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Mapping Spring Canola and Spring Wheat using Radarsat-2 and Landsat-8 Images with Google Earth Engine


Affiliations
1 The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China
 

Using remote sensing, it is difficult to accurately extract spring canola and wheat planting area with only optical images because both crops have the same growth period and similar spectral characteristics. Besides, optical images are susceptible to cloud contamination. Synthetic aperture radar is sensitive to canopy structure and is hardly influenced by weather; however, it is difficult to distinguish spring wheat and grass due to the similarity of both canopy structures during the major growth cycle. In order to resolve this problem, the present study proposed a method to extract spring canola and wheat by combining Radarsat-2 and Landsat-8 images based on Google Earth Engine. First, spring canola, forest, water and spring wheat and grass (both were regarded as one object) were extracted from Radarsat-2 image. Second, the cropland was extracted from Landsat-8 image. Third, synthetic mapping was carried out to achieve spring canola and wheat extraction. The result demonstrates that spring canola and wheat were successfully extracted with an overall accuracy of 96.04%.

Keywords

Google Earth Engine, Landsat-8, Radarsat-2, Spring Canola, Spring Wheat.
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  • Kim, Y., Jackson, T., Bindlish, R., Hong, S., Jung, G. and Lee, K., Retrieval of wheat growth parameters with radar vegetation indices. IEEE Geosci. Remote Sci., 2014, 11, 808-812.

  • Cable, J., Kovacs, J., Jiao, X. and Shang, J., Agricultural monitoring in northeastern Ontario, Canada, using multi-temporal polarimetric Radarsat-2 data. Remote Sensing, 2014, 6, 2343-2371.

  • Jiao, X., Kovacs, J. M., Shang, J., McNairn, H., Walters, D., Ma, B. and Geng, X., Object-oriented crop mapping and monitoring using multi-temporal polarimetric Radarsat-2 data. ISPRS J. Photogramm. Remote Sensing, 2014, 96, 38-46.

  • Wiseman, G., McNairn, H., Homayouni, S. and Shang, J. L., Radarsat-2 polarimetric SAR response to crop biomass for agricultural production monitoring. IEEE J-Stars, 2014, 7, 4461-4471.

  • Han, J. H. et al., Mapping above-ground biomass of winter oilseed rape using high spatial resolution satellite data at parcel scale under waterlogging conditions. Remote Sensing, 2017, 9, 238.

  • Mo, X. G., Chen, X. J., Hu, S., Liu, S. X. and Xia, J., Attributing regional trends of evapotranspiration and gross primary productivity with remote sensing: A case study in the north china plain. Hydrol. Earth Syst. Sci., 2017, 21, 295-310.

  • She, B., Huang, J. F., Zhang, D. Y. and Huang, L. S., Assessing and characterizing oilseed rape freezing injury based on MODIS and MERIS data. Int. J. Agric. Biol. Eng., 2017, 10, 143-157.

  • Wei, C. W., Huang, J. F., Wang, X. Z., Blackburn, G. A., Zhang, Y., Wang, S. S. and Mansaray, L. R., Hyperspectral characterization of freezing injury and its biochemical impacts in oilseed rape leaves. Remote Sensing Environ., 2017, 195, 56-66.

  • Jin, X. et al., Combined multi-temporal optical and radar parameters for estimating LAI and biomass in winter wheat using HJ and Radarsat-2 data. Remote Sensing, 2015, 7, 13251-13272.

  • Sulik, J. J. and Long, D. S., Spectral indices for yellow canola flowers. Int. J. Remote Sensing, 2015, 36, 2751-2765.

  • Ortiz-Monasterio, J. I. and Lobell, D. B., Remote sensing assessment of regional yield losses due to sub-optimal planting dates and fallow period weed management. Field Crop Res., 2007, 101, 80-87.

  • Sulik, J. J. and Long, D. S., Spectral considerations for modeling yield of canola. Remote Sensing Environ., 2016, 184, 161-174.

  • Jiao, X. F., Kovacs, J. M., Shang, J. L., McNairn, H., Walters, D., Ma, B. L. and Geng, X. Y., Object-oriented crop mapping and monitoring using multi-temporal polarimetric Radarsat-2 data. ISPRS J. Photogramm. Remote Sensing, 2014, 96, 38-46.

  • Thompson, A. A. and McLeod, I. H., The Radarsat-2 SAR processor. Can. J. Remote Sensing, 2004, 30, 336-344.

  • Clavet, D., Toutin, T. and Kharbouche, S., Radarsat-2: A new source for topographic data acquisition in the Canadian Arctic without field control. Can. J. Remote Sensing, 2011, 37, 529-534.

  • Tlili, A., Coulibaly, L., Hervet, E. and Adegbidi, H. G., Combination of spatial information and polarimetric mapping of forest compositions from SAR Radarsat-2 image. Can. J. Remote Sensing, 2012, 38, 324-335.

  • Patel, N. N. et al., Multitemporal settlement and population mapping from landsat using Google Earth Engine. Int. J. Appl. Earth Obs. Geoinf., 2015, 35, 199-208.

  • Tang, Z. et al., Assessing Nebraska play a wetland inundation status during 1985-2015 using Landsat data and Google Earth Engine. Environ. Monit. Assess., 2016, 188(12), 654.

  • Alonso, A., Munoz-Carpena, R., Kennedy, R. E. and Murcia, C., Wetland landscape spatio-temporal degradation dynamics using the new Google Earth engine cloud-based platform: Opportunities for non-speciatists in remote sensing. Trans. ASABE, 2016, 59, 1333-1344.

