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.

Early stress detection in grapevine – can remotely-sensed sun-induced chlorophyll fluorescence do the job?

2021 ◽  
Author(s):  
Georg Wohlfahrt ◽  
Albin Hammerle ◽  
Barbara Rainer ◽  
Florian Haas
Keyword(s):  
Chlorophyll Fluorescence ◽  
Vegetation Index ◽  
Near Infrared ◽  
Census Data ◽  
Meteorological Data ◽  
Vegetation Indices ◽  
Wavelength Region ◽  
Remote Sensing Data ◽  
Warning Signs ◽  
Mitigation Measures

&lt;p&gt;Ongoing changes in climate (both in the means and the extremes) are increasingly challenging grapevine production in the province of South Tyrol (Italy). Here we ask the question whether sun-induced chlorophyll fluorescence (SIF) observed remotely from space can detect early warning signs of stress in grapevine and thus help guide mitigation measures.&lt;/p&gt;&lt;p&gt;Chlorophyll fluorescence refers to light absorbed by chlorophyll molecules that is re-emitted in the red to far-red wavelength region. Previous research at leaf and canopy scale indicated that SIF correlates with the plant photosynthetic uptake of carbon dioxide as it competes for the same energy pool.&lt;/p&gt;&lt;p&gt;To address this question, we use time series of two down-scaled SIF products (GOME-2 and OCO-2, 2007/14-2018) as well as the original OCO-2 data (2014-2019). As a benchmark, we use several vegetation indices related to canopy greenness, as well as a novel near-infrared radiation-based vegetation index (2000-2019). Meteorological data fields are used to explore possible weather-related causes for observed deviations in remote sensing data. Regional DOC grapevine census data (2000-2019) are used as a reference for the analyses.&lt;/p&gt;

Estimation of chlorophyll‐a concentration in Lake Tai, China using in situ hyperspectral data

2006 ◽  
Vol 27 (19) ◽  
pp. 4267-4276 ◽  
Author(s):  
H. B. Jiao ◽  
Y. Zha ◽  
J. Gao ◽  
Y. M. Li ◽  
Y. C. Wei ◽  
...  
Keyword(s):  
Chlorophyll A ◽  
Hyperspectral Data ◽  
Chlorophyll A Concentration

scholarly journals TOTAL SUSPENDED SOLID DISTRIBUTION ANALYSIS USING SPOT-6 DATA IN SEGARA ANAKAN, CILACAP

2018 ◽  
Vol 5 (2) ◽  
pp. 177 ◽  
Author(s):  
Aisya Jaya Dhannahisvara ◽  
Hartono Harjo ◽  
Pramaditya Wicaksono ◽  
Ferman Setia Nugroho
Keyword(s):  
Chlorophyll A ◽  
Empirical Model ◽  
Field Data ◽  
Near Infrared ◽  
Total Suspended Solid ◽  
Suspended Solid ◽  
Distribution Analysis ◽  
Chlorophyll A Concentration ◽  
Accuracy Test ◽  
Suspended Solid Concentration

Spatial distribution and concentration of Total Suspended Solid (TSS) is one of the coastal parameters which are required to be examined in order to understand the quality of the water. Rapid development of remote sensing technology has resulted in the emergence of various methods to estimate TSS concentration. SPOT-6 data has spatial, spectral, and temporal characteristics that can be used to estimate TSS concentration. The purposes of this research are (1) to determine the best method for estimating TSS concentration, (2) to map TSS distribution, and (3) to determine the correlation between TSS concentration and chlorophyll-a concentration using SPOT-6 data in Segara Anakan. The estimation of TSS concentration in this research was performed using empirical model built from SPOT-6 and TSS field data. Bands used in this research are single band data (blue, green, red, and near infrared) and transformed bands such as band ratio (12 combinations), Normalized Difference Suspended Solid Index (NDSSI), and Suspended Solid Concentration Index (SSC). The result shows that blue, green, red, and near infrared bands and SSC index significantly correlated to TSS. Afterwards, regression analysis was performed to determine the function that can be used to predict TSS concentration using SPOT-6 data. Regression function used are linear and non-linear (exponential, logarithmic, 2nd order polynomial, and power). The best model was chosen based on the accuracy assessment using Standard Error of Estimate (SE). The selected model was used to calculate total TSS concentration and was correlated with chlorophyll-a field data. The result of accuracy test shows that the model from blue band has an accuracy of 70.68 %, green band 70.68 %, red band 75.73 %, near infrared band 65.58 %, and SSC 73.67 %. The accuracy test shows that red band produced the best prediction model for mapping TSS concentration distribution. The total TSS concentration, which was calculated using red band empirical model, is estimated to be 6.13 t. According to the correlation test, TSS concentration in Segara Anakan has no significant correlation with chlorophyll-a concentration, with a coefficient correlation value of -0.265.

