农作物遥感识别中的多源数据融合研究进展

doi:10.3864/j.issn.0578-1752.2015.06.09

Abstract

Abstract: Crop mapping by using the remotely-sensed images provide basic information for further geographical and ecological researches. A systematic review on the recent literature regarding crop mapping was carried out in order to improve our understanding on the integration and application of multi-source remote sensing data. The literature search was performed in Google Scholar, the ISI Web of Knowledge and CNKI (e.g. Topic =”crop + mapping”; Topic =”classification + multi-source”; timespan = 2000-2014). According to the thorough analysis on the existing publications, it is suggested that (1) there are two main ways to identify crop types based on the integration of multi-source data in order to expand the spatial and temporal scales. The techniques of multi-source data fusion, which are aimed at improving the spatial resolution, include image fusion, normal fuzzy distributed neural networks, component substitution, semi-physical fusion approach, and multiresolution wavelet decomposition. With the integrated application of such approaches, the spatial resolution and clarity of remote sensing images are raised; the effect of mixed pixels is weaken to some extent. Nevertheless, crop spectral information is partly lost or distorted. The techniques of multi-source data fusion, which are aimed at improving the temporal resolution, can be categorized into two types: the integration of the same data source, and the integration of different data sources. By using such approaches, the crossover of growth period among different crops can be effectively eliminated. But such approaches are susceptible to transformation models of spectral reflectance or vegetation indices, and the differences in band coverage among different remote sensing data. (2) The modes of multi-source data fusion can be categorized into three types according to the data types applied: integration of optical data, integration of optical and microwave data, and integration of remote sensing and ancillary data sources. Taking complementary advantages of various satellite data resources, these techniques of data fusion fully mine the differences of spectral, temporal and spatial characteristics, among various crop species. However, there still remain challenges in previous researches about the crop identification based on the fusion of multi-source remotely sensed data.

Key words: crop, multi-source, combining, remote sensing, identification

SONG Qian, ZHOU Qing-bo, WU Wen-bin, HU Qiong, YU Qiang-yi,TANG Hua-jun. Recent Progresses in Research of Integrating Multi-Source Remote Sensing Data for Crop Mapping[J].Scientia Agricultura Sinica, 2015, 48(6): 1122-1135.

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References

[1] 唐华俊, 吴文斌, 杨鹏, 周清波, 陈仲新. 农作物空间格局遥感监测研究进展. 中国农业科学. 2010, 43(14): 2879-2888.

Tang H J, Wu W B, Yang P, Zhou Q B, Chen Z X. Recent progresses in monitoring crop spatial patterns by using remote sensing technologies. Scientia Agricultura Sinica, 2010, 43(14): 2879-2888. (in Chinese)

[2] Ozdogan M. The spatial distribution of crop types from MODIS data: temporal unmixing using independent component analysis. Remote Sensing of Environment, 2010, 114(6): 1190-1204.

[3] 陈水森, 柳钦火, 陈良富, 李静, 刘强. 粮食作物播种面积遥感监测研究进展. 农业工程学报. 2005, 21(6): 166-171.

Cheng S S, Liu Q H, Chen L F, Li J, Liu Q. Review of research advances in remote sensing monitoring of grain crop area. Transactions of the Chinese Society of Agricultural Engineering, 2005, 21(6):166-171. (in Chinese)

[4] Xiao X, Boles S, Frolking S, Li C, Babu J Y, Salas W, Moore Iii B. Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images. Remote Sensing of Environment, 2006, 100(1): 95-113.

[5] Brown J C, Kastens J H, Coutinho A C, Victoria D D C, Bishop C R. Classifying multiyear agricultural land use data from Mato Grosso using time-series MODIS vegetation index data. Remote Sensing of Environment, 2013, 130: 39-50.

[6] Wardlow B D, Egbert S L, Kastens J H. Analysis of time-series MODIS 250 m vegetation index data for crop classification in the US Central Great Plains. Remote Sensing of Environment, 2007, 108(3): 290-310.

[7] Verburg P H, Mertz O, Erb K H, Haberl H, Wu W. Land system change and food security: towards multi-scale land system solutions. Curr Opin Environ Sustain, 2013, 5(5): 494-502.

