sup>2</sup> and root-mean-square errors (RMSE’s), the best biomass prediction model per satellite were chosen and maps were generated. The accuracy of predicted biomass maps were high for both Sentinel-2 (r<sup>2</sup>&amp;thinsp;=&amp;thinsp;0.92) and RapidEye data (r<sup>2</sup>&amp;thinsp;=&amp;thinsp;0.91).

scholarly journals DISEASE DETECTION FROM FIELD SPECTROMETER DATA

2013 ◽  
Vol 14 (2) ◽  
Author(s):  
O. H. Tawfik ◽  
H. Z. Mohd Shafri ◽  
Ali A. Mohammed
Keyword(s):  
Oil Palm ◽  
Large Scale ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Red Edge ◽  
Plant Stage ◽  
Simple Ratio ◽  
Stage 1 ◽  
Disease Stages ◽  
Spectrometer Data

ABSTRACT: Oil palm plants have been planted in large scale of areas. Ganoderma disease has been recognized and diagnosed in oil palm plants to infect almost half of the oil palm plants in Malaysia. To deal with this problem, the use of vegetation indices analysis on hyper spectral field data we will examine the ability of this data in discrimination between Ganoderma disease stages in oil palm plants which will be helpful in control the spread of the diseases. By using vegetation indices the oil palm plants could be classified into 1 (T1 healthy), 2 (T2 semi healthy) and 3 (T3 severe damage) plant classes accurately. The results showed that the best vegetation index is the Modified Red Edge Simple Ratio (MSR705) among the vegetation indices to discriminate between oil palm health stages. It was realized that the modification that was applied to the Modified Red Edge Simple Ratio (MSR705) index of Narrowband greenness VIs has been exhibited an acceptable results in differentiate between the oil palm plant stage 1 (T1 healthy) and stage 2 (T2 semi healthy). ABSTRAK: Tanaman kelapa sawit ditanam secara meluas.  Penyakit ganoderma dikenali dan didiagnosikan menjangkiti hampir separuh tanaman kelapa sawit di Malaysia. Untuk mengawal penyakit ini daripada merebak, analisis indeks tanaman dijalankan ke atas data kawasan spektrum melampau di mana keupayaan data ini diuji dalam membezakan peringkat-peringkat penyakit Ganoderma terhadap tanaman kelapa sawit. Dengan menggunakan indeks tanaman, kelapa sawit dapat diklasifikasikan kepada 1 (T1 sihat), 2 (T2 separa sihat) dan 3 (T3 rosak); kelas tanaman dengan tepat. Keputusan menunjukkan indeks tanaman terbaik sebagai Modified Red Edge Simple Ratio (MSR705) yang merupakan indeks tanaman dalam membezakan peringkat kesihatan kelapa sawit. Adalah didapati pengubahsuaian terhadap indeks Modified Red Edge Simple Ratio (MSR705) yang juga indeks Jalur Sempit Hijau VI telah memberikan keputusan yang munasabah dalam membezakan peringkat tanaman kelapa sawit peringkat 1 (T1 sihat) dan peringkat 2 (T2 separa sihat).

scholarly journals Aboveground Biomass Mapping of Crops Supported by Improved CASA Model and Sentinel-2 Multispectral Imagery

2021 ◽  
Vol 13 (14) ◽  
pp. 2755
Author(s):  
Peng Fang ◽  
Nana Yan ◽  
Panpan Wei ◽  
Yifan Zhao ◽  
Xiwang Zhang
Keyword(s):  
Aboveground Biomass ◽  
Production Management ◽  
Vegetation Index ◽  
Net Primary Productivity ◽  
Vegetation Indices ◽  
Biophysical Parameters ◽  
Edge Information ◽  
Casa Model ◽  
Red Edge ◽  
Biomass Mapping

The net primary productivity (NPP) and aboveground biomass mapping of crops based on remote sensing technology are not only conducive to understanding the growth and development of crops but can also be used to monitor timely agricultural information, thereby providing effective decision making for agricultural production management. To solve the saturation problem of the NDVI in the aboveground biomass mapping of crops, the original CASA model was improved using narrow-band red-edge information, which is sensitive to vegetation chlorophyll variation, and the fraction of photosynthetically active radiation (FPAR), NPP, and aboveground biomass of winter wheat and maize were mapped in the main growing seasons. Moreover, in this study, we deeply analyzed the seasonal change trends of crops’ biophysical parameters in terms of the NDVI, FPAR, actual light use efficiency (LUE), and their influence on aboveground biomass. Finally, to analyze the uncertainty of the aboveground biomass mapping of crops, we further discussed the inversion differences of FPAR with different vegetation indices. The results demonstrated that the inversion accuracies of the FPAR of the red-edge normalized vegetation index (NDVIred-edge) and red-edge simple ratio vegetation index (SRred-edge) were higher than those of the original CASA model. Compared with the reference data, the accuracy of aboveground biomass estimated by the improved CASA model was 0.73 and 0.70, respectively, which was 0.21 and 0.13 higher than that of the original CASA model. In addition, the analysis of the FPAR inversions of different vegetation indices showed that the inversion accuracies of the red-edge vegetation indices NDVIred-edge and SRred-edge were higher than those of the other vegetation indices, which confirmed that the vegetation indices involving red-edge information can more effectively retrieve FPAR and aboveground biomass of crops.

