时空协同的精准农业遥感研究

doi:10.12082/dqxxkx.2020.190726

地球信息科学学报 ›› 2020, Vol. 22 ›› Issue (4): 731-742.doi: 10.12082/dqxxkx.2020.190726

时空协同的精准农业遥感研究

吴志峰¹, 骆剑承^2,^3,^*(), 孙营伟^2,³, 吴田军⁴, 曹峥¹, 刘巍^2,³, 杨颖频^2,³, 王玲玉⁵

^1. 广州大学地理科学学院,广州 510006
^2. 中国科学院空天信息创新研究院,北京 100101
^3. 中国科学院大学,北京 100049
^4. 长安大学地质工程与测绘学院,西安 710064
^5. 贵州师范大学喀斯特研究院,贵阳 550001

收稿日期:2019-11-29 修回日期:2019-12-26 出版日期:2020-04-25 发布日期:2020-06-10
通讯作者: 骆剑承 E-mail:luojc@radi.ac.cn
作者简介:吴志峰（1969— ）,男,湖南湘潭人,博士,教授,博士生导师,研究方向为城市遥感与陆地生态遥感、复杂地表格局—过程—效应。E-mail:zfwu@gzhu.edu.cn
基金资助:
国家自然科学基金项目(41671430);国家自然科学基金项目(41631179);国家自然科学基金青年科学基金项目(41701472);NSFC-广东联合基金重点项目(U1901219)

Research on Precision Agricultural based on the Spatial-temporal Remote Sensing Collaboration

WU Zhifeng¹, LUO Jiancheng^2,^3,^*(), SUN Yingwei^2,³, WU Tianjun⁴, CAO Zheng¹, LIU Wei^2,³, YANG Yingpin^2,³, WANG Lingyu⁵

^1. School of Geographical Sciences, Guangzhou University, Guangzhou 510006, China
^2. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
^3. University of Chinese Academy of Sciences, Beijing 100049, China
^4. School of Geology Engineering and Geomatics, Chang'an University, Xi'an 710064, China
^5. Institute of Karst Science, Guizhou Normal University, Guiyang 550001, China

Received:2019-11-29 Revised:2019-12-26 Online:2020-04-25 Published:2020-06-10
Contact: LUO Jiancheng E-mail:luojc@radi.ac.cn
Supported by:
National Natural Science Foundation of China(41671430);National Natural Science Foundation of China(41631179);National Natural Science Foundation of China Youth Science Foundation Project(41701472);NSFC-Guangdong Joint Foundation Key Project(U1901219)

摘要/Abstract

摘要：

高分辨率遥感对地观测为我们从空间与时间2个维度客观反演地表格局—过程提供了有效的技术支撑。本文遵循时空协同的研究思路,基于高分辨率遥感影像,开展了农业遥感领域2个典型的问题研究：① 提出了一种基于影像视觉特征的耕地分区分层提取方法,该方法在利用DEM数据进行分区的基础上,根据不同区域内耕地所呈现的几何特征和纹理特征差异,分别设计了不同的耕地提取模型;② 构建了一种地块尺度的作物生长参数反演方法,方法以地块为基本单元,在空间、时间及属性组合约束下进行作物理化参数反演。本研究以贵州省安顺市西秀区和广西扶绥县耕地提取进行了耕地地块提取示范,以扶绥县进行了基于耕地地块和中空间分辨率时间序列遥感数据的甘蔗叶面积指数反演。其中,对于安顺市西秀区的耕地地块提取结果而言,形态精度（IoU）大于0.7的地块超过60%,规则耕地、梯田以及林草地等的类型精度均超过了80%;对于扶绥县甘蔗叶面积指数反演的结果而言,其结果可以较为精确地反映出基地甘蔗与非基地甘蔗的差异,基地甘蔗在品质上要优于非基地甘蔗。西南山地区的耕地形态提取/类型判别和地块甘蔗叶面积指数应用验证均证明了方法的可行性。结果表明,协同使用多源高分辨率数据是实现精准农业遥感研究的有效途径。

关键词: 精准农业, 高分遥感, 遥感图谱认知, 机器学习, 分区分层, 时空协同, 地块, 参数反演

Abstract:

