Beijing 010-82611269,13671083121
Shandong 0532-82861228,15563963062
Xian 029-81124223
/ En

PolyPen RP-410手持式植物反射光谱测量仪

PolyPen RP-410手持式植物反射光谱测量仪

PolyPen RP-410手持式植物反射光谱测量仪
  • 发布时间: 2018-11-15 11:52
  • 发布人:
  • 点击量: 2754

PolyPen RP-410手持式植物反射光谱测量仪

1.jpg

PolyPen RP 410手持式植物光谱测量仪通过内部光源(氙气白炽灯380-1050nm)测定植物叶片的反射光谱,也可以测定其他光源的透光度和吸光率。PolyPen在软件中内置了几乎所有常用的植物反射光谱指数公式,例如NDVI,PRI,NDGI等。测得的数据以图形或数据表的形式实时显示在仪器的显示屏上。这些数据都可以储存在仪器的内存里并传输到电脑里。

PolyPen RP 410由可充电锂电池供电,不需要使用电脑即可独立进行测量。仪器配备全彩色触屏显示器、内置光源、内置GPS和用于固定样品的无损叶夹。叶夹具备进行光源和检测器校准的标准参照物。

应用领域:

Ÿ植物反射光谱测量

Ÿ植物胁迫响应

Ÿ色素组成变化

Ÿ氮素含量变化

Ÿ产量估测

技术特点:

Ÿ目前最便携的测量植物叶片反射光谱的高光谱测量仪。

Ÿ自动计算常用的植物反射光谱指数,也可计算用户定制的指数,同时提供高精度反射光谱图。

Ÿ非破坏性原位测量。

Ÿ手持式仪器,电池供电,无需外部电脑,便于野外测量。

Ÿ内置GPS

技术参数:

Ÿ光谱检测范围:

PolyPen RP 410 UVIS光谱响应范围为380-790nm

PolyPen RP 410 NIR光谱响应范围为640-1050nm

Ÿ测量光谱曲线:反射率曲线、吸收率曲线

Ÿ内置植被指数:


2.jpg

PolyPen RP 410 UVIS:NDVI、SR、绿度指数、MCARI、TCARI、TVI、ZMI、SRPI、NPQI、PRI、NPCI、Carter指数、SIPI、GM1、ARI1、ARI2、CRI1、CRI2。

PolyPen RP 410 NIR:NDVI、SR、MCARI1、OSAVI、MCARI、TCARI、ZMI、Ctr2、GM2

Ÿ光源:氙气白炽灯380-1050nm

Ÿ光谱响应半宽度:8nm

Ÿ光谱杂散光:-30dB

Ÿ光学孔径:7mm

Ÿ扫描速度:约100ms

Ÿ触控屏:240×320像素,65535色

Ÿ内存:16MB(可存储4000组以上测量数据)

Ÿ系统数据:16位数模转换

Ÿ   3.jpg动态范围:高增益 1:4300;低增益 1:13000

Ÿ内置GPS模块:最大精度<1.5m

Ÿ通讯方式:USB

Ÿ软件功能:自动计算内置植被指数、计算用户自定义植被指数、实时显示数据图和数据表、数据导出为Excel、GPS地图、固件升级,Windows XP及以上系统适用

Ÿ光谱反射标准配件(选配):提供最高的漫反射值(99%)。光谱平面涵盖UV-VIS-NIR光谱,保证+/-1%的光学平面。用于光源和检测器的校准。

Ÿ尺寸:15×7.5×4cm

Ÿ重量:300g

Ÿ外壳:防水溅外壳

Ÿ电池:2600mAh可充电锂电池,通过USB接口连接电脑充电

Ÿ续航时间:可连续测量48小时

Ÿ工作条件:温度0~55℃,相对湿度0-95%(无冷凝水)

Ÿ存放条件:温度-10~60℃,相对湿度0-95%(无冷凝水)

