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遥感技术与应用  2019, Vol. 34 Issue (4): 756-765    DOI: 10.11873/j.issn.1004-0323.2019.4.0756
作物信息提取专栏     
基于时序定量遥感的冬小麦长势监测与估产研究
刘新杰1,2(),魏云霞2,焦全军2,孙奇2,3,刘良云2()
1. 中国国土勘测规划院 自然资源部土地利用重点实验室,北京 100035
2. 中国科学院遥感与数字地球研究所数字地球重点实验室,北京 100094
3. 中国矿业大学(北京)地球科学与测绘工程学院,北京 100083
Growth Monitoring and Yield Prediction of Winter Wheat based on Time-series Quantitative Remote Sensing Data
Xinjie Liu1,2(),Yunxia Wei2,Quanjun Jiao2,Qi Sun2,3,Liangyun Liu2()
1. Key Laboratory of Land Use, Ministry of Land and Resources, China Land Surveying and Planning Institute, Beijing 100035, China
2. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
3. College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
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摘要:

遥感技术是高效、客观监测农作物生长状态的重要手段,对农业生产管理具有重要意义。以安徽龙亢农场为研究区,收集了中高分辨率多源卫星遥感数据并进行了定量化处理,构建了冬小麦叶绿素密度、叶面积指数的遥感反演模型,生产了长时序冬小麦植被参数卫星遥感产品。通过监测冬小麦叶绿素密度、叶面积指数的时序变化规律,分析了不同品种冬小麦的长势情况,发现高产量小麦在越冬期长势显著优于低产量小麦。在此基础上,构建了基于归一化植被指数(NDVI)的冬小麦估产模型,结果表明:利用小麦抽穗期和乳熟期的累计NDVI值可以实现产量的精确估算,据此绘制了龙亢农场2017年冬小麦产量遥感估算地图,产量分布与实际种植情况吻合良好。实现了基于时序卫星定量遥感数据的冬小麦长势监测和产量预测,为区域范围内农作物长势监测提供了一种有效途径。

关键词: 时序定量遥感长势监测估产植被指数    
Abstract:

Remote sensing is an important approach for crop growth monitoring efficiently and subjectively, and is helpful for the agricultural productivity. In this paper, Longkang Farm in Anhui province, China, was selected as a case for the study. Remote sensing images with middle-high spatial resolution from different satellite-based sensors were collected and quantitively processed. Statistics models for the estimation of chlorophyll density and leaf area index were built based on vegetation indices. Time-series products of vegetation parameters were produced. We analyzed the temporal patterns of chlorophyll density and leaf area index and found that the high-yield wheat grew much better than the low-yield wheat during the winter. In addition, we built a yield prediction model based on the Normalized-Difference Vegetation Index (NDVI) for winter wheat. The results showed that, using accumulated NDVI at heading and milk stage, the yield can be accurately estimated. The winter wheat yield prediction map of Longkang farm was produced based on time-series satellite images. This study provided an efficient approach for crop growth monitoring.

Key words: Time-series quantitive remote sensing    Growth monitoring    Yield prediction    Vegetation indices
收稿日期: 2018-09-08 出版日期: 2019-10-16
ZTFLH:  TP751  
基金资助: 自然资源部土地利用重点实验室开放基金(KLLU201803);国家重点研发计划课题(2016YFD0300601);国家自然科学基金项目(41701396)
通讯作者: 刘良云     E-mail: liuxj@radi.ac.cn;liuly@radi.ac.cn
作者简介: 刘新杰(1989-),男,山东聊城人,博士,助理研究员,主要从事植被定量遥感研究。E?mail :liuxj@radi.ac.cn
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引用本文:

刘新杰,魏云霞,焦全军,孙奇,刘良云. 基于时序定量遥感的冬小麦长势监测与估产研究[J]. 遥感技术与应用, 2019, 34(4): 756-765.

Xinjie Liu,Yunxia Wei,Quanjun Jiao,Qi Sun,Liangyun Liu. Growth Monitoring and Yield Prediction of Winter Wheat based on Time-series Quantitative Remote Sensing Data. Remote Sensing Technology and Application, 2019, 34(4): 756-765.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2019.4.0756        http://www.rsta.ac.cn/CN/Y2019/V34/I4/756

图1  研究区地理位置
日期 3/25 4/2 4/10 4/18 5/6 5/17 5/24 5/31
生育期 返青期 起身初期 起身后期 拔节期 抽穗期 灌浆初期 灌浆后期 乳熟期
表1  2002年小汤山实验所测量的冬小麦时间表
样方编号 分场 品种名称 亩产量
1 一分场 济麦22 1 124
2 一分场 济麦22 998
3 二分场 龙科1109 1205
4 二分场 济麦22 1049
5 九分场 烟农19 906
6 九分场 烟农19 821
7 三分场 烟农19 934
8 三分场 烟农19 889
9 七分场 华成3366 982
10 七分场 华成3366 1 011
11 四分场 泛麦5号 1 074
12 四分场 安农0711 917
13 十分场 华成3366 857
14 十分场 华成3366 913
15 五分场 安农大1216 1 205
16 五分场 烟农19 922
17 六分场 华成3366 1 024
18 六分场 未来0818 1 009
19 八分场 未来0818 812
20 八分场 未来0818 666
表2  研究区各样区冬小麦产量
卫星/传感器

空间分辨率

(可见光-近红外波段)

重访周期 景数 日期
Landsat-8 OLI 30 m 16 d 5 2016/12/7、2017/4/30、5/16、5/25、6/17
Sentinel-2A 10 m 10 d 2 2016/11/28、2017/3/26
GF-1 PMS 8 m 4 d 1 2017/4/17
HJ-1A/B CCD 30 m 单星4 d,双星2 d 11

