基于星-机光谱融合的棉花叶片SPAD值反演

doi:10.3864/j.issn.0578-1752.2022.24.004

摘要/Abstract

摘要：

【目的】为提高棉花叶片叶绿素含量的反演精度，并掌握其在山东省夏津县的空间分布特征。【方法】本研究以山东省德州市夏津县为研究区，以夏津县大李庄棉田为试验区，通过SPAD（soil and plant analyzer development，SPAD）仪实地测定试验区棉花叶片叶绿素含量的相对值（SPAD值），并获取同期试验区无人机（unmanned aerial vehicle，UAV）近地多光谱图像和研究区Sentinel-2A MSI（MSI）卫星影像；然后分别基于UAV和MSI的光谱反射率，构建并筛选最优光谱参量，采用多元线性回归（multiple linear regression，MLR）建立SPAD值定量反演模型；最后采用二次多项式拟合法融合UAV和Sentinel-2A MSI对应的最优光谱参量，对比分析融合前后模型效果，优选最佳反演模型，实现研究区SPAD值反演。【结果】研究表明，(REG-R)/(REG+R)、R/G、CL(red edge)、NDVI可作为SPAD值的最优光谱参量；基于UAV图像的定量反演模型精度优于基于MSI影像的模型；基于二次多项式拟合后建模R²提高了0.015—0.057，RMSE降低了0.457—0.638，验证R²提高了0.040—0.085，RMSE降低了0.387—0.397，RPD提高了0.020—0.139；将融合后的MSI光谱参量代入基于UAV图像的反演模型（Fused MSI-Mod_UAV），也可获得较高的反演精度，建模R²达0.672，RMSE为3.982，验证R ²达0.713，RMSE为3.859，RPD为1.685；基于上述模型进行研究区棉花叶片SPAD值反演分析，试验区整体呈南高北低的分布趋势，研究区呈中间低、四周高的分布趋势，均与实地情况一致，具有较好的预测效果。【结论】采用二次多项式拟合法融合无人机和卫星影像数据，可较好地实现区域高精度作物生长指标的定量反演，研究结果可丰富多源遥感融合理论与技术，为后续棉花长势监测与精准生产提供技术参考和数据支持。

关键词: SPAD值, 无人机, Sentinel-2A MSI, 反演模型, 二次多项式拟合法

Abstract:

【Objective】The aim of this study was to improve the inversion accuracy of chlorophyll content in cotton leaves, and to grasp its spatial distribution characteristics in Xiajin county, Shandong province. 【Method】Taking Xiajin county, Dezhou city, Shandong province as the study area and Dalizhuang cotton field in Xiajin county as the test area, the relative value of chlorophyll content (SPAD value) in cotton leaves in the experimental area was measured by SPAD (soil and plant analyzer development), and obtained the near earth multispectral image of unmanned aerial vehicle (UAV) and Sentinel-2A MSI (MSI) satellite image in the study area in the same period; Then, based on the spectral reflectance of UAV and MSI satellite images, the optimal spectral parameters were constructed and selected, and the inversion model of SPAD value was established by multiple linear regression (MLR); Finally, the quadratic polynomial fitting method was used to fuse the optimal spectral parameters corresponding to UAV and Sentinel-2A MSI. By comparing and analyzing the model effects before and after fusion, the inversion model was optimized, and the SPAD value inversion of the study area was realized. 【Result】(REG-R)/(REG+R), R/G, Cl(red edge) and NDVI could be the optimal spectral parameters of SPAD value. The precision of cotton leaf SPAD inversion model based on UAV near ground image was better than that based on satellite image; After quadratic polynomial fitting, the calibration R² was increased by 0.015-0.057, and RMSE was decreased by 0.457-0.638, while the validation R² was increased by 0.040-0.085, RMSE was decreased by 0.387-0.397, and RPD was increased by 0.020-0.139. The fused spectral parameters based on Sentinel-2A MSI image were input to the inversion model based on UAV data (Fused MSI-Mod_UAV), the high inversion accuracy of SPAD value in cotton leaves could be obtained, with the model calibration R² up to 0.672, RMSE of 3.982, validation R² up to 0.713, RMSE of 3.859, and RPD of 1.685. Based on the above model, two inversion prediction maps of different scales were obtained. The SPAD value of cotton leaves in the test area showed the distribution trend of high in the south and low in the north, and the study area showed the distribution trend of low in the middle and high around, which were consistent with the field situation and showed the model had a good prediction effect. 【Conclusion】Therefore, the fusion of UAV and satellite image data by using quadratic polynomial fitting method could better realize the quantitative inversion of regional high-precision crop growth indicators. The research results could enrich the theory and technology of multi-source remote sensing fusion, and provide the technical reference and data support for cotton growth monitoring and precision production.