[8] Husak G J, Marshall M T, Michaelsen J, Pedreros D, Funk C, Galu G. Crop area estimation using high and medium resolution satellite imagery in areas with complex topography. Journal of Geophysical Research, 2008, 113(D14): D14112.

[9] Zhong L, Gong P, Biging G S. Efficient corn and soybean mapping with temporal extendability: A multi-year experiment using Landsat imagery. Remote Sensing of Environment, 2014, 140: 1-13.

[10] Xiao X, Boles S, Frolking S, Li C, Babu J Y, Salas W, Moore Iii B. Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images. Remote Sensing of Environment, 2006, 100(1): 95-113.

[11] Pan Y, Li L, Zhang J, Liang S, Hou D. Crop area estimation based on MODIS-EVI time series according to distinct characteristics of key phenology phases: a case study of winter wheat area estimation in small-scale area. Journal of Remote Sensing, 2011, 15(3): 578-594.

[12] Turker M, Ozdarici A. Field-based crop classification using SPOT4, SPOT5, IKONOS and QuickBird imagery for agricultural areas: a comparison study. International Journal of Remote Sensing, 2011, 32(24): 9735-9768.

[13] Le Maire G, Dupuy S, Nouvellon Y, Loos R A, Hakamada R. Mapping short-rotation plantations at regional scale using MODIS time series: case of eucalypt plantations in Brazil. Remote Sensing of Environment, 2014, 152: 136-149.

[14] 顾晓鹤, 潘耀忠, 朱秀芳, 张锦水, 韩立建, 王双. MODIS 与 TM 冬小麦种植面积遥感测量一致性研究——小区域实验研究. 遥感学报. 2007, 11(5): 350-358.

Gu X H, Pan Y Z, Zhu X F, Zhang J S, Han L J, Wang S. Consistency study between MODIS and TM on winter wheat plant area monitoring-a case in small area. Journal of Remote Sensing. 2007, 11(5): 350-358. (in Chinese)

[15] Hong G, Zhang A, Zhou F, Townley-Smith L, Brisco B, Olthof I. Crop-type identification potential of Radarsat-2 and MODIS images for the Canadian prairies. Canadian Journal of Remote Sensing, 2011, 37(1): 45-54.

[16] Mcnairn H, Shang J, Champagne C, Jiao X. TerraSAR-X and RADARSAT-2 for crop classification and acreage estimation. 2009.

[17] 吴文斌, 杨鹏, 李正国, 陈仲新, 周清波, 唐华俊. 农作物空间格局变化研究进展评述. 中国农业资源与区划, 2014, 35(1): 12-20.

Wu W B, Yang P, Li Z G, Chen Z X, Zhou Q B, Tang H J. Overview of research progress in crop spatial pattern changes. Chinese Journal of Agricutural Resources and Regional Planning, 2014, 35(1): 12-20. (in Chinese)

[18] Alcantara C, Kuemmerle T, Baumann M, Bragina E V, Griffiths P, Hostert P, Knorn J, M U Ller D, Prishchepov A V, Schierhorn F, Others. Mapping the extent of abandoned farmland in Central and Eastern Europe using MODIS time series satellite data. Environmental Research Letters, 2013, 8(3): 1-9.

[19] Brown J C, Kastens J H, Coutinho A C, Victoria D D C, Bishop C R. Classifying multiyear agricultural land use data from Mato Grosso using time-series MODIS vegetation index data. Remote Sensing of Environment, 2013, 130: 39-50.

[20] Pan Y. Winter wheat area estimate from MODIS-EVIi time series using the crop proportion phenology index. 2012.

[21] Zhong L, Gong P, Biging G S. Efficient corn and soybean mapping with temporal extendability: a multi-year experiment using Landsat imagery. Remote Sensing of Environment, 2014, 140: 1-13.

[22] Bouvet A, Le Toan T. Use of ENVISAT/ASAR wide-swath data for timely rice fields mapping in the Mekong River Delta. Remote Sensing of Environment, 2011, 115(4): 1090-1101.

[23] Mcnairn H, Shang J, Champagne C, Jiao X. TerraSAR-X and RADARSAT-2 for crop classification and acreage estimation. 2009.