A compact hyperspectral camera for measurement of perfusion parameters in medicine

2018 ◽  
Vol 63 (5) ◽  
pp. 519-527 ◽  
Author(s):  
Axel Kulcke ◽  
Amadeus Holmer ◽  
Philip Wahl ◽  
Frank Siemers ◽  
Thomas Wild ◽  
...  
Keyword(s):  
Clinical Practice ◽  
Near Infrared ◽  
Chronic Wounds ◽  
Relative Concentration ◽  
Problem Area ◽  
Hyperspectral Data ◽  
Perfusion Index ◽  
Wound Treatment ◽  
Two Parameters ◽  
Hyperspectral Camera

Abstract Worldwide, chronic wounds are still a major and increasing problem area in medicine with protracted suffering of patients and enormous costs. Beside conventional wound treatment, for instance kinds of oxygen therapy and cold plasma technology have been tested, providing an improvement in the perfusion of wounds and their healing potential, but these methods are unfortunately not sufficiently validated and accepted for clinical practice to date. Using hyperspectral imaging technology in the visible (VIS) and near infrared (NIR) region with high spectral and spatial resolution, perfusion parameters of tissue and wounds can be determined. We present a new compact hyperspectral camera which can be used in clinical practice. From hyperspectral data the hemoglobin oxygenation (StO2), the relative concentration of hemoglobin [tissue hemoglobin index (THI)] and the so-called NIR-perfusion index can be determined. The first two parameters are calculated from the VIS-part of the spectrum and represent the perfusion of superficial tissue layers, whereas the NIR-perfusion index is calculated from the NIR-part representing the perfusion in deeper layers. First clinical measurements of transplanted flaps and chronic ulcer wounds show, that the perfusion level can be determined quantitatively allowing sensitive evaluation and monitoring for an optimization of the wound treatment planning and for validation of new treatment methods.

scholarly journals Vertical distribution of chlorophyll <I>a</I> concentration and phytoplankton community composition from in situ fluorescence profiles: a first database for the global ocean

2015 ◽  
Vol 7 (2) ◽  
pp. 261-273 ◽  
Author(s):  
R. Sauzède ◽  
H. Lavigne ◽  
H. Claustre ◽  
J. Uitz ◽  
C. Schmechtig ◽  
...  
Keyword(s):  
Chlorophyll Fluorescence ◽  
Chlorophyll A ◽  
Community Composition ◽  
Phytoplankton Biomass ◽  
Phytoplankton Community ◽  
Environmental Data ◽  
Global Ocean ◽  
Control Procedure ◽  
Chlorophyll A Concentration ◽  
Phytoplankton Community Composition