High-resolution remote-sensing earth observation provide us with effective technical support for objectively inverting the surface patterns-process from the dimensions of space and time. This paper follows the research idea of space-temporal collaboration, and based on the high-resolution remote sensing images, we explored two typical problems in the agricultural remote sensing field: (1) proposed a division control and stratified extraction method for geo-parcel based on visual characteristics of images. Based on the division of DEM, we have designed different geo-parcel extraction models based on the differences in geometric and texture features in the division regions; (2) proposed a method for crop growth parameters inversing at the geo-parcel scale. Geo-parcel is the basic unit to perform physical parameter inversion under the constraints of space-time-attribute combination. The study taking geo-parcels extraction in in Xixiu District of Anshun City in Guizhou Province and Fusui County in Guangxi as examples for the division control and stratified extraction method, and taking inversion of sugarcane leaf area index in Fusui County of Guangxi Province as examples for the method of crop growth parameters inversing at the geo-parcel scale. For the extraction of cultivated land in Xixiu District, The number of geo-parcels with morphological accuracy (IoU) greater than 0.7 accounts for more than 60%, and the accuracy of the types of regular geo-parcels, terraces, forests and grasslands exceeded 80%; also, for the inversion results of sugarcane leaf area index in Fusui County, the results can accurately reflect the difference between base sugarcane and non-base sugarcane, and the base sugarcane is superior in quality to non-base sugarcane. It shows that Spatial-temporal collaboration use of multi-source high-resolution data is an effective way to achieve accurate agricultural remote sensing research.

Key words: precision agriculture, high-resolution remote sensing, spatial-spectral cognition of remote sensing, machine learning, division and stratify, spatial-temporal collaboration, geo-parcel, parameter inversion

吴志峰, 骆剑承, 孙营伟, 吴田军, 曹峥, 刘巍, 杨颖频, 王玲玉. 时空协同的精准农业遥感研究[J]. 地球信息科学学报, 2020, 22(4): 731-742.DOI:10.12082/dqxxkx.2020.190726

WU Zhifeng, LUO Jiancheng, SUN Yingwei, WU Tianjun, CAO Zheng, LIU Wei, YANG Yingpin, WANG Lingyu. Research on Precision Agricultural based on the Spatial-temporal Remote Sensing Collaboration[J]. Journal of Geo-information Science, 2020, 22(4): 731-742.DOI:10.12082/dqxxkx.2020.190726

