基于Sentinel-2时间序列数据及物候特征的棉花种植区提取

doi:10.12118/j.issn.1000-6060.2022.119

Abstract

Abstract:

Cotton is an important economic crop in China and is cultivated on a large scale in Xinjiang. The timely and accurate acquisition of cotton planting areas is significant to agricultural policy formulation and economic development. This study takes the Ugan River-Kuqa River delta oasis cotton as the main research object and uses the Sentinel-2 data of 36 scenes from 2018 to 2020 (1 scene/month) to construct a normalized vegetation index (NDVI) and red-edge normalized vegetation index (RENDVI) time-series data; The Savitzky-Golay filtering method is used to smooth and reconstruct the time-series data, and 11 phenological features are extracted. The out-of-bag method is used to optimize the 11 phenological features. Time-series data NDVI Fit and RENDVI₇₈₃ Fit, phenological feature RENDVI₇₈₃ Ph, and phenological feature optimization were combined to construct six different feature datasets, which were classified and extracted by random forest classification (RFC). The maximum likelihood and support vector machine were used to verify the classification effect. The results are as follows: (1) The changing trends of NDVI and RENDVI₇₈₃ time-series data are relatively consistent. Cotton has an obvious upward trend from May (seedling stage) to early August (blooming stage) and reaches a peak from the end of August to September (flowering and boll stage). Compared with NDVI, the peak value of the RENDVI₇₈₃ time-series curve composed of the red-edge band is increased from 0.7 to 0.9, and the cotton discrimination effect is better. (2) Among the 11 phenological features, the maximum value of the fitting function, the length of the growing season, the amplitude of the growing season, the end of the growing season, the large integral of the growing season, and the small integral of the growing season contributed the most to the classification effect, with importance scores of 1.43, 1.40, 1.23, 1.16, 1.02, and 1.01, respectively. (3) The RFC method has the best classification accuracy for the feature dataset (RENDVI₇₈₃ Fit + phenological feature optimal combination). The overall accuracy and Kappa coefficient are 92.20% and 0.92, respectively. (4) The cotton classification accuracy in the study area reached 91.02%, and the planting area was approximately 3424 km², accounting for 24.67% of the total area of the study area.

Key words: NDVI time-series, RENDVI time-series, phenological characteristics, out-of-bag error method, random forest classification

Mihray MOYIDIN, Mamat SAWUT, LI Jinzhao. Extraction of cotton planting area based on Sentinel-2 time series data and phenological characteristics[J].Arid Land Geography, 2022, 45(6): 1847-1859.

Figures/Tables 14

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References 33

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特征	指数全称	计算公式
光谱特征	归一化植被指数（NDVI）	(B8-B4)/(B8+B4)
红边特征	红边1归一化植被指数（RENDVI₇₀₅）	(B8-B5)/(B8+B5)
	红边2归一化植被指数（RENDVI₇₄₀）	(B8-B6)/(B8+B6)
	红边3归一化植被指数（RENDVI₇₈₃）	(B8-B7)/(B8+B7)

物候特征	特征含义
生长季开始	左边NDVI增加到用户定义的拟合函数值的时刻
生长季结束	右边NDVI减少到用户定义的拟合函数值的时刻
生长季长度	生长结束与生长季开始的时间差值
生长季基值	拟合函数左右2部分NDVI最小值的平均值
生长季中期	左边增加到80％和右边降低到80％的平均时间
拟合函数最大值	NDVI最大值
生长季振幅	最大值和基值之间均值的差
生长季左导数	NDVI增加到左边振幅80％的时间
生长季右导数	NDVI减少到右边振幅80%的时间
生长季大积分	从生长季开始到生长季结束的区域面积
生长季小积分	NDVI拟合曲线与生长季开始到结束的分量

方案	特征集	波段数目
A	重构后的NDVI时序数据（NDVI Fit）	36
B	重构后的RENDVI₇₈₃时序数据（RENDVI₇₈₃ Fit）	36
C	重构后的RENDVI₇₈₃ 11个物候特征（RENDVI₇₈₃ Ph）	11
D	物候特征优选组合	6
E	重构后的RENDVI₇₈₃时序数据+11个物候特征（RENDVI₇₈₃ Fit+RENDVI₇₈₃ Ph）	47
F	重构后的RENDVI₇₈₃时序数据+物候特征优选组合	42

分类方法	分类精度	A方案	B方案	C方案	D方案	E方案	F方案
MLC	制图精度	79.92	82.06	79.36	82.89	85.05	86.66
MLC	用户精度	71.70	78.39	76.54	78.51	81.92	80.59
SVM	制图精度	83.01	85.03	86.11	90.09	89.33	90.88
SVM	用户精度	76.22	78.21	79.55	82.56	84.17	88.44
RFC	制图精度	87.14	89.07	90.54	91.07	87.09	88.89
RFC	用户精度	79.66	82.32	83.38	85.41	89.36	94.12

Extraction of cotton planting area based on Sentinel-2 time series data and phenological characteristics

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卫星名称	获取时间	影像数量	波段数	空间分辨率/m
Sentinel-2	2018年1—12月	12	12	10
	2019年1—12月	12	12	10
	2020年1—12月	12	12	10