Abstract. In vivo chlorophyll a fluorescence is a proxy of chlorophyll a concentration, and is one of the most frequently measured biogeochemical properties in the ocean. Thousands of profiles are available from historical databases and the integration of fluorescence sensors to autonomous platforms has led to a significant increase of chlorophyll fluorescence profile acquisition. To our knowledge, this important source of environmental data has not yet been included in global analyses. A total of 268 127 chlorophyll fluorescence profiles from several databases as well as published and unpublished individual sources were compiled. Following a robust quality control procedure detailed in the present paper, about 49 000 chlorophyll fluorescence profiles were converted into phytoplankton biomass (i.e., chlorophyll a concentration) and size-based community composition (i.e., microphytoplankton, nanophytoplankton and picophytoplankton), using a method specifically developed to harmonize fluorescence profiles from diverse sources. The data span over 5 decades from 1958 to 2015, including observations from all major oceanic basins and all seasons, and depths ranging from the surface to a median maximum sampling depth of around 700 m. Global maps of chlorophyll a concentration and phytoplankton community composition are presented here for the first time. Monthly climatologies were computed for three of Longhurst's ecological provinces in order to exemplify the potential use of the data product. Original data sets (raw fluorescence profiles) as well as calibrated profiles of phytoplankton biomass and community composition are available on open access at PANGAEA, Data Publisher for Earth and Environmental Science. Raw fluorescence profiles: http://doi.pangaea.de/10.1594/PANGAEA.844212 and Phytoplankton biomass and community composition: http://doi.pangaea.de/10.1594/PANGAEA.844485

Wildfire detection and monitoring by using PRISMA hyperspectral data and convolutional neural networks

2021 ◽  
Author(s):  
Dario Spiller ◽  
Luigi Ansalone ◽  
Nicolas Longépé ◽  
James Wheeler ◽  
Pierre Philippe Mathieu
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Near Infrared ◽  
Fire Detection ◽  
Input Image ◽  
Hyperspectral Data ◽  
Observation Data ◽  
Hyperspectral Camera

&lt;p&gt;Over the last few years, wildfires have become more severe and destructive, having extreme consequences on local and global ecosystems. Fire detection and accurate monitoring of risk areas is becoming increasingly important. Satellite remote sensing offers unique opportunities for mapping, monitoring, and analysing the evolution of wildfires, providing helpful contributions to counteract dangerous situations.&lt;/p&gt;&lt;p&gt;Among the different remote sensing technologies, hyper-spectral (HS) imagery presents nonpareil features in support to fire detection. In this study, HS images from the Italian satellite PRISMA (PRecursore IperSpettrale della Missione Applicativa) will be used. The PRISMA satellite, launched on 22 March 2019, holds a hyperspectral and panchromatic&amp;#160; payload which is able to acquire images with a worldwide coverage. The hyperspectral camera works in the spectral range of 0.4&amp;#8211;2.5 &amp;#181;m, with 66 and 173 channels in the VNIR (Visible and Near InfraRed) and SWIR (Short-Wave InfraRed) regions, respectively. The average spectral resolution is less than 10 nm on the entire range with an accuracy of &amp;#177;0.1 nm, while the ground sampling distance of PRISMA images is about 5 m and 30 m for panchromatic and hyperspectral camera, respectively.&lt;/p&gt;&lt;p&gt;This work will investigate how PRISMA HS images can be used to support fire detection and related crisis management. To this aim, deep learning methodologies will be investigated, as 1D convolutional neural networks to perform spectral analysis of the data or 3D convolutional neural networks to perform spatial and spectral analyses at the same time. Semantic segmentation of input HS data will be discussed, where an output image with metadata will be associated to each pixels of the input image. The overall goal of this work is to highlight how PRISMA hyperspectral data can contribute to remote sensing and Earth-observation data analysis with regard to natural hazard and risk studies focusing specially on wildfires, also considering the benefits with respect to standard multi-spectral imagery or previous hyperspectral sensors such as Hyperion.&lt;/p&gt;&lt;p&gt;The contributions of this work to the state of the art are the following:&lt;/p&gt;&lt;ul&gt;&lt;li&gt;Demonstrating the advantages of using PRISMA HS data over using multi-spectral data.&lt;/li&gt; &lt;li&gt;Discussing the potentialities of deep learning methodologies based on 1D and 3D convolutional neural networks to catch spectral (and spatial for the 3D case) dependencies, which is crucial when dealing with HS images.&lt;/li&gt; &lt;li&gt;Discussing the possibility and benefit to integrate HS-based approach in future monitoring systems in case of wildfire alerts and disasters.&lt;/li&gt; &lt;li&gt;Discussing the opportunity to design and develop future missions for HS remote sensing specifically dedicated for fire detection with on-board analysis.&lt;/li&gt; &lt;/ul&gt;&lt;p&gt;To conclude, this work will raise awareness in the potentialities of using PRISMA HS data for disasters monitoring with specialized focus on wildfires.&lt;/p&gt;

scholarly journals Estimating Rice Agronomic Traits Using Drone-Collected Multispectral Imagery

