Cropland Mapping through Integration of Segmentation and Classification Techniques in Google Earth Engine

Document Type : Original Article


1 M.Sc. Student in the Dep. of Remote Sensing and Photogrammetry, K.N. Toosi University of Technology

2 Prof. of Dep. of Remote Sensing and Photogrammetry, K.N. Toosi University of Technology

3 Assistant Prof., Dep. of Geodesy and Surveying Engineering, University of Tafresh


Map of croplands is one of the information layers required in the efficient management of these lands. Having such maps makes it possible to monitor agricultural fields during the growing season continuously. In this study, a solution to produce map of Shahrekord’s agricultural lands in two agricultural and non-agricultural classes is presented using the time series of different extracted indices from Sentinel-2 images. Since the use of large data sources is one of the obstacles to the development of methods based on the time series of satellite images, the Google Earth engine processing platform has been used in this study. The proposed method is based on integrating supervised pixel-based classification results with segmentation results. First, training data of supervised classification is provided in a rigorous refining process without the need of collected data from field surveys or interpretation of high-resolution satellite images. Then, by calculating the separability of the two target classes in the time series of each index, the optimal indices are selected. Finally, by combining the results of segmentation and classification methods based on the votes obtained from the classification results, agricultural or non-agricultural class is assigned to each of the image segments. In addition to incorporating spatial information including edges and spatial proximity, this method has been able to improve the noise and porous results of pixel-based classification and has increased the overall accuracy of the final map from 90.7% to 96.05%. Also, user accuracy of both agricultural and non-agricultural classes show an improvement of 3.27 and 7.97%, respectively.