Key words: SPAD value, UAV, Sentinel-2A MSI, inversion model, quadratic polynomial fitting method

王淑婷, 孔雨光, 张赞, 陈红艳, 刘鹏. 基于星-机光谱融合的棉花叶片SPAD值反演[J]. 中国农业科学, 2022, 55(24): 4823-4839.

WANG ShuTing, KONG YuGuang, ZHANG Zan, CHEN HongYan, LIU Peng. SPAD Value Inversion of Cotton Leaves Based on Satellite-UAV Spectral Fusion[J]. Scientia Agricultura Sinica, 2022, 55(24): 4823-4839.

0
/ / 推荐

导出引用管理器 EndNote|Reference Manager|ProCite|BibTeX|RefWorks

链接本文: https://www.chinaagrisci.com/CN/10.3864/j.issn.0578-1752.2022.24.004

https://www.chinaagrisci.com/CN/Y2022/V55/I24/4823

图/表 17

图1

表1

表2

图2

表3

表4

表5

图3

图4

表6

表7

表8

图5

图6

表9

图7

表10

参考文献 50

[1]	苏伟, 赵晓凤, 孙中平, 张明政, 邹再超, 王伟, 史园莉. 基于Sentinel-2A影像的玉米冠层叶绿素含量估算. 光谱学与光谱分析, 2019, 39(5): 1535-1542.
	SU W, ZHAO X F, SUN Z P, ZHANG M Z, ZOU Z C, WANG W, SHI Y L. Estimating the corn canopy chlorophyll content using the Sentinel-2A image. Spectroscopy and Spectral Analysis, 2019, 39(5): 1535-1542. (in Chinese)
[2]	周敏姑, 邵国敏, 张立元, 刘治开, 韩文霆. 基于无人机遥感的冬小麦叶绿素含量多光谱反演. 节水灌溉, 2019, (9): 40-45.
	ZHOU M G, SHAO G M, ZHANG L Y, LIU Z K, HAN W T. Multi-spectral inversion of SPAD value of winter wheat based on unmanned aerial vehicle remote sensing. Water Saving Irrigation, 2019, (9): 40-45. (in Chinese)
[3]	陈硕博. 无人机多光谱遥感反演棉花光合参数与水分的模型研究[D]. 西安: 西北农林科技大学, 2019.
	CHEN S B. Modeling of cotton photosynthetic parameters and water content retrieval by multi-spectral remote sensing of UAV[D]. Xi’an: Northwest A&F University, 2019. (in Chinese)
[4]	HATAM Z, SABET M S, MALAKOUTI M J, BIDGOLI A M, HOMAEE M. Zinc and potassium fertilizer recommendation for cotton seedlings under salinity stress based on gas exchange and chlorophyll fluorescence responses. South African Journal of Botany, 2020, 130: 155-164. doi: 10.1016/j.sajb.2019.11.032
[5]	鱼欢, 邬华松, 王之杰. 利用SPAD和Dualex快速、无损诊断玉米氮素营养状况. 作物学报, 2010, 36(5): 840-847.
	YU H, WU H S, WANG Z J. Evaluation of SPAD and Dualex for in season corn nitrogen status estimation. Acta Agronomica Sinica, 2010, 36(5): 840-847. (in Chinese)
[6]	LI X J, DU H Q, ZHOU G M, MAO F J, ZHANG M, HAN N, FAN W L, LIU H, HUANG Z H, HE S B, MEI T T. Phenology estimation of subtropical bamboo forests based on assimilated MODIS LAI time series data. ISPRS Journal of Photogrammetry and Remote Sensing, 2021, 173(6): 262-277. doi: 10.1016/j.isprsjprs.2021.01.018
[7]	KOTIKOT A M, FLORES A, GRIFFIN R E, SEDAH A, NYAGA J, MUGO R, LIMAYE A, IRWIN D E. Mapping threat to agriculture in East Africa: Performance of MODIS derived LST for frost identification in Kenya’s tea plantations. International Journal of Applied Earth Observation and Geoinformation, 2018, 72: 131-139. doi: 10.1016/j.jag.2018.05.009
[8]	HOLZMAN M E, CARMONA F, RIVAS R, NICLOS R. Early assessment of crop yield from remotely sensed water stress and solar radiation data. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 145: 297-308. doi: 10.1016/j.isprsjprs.2018.03.014
[9]	GHOSH S, MISHRA D R, GITELSON A A. Long-term monitoring of biophysical characteristics of tidal wetlands in the northern Gulf of Mexico—A methodological approach using MODIS. Remote Sensing of Environment, 2016, 173: 39-58. doi: 10.1016/j.rse.2015.11.015
[10]	LIAQAT M U, CHEEMA M J M, HUANG W J, MAHMOOD T, ZAMAN M, KHAN M M. Evaluation of MODIS and Landsat multiband vegetation indices used for wheat yield estimation in Irrigated Indus Basin. Computers and Electronics in Agriculture, 2017, 138: 39-47. doi: 10.1016/j.compag.2017.04.006
[11]	MOKHTARI A, NOORY H, POURSHAKOURI F, HAGHIGHATMEHE P, AFRASIABIAN Y, RAZAVI M, FEREYDOONI F, NAENI A S. Calculating potential evapotranspiration and single crop coefficient based on energy balance equation using Landsat 8 and Sentinel-2. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 154: 231-245. doi: 10.1016/j.isprsjprs.2019.06.011