  • Dong, J. W. et al., Mapping paddy rice planting area in northeastern asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine. Remote Sensing Environ., 2016, 185, 142-154.

  • Goldblatt, R., You, W., Hanson, G. and Khandelwal, A. K., Detecting the boundaries of urban areas in India: A dataset for pixel-based image classification in Google Earth Engine. Remote Sensing, 2016, 8, 634-652.

  • Xiong, J. et al., Automated cropland mapping of continental Africa using Google Earth Engine cloud computing. ISPRS J. Photogramm. Remote Sensing, 2017, 126, 225-244.

  • Morena, L. C., James, K. V. and Beck, J., An introduction to the Radarsat-2 mission. Can. J. Remote Sensing, 2004, 30, 221-234.

  • Ali, Z., Kroupnik, G., Matharu, G., Graham, J., Barnard, I., Fox, P. and Raimondo, G., Radarsat-2 space segment design and its enhanced capabilities with respect to Radarsat-1. Can. J. Remote Sensing, 2004, 30, 235-245.

  • Yang, Z., Shao, Y., Li, K., Liu, Q. B., Liu, L. and Brisco, B., An improved scheme for rice phenology estimation based on timeseries multispectral HJ-1a/b and polarimetric Radarsat-2 data. Remote Sensing Environ., 2017, 195, 184-201.

  • Nguyen, D. B. and Wagner, W., European rice cropland mapping with SENTINEL-1 data: The mediterranean region case study. Water, 2017, 9, 392.

  • Tian, H., Li, W., Wu, M., Huang, N., Li, G., Li, X. and Niu, Z., Dynamic monitoring of the largest freshwater lake in China using a new water index derived from high spatiotemporal resolution SENTINEL-1a data. Remote Sensing, 2017, 9, 521-539.

  • Tollefson, J., Landsat 8 to the rescue. Nature, 2013, 494, 13-14.

  • Roy, D. P. et al., Landsat-8: Science and product vision for terrestrial global change research. Remote Sensing Environ., 2014, 145, 154-172.

  • Irons, J. R., Dwyer, J. L. and Barsi, J. A., The next Landsat satellite: The Landsat data continuity mission. Remote Sensing Environ., 2012, 122, 11-21.

  • Qiao, C., Luo, J. C., Sheng, Y. W., Shen, Z. F., Zhu, Z. W. and Ming, D. P., An adaptive water extraction method from remote sensing image based on NDWI. J. Indian Soc. Remote, 2012, 40, 421-433.

  • McFeeters, S. K., Using the normalized difference water index (NDWI) within a geographic information system to detect swimming pools for mosquito abatement: a practical approach. Remote Sensing, 2013, 5, 3544-3561.

  • Li, W., Niu, Z., Liang, X. L., Li, Z. Y., Huang, N., Gao, S., Wang, C. and Muhammad, S., Geostatistical modeling using Lidarderived prior knowledge with Spot-6 data to estimate temperate forest canopy cover and above-ground biomass via stratified random sampling. Int. J. Appl. Earth Obs. Geoinf., 2015, 41, 88-98.

  • Cable, J. W., Kovacs, J. M., Jiao, X. F. and Shang, J. L., Agricultural monitoring in northeastern Ontario, Canada, using multi-temporal polarimetric Radarsat-2 data. Remote Sensing, 2014, 6, 2343-2371.

  • Hasituya and Chen, Z. X., Mapping plastic-mulched farmland with multi-temporal Landsat-8 data. Remote Sensing, 2017, 9, 557.

  • Bellon, B., Begue, A., Lo Seen, D., de Almeida, C. A. and Simoes, M., A remote sensing approach for regional-scale mapping of agricultural land-use systems based on NDVI time series. Remote Sensing, 2017, 9, 600.

  • Jiang, D., Huang, Y. H., Zhuang, D. F., Zhu, Y. Q., Xu, X. L. and Ren, H. Y., A simple semi-automatic approach for land cover classification from multispectral remote sensing imagery. PLoS ONE, 2012, 7(9), e45889.


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  • Mapping Spring Canola and Spring Wheat using Radarsat-2 and Landsat-8 Images with Google Earth Engine

Abstract Views: 416  |  PDF Views: 130

Authors

Haifeng Tian
The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China
Meng Meng
The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China
Mingquan Wu
The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China
Zheng Niu
The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China

Abstract


Using remote sensing, it is difficult to accurately extract spring canola and wheat planting area with only optical images because both crops have the same growth period and similar spectral characteristics. Besides, optical images are susceptible to cloud contamination. Synthetic aperture radar is sensitive to canopy structure and is hardly influenced by weather; however, it is difficult to distinguish spring wheat and grass due to the similarity of both canopy structures during the major growth cycle. In order to resolve this problem, the present study proposed a method to extract spring canola and wheat by combining Radarsat-2 and Landsat-8 images based on Google Earth Engine. First, spring canola, forest, water and spring wheat and grass (both were regarded as one object) were extracted from Radarsat-2 image. Second, the cropland was extracted from Landsat-8 image. Third, synthetic mapping was carried out to achieve spring canola and wheat extraction. The result demonstrates that spring canola and wheat were successfully extracted with an overall accuracy of 96.04%.

Keywords


Google Earth Engine, Landsat-8, Radarsat-2, Spring Canola, Spring Wheat.

References





DOI: https://doi.org/10.18520/cs%2Fv116%2Fi2%2F291-298