软件界面

4.jpg

应用案例

5.jpg

德国波恩大学使用RP400光谱仪测量反射光谱植被指数PRI、NPCI、GI、NDVI等研究铁毒害对水稻的影响(Wu,2019)。

6.jpg

欧盟委员会联合研究中心通过无人机遥测技术研究叶缘焦枯病菌在橄榄树中的感染。同时通过FluorPen叶绿素荧光仪和RP400光谱仪直接检测叶片的叶绿素荧光和反射光谱植被指数,用于对照修正无人机遥测数据。研究结果发表在《Nature Plants》(Zarco-Tejada,2018)。

参考文献

1. Poblete, T., Camino, C., Beck, P. S. A.,A., Hornero, A., et al. 2020. Detection of Xylella fastidiosa in  fastidiosa infection symptoms with airborne multispectr tral and thermal imagery: Assessing bandset redu eduction performance from hyperspectral analysis. ISPRS Journal of Photogrammetry and Remote Sensing, 162, 27–40.

2. Junker L. V., Rascher U., Jaenicke H., et al. 2019. Detection of plant stress responses in aphid-infested lettuce using non-invasive detection methods. Integrated Protection in Field Vegetables IOBC OBC-WPRS Bulletin Vol.142, 2019 . 8-16 8

3. Wu, L.B., Holtkamp, F., Wairich, A., & Frei, M. 2019. Potassium Ion Channel Gene OsAKT1 Affects Iron Translocation in Rice Plants Exposed to Iron Toxicity. Frontiers in Plant Science, 10.

4. Bartak, M., Hajek, J., Morkusova, J., et al. 2018. Dehydration-induced changes in spec pectral reflectance indices and chlorophyll fluorescence of Antarctic e of Antarctic lichens with different thallus color, and intrathall intrathalline photobiont. Acta Physiologiae Plantarum, 40(10 10).

5. Bartak, M., Mishra, K.B., Mareckova A, M. 2018. Spectral reflectance indices sense desiccation induced changes in the thalli of Antarctic lichen Dermatocarpon polyphyllizum. Czech Polar Reports 8 (2): 249-259.

6. Gálvez, S., Mérida-García, R., Camino Ino, C. et al. 2018. Hotspots in the genomic architectu hitecture of field droughtresponses in wheat as breeding targets. Functional & Integrative Genomics.

7. Nuttall, J. G., Perry, E. M., Delahunt Ty, A. J. et al. 2018. Frost response in wheat and early detection using proximal sensors. Journal of Agrono f Agronomy and Crop Science, 205(2), 220–234.

8. Sytar O., Zivcak M., Olsovska K., Breststic M. 2018 Perspectives in High-Throughput Phenotyping of Qualitative Traits at the Whole-Plant Level. In: Sengar R., Singh A. (eds) Eco-friendly Agro-biolog logical Techniques for Enhancing Crop Productivity. Springer, Singapore.

9. Zarco-Tejada, P. J., Camino, C., Beck, P. S. A., Calderon, R., Hornero, A., et al. 2018. Previsual symptoms of Xylella fastidiosa infection revealed in spectral plant-trait alterations. Nature Plants, 4(7), 4 ts, 4(7), 432–439.

10. A Niglas, et al. 2017. Short-term effects of light quality on leaf gas exchange and hydraulic properties of silver birch (Betula pendula). Tree Physiology 37(9): 1218-1228

11. M Ashrafuzzaman, et al. 2017. Diagnosing ozone stress and differential tolerance in rice (Oryza sativa L.) with ethylenediurea (EDU). Environmental Pollution 230: 339-350

12. M López-López, et al. 2016. Early Detection and Quantification of Almond Red Leaf Blotch Using High-Resolution Hyperspectral and Thermal Imagery. Remote Sens. 8(4): 276

13. PJ Zarco-Tejada, et al. 2016. Seasonal stability of chlorophyll fluorescence quantified from airborne hyperspectral imagery as an indicator of net photosynthesis in the context of precision agriculture. Remote Sensing of Environment 179: 89-103

14. VV Ptushenko, et al. 2015. Possible reasons of a decline in growth of Chinese cabbage under a combined narrowband red and blue light in comparison with illumination by high-pressure sodium lamp. Scientia Horticulturae 194: 267-277