[24] Jia K, Wu B, Li Q. Crop classification using HJ satellite multispectral data in the North China Plain. Journal of Applied Remote Sensing, 2013, 7(1): 7-12.

[25] Turker M, Ozdarici A. Field-based crop classification using SPOT4, SPOT5, IKONOS and QuickBird imagery for agricultural areas: a comparison study. International Journal of Remote Sensing, 2011, 32(24): 9735-9768.

[26] Yang C, Everitt J H, Murden D. Evaluating high resolution SPOT 5 satellite imagery for crop identification. Computers and Electronics in Agriculture, 2011, 75(2): 347-354.

[27] Mathur A, Foody G M. Crop classification by support vector machine with intelligently selected training data for an operational application. International Journal of Remote Sensing, 2008, 29(8): 2227-2240.

[28] Ozdogan M. The spatial distribution of crop types from MODIS data: Temporal unmixing using Independent Component Analysis. Remote Sensing of Environment, 2010, 114(6): 1190-1204.

[29] Hongbo Q, Dengfa C. Application of EOS/MODIS-NDVI at different time sequences on monitoring winter wheat acreage in Henan Province, 2009.

[30] 闫慧敏, 黄河清, 肖向明, 姜鲁光. 鄱阳湖农业区多熟种植时空格局特征遥感分析. 生态学报, 2008, 28(9): 4517-4523.

Yan H M, Huang H Q, Xiao X M, Jiang L G. Spatio-temporal distribution of multiple cropping systems in the Poyang Lake region. Acta Ecologica Sinica, 2008, 28(9): 4517-4523. (in Chinese)

[31] 张锦水, 申克建, 潘耀忠, 李苓苓, 侯东. HJ-1号卫星数据与统计抽样相结合的冬小麦区域面积估算. 中国农业科学, 2010, 43(16): 3306-3315.

Zhang J S, Shen K J, Pan Y Z, Li L L, Hou D. HJ-1 Remotely sensed data and sampling method for wheat area estimation. Scientia Agricultura Sinica, 2010, 43(16): 3306-3315. (in Chinese)

[32] 郑长春, 王秀珍, 黄敬峰. 基于特征波段的 SPOT-5 卫星影像水稻面积信息自动提取的方法研究. 遥感技术与应用, 2008, 23(3): 294-299.

Zheng C C, Wang X Z, Huang J F. Decision tree algorithm of automatically extracting paddy rice information from SPOT-5 Images based on characteristic bands. Remote Sensing Technology and Application, 2008, 23(3): 294-299. (in Chinese)

[33] Comber A, Fisher P, Brunsdon C, Khmag A. Spatial analysis of remote sensing image classification accuracy. Remote Sensing of Environment, 2012, 127: 237-246.

[34] Abdikan S, Balik Sanli F, Sunar F, Ehlers M. A comparative data-fusion analysis of multi-sensor satellite images. International Journal of Digital Earth, 2014, 7(8): 671-687.

[35] 蒋楠, 李卫国, 杜培军. 不同遥感数据融合方法在南方水稻面积监测中的应用研究.西南大学学报, 2012,34(6):20-24.

Jiang N, Li W G, Du P J. Application of different remote sensing data fusion methods to rice area monitoring in South Area. Journal of Southwest University, 2012, 34(6): 20-24. (in Chinese)

[36] 何馨. 基于多源数据融合的玉米种植面积遥感提取研究[D]. 南京:南京信息工程大学,2010.

He X. Study of extracting corn planting area using the fusion technologies of multi-source remote sensing data[D]. Jiangsu: Nanjing University of Information Science & Technology, 2010. (in Chinese)

[37] 赵天杰, 李新武, 张立新, 王芳. 双频多极化SAR数据与多光谱数据融合的作物识别. 地球信息科学学报, 2009, 11(1): 84-90.

Zhao T J, Li X W, Zhang L X, Wang F. Agrotype recognition using dual-frequency multi-polarization SAR data and multi-spectral data. Journal of Geo-information Science, 2009, 11(1): 84-90. (in Chinese)

[38] Gao F, Masek J, Schwaller M, Hall F. On the blending of the Landsat and MODIS surface reflectance: Predicting daily Landsat surface reflectance. Geoscience and Remote Sensing, IEEE Transactions on, 2006, 44(8): 2207-2218.