[12]	张卓然, 常庆瑞, 张延龙, 班松涛, 由明明. 基于支持向量机的棉花冠层叶片叶绿素含量高光谱遥感估算. 西北农林科技大学(自然科学版), 2018, 46(11): 39-45.
	ZHANG Z R, CHANG Q R, ZHANG Y L, BAN S T, YOU M M. Hyperspectral estimation of chlorophyll remote sensing content of cotton canopy leaves based on support vector machine. Journal of Northwest A&F University (Natural Science Edition), 2018, 46(11): 39-45. (in Chinese)
[13]	LIU J B, HAN J C, CHEN X, SHI L, ZHANG L. Nondestructive detection of rape leaf chlorophyll level based on Vis-NIR spectroscopy. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2019, 222: 117202.
[14]	张东彦, 宋晓宇, 马智宏, 杨贵军, 黄文江, 王纪华. 扫描成像光谱仪和地物光谱仪在单叶尺度上的对比研究. 中国农业科学, 2010, 43(11): 2239-2245.
	ZHANG D Y, SONG X Y, MA Z H, YANG G J, HUANG W J, WANG J H. Assessment of the developed pushbroom imaging spectrometer in single leaf scale. Scientia Agricultura Sinica, 2010, 43(11): 2239-2245. (in Chinese)
[15]	唐普恩, 丁建丽, 葛翔宇, 张振华. 基于Sentinel-2A影像干旱区棉花叶片SPAD数字制图. 生态学报, 2020, 40(22): 8326-8335.
	TANG P E, DING J L, GE X Y, ZHANG Z H. SPAD digital mapping of cotton leaves in arid area based on Sentinel-2A image. Acta Ecologica Sinica, 2020, 40(22): 8326-8335. (in Chinese)
[16]	MALHI R K M, KIRAN G S. Empirical modelling for retrieval of foliar traits in cotton crop using spatial data. Current Science, 2019, 116(12): 2089-2096. doi: 10.18520/cs/v116/i12/2089-2096
[17]	BALLESTEROS R, ORTEGA J F, HERNANDEZ D. Onion biomass monitoring using UAV based RGB imaging. Precision Agriculture, 2018, 19(5): 840-857. doi: 10.1007/s11119-018-9560-y
[18]	奚雪, 赵庚星, 高鹏, 崔昆, 李涛. 基于Sentinel卫星及无人机多光谱的滨海冬小麦种植区土壤盐分反演研究——以黄三角垦利区为例. 中国农业科学, 2020, 53(24): 5005-5016.
	XI X, ZHAO G X, GAO P, CUI K, LI T. Inversion of soil salinity in coastal winter wheat growing area based on sentinel satellite and unmanned aerial vehicle multi-spectrum—A case study in Kenli District of the Yellow River Delta. Scientia Agricultura Sinica, 2020, 53(24): 5005-5016. (in Chinese)
[19]	史舟, 徐冬云, 滕洪芬, 胡月明, 潘贤章, 张甘霖. 土壤星地传感技术现状与发展趋势. 地理科学进展, 2018, 37(1): 79-92. doi: 10.18306/dlkxjz.2018.01.009
	SHI Z, XU D Y, TENG H F, HU Y M, PAN X Z, ZHANG G L. Soil information acquisition based on remote sensing and proximal soil sensing: Current status and prospect. Progress in Geography, 2018, 37(1): 79-92. (in Chinese) doi: 10.18306/dlkxjz.2018.01.009
[20]	陈俊英, 王新涛, 张智韬, 韩佳, 姚志华, 魏广飞. 基于无人机-卫星遥感尺度的土壤盐渍化监测方法. 农业机械学报, 2019, 50(12): 161-169.
	CHEN J Y, WANG X T, ZHANG Z T, HAN J, YAO Z H, WEI G F. Soil salinization monitoring method based on UAV satellite remote sensing scale up. Transactions of the Chinese Society for Agricultural Machinery, 2019, 50(12): 161-169. (in Chinese)
[21]	田明璐, 班松涛, 常庆瑞, 马文君, 殷紫, 王力. 基于无人机成像光谱仪数据的棉花叶绿素含量反演. 农业机械学报, 2016, 47(11): 285-293.
	TIAN M L, BAN S T, CHANG Q R, MA W J, YIN Z, WANG L. Estimation of SPAD value of cotton leaf using hyperspectral images from UAV based imaging spectroradiometer. Transactions of the Chinese Society for Agricultural Machinery, 2016, 47(11): 285-293. (in Chinese)
[22]	毛智慧, 邓磊, 孙杰, 张爱武, 陈向阳, 赵云. 无人机多光谱遥感在玉米冠层叶绿素预测中的应用研究. 光谱学与光谱分析, 2018, 38(9): 2923-2931.
	MAO Z H, DENG L, SUN J, ZHANG A W, CHEN X Y, ZHAO Y. Research on the application of UAV multispectral remote sensing in the maize chlorophyll prediction. Spectroscopy and Spectral Analysis, 2018, 38(9): 2923-2931. (in Chinese)
[23]	CHEN H Y, MA Y, ZHU A X, WANG Z R, GENG X Z, WEI Y N. Soil salinity inversion based on differentiated fusion of satellite image and ground spectra. International Journal of Applied Eaeth Observation and Geoinformation, 2021, 101(2): 1-11.
[24]	齐小玲, 吴健平. 多源遥感影像融合及其关键技术探讨. 现代测绘, 2003, 26(3): 20-22.
