A Method for Summer Maize Phenology Monitoring by MODIS Data

Li Ying; Chen Huailiang; Li Yaohui; Wang Xiuping; Zhang Fangmin

doi:10.11898/1001-7313.20180110

Vol.29, NO.1, 2018

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Article Navigation > Journal of Applied Meteorological Science > 2018 > 29(1): 111-119

Li Ying, Chen Huailiang, Li Yaohui, et al. A method for summer maize phenology monitoring by MODIS data. J Appl Meteor Sci, 2018, 29(1): 111-119. DOI: 10.11898/1001-7313.20180110.

Citation:

Li Ying, Chen Huailiang, Li Yaohui, et al. A method for summer maize phenology monitoring by MODIS data. J Appl Meteor Sci, 2018, 29(1): 111-119. DOI: 10.11898/1001-7313.20180110.

Li Ying, Chen Huailiang, Li Yaohui, et al. A method for summer maize phenology monitoring by MODIS data. J Appl Meteor Sci, 2018, 29(1): 111-119. DOI: 10.11898/1001-7313.20180110.

Citation:

Li Ying, Chen Huailiang, Li Yaohui, et al. A method for summer maize phenology monitoring by MODIS data. J Appl Meteor Sci, 2018, 29(1): 111-119. DOI: 10.11898/1001-7313.20180110.

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A Method for Summer Maize Phenology Monitoring by MODIS Data

DOI: 10.11898/1001-7313.20180110

1.
Henan Key Laboratory of Agrometeorological Support and Applied Technique, CMA, Zhengzhou 450003
2.
Henan Institute of Meteorological Sciences, Zhengzhou 450003
3.
Henan Meteorological Administration, Zhengzhou 450003
4.
High-tech Industrial Development Zone, Chengdu 610016
5.
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Jiangsu Key Laboratory of Agricultural Meteorology, Nanjing University of Information & Technology, Nanjing 210044

Received Date: 2017-05-24
Rev Recd Date: 2017-09-26
Publish Date: 2018-01-31

Abstract

Abstract

Crop phenology period is an important feature of the agricultural eco-system. It is important to investigate the crop phenology period in large area for precision crop management and yield forecast by remote sensing technical. However, there are still some limitations in this approach, such as the investigation precision is restricted by the investigation area, and different types of crop growth curves don't match with the crop phenology period very well.Therefore, data from moderate-resolution imaging spectroradiometer (MODIS), and summer maize growth stages by field observations from 23 agricultural meteorological stations in Henan Province are adopted to improve the identification efficiency and accuracy. Normalized difference vegetation index (NDVI) growth curve with the time resolution of 1 d is reconstructed by denoising, smooth processing and logistic curve fitting. Crop growth feature points on the reconstructed growth curve are extracted by using dynamic threshold method and curvature extremum method. The optimum matching relationship between feature points and maize growth stages is constructed by based on the feature points, their occurrence date, and observed dates of growth stages. By matching with the investigated growth stage dates in 2013-2014, values of dynamic threshold 1 is chosen to extract the 7-leaf stage, with the root mean square error (RMSE) of 5.4 d. Values of minimum curvature is chosen to extract the jointing stage, with the RMSE of 6.4 d. Values of dynamic threshold 2 is chosen to extract the tasseling stage, with the RMSE of 6.0 d. By validation with the investigated growth stage dates in 2015, RMSE with the selected 3 key growth stages are all less than 6 d. The accuracy is higher than the earlier proposed methods to extract maize growth stages by using MODIS or other similar lower/medium spatial resolution remote sensing data. Map of summer maize critical phenology in Henan Province demonstrates that most of summer maize in the research areas enter 7-leaf stage, jointing stage and tasseling stage in June 25 to 27 June, 10 July to 17 July and 27 July to 2 August, respectively. Numbers of pixels, which enter 7-leaf stage, jointing stage and tasseling stage on the above-mentioned dates, account for 45.6%, 66.8% and 71% of the total, respectively. Maize growth stages obtained by the method proposed by this research can be used in crop management and grain yield forecast.
- normalized difference vegetation index;
- crop growth curve;
- SG filter;
- Logistic fitting

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References(33)

Figures and Tables

Fig. 1 The distribution of agricultural meteorological observation stations in Henan Province

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图 2 利用两种方法提取曲线特征点位

(a)动态阈值法，(b)曲率法

Fig. 2 Curve feature point extraction using two methods

(a)dynamic threshold method, (b)curvature method

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Fig. 3 Error distribution between extracted and observed maize entered growth stages in each site in 2015

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图 4 2015年利用MODIS NDVI时序数据监测夏玉米进入关键物候期的时间

(a)七叶期，(b)拔节期，(c)抽雄期

Fig. 4 Dates of summer maize entered key growth stages monitored by using time-series MODIS NDVI data in 2015

(a)seven-leaf, (b)jointing, (c)tasseling

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Table 1 Root mean square error between feature points and maize phenology in 2013 and 2014(unit: d)

物候期	动态阈值1	动态阈值2	曲率最大值1	曲率最小值	曲率最大值2
播种期	23.0	54.3	29.0	38.6	48.2
出苗期	16.9	48.0	22.8	32.3	42.0
三叶期	12.6	43.6	18.4	27.9	37.5
七叶期	5.4	32.4	8.2	16.9	26.4
拔节期	15.0	19.2	9.6	6.4	13.5
抽雄期	32.0	6.0	25.9	16.5	8.2
开花期	33.4	6.4	27.3	17.9	9.4
吐丝期	34.5	6.9	28.4	19.0	10.4
乳熟期	57.0	26.3	50.9	41.4	32.0
成熟期	79.8	48.6	73.7	64.0	54.5

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