图/表 9

图1

图2

图3

图4

图5

图6

图7

表1

图8

参考文献 42

[1]	吴炳方, 蒙继华, 李强子 . 国外农情遥感监测系统现状与启示[J]. 地球科学进展, 2010,25(10):1003-1012.
	[ Wu B F, Meng J H, Li Q Z . Review of overseas crop monitoring systems with remote sensing[J]. Advances in Earth Science, 2010,25(10):1003-1012. ]
[2]	史舟, 梁宗正, 杨媛媛 , 等. 农业遥感研究现状与展望[J]. 农业机械学报, 2015,46(2):247-260.
	[ Shi Z, Liang Z Z, Yang Y Y , et al. Status and prospect of agricultural remote sensing[J]. Transactions of the Chinese Society for Agricultural Machinery, 2015,46(2):247-260.]
[3]	Moran M S, Inoue Y, Barnes E M . Opportunities and limitations for image-based remote sensing in precision crop management[J]. Remote Sensing of Environment, 1997,61(3):319-346.
[4]	Yang B J, Pei Z Y, Zhou Q B . Key technologies of crop monitoring using remote sensing at a national scale: Progress and problems[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2002,18(3):191-194.
[5]	蒙继华, 吴炳方, 杜鑫 , 等. 遥感在精准农业中的应用进展及展望[J]. 国土资源遥感, 2011,23(3):1-7.
	[ Meng J H, Wu B F, Du X . A review and outlook of applying remote sensing to precision agriculture[J]. Remote Sensing for Land & Resources, 2011,23(3):1-7. ]
[6]	刘亮, 姜小光, 李显彬 , 等. 利用高光谱遥感数据进行农作物分类方法研究[J]. 中国科学院研究生院学报, 2006,23(4):484-488.
	[ Liu L, Jiang X G, Li X B , et al. Study on classification of agricultural crop by hyperspectral remote sensing data[J]. Journal of the Graduate School of the Chinese Academy of Sciences, 2006,23(4):484-488. ]
[7]	蒙继华, 付伟, 徐晋 , 等. 遥感在种植业保险估损中的应用[J]. 遥感技术与应用, 2017,32(2):238-246.
	[ Meng J H, Fu W, Xu J , et al. Remote sensing application in insurance loss estimation of farming industry[J]. Remote Sensing Technology and Application, 2017,32(2):238-246. ]
[8]	邵美珍, 邓广林, 徐希孺 . 基于主成分分析的混合象元分解[J]. 中国空间科学技术, 1989,9(5):63-68.
	[ Shao M Z, Deng G L, Xu X R . Pixel decomposition based on principal component analysis[J]. Chinese Space Science and Technology, 1989,9(5):63-68. ]
[9]	Canny J . A computational approach to edge detection[J]. IEEE Transactions on pattern analysis and machine intelligence, 1986(6):679-698.
[10]	DeFries R S, Townshend J R G . NDVI-derived land cover classifications at a global scale[J]. International Journal of Remote Sensing, 1994,15(17):3567-3586.
[11]	Hay G J, Niemann K O, McLean G F . An object-specific image-texture analysis of H-resolution forest imagery[J]. Remote Sensing of Environment, 1996,55(2):108-122.
[12]	Huang X, Zhang L, Li P . A multiscale feature fusion approach for classification of very high resolution satellite imagery based on wavelet transform[J]. International Journal of Remote Sensing, 2008,29(20):5923-5941.
[13]	Bau T C, Sarkar S, Healey G . Hyperspectral region classification using a three-dimensional Gabor filterbank[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010,48(9):3457-3464.
[14]	Su W, Li J, Chen Y , et al. Textural and local spatial statistics for the object-oriented classification of urban areas using high resolution imagery[J]. International Journal of Remote Sensing, 2008,29(11):3105-3117.
[15]	李秦, 高锡章, 张涛 , 等. 最优分割尺度下的多层次遥感地物分类实验分析[J]. 地球信息科学学报, 2011,13(3):409-417.
	[ Li Q, Gao X Z, Zhang T , et al. Optimal segmentation scale selection and evaluation for multi-layer image recognition and classification[J]. Journal of Geo-information Science, 2011,13(3):409-417. ]
[16]	Wang M, Li R . Segmentation of high spatial resolution remote sensing imagery based on hard-boundary constraint and two-stage merging[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014,52(9):5712-5725.
[17]	黄慧萍 . 面向对象影像分析中的尺度问题研究[D]. 北京:中国科学院研究生院, 2003.
	[ Huang H P . Scale issues in object-oriented image analysis[D]. Beijing:Graduate school of Chinese Academy of Sciences, 2003. ]
[18]	Baatz M, Schäpe A . An optimization approach for high quality multi-scale image segmentation [C]//Proceedings of the Beiträge zum AGIT-Symposium, Salzburg, Austria, 2000: 12-23.
[19]	王金甲, 陈浩, 刘青玉 . 大数据下的深度学习研究[J]. 高技术通讯, 2017,27(1):27-37.
	[ Wang J J, Chen H, Liu Q Y . The study of deep learning under big data[J]. Chinese High Technology Letters, 2017,27(1):27-37. ]
[20]	LeCun Y, Bengio Y, Hinton G . Deep learning[J]. Nature, 2015,521(7553):436-444.
[21]	Hinton G E, Osindero S, Teh Y W . A fast learning algorithm for deep belief nets[J]. Neural computation, 2006,18(7):1527-1554. doi: 10.1162/neco.2006.18.7.1527
[22]	Krizhevsky A, Sutskever I, Hinton G E . Imagenet classification with deep convolutional neural networks[C]// Advances in neural information processing systems, 2012: 1097-1105.