	QI X L, WU J P. Study on multi-source RS images fusion and its key techniques. Modern Surveying and Mapping, 2003, 26(3): 20-22. (in Chinese)
[25]	PENG Y, NGUY-ROBERTSON A, ARKEBAUER T, GITELSON A A. Assessment of canopy chlorophyll content retrieval in maize and soybean: Implications of hysteresis on the development of generic algorithms. Remote Sensing, 2017, 9(3): 226. doi: 10.3390/rs9030226
[26]	贾博中. 基于MODIS与无人机的内蒙古沿黄平原区玉米信息反演与产量估测[D]. 内蒙古: 内蒙古农业大学, 2021.
	JIA B Z. Maize information inversion and yield estimation a plain along the Yellow River in Inner Mongolia based on MODIS and UAV[D]. Inner Mongolia: Inner Mongolia Agricultural University, 2021. (in Chinese)
[27]	JIN X L, LI Z H, FENG H K, XU X G, YANG G J. Newly combined spectral indices to improve estimation of total leaf chlorophyll content in cotton. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(11): 4589-4600. doi: 10.1109/JSTARS.2014.2360069
[28]	易秋香. 基于Sentinel-2多光谱数据的棉花叶面积指数估算. 农业工程学报, 2019, 35(16): 189-197.
	YI Q X. Remote estimation of cotton LAI using Sentinel-2 multispectral data. Transactions of the Chinese Society of Agricultural Engineering, 2019, 35(16): 189-197. (in Chinese)
[29]	刘金然. 基于无人机遥感影像的棉花主要生长参数反演[D]. 济南: 山东师范大学, 2019.
	LIU J R. Inversion of cotton main growth parameters based on UAV sensing image[D]. Ji’nan: Shandong Normal University, 2019. (in Chinese)
[30]	张卓然. 棉花高光谱特征及其农学参数遥感反演研究[D]. 杨凌: 西北农林科技大学, 2018.
	ZHANG Z R. Research on hyperspecrtal characteristic of cotton and remote sensing inversion about cotton agronomic parameters[D]. Yangling: Northwest A&F University, 2018. (in Chinese)
[31]	ZHANG S M, ZHAO G X, LANG K, SU B W, CHEN X N, XI X, ZHANG H B. Integrated satellite, unmanned aerial vehicle (UAV) and ground inversion of the SPAD of winter wheat in the reviving stage. Sensors, 2019, 19(7): 1485. doi: 10.3390/s19071485
[32]	李敏. 基于RS和GIS的县域棉花信息提取及分区管理研究——以山东省夏津县为例[D]. 泰安: 山东农业大学, 2012.
	LI M. Cotton information extraction and management zoning based on RS and GIS at county scale—A case study in Xiajin county, Shandong province[D]. Taian: Shandong Agricultural University, 2012. (in Chinese)
[33]	张同瑞, 赵庚星, 高明秀, 常春艳, 王卓然. 基于近地多光谱和OLI影像的黄河三角洲冬小麦种植区盐分估算及遥感反演——以山东省垦利县和无棣县为例. 自然资源学报, 2016, 31(6): 1051-1060.
	ZHANG T R, ZHAO G X, GAO M X, CHANG C Y, WANG Z R. Soil salinity estimation and remote sensing inversion based on near-ground multispectral and TM imagery in winter wheat growing area in the Yellow River Delta—Case study in Kenli county and Wudi county, Shandong province. Journal of Natural Resources, 2016, 31(6): 1051-1060. (in Chinese)
[34]	梁亮, 杨敏华, 张连蓬, 林卉, 周兴东. 基于SVR算法的小麦冠层叶绿素含量高光谱反演. 农业工程学报, 2012, 28(20): 163-172.
	LIANG L, YANG M H, ZHANG L P, LIN H, ZHOU X D. Chlorophyll content inversion with hyperspectral technology for wheat canopy based on support vector regression algorithm. Transactions of the Chinese Society of Agricultural Engineering, 2012, 28(20): 163-172. (in Chinese)
[35]	李粉玲, 王力, 刘京, 常庆瑞. 基于高分一号卫星数据的冬小麦叶片SPAD值遥感估算. 农业机械学报, 2015, 46(9): 273-281.
	LI F L, WANG L, LIU J, CHANG Q R. Remote sensing estimation of SPAD value for wheat leaf based on GF-1 data. Transactions of the Chinese Society for Agricultural Machinery, 2015, 46(9): 273-281. (in Chinese)
[36]	CAO Q, MIAO Y X, SHEN J N, YU W F, YUAN F, CHENG S S, HUANG S Y, WANG H Y, YANG W, LIU F Y. Improving in-season estimation of rice yield potential and responsiveness to topdressing nitrogen application with crop circle actice crop canopy sensor. Precision Agriculture, 2016, 17: 136-154. doi: 10.1007/s11119-015-9412-y
[37]	HABOUSANE D, MILLER J R, PATTEY E, ZARCO-TEJADA P J, STRACHAN I B. Hyperspectra vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sensing of Environment, 2004, 90(3): 337-352. doi: 10.1016/j.rse.2003.12.013