2016/11/15、2017/2/10、2/15、2/18、2/19、2/27、3/7、4/1、

4/22、4/30, 5/12

表3  所使用卫星传感器空间分辨率、重访周期、收集影像数量和获取时间
光谱指数 公式 参考文献
Viopt (1+0.45)((R800)2+1)/(R670+0.45) [11]
NDVIg-b (R573-R440)/(R573+R440) [12]
RVI I R810/R660 [13]
RVI II R810/R560 [14]
NRI (R800/R550,target/(R800/R550)reference [15]
MCARI/MTVI2

MCARI/MTVI2

MCARI:R700-R670-0.2(R700-R550)/(R700/R670)

MTVI2:1.5(1.2(R800-R550)-2.5(R670-R550))/sqrt((2R800+1)2- (6R800-5sqrt(R670))-0.5)

[16]
DCNI (R720-R700)/(R700-R670)/(R720-R670+0.03) [16]
MCARI (R700-R670-0.2(R700-R550))(R700/R670) [16]
TCARI 3((R700-R670-0.2(R700-R550))(R700/R670)) [17]
TCARI/OSAVI

TCARI/OSAVI

TCARI:3((R700-R670-0.2(R700-R550))(R700/R670))

OSAVI:1.16(R800-R670)/(R800+R670+0.16)

[18]
MTCI (R750-R710)/(R710-R680) [19]
R-M R750/R720-1 [20]
REP-LI

700+40(Rre-R700)/(R740-R700)

Rre:R670+R780)/2

[21]
NDVI (R800-R670)/(R800+R670) [22]
RVI R800/R670 [23]
SR I R801/R670 [24]
SR705 R750/R705 [25]
ND705 (R750-R705)/ (R750+R705) [25]
GNDVI (R801-R550)/ (R801+R550) [17]
OSAVI (1+0.16)·(R801-R670)/(R801+R670+0.16) [26]
MSR705 (R750-R445)/( R705- R445) [25]
MND705 (R750-R705)/( R750+R705- 2R445) [25]
MSAVI2 0.5{(2R800+1)-[(2R800+1)2-8(R800-R670)]1/2} [27]
PSRI (R680-R500)/R750 [28]
PRI (R570-R531)/( R570+R531) [29]
NDPI (R680-R430)/( R680+R430) [30]
SIPI (R800-R445)/( R800-R680) [30]
表4  常用植被指数及计算公式
植被指数 模型 R 2
SR705 y=0.1204x-0.0109 0.67
RVI I y=0.0089x+0.0308 0.67
RVI II y=0.024x-0.0276 0.69
R-M y=0.04x+0.0314 0.68
SR I y=0.0084x+0.0341 0.66
VIopt y=0.1827x-0.484 0.64
MSR705 y=0.0263x-0.0173 0.65
MTCI y=0.0542x-0.0642 0.55
表5  基于不同植被指数的叶绿素密度(mg/cm2)反演模型
植被指数 模型 R 2
VIopt y=3.5404x-9.2439 0.82
RVI y=0.2222x+0.4187 0.81
OSAVI y=7.3238x-2.1801 0.80
RVI II y=0.5382x-0.7119 0.77
SR705 y=0.9114x-0.8232 0.77
NDVI y=0.1524e3.5427 x 0.87
ND705 y=-19.469x+4.1196 0.76
GNDVI y=5.479e-10.76 x 0.78
表6  基于不同植被指数的LAI反演模型
图2  不同卫星传感器可见光-近红外波段光谱响应函数
卫星数据 植被参数 植被指数 模型 R2
GF-1 叶绿素 RVI II y =0.0227x - 0.0307 0.70
LAI NDVI y = 4.5175x 2.2089 0.83
HJ-1A CCD1 叶绿素 RVI II y=0.0194x-0.0167 0.70
LAI NDVI y = 4.409x 2.1744 0.83
HJ-1A CCD2 叶绿素 RVI II y=0.0188x-0.0152 0.69
LAI NDVI y = 4.4726x 2.164 0.83
HJ-1B CCD1 叶绿素 RVI II y = 0.0189x - 0.0154 0.69
LAI NDVI y = 4.3389x 2.1946 0.83
HJ-1B CCD2 叶绿素 RVI II y = 0.0187x - 0.0127 0.69
LAI NDVI y = 4.4497x 2.1748 0.83
Sentinel-2 叶绿素 RVI II y = 0.0221x - 0.026 0.69
LAI NDVI y = 4.1565x 2.2538 0.84
Landsat8 叶绿素 RVI II y = 0.0207x - 0.0221 0.69
LAI NDVI y = 4.2303x 2.286 0.84
表7  各卫星传感器植被参数反演模型
图3  基于地面光谱数据的龙亢农场叶绿素密度与LAI反演模型验证结果
图4  基于卫星遥感数据的龙亢农场叶绿素密度与LAI反演模型验证结果
图5  不同产量冬小麦植被参数时序变化曲线
图6  基于抽穗期与乳熟期累计NDVI的冬小麦估产模型
生育期 NDVI估产模型
公式 R 2
越冬期 y=396.46x+770.72 0.10
返青期 y=600.73x+797.66 0.11
起身期 y=1227.6x+291.13 0.44
拔节期 y= 763.02x+604.39 0.56
抽穗期 y=903.61x+352.96 0.74
乳熟期 y=1314.8x+104.25 0.72
表8  基于NDVI在不同生育期建立的估产模型
图7  基于Landsat-8 OLI数据的2017年龙亢农场冬小麦产量预测图
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