[23]	Cheng G, Zhou P, Han J . Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016,54(12):7405-7415.
[24]	Zhao W, Du S . Learning multiscale and deep representations for classifying remotely sensed imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016,113:155-165.
[25]	Zhao W, Du S, Emery W J . Object-based convolutional neural network for high-resolution imagery classification[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017,10(7):3386-3396.
[26]	刘浩, 骆剑承, 黄波 , 等. 基于特征压缩激活Unet网络的建筑物提取[J/OL]. 地球信息科学学报, 2019,21(11):1-11.
	[ Liu H, Luo J C, Huang B , et al. Building roof extraction based on SE-Unet[J]. Journal of Geo-information Science, 2019,21(11):1-11. ]
[27]	吴炳方, 张峰, 刘成林 , 等. 农作物长势综合遥感监测方法[J]. 遥感学报, 2004,8(6):498-514.
	[ Wu B F, Zhang F, Liu C L , et al. An Integrated Method for Crop Condition Monitoring[J]. Journal of Remote Sensing, 2004,8(6):498-514.]
[28]	李峰, Alchanatis Victor, 赵红 , 等. 基于PLSR方法的马铃薯叶片氮素含量机载高光谱遥感反演[J]. 中国农业气象, 2014,35(3):338-343.
	[ Li F, Alchanatis V, Zhao H et al. PLSR-based airborne hyperspectral remote sensing retrieval of Leaf nitrogen content in potato fields[J]. Chinese Journal of Agrometeorology, 2014,35(3):338-343. ]
[29]	郭琳, 裴志远, 张松龄 , 等. 基于环境星CCD图像的甘蔗叶面积指数反演方法[J]. 农业工程学报, 2010,26(10):201-205.
	[ Guo L, Pei Z Y, Zhang S L et al. Estimation method of sugarcane leaf area index using HJ CCD images[J]. Transactions of the Chinese Society of Agricultural Engineering, 2010,26(10):201-205. ]
[30]	甘海明, 岳学军, 洪添胜 , 等. 基于深度学习的龙眼叶片叶绿素含量预测的高光谱反演模型[J]. 华南农业大学学报, 2018,39(3):108-116.
	[ Gan H M, Yue X J, Hong T S , et al. A hyperspectral inversion model for predicting chlorophyll content of longan leaves based on deep learning[J]. Journal of South China Agricultural University, 2018,39(3):108-116. ]
[31]	马茵驰, 阎广建, 丁文 , 等. 基于人工神经网络方法的冬小麦叶面积指数反演[J]. 农业工程学报, 2009,25(12):187-192.
	[ Ma Y C, Yan G J, Ding W , et al. Leaf area index retrieval of winter wheat using artificial neural network[J]. Transactions of the CSAE, 2009,25(12):187-192.]
[32]	李宗南, 陈仲新, 王利民 , 等. 基于ACRM模型不同时期冬小麦LAI和叶绿素反演研究[J]. 中国农业资源与区划, 2012(1):3-7.
	[ Li Z N, Cheng Z X, Wang L M , et al. Inversion of LAI and chlorophyll content of winter wheat at different stages using two-layer canopy reflectance model[J]. Chinese Journal of Agricultural Resources and Regional Planning, 2012(1):3-7. ]
[33]	陈述彭, 岳天祥, 励惠国 . 地学信息图谱研究及其应用[J]. 地理研究, 2000,19(4):337-343.
	[ Chen S, Yue T, Li H . Studies on Geo-informatic Tupu and its application[J]. Geographical Research, 2000,19(4):337-343. ]
[34]	赵英时 . 遥感应用分析原理与方法[M]. 北京: 科学出版社, 2013.
	[ Zhao Y S . Principles and methods of remote sensing application analysis[M]. Beijing: Science Press, 2013.
[35]	Liang S L . Advances in land remote sensing: System, modeling, inversion and application. Maryland, USA: Springer.
[36]	Chopping M J . Advances in land remote sensing: System, modeling, inversion and application[M]. Springer Science & Business Media, 2008.
[37]	邵芸, 郭华东, 范湘涛 , 等. 水稻时域散射特征分析及其应用研究[J]. 遥感学报, 2001,5(5):340-345.
	[ Shao Y, Guo H D, Fan X T , et al. Studies on rice backscatter signatures in time domain and its applications[J]. Journal of Remote Sensing, 2001,5(5):340-345. ]
[38]	化国强, 王晶晶, 黄晓军 , 等. 基于全极化SAR数据散射机理的农作物分类[J]. 江苏农业学报, 2011,27(5):978-982.
	[ Hua G Q, Wang J J, Huang X J , et al. Crop classification based on scattering model using full-polarization SAR data[J]. Jiangsu Journal of Agricultural Sciences, 2011,27(5):978-982. ]
[39]	王迪, 周清波, 陈仲新 , 等. 基于合成孔径雷达的农作物识别研究进展[J]. 农业工程学报, 2014,30(16):203-212.
	[ Wang D, Zhou Q B, Chen Z X , et al. Research advances on crop identification using synthetic aperture radar[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2014,30(16):203-212. ]
[40]	向海燕 . 基于光学和SAR数据的多云雾山区土地覆被分类[D]. 重庆:西南大学, 2018.
	[ Xiang H Y . Land cover classification in cloudy and hilly regions based on optical and SAR data[D]. Chongqing: Southwest University, 2018. ]
[41]	Zhou Y, Luo J, Feng L , et al. Long-short-term-memory-based crop classification using high-resolution optical images and multi-temporal SAR data[J]. GIScience & Remote Sensing, 2019,56(8):1170-1191.
[42]	骆剑承, 吴田军, 吴志峰 , 等. 地理图斑智能计算及模式挖掘方法研究[J]. 地球信息科学学报, 2020,22(1):57-75.
	[ Luo J C, Wu T J, Wu Z F , et al. Research on the methods of intelligent computation and pattern mining based ongeo-parcels[J]. Journal of Geo-information Science, 2020,22(1) 57-75. ]