[38]	纪伟帅, 陈红艳, 王淑婷, 张玉婷. 基于无人机多光谱的华北平原花岭期棉花叶片SPAD建模方法研究. 中国农学通报, 2021, 37(22): 143-150.
	JI W S, CHEN H Y, WANG S T, ZHANG Y T. Modeling method of cotton leaves SPAD at flowering and boll stage in North China Plain based on UAV multispectrum. Chinese Agricultural Science Bulletin, 2021, 37(22): 143-150. (in Chinese)
[39]	王丹阳, 陈红艳, 王桂峰, 丛津桥, 王向峰, 魏学文. 无人机多光谱反演黄河口重度盐渍土盐分的研究. 中国农业科学, 2019, 52(10): 1698-1709.
	WANG D Y, CHEN H Y, WANG G F, CONG J Q, WANG X F, WEI X W. Salinity inversion of severe soil in the Yellow River estuary based on UAV multi-spectral. Scientia Agricultura Sinica, 2019, 52(10): 1698-1709. (in Chinese)
[40]	MIURA T, HUETE A, YOSHIOKA H. An empirical investigation of cross sensor relationships of NDVI and red/near infrared reflectance using EO-1 hyperion data. Remote Sensing of Environment, 2006, 100(2): 223-236. doi: 10.1016/j.rse.2005.10.010
[41]	TRISHCHENKO A P. Effects of spectral response function on surface reflectance and NDVI measured with moderate resolution satellite sensors: Extension to AVHRR NOAA-17 and METOP-A. Remote Sensing of Environment, 2009, 113(2): 335-341. doi: 10.1016/j.rse.2008.10.002
[42]	陈文娇, 翁永玲, 范兴旺, 曹一茹. 基于光谱转换的土壤盐分反演与动态分析. 东南大学学报(自然科学版), 2017. 47(6): 1233-1238.
	CHEN W J, WENG Y L, FAN X W, CAO Y R. Soil salinity retrieval and dynamic analysis based on spectral band inter-calibration. Journal of Southeast University (Natural Science Edition), 2017, 47(6): 1233-1238. (in Chinese)
[43]	MA Y, CHEN H Y, ZHAO G X, WANG Z R, WANG D Y. Spectral index fusion for salinized soil salinity inversion using Sentinel-2A and UAV images in a coastal area. IEEE Access, 2020, 8: 159595-159608. doi: 10.1109/ACCESS.2020.3020325
[44]	刘爽, 吴永波. 城市土壤压实对树木叶片叶绿素及光合生理特性的影响. 生态环境学报, 2010, 19(1): 172-176.
	LIU S, WU Y B. The impact of soil compaction on chlorophyll and photosynthetic characteristics of trees. Ecology and Environmental Sciences, 2010, 19(1): 172-176. (in Chinese)
[45]	李美平, 朱宇恩, 尚晋伟, 郑国璋. 城市路口差异对行道树叶片叶绿素含量的影响研究. 环境污染与防治, 2010, 32(9): 37-40, 49.
	LI M P, ZHU Y E, SHANG J W, ZHENG G Z. Influence of road intersection difference on chlorophyll content of roadside tree. Environmental Pollution & Control, 2010, 32(9): 37-40, 49. (in Chinese)
[46]	李萍, 赵庚星, 高明秀, 常春燕, 王卓然, 张同瑞, 安德玉, 贾吉超. 黄河三角洲土壤含水量状况的高光谱估测与遥感反演. 土壤学报, 2015, 52(6): 1262-1272.
	LI P, ZHAO G X, GAO M X, CHANG C Y, WANG Z R, ZHANG T R, AN D Y, JIA J C. Hyperspectral estimation and remote sensing reteieval of soil water regime in the Yellow River Delta. Acta Pedologica Sinica, 2015, 52(6): 1262-1272. (in Chinese)
[47]	FAN X W, WENG Y L, TAO J M. Towards decadal soil salinity mapping using Landsat time series data. Internationl Journal of Applied Earth Observation and Geoinformation, 2016, 52: 32-41.
[48]	依尔夏提·阿不来提, 买买提·沙吾提, 白灯莎·买买提艾力, 安申群, 马春玥. 基于随机森林算法的棉花叶片叶绿素含量估算. 作物学报, 2019, 45(1): 81-90. doi: 10.3724/SP.J.1006.2019.84058
	ABLET E, SAWUT M, MAIMAITIAILI B, AN S Q, MA C Y. Estimation of leaf chlorophyll content in cotton based on the random forest approach. Acta Agronomica Sinica, 2019, 45(1): 81-90. (in Chinese) doi: 10.3724/SP.J.1006.2019.84058
[49]	王烁, 常庆瑞, 刘梦云, 严林, 李媛媛, 刘秀英. 基于高光谱遥感的棉花叶片叶绿素含量估算. 中国农业大学学报, 2017, 22(4): 16-27.
	WANG S, CHANG Q R, LIU M Y, YAN L, LI Y Y, LIU X Y. Estimation on chlorophyll content of cotton based on optimized spectral index. Journal of China Agricultural University, 2017, 22(4): 16-27. (in Chinese)
[50]	黄春燕. 基于高光谱数据的北疆棉花遥感监测研究[D]. 石河子: 石河子大学, 2005.
	HUANG C Y. Study on monitoring of remote sensing of cotton in north of XinJiang with hyperspectral data[D]. Shihezi: Shihezi University, 2005. (in Chinese)