scholarly journals Effects of Crop Leaf Angle on LAI-Sensitive Narrow-Band Vegetation Indices Derived from Imaging Spectroscopy

2018 ◽  
Vol 8 (9) ◽  
pp. 1435 ◽  
Author(s):  
Xiaochen Zou ◽  
Iina Haikarainen ◽  
Iikka Haikarainen ◽  
Pirjo Mäkelä ◽  
Matti Mõttus ◽  
...  
Keyword(s):  
Chlorophyll Content ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Imaging Spectroscopy ◽  
Leaf Angle ◽  
Field Crops ◽  
Leaf Chlorophyll ◽  
Simple Ratio ◽  
The Relationship

Leaf area index (LAI) is an important biophysical variable for understanding the radiation use efficiency of field crops and their potential yield. On a large scale, LAI can be estimated with the help of imaging spectroscopy. However, recent studies have revealed that the leaf angle greatly affects the spectral reflectance of the canopy and hence imaging spectroscopy data. To investigate the effects of the leaf angle on LAI-sensitive narrowband vegetation indices, we used both empirical measurements from field crops and model-simulated data generated by the PROSAIL canopy reflectance model. We found the relationship between vegetation indices and LAI to be notably affected, especially when the leaf mean tilt angle (MTA) exceeded 70 degrees. Of the indices used in the study, the modified soil-adjusted vegetation index (MSAVI) was most strongly affected by leaf angles, while the blue normalized difference vegetation index (BNDVI), the green normalized difference vegetation index (GNDVI), the modified simple ratio using the wavelength of 705 nm (MSR705), the normalized difference vegetation index (NDVI), and the soil-adjusted vegetation index (SAVI) were only affected for sparse canopies (LAI < 3) and MTA exceeding 60°. Generally, the effect of MTA on the vegetation indices increased as a function of decreasing LAI. The leaf chlorophyll content did not affect the relationship between BNDVI, MSAVI, NDVI, and LAI, while the green atmospherically resistant index (GARI), GNDVI, and MSR705 were the most strongly affected indices. While the relationship between SR and LAI was somewhat affected by both MTA and the leaf chlorophyll content, the simple ratio (SR) displayed only slight saturation with LAI, regardless of MTA and the chlorophyll content. The best index found in the study for LAI estimation was BNDVI, although it performed robustly only for LAI > 3 and showed considerable nonlinearity. Thus, none of the studied indices were well suited for across-species LAI estimation: information on the leaf angle would be required for remote LAI measurement, especially at low LAI values. Nevertheless, narrowband indices can be used to monitor the LAI of crops with a constant leaf angle distribution.

scholarly journals Spectral variables, growth analysis and yield of sugarcane

2005 ◽  
Vol 62 (3) ◽  
pp. 199-207 ◽  
Author(s):  
Maurício dos Santos Simões ◽  
Jansle Vieira Rocha ◽  
Rubens Augusto Camargo Lamparelli
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Lower Amount ◽  
Total Biomass ◽  
Area Index ◽  
Spectral Vegetation Indices ◽  
Green Vegetation ◽  
Highly Correlated ◽  
Simple Ratio

Spectral information is well related with agronomic variables and can be used in crop monitoring and yield forecasting. This paper describes a multitemporal research with the sugarcane variety SP80-1842, studying its spectral behavior using field spectroscopy and its relationship with agronomic parameters such as leaf area index (LAI), number of stalks per meter (NPM), yield (TSS) and total biomass (BMT). A commercial sugarcane field in Araras/SP/Brazil was monitored for two seasons. Radiometric data and agronomic characterization were gathered in 9 field campaigns. Spectral vegetation indices had similar patterns in both seasons and adjusted to agronomic parameters. Band 4 (B4), Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI), and Soil Adjusted Vegetation Index (SAVI) increased their values until the end of the vegetative stage, around 240 days after harvest (DAC). After that stage, B4 reflectance and NDVI values began to stabilize and decrease because the crop reached ripening and senescence stages. Band 3 (B3) and RVI presented decreased values since the beginning of the cycle, followed by a stabilization stage. Later these values had a slight increase caused by the lower amount of green vegetation. Spectral variables B3, RVI, NDVI, and SAVI were highly correlated (above 0.79) with LAI, TSS, and BMT, and about 0.50 with NPM. The best regression models were verified for RVI, LAI, and NPM, which explained 0.97 of TSS variation and 0.99 of BMT variation.

scholarly journals A Transformed Triangular Vegetation Index for Estimating Winter Wheat Leaf Area Index