2019 ◽  
Vol 11 (5) ◽  
pp. 545 ◽  
Author(s):  
Dimitris Stavrakoudis ◽  
Dimitrios Katsantonis ◽  
Kalliopi Kadoglidou ◽  
Argyris Kalaitzidis ◽  
Ioannis Gitas
Keyword(s):  
Near Infrared ◽  
Agronomic Traits ◽  
Oryza Sativa L ◽  
N Uptake ◽  
Vegetation Indices ◽  
Growth Stages ◽  
Linear Regression Models ◽  
Remotely Sensed Data ◽  
Cost Efficient ◽  
N Management

The knowledge of rice nitrogen (N) requirements and uptake capacity are fundamental for the development of improved N management. This paper presents empirical models for predicting agronomic traits that are relevant to yield and N requirements of rice (Oryza sativa L.) through remotely sensed data. Multiple linear regression models were constructed at key growth stages (at tillering and at booting), using as input reflectance values and vegetation indices obtained from a compact multispectral sensor (green, red, red-edge, and near-infrared channels) onboard an unmanned aerial vehicle (UAV). The models were constructed using field data and images from two consecutive years in a number of experimental rice plots in Greece (Thessaloniki Regional Unit), by applying four different N treatments (C0: 0 N kg∙ha−1, C1: 80 N kg∙ha−1, C2: 160 N kg∙ha−1, and C4: 320 N kg∙ha−1). Models for estimating the current crop status (e.g., N uptake at the time of image acquisition) and predicting the future one (e.g., N uptake of grains at maturity) were developed and evaluated. At the tillering stage, high accuracies (R2 ≥ 0.8) were achieved for N uptake and biomass. At the booting stage, similarly high accuracies were achieved for yield, N concentration, N uptake, biomass, and plant height, using inputs from either two or three images. The results of the present study can be useful for providing N recommendations for the two top-dressing fertilizations in rice cultivation, through a cost-efficient workflow.

scholarly journals Early Identification of Root Rot Disease by Using Hyperspectral Reflectance: The Case of Pathosystem Grapevine/Armillaria

2021 ◽  
Vol 13 (13) ◽  
pp. 2436
Author(s):  
Federico Calamita ◽  
Hafiz Ali Imran ◽  
Loris Vescovo ◽  
Mohamed Lamine Mekhalfi ◽  
Nicola La Porta
Keyword(s):  
Root Rot ◽  
Woody Plants ◽  
Near Infrared ◽  
Vegetation Indices ◽  
Cost Effective ◽  
Disease Diagnosis ◽  
Hyperspectral Data ◽  
Detection Methods ◽  
Root Rot Disease ◽  
Rot Disease

Armillaria genus represents one of the most common causes of chronic root rot disease in woody plants. Prompt recognition of diseased plants is crucial to control the pathogen. However, the current disease detection methods are limited at a field scale. Therefore, an alternative approach is needed. In this study, we investigated the potential of hyperspectral techniques to identify fungi-infected vs. healthy plants of Vitis vinifera. We used the hyperspectral imaging sensor Specim-IQ to acquire leaves’ reflectance data of the Teroldego Rotaliano grapevine cultivar. We analyzed three different groups of plants: healthy, asymptomatic, and diseased. Highly significant differences were found in the near-infrared (NIR) spectral region with a decreasing pattern from healthy to diseased plants attributable to the leaf mesophyll changes. Asymptomatic plants emerged from the other groups due to a lower reflectance in the red edge spectrum (around 705 nm), ascribable to an accumulation of secondary metabolites involved in plant defense strategies. Further significant differences were observed in the wavelengths close to 550 nm in diseased vs. asymptomatic plants. We evaluated several machine learning paradigms to differentiate the plant groups. The Naïve Bayes (NB) algorithm, combined with the most discriminant variables among vegetation indices and spectral narrow bands, provided the best results with an overall accuracy of 90% and 75% in healthy vs. diseased and healthy vs. asymptomatic plants, respectively. To our knowledge, this study represents the first report on the possibility of using hyperspectral data for root rot disease diagnosis in woody plants. Although further validation studies are required, it appears that the spectral reflectance technique, possibly implemented on unmanned aerial vehicles (UAVs), could be a promising tool for a cost-effective, non-invasive method of Armillaria disease diagnosis and mapping in-field, contributing to a significant step forward in precision viticulture.