波段名称 Band name	无人机 UAV			卫星 Sentinel-2A MSI
波段名称 Band name	波段 Band	中心波长 Central wavelength (nm)	带宽 Bandwidth (nm)	波段 Band	中心波长 Central wavelength (nm)	带宽 Bandwidth (nm)
G	Green	550	40	B3-Green	560	45
R	Red	660	40	B4-Red	665	38
REG	Red Edge	735	10	B6-Vegetation Red Edge	740	18
NIR	Near IR	790	40	B7- Vegetation Red Edge	783	28

植被指数 Vegetation index	名称 Name	计算公式 Calculation formula	参考文献 Reference
RVI	比值植被指数 Ratio vegetation index	NIR/R	[22]
DVI	差值植被指数 Difference vegetation index	NIR-R
GNDVI	绿色归一化植被指数 Green normalized difference vegetation index	(NIR-G)/(NIR+R)
NDRE	红边植被指数 Normalized difference red edge	(NIR-REG)/(NIR+REG)	[29]
CL(red edge)	红边叶绿素指数Ⅱ Red edge chlorophyII index	REG/R-1	[31]
MCARI	改进叶绿素吸收比值指数 Modified chlorophyII absorption ratio index	[(REG-R)-0.2×(REG-G)] ×(REG/R)
TCARI	转化叶绿素吸收比值指数 Transformed chlorophyII absorption ratio index	3×[(REG-R)-0.2×(REG-G)×(REG/R)]
OSAVI	优化型土壤调节植被指数 Optimized soil adjusted vegetation index	1.16×(NIR-R)/(NIR+R+0.16)
NDVI	归一化植被指数 Normalized difference vegetation index	(NIR-R)/(NIR+R)
CIRE	红边叶绿素指数Ⅰ Red edge chlorophyII index	NIR/REG-1	[36]
MSR	改进简单比值植被指数 Modified simple ratio	(NIR/R-1)(NIR/R+1)^0.5	[37]
RDVI	重归一化植被指数 Renormalized difference vegetation index	(NIR-R)/(NIR+R)^0.5	[37]
(REG-R)/(REG+R)	—	(REG-R)/(REG+R)	[38]
R/G	—	R/G
sqrt(R2+G^2)	—	sqrt(R^2+G^2)
G×R	—	G×R
G×R×REG	—	G×R×REG
REG-R	—	REG-R
REG-NIR	—	REG-NIR