2019 ◽  
Vol 12 (1) ◽  
pp. 16 ◽  
Author(s):  
Naichen Xing ◽  
Wenjiang Huang ◽  
Qiaoyun Xie ◽  
Yue Shi ◽  
Huichun Ye ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Saturation Effect ◽  
Estimation Accuracy ◽  
Growth Monitoring ◽  
Area Index ◽  
Multispectral Data ◽  
Red Edge

Leaf area index (LAI) is a key parameter in plant growth monitoring. For several decades, vegetation indices-based empirical method has been widely-accepted in LAI retrieval. A growing number of spectral indices have been proposed to tailor LAI estimations, however, saturation effect has long been an obstacle. In this paper, we classify the selected 14 vegetation indices into five groups according to their characteristics. In this study, we proposed a new index for LAI retrieval-transformed triangular vegetation index (TTVI), which replaces NIR and red bands of triangular vegetation index (TVI) into NIR and red-edge bands. All fifteen indices were calculated and analyzed with both hyperspectral and multispectral data. Best-fit models and k-fold cross-validation were conducted. The results showed that TTVI performed the best predictive power of LAI for both hyperspectral and multispectral data, and mitigated the saturation effect. The R2 and RMSE values were 0.60, 1.12; 0.59, 1.15, respectively. Besides, TTVI showed high estimation accuracy for sparse (LAI < 4) and dense canopies (LAI > 4). Our study provided the value of the Red-edge bands of the Sentinel-2 satellite sensors in crop LAI retrieval, and demonstrated that the new index TTVI is applicable to inverse LAI for both low-to-moderate and moderate-to-high vegetation cover.

scholarly journals Crop Type Classification Using Vegetation Indices of RapidEye Imagery

2014 ◽  
Vol XL-7 ◽  
pp. 195-198 ◽  
Author(s):  
M. Ustuner ◽  
F. B. Sanli ◽  
S. Abdikan ◽  
M. T. Esetlili ◽  
Y. Kurucu
Keyword(s):  
Remote Sensing ◽  
Classification Accuracy ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Support Vector ◽  
Red Edge ◽  
Crop Type ◽  
Type Classification

Cutting-edge remote sensing technology has a significant role for managing the natural resources as well as the any other applications about the earth observation. Crop monitoring is the one of these applications since remote sensing provides us accurate, up-to-date and cost-effective information about the crop types at the different temporal and spatial resolution. In this study, the potential use of three different vegetation indices of RapidEye imagery on crop type classification as well as the effect of each indices on classification accuracy were investigated. The Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), and the Normalized Difference Red Edge Index (NDRE) are the three vegetation indices used in this study since all of these incorporated the near-infrared (NIR) band. RapidEye imagery is highly demanded and preferred for agricultural and forestry applications since it has red-edge and NIR bands. The study area is located in Aegean region of Turkey. Radial Basis Function (RBF) kernel was used here for the Support Vector Machines (SVMs) classification. Original bands of RapidEye imagery were excluded and classification was performed with only three vegetation indices. The contribution of each indices on image classification accuracy was also tested with single band classification. Highest classification accuracy of 87, 46 % was obtained using three vegetation indices. This obtained classification accuracy is higher than the classification accuracy of any dual-combination of these vegetation indices. Results demonstrate that NDRE has the highest contribution on classification accuracy compared to the other vegetation indices and the RapidEye imagery can get satisfactory results of classification accuracy without original bands.

scholarly journals Use of Selected Spectral Ratios to Assess the Response of Pineapple to Potassium Nutrition

2021 ◽  
Vol 1 (1) ◽  
pp. 16-22
Author(s):  
Siva K. Balasundram ◽  
Yen Mee Chong
Keyword(s):  
Vegetation Index ◽  
Spectral Response ◽  
Vegetation Indices ◽  
Biochemical Properties ◽  
Canopy Reflectance ◽  
Red Edge ◽  
K Deficiency ◽  
High Precipitation ◽  
Spectral Ranges ◽  
Biophysical Changes

Potassium (K) nutrition in pineapple grown on tropical peat can be problematic due to high precipitation which encourages leaching losses. Non-destructive tools that can assess K deficiency and the accompanying changes in biophysical and biochemical properties within pineapple is a good strategy to employ. In this study, we assessed the biophysical changes in pineapple (var. MD2) in response to different K rates by using a hyperspectral approach. K deficiency was detected at 171 days after planting. Shortage of K also exhibited a shift in red edge towards shorter wavelengths between 500-700 nm. In addition, spectral ranges of 430-680 nm, as well as 680-752 nm were found to be most effective in differentiating spectral response to varying K rates. Three vegetation indices, i.e. Normalized Pigment Chlorophyll Index (NPCI), Plant Senescence Index (PSRI) and Red-edge Vegetation Index (RVSI) were found to best describe K treatment effects on pineapple canopy reflectance. This study could be extended further to include pineapple varieties other than MD2, and also key nutrients, such as N and P, for better fertilizer management in peat-grown pineapple.

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