scholarly journals SPECTRAL FEATURE ANALYSIS FOR QUANTITATIVE ESTIMATION OF CYANOBACTERIA CHLOROPHYLL-A

2016 ◽  
Vol XLI-B7 ◽  
pp. 91-98
Author(s):  
Yi Lin ◽  
Zhanglin Ye ◽  
Yugan Zhang ◽  
Jie Yu
Keyword(s):  
Chlorophyll A ◽  
Vegetation Index ◽  
Quantitative Estimation ◽  
Vegetation Indices ◽  
Spectral Feature ◽  
Hyperspectral Data ◽  
Single Band ◽  
Band Width ◽  
Band Model ◽  
Cyanobacteria Bloom

In recent years, lake eutrophication caused a large of Cyanobacteria bloom which not only brought serious ecological disaster but also restricted the sustainable development of regional economy in our country. <i>Chlorophyll-a</i> is a very important environmental factor to monitor water quality, especially for lake eutrophication. Remote sensed technique has been widely utilized in estimating the concentration of <i>chlorophyll-a</i> by different kind of vegetation indices and monitoring its distribution in lakes, rivers or along coastline. For each vegetation index, its quantitative estimation accuracy for different satellite data might change since there might be a discrepancy of spectral resolution and channel center between different satellites. The purpose this paper is to analyze the spectral feature of <i>chlorophyll-a</i> with hyperspectral data (totally 651 bands) and use the result to choose the optimal band combination for different satellites. The analysis method developed here in this study could be useful to recognize and monitor cyanobacteria bloom automatically and accrately. <br><br> In our experiment, the reflectance (from 350nm to 1000nm) of wild cyanobacteria in different consistency (from 0 to 1362.11ug/L) and the corresponding <i>chlorophyll-a</i> concentration were measured simultaneously. Two kinds of hyperspectral vegetation indices were applied in this study: simple ratio (SR) and narrow band normalized difference vegetation index (NDVI), both of which consists of any two bands in the entire 651 narrow bands. Then multivariate statistical analysis was used to construct the linear, power and exponential models. After analyzing the correlation between <i>chlorophyll-a</i> and single band reflectance, SR, NDVI respetively, the optimal spectral index for quantitative estimation of cyanobacteria <i>chlorophyll-a</i>, as well corresponding central wavelength and band width were extracted. Results show that: Under the condition of water disturbance, SR and NDVI are both suitable for quantitative estimation of <i>chlorophyll-a</i>, and more effective than the traditional single band model; the best regression models for SR, NDVI with <i>chlorophyll-a</i> are linear and power, respectively. Under the condition without water disturbance, the single band model works the best. For the SR index, there are two optimal band combinations, which is comprised of infrared (700nm-900nm) and blue-green range (450nm-550nm), infrared and red range (600nm-650nm) respectively, with band width between 45nm to 125nm. For NDVI, the optimal band combination includes the range from 750nm to 900nm and 700nm to 750nm, with band width less than 30nm. For single band model, band center located between 733nm-935nm, and its width mustn’t exceed the interval where band center located in. <br><br> This study proved , as for SR or NDVI, the centers and widths are crucial factors for quantitative estimating <i>chlorophyll-a</i>. As for remote sensor, proper spectrum channel could not only improve the accuracy of recognizing cyanobacteria bloom, but reduce the redundancy of hyperspectral data. Those results will provide better reference for designing the suitable spectrum channel of customized sensors for cyanobacteria bloom monitoring at a low altitude. In other words, this study is also the basic research for developing the real-time remote sensing monitoring system with high time and high spatial resolution.