[39] Shettigara V K. A generalized component substitution technique for spatial enhancement of multispectral images using a higher resolution data set. Photogrammetric Engineering and Remote Sensing, 1992, 58(5): 561-567.

[40] Roy D P, Ju J, Lewis P, Schaaf C, Gao F, Hansen M, Lindquist E. Multi-temporal MODIS--Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data. Remote Sensing of Environment, 2008, 112(6): 3112-3130.

[41] Yocky D A. Multiresolution wavelet decomposition image merger of landsat thematic mapper and SPOT panchromatic data. Photogrammetric Engineering and Remote Sensing, 1996, 62(9): 1067-1074.

[42] Wardlow B D, Egbert S L. Large-area crop mapping using time-series MODIS 250 m NDVI data: An assessment for the US Central Great Plains. Remote Sensing of Environment, 2008, 112(3): 1096-1116.

[43] 顾晓鹤, 何馨, 郭伟, 黄文江, 燕荣江. 基于 MODIS 与 TM 时序插补的省域尺度玉米遥感估产. 农业工程学报, 2010, 26(2): 53-58.

Gu X H, He X, Guo W, Huang W J, Xian R J. Maize yield estimation at province scale by interpolation of TM and MODIS time-series images. Transactions of the CSAE, 2010, 26(2): 53-58. (in Chinese)

[44] 邬明权, 牛铮, 王长耀. 利用遥感数据时空融合技术提取水稻种植面积. 农业工程学报, 2010, 26(2): 48-52.

Wu M Q, Niu Z, Wang C Y. Mapping paddy fields by using spatial and temporal remote sensing data fusion technology. Transactions of the Chinese Society of Agricultural Engineering, 2010, 26(2): 48-52. (in Chinese)

[45] Singh D. Generation and evaluation of gross primary productivity using Landsat data through blending with MODIS data. International Journal of Applied Earth Observation and Geoinformation, 2011, 13(1): 59-69.

[46] Singh D. Evaluation of long-term NDVI time series derived from Landsat data through blending with MODIS data. Atmosfera, 2012, 25(1): 43-63.

[47] Watts J D, Powell S L, Lawrence R L, Hilker T. Improved classification of conservation tillage adoption using high temporal and synthetic satellite imagery. Remote Sensing of Environment, 2011, 115(1): 66-75.

[48] Wu B, Yan N, Xiong J, Bastiaanssen W, Zhu W, Stein A. Validation of ETWatch using field measurements at diverse landscapes: A case study in Hai Basin of China. Journal of hydrology, 2012, 436: 67-80.

[49] Foerster S, Kaden K, Foerster M, Itzerott S. Crop type mapping using spectral–temporal profiles and phenological information. Computers and Electronics in Agriculture, 2012, 89: 30-40.

[50] 郝鹏宇, 牛铮, 王力, 王秀兰, 王长耀. 基于历史时序植被指数库的多源数据作物面积自动提取方法. 农业工程学报, 2012, 28(23): 123-131.

Hao P Y, Niu Z, Wang L，Wang X L, Wang C Y,. Multi-source automatic crop pattern mapping based on historical vegetation index profiles. Transactions of the Chinese Society of Agricultural Engineering, 2012, 28(23): 123-131. (in Chinese)

[51] Maselli F, Gilabert M A, Conese C. Integration of high and low resolution NDVI data for monitoring vegetation in Mediterranean environments. Remote Sensing of Environment, 1998, 63(3): 208-218.

[52] Esch T, Metz A, Marconcini M, Keil M. Combined use of multi-seasonal high and medium resolution satellite imagery for parcel-related mapping of cropland and grassland. International Journal of Applied Earth Observation and Geoinformation, 2014, 28: 230-237.

[53] Witharana C, Civco D L, Meyer T H. Evaluation of data fusion and image segmentation in earth observation based rapid mapping workflows. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 87: 1-18.