样本类型 Sample type	样本数 Number of samples	最大值 Maximum value	最小值 Minimum value	平均值 Average value	标准差 Standard deviation
建模集 Calibration set	64	59.5	20.2	43.5	7.8
验证集 Validation set	31	68.2	17.1	43.3	8.3
全部 All the samples	95	68.2	17.1	43.3	8.6
研究区 Study area	58	63.1	21.5	46.9	8.1

序号 Number	光谱参量 Spectral parameter	相关系数 Correlation coefficient
序号 Number	光谱参量 Spectral parameter	无人机 UAV	卫星 Sentinel-2A MSI
1	NDVI	0.688**	0.559**
2	RVI	0.655**	0.518*
3	DVI	0.300	0.558*
4	MSR	0.683**	0.534
5	RDVI	0.530*	0.553*
6	GNDVI	-0.613	0.542
7	CIRE	-0.547*	0.411
8	OSAVI	0.531*	0.545*
9	CL(red edge)	0.756**	0.549*
10	MCARI	0.687**	0.517*
11	TCARI	-0.490	-0.496
12	NDRE	-0.196	0.412
13	(REG-R)/(REG+R)	0.800**	0.557**
14	R/G	-0.768**	-0.547**
15	sqrt(R^2+G^2)	-0.653**	-0.529*
16	G×R	-0.626**	-0.541*
17	G×R×REG	-0.405	-0.510
18	REG-R	0.680**	0.548**
19	REG-NIR	0.023	-0.505*

数据源 Data source	反演模型 Inversion model	建模精度 Calibration accuracy
数据源 Data source	反演模型 Inversion model	决定系数 R2	均方根误差RMSE	决定系数 R2	均方根误差RMSE	相对分析误差 RPD
无人机 UAV	Y=37.362+1.093×NDVI+1.26×CL(red edge)- 4.946×R/G+8.286×(REG-R)/(REG+R)	0.709	3.782	0.753	3.589	2.045
卫星 Sentinel-2A MSI	Y=-39.914+152.318×NDVI-2.267×CL(red edge)- 4.990×R/G-25.234×(REG-R)/(REG+R)	0.452	5.823	0.447	5.909	1.521