参数	符号	范围	个数	分布
太阳天顶角/°	?_s	-	1	固定值
观测天顶角/°	?_v	-	1	固定值
相对方位角/°	Φ	-	1	固定值
叶面积指数/(m²/m²)	LAI	0.1~6	25	均匀分布
平均叶倾角/°	ALA	20~70	5	均匀分布
热点参数	Hot	0.05~0.5	5	均匀分布
散射光比例	skyl	-	1	固定值
土壤相对湿度	α_soil	0.0~1.0	5	均匀分布
叶绿素a/b浓度/(μg/cm²)	C_ab	5~100	22	均匀分布
胡萝卜素浓度/(μg/cm²)	C_ar	0.001~25	3	均匀分布
等效水分厚度/cm	C_w	0.001~0.05	3	均匀分布
干物质含量/(g/cm²)	C_m	0.001~0.02	5	均匀分布
棕色素相对含量	C_bp	0	1	固定值
叶片结构参数	N	1~3	5	均匀分布

时空协同的精准农业遥感研究

Research on Precision Agricultural based on the Spatial-temporal Remote Sensing Collaboration

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 9

参考文献 42

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	刘明杰, 徐卓揆, 郜允兵, 杨晶, 潘瑜春, 高秉博, 周艳兵, 周万鹏, 王凌. 基于机器学习的稀疏样本下的土壤有机质估算方法[J]. 地球信息科学学报, 2020, 22(9): 1799-1813.
[2]	陈昂, 杨秀春, 徐斌, 金云翔, 张文博, 郭剑, 邢晓语, 杨东. 基于面向对象与深度学习的榆树疏林识别方法研究[J]. 地球信息科学学报, 2020, 22(9): 1897-1909.
[3]	刘新, 赵宁, 郭金运, 郭斌. 基于LSTM神经网络的青藏高原月降水量预测[J]. 地球信息科学学报, 2020, 22(8): 1617-1629.
[4]	李婉, 牛陆, 陈虹, 吴骅. 基于随机森林算法的地表温度鲁棒降尺度方法[J]. 地球信息科学学报, 2020, 22(8): 1666-1678.
[5]	毛亚萍, 房世峰. 基于机器学习的参考作物蒸散量估算研究[J]. 地球信息科学学报, 2020, 22(8): 1692-1701.
[6]	赵青松, 兰安军, 范泽孟, 杨青. 贵州省不同地貌形态类型土壤侵蚀强度变化的定量分析[J]. 地球信息科学学报, 2020, 22(7): 1555-1566.
[7]	贾涛, 杨仕浩, 李欣, 鄢鹏高, 喻雪松, 罗希, 陈凯. 武汉居民建筑物碳排放反演计算和时空分析[J]. 地球信息科学学报, 2020, 22(5): 1063-1072.
[8]	郭云开, 张晓炯, 许敏, 刘雨玲, 钱佳, 章琼. 路域植被等效水厚度估算模型研究[J]. 地球信息科学学报, 2020, 22(2): 308-315.
[9]	杨丹, 周亚男, 杨先增, 郜丽静, 冯莉. LSTM支持下时序Sentinel-1A数据的太白山区植被制图[J]. 地球信息科学学报, 2020, 22(12): 2445-2455.
[10]	何红术, 黄晓霞, 李红旮, 倪凌佳, 王新歌, 陈崇, 柳泽. 基于改进U-Net网络的高分遥感影像水体提取[J]. 地球信息科学学报, 2020, 22(10): 2010-2022.
[11]	高亮, 杜鑫, 李强子, 王红岩, 张源, 王思远. 融合土地覆盖和土壤水分产品的近地表空气温度空间化方法[J]. 地球信息科学学报, 2020, 22(10): 2023-2037.
[12]	郭子慧, 刘伟. 深度学习和遥感影像支持的矢量图斑地类解译真实性检查方法[J]. 地球信息科学学报, 2020, 22(10): 2051-2061.
[13]	叶杰, 孟凡晓, 白潍铭, 张斌, 郑金明. “四同”条件下周口城区高分一号遥感影像分类对比研究[J]. 地球信息科学学报, 2020, 22(10): 2088-2097.
[14]	方志祥, 倪雅倩, 黄守倩. 顾及上网行为特征的手机用户停留行为预测方法[J]. 地球信息科学学报, 2020, 22(1): 136-144.
[15]	骆剑承, 吴田军, 吴志峰, 周亚男, 郜丽静, 孙营伟, 吴炜, 杨颖频, 胡晓东, 张新, 沈占锋. 地理图斑智能计算及模式挖掘方法研究[J]. 地球信息科学学报, 2020, 22(1): 57-75.