15. VV Ptushenko, et al. 2014. Chlorophyll fluorescence induction, chlorophyll content, and chromaticity characteristics of leaves as indicators of photosynthetic apparatus senescence in arboreous plants. Biochemistry (Moscow) 79: 260-272

内置计算公式的植物光谱指数:

Ÿ归一化差值植被指数Normalized Difference Vegetation Index (NDVI)

参考文献:Rouse et al. (1974)

公式:NDVI = (RNIR - RRED ) / (RNIR + RRED )

Ÿ简单比值植被指数Simple Ratio Index (SR)

参考文献:Jordan (1969); Rouse et al. (1974)

公式:SR = RNIR / RRED

Ÿ改进的叶绿素吸收反射指数1 Modified Chlorophyll Absorption in Reflectance Index 1 (MCARI1)

参考文献:Haboudane et al. (2004)

公式:MCARI1 = 1.2 * [2.5 * (R790- R670) - 1.3 * (R790- R550)]

Ÿ最优化土壤调整植被指数Optimized Soil-Adjusted Vegetation Index (OSAVI)

参考文献:Rondeaux et al. (1996)

公式OSAVI = (1 + 0.16) * (R790- R670) / (R790- R670 + 0.16)

Ÿ绿度指数Greenness Index (G)

公式G = R554 / R677

Ÿ改进的叶绿素吸收反射指数Modified Chlorophyll Absorption in Reflectance Index (MCARI)

参考文献:Daughtry et al. (2000)

公式MCARI = [(R700- R670) - 0.2 * (R700- R550)] * (R700/ R670)

Ÿ转换类胡萝卜素指数Transformed CAR Index (TCARI)

参考文献Haboudane et al. (2002)

公式TSARI = 3 * [(R700- R670) - 0.2 * (R700- R550) * (R700/ R670)]

Ÿ三角植被指数Triangular Vegetation Index (TVI)

参考文献Broge and Leblanc (2000)

公式TVI = 0.5 * [120 * (R750- R550) - 200 * (R670- R550)]

ŸZarco-Tejada & Miller 指数Zarco-Tejada & Miller Index (ZMI)

参考文献Zarco-Tejada et al. (2001)

公式ZMI = R750 / R710

Ÿ简单比值色素指数Simple Ratio Pigment Index (SRPI)

参考文献Peñuelas et al. (1995)

公式SRPI = R430 / R680

Ÿ归一化脱镁作用指数Normalized Phaeophytinization Index (NPQI)

参考文献Barnes et al. (1992)

公式NPQI = (R415- R435) / (R415+ R435)

Ÿ光化学植被反射指数Photochemical Reflectance Index (PRI)

参考文献Gamon et al. (1992)

公式PRI = (R531- R570) / (R531+ R570)

Ÿ归一化色素叶绿素指数Normalized Pigment Chlorophyll Index (NPCI)

参考文献Peñuelas et al. (1994)

公式NPCI = (R680- R430) / (R680+ R430)

ŸCarter指数Carter Indices

参考文献Carter (1994), Carter et al. (1996)

公式Ctr1 = R695 / R420; Ctr2 = R695 / R760

Ÿ结构加强色素指数Structure Intensive Pigment Index (SIPI)

参考文献Peñuelas et al. (1995)

公式SIPI = (R790- R450) / (R790+ R650)

ŸGitelson and Merzlyak 指数 Gitelson and Merzlyak Indices

参考文献Gitelson & Merzlyak (1997)

公式GM1 = R750/ R550; GM2 = R750/ R700

Ÿ花青素反射指数Anthocyanin Reflectance Indices

参考文献:Gitelson et al. (2001)

公式:ARI1 = 1/R550 – 1/R700; ARI2 = R800 * (1/R550 – 1/R700)

Ÿ类胡萝卜素反射指数Carotenoid Reflectance Indices

参考文献:Gitelson et al. (2002)

公式:CRI1 = 1/R510 – 1/R550; CRI2 = 1/R510 – 1/R700

产地:欧洲

注:本文转载自易科泰,转载目的在于传递更多信息,并不代表本网赞同其观点和对其真实性负责。如有侵权行为,请联系我们,我们会及时删除。