[54] Prasad S, Gamba P, Herold M. Foreword to the special issue on earth observation approaches for large area land monitoring with multiple sensors and resolutions. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6(5): 2075-2076.

[55] Shang J, Mcnairn H, Champagne C, Jiao X. Contribution of multi frequency, multi sensor, and multi temporal radar data to operational annual crop mapping. IEEE Geoscience and Remote Sensing Letters, 2009, 3(1): III378-III381. doi: 10.1109/IGARSS.2008.4779362.

[56] Peña-Barragán J M, Ngugi M K, Plant R E, Six J. Object-based crop identification using multiple vegetation indices, textural features and crop phenology. Remote Sensing of Environment, 2011, 115(6): 1301-1316.

[57] Conrad C, Fritsch S, Zeidler J, R U Cker G, Dech S. Per-field irrigated crop classification in arid Central Asia using SPOT and ASTER data. Remote Sensing, 2010, 2(4): 1035-1056.

[58] Conrad C, Goessl A, Lex S, Metz A, Esch T, Konrad C, Goettlicher G, Dech S. Mapping crop distribution in administrative districts of southwest Germany using multi-sensor remote sensing data. 2010.

[59] 蔡学良, 崔远来. 基于异源多时相遥感数据提取灌区作物种植结构. 农业工程学报, 2009, 25(8): 124-130.

Cai X L, Cui Y L. Crop planting structure extraction in irrigated areas from multi-sensor and multi-temporal remote sensing data. Transactions of the Chinese Society of Agricultural Engineering, 2009, 25(8): 124-130. (in Chinese)

[60] 顾晓鹤, 韩立建, 王纪华, 黄文江, 何馨. 中低分辨率小波融合的玉米种植面积遥感估算. 农业工程学报, 2012, 28(3): 203-209.

Gu X H, Han L J, Wang J H, He X. Estimation of maize planting area based on wavelet fusion of multi-resolution images. Transactions of the Chinese Society of Agricultural Engineering, 2012, 28(3): 203-209. (in Chinese)

[61] 李颖, 陈秀万, 段红伟, 沈阳. 多源多时相遥感数据在冬小麦识别中的应用研究. 地理与地理信息科学, 2010, 26(4): 47-49.

Li Y, Chen X W, Duan H W, Shen Y. Application of multi-source and multi-temporal remote sensing data in winter wheat identification. Geography and Geo-Information Science, 2010, 26(4): 47-49. (in Chinese)

[62] Zhang W, Li A, Jin H, Bian J, Zhang Z, Lei G, Qin Z, Huang C. An enhanced spatial and temporal data fusion model for fusing landsat and MODIS surface reflectance to generate high temporal landsat-like data. Remote Sensing, 2013, 5(10): 5346-5368.

[63] Larra N Aga A, A Lvarez-Mozos J, Albizua L. Crop classification in rain-fed and irrigated agricultural areas using Landsat TM and ALOS/PALSAR data. Canadian Journal of Remote Sensing, 2011, 37(1): 157-170.

[64] Hong G, Zhang A, Zhou F, Townley-Smith L, Brisco B, Olthof I. Crop-type identification potential of Radarsat-2 and MODIS images for the Canadian prairies. Canadian Journal of Remote Sensing, 2011, 37(1): 45-54.

[65] Mcnairn H, Champagne C, Shang J, Holmstrom D, Reichert G. Integration of optical and Synthetic Aperture Radar (SAR) imagery for delivering operational annual crop inventories. ISPRS Journal of Photogrammetry and Remote Sensing, 2009, 64(5): 434-449.

[66] Jain M, Mondal P, Defries R S, Small C, Galford G L. Mapping cropping intensity of smallholder farms: A comparison of methods using multiple sensors. Remote Sensing of Environment, 2013, 134: 210-223.

[67] Ban Y. Synergy of multitemporal ERS-1 SAR and Landsat TM data for classification of agricultural crops. Canadian Journal of Remote Sensing, 2003, 29(4): 518-526.

[68] Bruzzone L, Prieto D F, Serpico S B. A neural-statistical approach to multitemporal and multisource remote-sensing image classification. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(3): 1350-1359.

