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E-mail
lina-he@zolix.com.cn
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Phone
13810146393
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Address
Feihong Road, Nanhu Avenue, Liangxi District, Wuxi City, Jiangsu Province
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Jiangsu Shuangli Hepu Technology Co., Ltd
Portable daylight induced chlorophyll fluorescence imaging
NegotiableUpdate on 01/09
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Overview
Portable daylight induced chlorophyll fluorescence imaging: 1. Excellent optical performance to meet the requirements of detecting weak fluorescence signals; 2. Built in push scan technology reduces system complexity; 3. Equipped with automatic exposure, automatic focusing, and automatic scanning speed matching; 4. Diversified testing plan - tripod near scale observation, outdoor fixed-point observation, continuous observation at tower base
Product Details
Portable daylight induced chlorophyll fluorescence imaging
1. Introduction to Chlorophyll Fluorescence Imaging Spectrometer System
Basic principle:
The inversion mechanism of Solar Induced Chlorophyll Fluorescence (SIF): The solar radiation spectral lines pass through the absorption of various components in the solar atmosphere and the Earth's atmosphere, and when they reach the sensor, there are absorption valleys with different widths (0.1nm-10nm) and depths, known as Fraunhofer Lines. SIF, as a surface emission signal, is superimposed on reflection information to change the depth of the Fraunhofer dark line. By utilizing the "well" filling effect of SIF on the Fraunhofer dark line, remote sensing inversion of SIF can be achieved by comparing the original dark line depth with the depth of the dark line filled with SIF. Due to the use of at least one Fraunhofer dark line in the inversion of SIF, and for any dark line, the higher the spectral resolution of the sensor, the deeper the observed depth of the original dark line, and the more obvious the filling effect of SIF on the dark line, the stronger the robustness of SIF inversion. Therefore, remote sensing inversion of SIF needs to be achieved under high spectral resolution conditions, and the core issue is how to accurately obtain the original dark lines that have not been filled with fluorescence and the dark lines that have been filled with fluorescence.
Sun/Solar induced Chlorophyll Fluorescence (SIF) is a spectral signal (650-800 nm) emitted by the photosynthetic center of plants under sunlight conditions. It has two peaks, red light (around 690 nm) and near-infrared light (around 740 nm), and can directly reflect the dynamic changes of actual photosynthesis in plants.
SIF remote sensing is a rapidly developing vegetation remote sensing technology in recent years, which can make up for the shortcomings of current vegetation remote sensing observations and provide new ideas and technologies for carbon cycling and vegetation monitoring of terrestrial ecosystems.
Vegetation remote sensing, represented by vegetation indices based on "greenness" observations (such as NDVI), has greatly promoted the understanding and recognition of the Earth's biosphere at a macro scale in the past 30 years. However, it can only detect the "potential photosynthesis" of plants through "greenness".
Chlorophyll fluorescence has unique technological advantages in the detection of vegetation photosynthetic physiology and is a direct detection method for "actual photosynthesis". It can be said that vegetation chlorophyll fluorescence remote sensing is the most breakthrough research frontier in the field of vegetation remote sensing in the past 10 years. With the development of research and technology, SIF remote sensing has made significant progress in the past decade. SIF is a typical representative of measuring chlorophyll fluorescence under light. By measuring the filling of Fraunhofer dark lines in the downward solar spectrum by measuring the upward radiance, the intensity of chlorophyll fluorescence emitted by vegetation can be inverted. The following spectral curve is usually obtained.
Figure 1 Retrieval of chlorophyll fluorescence intensity emitted by vegetation
Portable daylight induced chlorophyll fluorescence imagingTechnical Indicators:
•Imaging spectral range: 670-780nm (650-800nm)
•Imaging sensor: SCMOS (cooled/uncooled)
•Spectral resolution: 0.3~0.4nm
•Spectral sampling interval: 0.1nm
•Number of spectral bands: 100-1000 bands
•SNR: better than 1000:1 (peak signal-to-noise ratio)
•Incident slit width: 30 μ m
•Imaging lens: 25mm fixed focus lens
•Spatial image resolution: ≥ 1200 * 1000
•Frame rate: 1fps~100fps
•Interface: USB 3.0
•Weight:<2.27Kg;
Main functions:
•Dynamic exposure, autofocus, automatic scanning speed matching
•Shutter Shutter
•Radiance, uniformity, lens, reflectance calibration
•Real time collection of sunlight (cosine corrector)
•Built-in battery
•Built in push scan imaging
•Auxiliary camera monitoring
•infrared thermography
•Real time inversion output
Figure 2 System Hardware and Software Interface
Data processing and analysis:
•Storage and output of reflectance spectrum data
•Hyperspectral data cube plot
•Real time solar spectrum acquisition/calibration
•Vegetation index: Normalization index N DV I, ratio index SR, improved chlorophyll absorption and reflection index MCARI, improved chlorophyll absorption and reflection index 1MCAI1, optimized soil adjusted vegetation index OSAVI, etc. It also supports custom band calculation formulas.
Inverse model:
Technical features:
Real time same frame parallel acquisition function of reference light source intensity information and target image to be tested
In the incident slit area of the SIF imaging spectrometer, a structure is designed specifically for collecting real-time reference light source intensity information through optical fibers. The cosine correction module will homogenize the collected light from all directions and then conduct it through the optical fiber to the front end of the incident slit, ensuring that during scanning imaging, each frame rate will record its independent and synchronized corresponding reference light source information. To avoid calibration errors caused by changes in light intensity, ensure the relative independence of calibration, and provide support for quantitative research.
Figure 3 Real time fiber optic intensity acquisition
Band customization and sampling interval customization
On the software interface, custom spectral start band, end band, and interval parameters can be set; And it supports multiple similar operations, setting the accuracy of interest regions and spectral subdivision, reducing the number of bands in non interest regions, thereby increasing the extraction of effective signals and reducing redundant data, improving acquisition efficiency while ensuring data accuracy.
Figure 4 Band Custom Setting Interface
Data correction function
Reflectivity correction function:
•Using standard whiteboards, grey cloth, etc. as reference boards, calibrate the reflectance of the collected raw DN value data;
•Using cosine correction module to real-time collect data on light intensity, dark background, etc. for reflectance calibration;
Radiometric correction function:
•Relative radiance calibration;
•Absolute radiance calibration;
Real time ambient light (intensity) correction processing
Under normal circumstances, SIF imaging systems collect data under very good lighting conditions. However, occasional cloud cover and other factors can cause inconsistency between the target area and the system's lighting collection. Real time reference light source information corresponding to each frame rate can be used to correct the light intensity and perform algorithm processing on the target image.
*The red area is the image area of the reference light source recorded by the light intensity sensor (occupying multiple pixels);
*The yellow area is the target area that needs to be calibrated;
*Unique software data analysis function;
Figure 5 Real time intensity correction data processing
Real time inversion function
Firstly, it is necessary to perform real-time reflectance calibration on the original DN value data. The system software has a folder for storing corresponding white frame and dark background data. After the collection is performed, reflectance calibration will be automatically performed; Secondly, by combining the selected mathematical model, the inversion results of the corresponding model indicators can be output.
Figure 6 Real time inversion
2. Actual application:
Actual measurement data and inversion output results.
Figure 7: Actual measured images and spectra
Figure 8 inversion chart
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