Study on Variation Curve and Fitting Model of Winter Wheat Canopy NDVI

Water Saving Irrigation ›› 2018 ›› (12) : 97-103.

CUI Ting1，2 ，ZHANG Zhi-tao1，2 ，CUI Chen-feng1，2 ，BIAN Jiang1 ，CHEN Shuo-bo1 ，WANG Hai-feng1

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Normalized Difference Vegetation Index ( NDVI) is an important tool for assessing crop growth condition and has been widely used in agriculture field． It is a vegetation index based on the high absorption rate of red light and high reflectance of near－infrared light． And it has been found to be dynamic at different times in a day because of crop itself and environmental factors，so the accurate determination of NDVI is difficult． To explore daily changes of NDVI at main growth stages of winter wheat，the winter wheat canopy reflectances in the 656 and 770 nm wavelengths were obtained by Greenseeker to compute NDVI． The data were obtained in successive hours at reviving stage， jointing stage，and heading stage，respectively． The study proves that the winter wheat canopy NDVI values are dynamic in different periods of a day; the NDVI data demonstrates clear parabolic shaped diurnal variations; it decreases gradually from 8 ∶ 00AM，and reaches to its minimum at 13 ∶ 00PM or 14 ∶ 00PM followed by a rapid increase in the afternoon． In order to describe the variations of the daily NDVI values，the quadratic polynomial regression，Gauss function and Sine function was used to fit the normalized NDVI daily variation curve respectively． Before fitting，a normalization processing for the original data was made to limit the data in the same range，which was convenient for the compare of different models． In significance test，the selected models were all statistically significant ( P＜0．01) in the three growing stages of winter wheat． And the three models had the best fitting effect in the jointing period with the coefficient of determination ( Ｒ2 ) all above 0．9． But the quadratic polynomial model expressed better stability than the other two models． Then the predicted and measured values were compared and the best fitting model was found by root mean square error ( ＲMSE) and mean absolute error( MAE) ． The results show that all three models have good fitting effects，however，the prediction precision of quadratic polynomial model is the best with the ＲMSE of 0．212，0．213 and 0．187，and MAE of 0．165，0．162 and 0．142 in the three stages of reviving，jointing and heading，and the other two models show almost the same prediction accuracy． This study can provide a reference for the future study of the NDVI daily change process and NDVI accurate monitoring of winter wheat．

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winter wheat / NDVI / daily changes / normalization / model

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CUI Ting, ZHANG Zhi-tao, CUI Chen-feng, BIAN Jiang, CHEN Shuo-bo, WANG Hai-feng. Study on Variation Curve and Fitting Model of Winter Wheat Canopy NDVI. Water Saving Irrigation. 2018, 0(12): 97-103

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[1]刘先华, 陈佐忠, 秋山侃, 等.定居放牧方式下归一化植被指数(NDVI)的空间变化特征[J]. 植物生态学报, 2000, (06): 662-666.
[2]韩衍欣, 蒙继华, 徐晋.基于NDVI与物候修正的大豆长势评价方法[J]. 农业工程学报, 2017, (02): 177-182.
[3]虞连玉, 蔡焕杰, 姚付启, 等.植被指数反演冬小麦植被覆盖度的适用性研究[J]. 农业机械学报, 2015, (01): 231-239.
[4]贺振, 贺俊平.近32年黄河流域植被覆盖时空演化遥感监测[J]. 农业机械学报, 2017, (02): 179-185.
[5]王利民, 刘佳, 杨玲波, 等.基于NDVI加权指数的冬小麦种植面积遥感监测[J]. 农业工程学报, 2016, (17): 127-135.
[6]JOVANOVIC M M, MILANOVIC M M, ZORN M.THE USE OF NDVI AND CORINE LAND COVER DATABASES FOR FOREST MANAGEMENT IN SERBIA[J].Acta Geogr Slov, 2018, 58(1):109-123
[7]李新伟, 余炳凤, 吕新, 等.不同氮水平下棉花冠层NDVI分析与产量估测[J]. 农业机械学报, 2014, (07): 231-236.
[8]MKHABELA M S, BULLOCK P, RAJ S, et al.Crop yield forecasting on the Canadian Prairies using MODIS NDVI data[J].Agricultural and Forest Meteorology, 2011, 151(3):385-393
[9]SHARMA L K, BU H G, DENTON A, et al.Active-Optical Sensors Using Red NDVI Compared to Red Edge NDVI for Prediction of Corn Grain Yield in North Dakota,USA[J].Sensors, 2015, 15(11):27832-27853
[10]刘云, 孙丹峰, 宇振荣, 等.基于NDVI-Ts特征空间的冬小麦水分诊断与长势监测[J]. 农业工程学报, 2008, (05): 147-151.
[11]赵明家, 张忠庆, 李志洪.基于高光谱优化NDVI值反演玉米叶片氮含量[J]. 中国农业大学学报, 2014, (06): 56-61.
[12]刘明, 冯锐, 纪瑞鹏, 等.基于MODIS-NDVI的春玉米叶面积指数和地上生物量估算[J]. 中国农学通报, 2015, (06): 80-87.
[13]WANG Q, ADIKU S, TENHUNEN J, et al.On the relationship of NDVI with leaf area index in a deciduous forest site[J].Remote Sensing of Environment, 2005, 94(2):244-255
[14]王晓飞, 李志洪, 袁家萍, 等.玉米品种冠层NDVI与叶绿素的关系[J]. 中国农学通报, 2010, (16): 175-179.
[15]杜加强, 赵晨曦, 贾尔恒·阿哈提, 等.近30a新疆月NDVI动态变化及其驱动因子分析[J]. 农业工程学报, 2016, (05): 172-181.
[16]SUN Y L, YANG Y L, ZHANG Y, et al.Assessing vegetation dynamics and their relationships with climatic variability in northern China[J]. Phys Chem Earth, 2015, 87-88: 79-86.
[17]CARTER G A.Reflectance wavebands and indices for remote estimation of photosynthesis and stomatal conductance in pine canopies[J].Remote Sensing of Environment, 1998, 63(1):61-72
[18]CHOUDHURY B J.Estimating gross photosynthesis using satellite and ancillary data: Approach and preliminary results[J].Remote Sensing of Environment, 2001, 75(1):1-21
[19]FENSHOLT R, SANDHOLT I, STISEN S, et al.Analysing NDVI for the African continent using the geostationary meteosat second generation SEVIRI sensor[J].Remote Sensing of Environment, 2006, 101(2):212-229
[20]UUDUS B, PARK K A, KIM K R, et al.Diurnal variation of NDVI from an unprecedented high-resolution geostationary ocean colour satellite[J].Remote Sens Lett, 2013, 4(7):639-647
[21]ANDERSON M C, NEALE C M U, LI F, et al.Upscaling ground observations of vegetation water content,canopy height,and leaf area index during SMEX02 using aircraft and Landsat imagery[J].Remote Sensing of Environment, 2004, 92(4):447-464
[22]张智韬, 兰玉彬, 郑永军, 等.影响大豆NDVI的气象因素多元回归分析[J]. 农业工程学报, 2015, (05): 188-193.
[23]ZHANG Z, LAN Y, WU P, et al.Model of soybean NDVI change based on time series[J].International Journal of Agricultural and Biological Engineering, 2014, 7(5):64-70

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Received	Revised	Published
2018-07-01	2018-08-10	2018-12-15
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2018-12-31

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