scholarly journals SPECTRAL FEATURE ANALYSIS FOR QUANTITATIVE ESTIMATION OF CYANOBACTERIA CHLOROPHYLL-A

2016 ◽  
Vol XLI-B7 ◽  
pp. 91-98
Author(s):  
Yi Lin ◽  
Zhanglin Ye ◽  
Yugan Zhang ◽  
Jie Yu
Keyword(s):  
Chlorophyll A ◽  
Vegetation Index ◽  
Quantitative Estimation ◽  
Vegetation Indices ◽  
Spectral Feature ◽  
Hyperspectral Data ◽  
Single Band ◽  
Band Width ◽  
Band Model ◽  
Cyanobacteria Bloom

In recent years, lake eutrophication caused a large of Cyanobacteria bloom which not only brought serious ecological disaster but also restricted the sustainable development of regional economy in our country. &lt;i&gt;Chlorophyll-a&lt;/i&gt; is a very important environmental factor to monitor water quality, especially for lake eutrophication. Remote sensed technique has been widely utilized in estimating the concentration of &lt;i&gt;chlorophyll-a&lt;/i&gt; by different kind of vegetation indices and monitoring its distribution in lakes, rivers or along coastline. For each vegetation index, its quantitative estimation accuracy for different satellite data might change since there might be a discrepancy of spectral resolution and channel center between different satellites. The purpose this paper is to analyze the spectral feature of &lt;i&gt;chlorophyll-a&lt;/i&gt; with hyperspectral data (totally 651 bands) and use the result to choose the optimal band combination for different satellites. The analysis method developed here in this study could be useful to recognize and monitor cyanobacteria bloom automatically and accrately. &lt;br&gt;&lt;br&gt; In our experiment, the reflectance (from 350nm to 1000nm) of wild cyanobacteria in different consistency (from 0 to 1362.11ug/L) and the corresponding &lt;i&gt;chlorophyll-a&lt;/i&gt; concentration were measured simultaneously. Two kinds of hyperspectral vegetation indices were applied in this study: simple ratio (SR) and narrow band normalized difference vegetation index (NDVI), both of which consists of any two bands in the entire 651 narrow bands. Then multivariate statistical analysis was used to construct the linear, power and exponential models. After analyzing the correlation between &lt;i&gt;chlorophyll-a&lt;/i&gt; and single band reflectance, SR, NDVI respetively, the optimal spectral index for quantitative estimation of cyanobacteria &lt;i&gt;chlorophyll-a&lt;/i&gt;, as well corresponding central wavelength and band width were extracted. Results show that: Under the condition of water disturbance, SR and NDVI are both suitable for quantitative estimation of &lt;i&gt;chlorophyll-a&lt;/i&gt;, and more effective than the traditional single band model; the best regression models for SR, NDVI with &lt;i&gt;chlorophyll-a&lt;/i&gt; are linear and power, respectively. Under the condition without water disturbance, the single band model works the best. For the SR index, there are two optimal band combinations, which is comprised of infrared (700nm-900nm) and blue-green range (450nm-550nm), infrared and red range (600nm-650nm) respectively, with band width between 45nm to 125nm. For NDVI, the optimal band combination includes the range from 750nm to 900nm and 700nm to 750nm, with band width less than 30nm. For single band model, band center located between 733nm-935nm, and its width mustn’t exceed the interval where band center located in. &lt;br&gt;&lt;br&gt; This study proved , as for SR or NDVI, the centers and widths are crucial factors for quantitative estimating &lt;i&gt;chlorophyll-a&lt;/i&gt;. As for remote sensor, proper spectrum channel could not only improve the accuracy of recognizing cyanobacteria bloom, but reduce the redundancy of hyperspectral data. Those results will provide better reference for designing the suitable spectrum channel of customized sensors for cyanobacteria bloom monitoring at a low altitude. In other words, this study is also the basic research for developing the real-time remote sensing monitoring system with high time and high spatial resolution.

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