[69] Brisco B, Brown R J. Multidate SAR/TM synergism for crop classification in western Canada. Photogrammetric Engineering and Remote Sensing, 1995, 61(8): 1009-1014.

[70] 宫鹏. 遥感科学与技术中的一些前沿问题. 遥感学报, 2009, 13(1): 13-23.

Gong P. Some essential questions in remote sensing science and technology. Journal of Remote Sensing, 2009, 13(1): 13-23. (in Chinese)

[71] Blaes X, Vanhalle L, Defourny P. Efficiency of crop identification based on optical and SAR image time series. Remote Sensing of Environment, 2005, 96(3): 352-365.

[72] Mcnairn H, Champagne C, Shang J, Holmstrom D, Reichert G. Integration of optical and Synthetic Aperture Radar (SAR) imagery for delivering operational annual crop inventories. ISPRS Journal of Photogrammetry and Remote Sensing, 2009, 64(5): 434-449.

[73] 贾坤, 李强子, 田亦陈, 吴炳方, 张飞飞, 蒙继华. 微波后向散射数据改进农作物光谱分类精度研究. 光谱学与光谱分析, 2011, 31(2): 483-487.

Jia K, Li Q Z, Tian Y C, Wu B F, Zhang F F, Meng J H. Accuracy improvement of spectral classification of crop using microwave backscatter data. Spectroscopy and Spectral Analysis, 2011, 31(2): 483-487. (in Chinese)

[74] 刘达. 遥感与作物生长模型同化评估水稻长势[D]. 北京:中国地质大学, 2013.

Liu D. Monitoring rice growth by assimilation of remote sensing and crop growth model[D]. Beijing: China University of Geosciences, 2013. (in Chinese)

[75] Frolking S, Qiu J, Boles S, Xiao X, Liu J, Zhuang Y, Li C, Qin X. Combining remote sensing and ground census data to develop new maps of the distribution of rice agriculture in China. Global Biogeochemical Cycles, 2002, 16(4): 31-38.

[76] Leff B, Ramankutty N, Foley J A. Geographic distribution of major crops across the world. Global Biogeochemical Cycles, 2004, 18(1), doi: 10.1029/2003GB002108.

[77] Thenkabail P S B C M T. An irrigated area map of the world (1999) derived from remote sensing. Colombo, Sri Lanka: International Water Management Institute (IWMI) Research Report, 2006(105).

[78] 彭代亮. 基于统计与 MODIS 数据的水稻遥感估产方法研究[D].杭州: 浙江大学, 2009.

Peng D L. The study on the method of rice yield estimation using statistical and MODIS data[D]. Hangzhou: Zhejiang University, 2009. (in Chinese)

[79] 董芳. 基于多源数据的丘陵区苹果园地信息遥感提取技术研究[D]. 山东: 山东农业大学, 2012.

Dong F. Remote sensing techniques of apple orchard information extraction based on multi-source data in hilly areas[D]. Shandong: Shandong Agricultural University, 2012. (in Chinese)

[80] You L, Wood S. An entropy approach to spatial disaggregation of agricultural production. Agricultural Systems, 2006, 90(1): 329-347.

[81] Liu J, Fritz S, Van Wesenbeeck C, Fuchs M, You L, Obersteiner M, Yang H. A spatially explicit assessment of current and future hotspots of hunger in Sub-Saharan Africa in the context of global change. Global and Planetary Change, 2008, 64(3): 222-235.

[82] 刘珍环, 李正国, 唐鹏钦, 李志鹏, 吴文斌, 杨鹏, 游良志, 唐华俊. 近30年中国水稻种植区域与产量时空变化分析. 地理学报. 2013, 68(5): 680-693.

Liu Z H, Li Z G, Tang P Q, Li Z P,Wu W B, Yang P, You L Z, Tang H J. Spatial-temporal changes of rice area and production in China during 1980-2010. Acta Geographica Sinica, 2013, 68(5): 680-693. (in Chinese)

[83] Gallego J, Kravchenko A N, Kussul N N, Skakun S V, Shelestov A Y, Grypych Y A. Efficiency assessment of different approaches to crop classification based on satellite and ground observations. Journal of Automation and Information Sciences, 2012, 44(5): 67-80.

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