Achanta, R. & Süsstrunk, S., 2017, Superpixels and Polygons Using Simple Non-Iterative Clustering, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
Barnes, E.M., Clarke, T.R., Richards, S.E., Colaizzi, P.D., Haberland, J., Kostrzewski, M., Waller, P., Choi, C., Riley, E., Thompson, T., Lascano, R.J., Li, H., Moran & M.S., 2000, Coincident Detection of Crop Water Stress, Nitrogen Status and Canopy Density Using Ground Based Multispectral Data, Proceedings of the Fifth International Conference on Precision Agriculture, Bloomington, MN, USA, Vol. 1619.
Belgiu, M. & Drăguţ, L., 2016, Random Forest in Remote Sensing: A Review of Applications and Future Directions, ISPRS Journal of Photogrammetry and Remote Sensing, 114, PP. 24-31.
Blaschke. T., 2010, Object Based Image Analysis for Remote Sensing, ISPRS Journal of Photogrammetry and Remote Sensing, 65(1), PP. 2-16.
Buchhorn, M., Lesiv, M., Tsendbazar, N.-E., Herold, M., Bertels, L. & Smets, B., 2020, Copernicus Global Land Cover Layers—Collection 2, Remote Sensing, 12(6), e1044.
Carrasco, Luis, A.W., O’Neil, Morton R.D. & Rowland, C.S., 2019, Evaluating Combinations of Temporally Aggregated Sentinel-1, Sentinel-2 and Landsat 8 for Land Cover Mapping with Google Earth Engine, Remote Sensing, 11(3), P. 288.
Chen, B., Xiao, X., Li, X., Pan, L., Doughty, R., Jinwei Dong, J.M., Qin, Y., Zhao, B., Wu, Z., Sun, R., Lan, G., Xie, G., Clinton, N. & Giri, C. 2017, A Mangrove Forest Map of China in 2015: Analysis of Time Series Landsat 7/8 and Sentinel-1A Imagery in Google Earth Engine Cloud Computing Platform, ISPRS Journal of Photogrammetry and Remote Sensing, 131, PP. 104-120.
Csillik, O., Belgiu, M., Asner, G.P. & Kelly, M., 2019, Object-Based Time-Constrained Dynamic Time Warping Classification of Crops Using Sentinel-2, Remote Sensing, 11(10).
Du, Z., Yang, J., Ou, C. & Zhang, T., 2019, Smallholder Crop Area Mapped with a Semantic Segmentation Deep Learning Method, Remote Sensing, 11(7), P. 888.
Farajzadeh, M., Khoorani, A., Bazgeer, S., Zyaeeyan, P., 2013, Estimation Rainfed Wheat Yield Using Agro Climatic and Remote Sensing Indices in Kurdistan Province, Iran, Iranian Journal of Remote Sensing & GIS, 5(2), PP. 35-52.
Gitelson, A.A., Gritz, Y. & Merzlyak., MN., 2003, Relationships between Leaf Chlorophyll Content and Spectral Reflectance and Algorithms for Non-Destructive Chlorophyll Assessment in Higher Plant Leaves, Journal of Plant Physiology, 160(3), PP. 271-282.
Gobron, N., Pinty, B., Verstraete, M.M. & Widlowski, J.-L., 2000, Advanced Vegetation Indices Optimized for Up-Coming Sensors: Design, Performance, and Applications, IEEE Transactions on Geoscience and Remote Sensing, 38(6), PP. 2489-2505.
Gorelick, N., Hancher, M., Dixon, M. & Ilyushchenko, S., 2017. Google Earth Engine: Planetary-scale Geospatial Analysis for Everyone, Remote Sensing of Environment, 202(1), PP. 18-27.
Hermann, I., Pimstein, A., Karniel, A., Cohen, Y., Alchanatis, V. & Bonfil, D.J., 2011, LAI Assessment of Wheat and Potato Crops by VENμS and Sentinel-2 Bands, Remote Sensing of Environment, 115(8), PP. 2141-2151.
Huabing, H., Chen, Y., Clinton, N., Wang, J.Wang, X.Y., Liu, C.,X., Gong, P., Yang, J., Bai, Y.Q., Zheng, Y.M. & Zhu, Z., 2017, Mapping Major Land Cover Dynamics in Beijing Using all Landsat Images in Google Earth Engine, Remote Sensing of Environment, 202, PP. 166-176.
Huete, A., Didana, K., Miuraa, T., Rodrigueza, E.P., Gaoa, X. & Ferreira, L.G., 2002, Overview of the Radiometric and Biophysical Performance of the MODIS Vegetation Indices, Remote Sensing of Environment, 83(2002), PP. 195-213 .
Huete, A.R., 1988, A Soil-Adjusted Vegetation Index (SAVI), Remote Sensing of Environment, 25(3), PP. 295-309.
Hunt, E.R., Daughtry, C.S.T., Eitel, J.U.H. & Long, D.S., 2011, Remote Sensing Leaf Chlorophyll Content Using a Visible Band Index, Agronomy Journal, 103(4), PP. 1090-1099.
Jensen, J.R., 1996, Introductory Digital Image Processing: A Remote Sensing Perspective, Prentice-Hall Inc.
Kobayashi, N., Tani, H., Wang, X. & Sonobe, R., 2020, Crop Classification Using Spectral Indices Derived from Sentinel-2A Imagery, Journal of Information and Telecommunication, 4(1), PP. 67-90.
Liu, X., Hu, G., Chen, Y., Li, X., Xu, X., Li, S., Pei, F. & Wang, S., 2018, High-Resolution Multi-Temporal Mapping of Global Urban Land Using Landsat Images Based on the Google Earth Engine Platform, Remote Sensing of Environment, 209, PP. 227-239.
Martone, M., Rizzoli, P., González, C., Bueso-Bello, J.-L., Zink, M., Krieger, G. & Moreira, A., 2018, The Global Forest/Non-Forest Map from TanDEM-X Interferometric SAR Data, Remote Sensing of Environment, 205, PP. 352-373.
Matkan, A.A., Ashourloo, D., Salehi, H., 2017, Classification Performance Improvement of Agricultural Crops in Multitemporal Images Using Textural Information in Ghorveh County, Iranian Journal of Remote Sensing & GIS, 8(4), PP. 65-78.
McFeeters, S.K., 1996, The Use of the Normalized Difference Water Index (NDWI) in the Delineation of Open Water Features, International Journal of Remote Sensing, 17(7), PP. 1425-1432.
Mulder, V.L., de Bruin , S., Schaepman, M. & Mayr, T.R., 2011, The Use of Remote Sensing in Soil and Terrain Mapping—A Review, Geoderma, 162(1-2), PP. 1-19.
Nematollahi, H., Ashourloo, D., Alimohammadi, A., Khodabandehloo, E., & Radiom, S. ,2018, Development and application of crop and field condition indices using time-series satellite images of Sentinel-2, Iranian Journal of Remote Sensing & GIS, 10(3), PP. 105-122.
Nyaga, J.W., Markert, K.N., Thomas, A.B., Mugo, R.M.,Wahome, A.M. & Irwin, D., 2019, Water Quality Monitoring of In-Land Lakes in East Africa. AGUFM, 40(1), PP. 1-7.
Padma, S. & Sanjeevi, S., 2014, Jeffries Matusita Based Mixed-Measure for Improved Spectral Matching in Hyperspectral Image Analysis, International Journal of Applied Earth Observation and Geoinformation, 32, PP. 138-151.
Richards, J.A., 1999, Remote Sensing Digital Image Analysis, s.l.:Springer.
Richardson, A.J. & Wiegand., C.L., 1977, Distinguishing Vegetation from Soil Background Information, Photogrammetric Engineering and Remote Sensing, 43(12), PP. 1541-52.
Teluguntla, P., Thenkabail, P.S., Oliphant, A., Xiong, J., Gumma, M.K., Congalton, R.G., Yadav, K. & Huete, A, 2018, A 30-m Landsat-Derived Cropland Extent Product of Australia and China Using Random Forest Machine Learning Algorithm on Google Earth Engine Cloud Computing Platform, ISPRS Journal of Photogrammetry and Remote Sensing, 144(October 2018), PP. 325-340.
Teluguntla, P.G., Thenkabail, P.S., Gumma, M.K., Xiong, J., Giri, C., Milesi, C., Ozdogan, M., Congalton, R., Tilton, J., Tsagaan Sankey, T., Massey, R., Phalke, A. & Yadav, K., 2015, Global Cropland Area Database (GCAD) Derived from Remote Sensing in Support of Food Security in the Twenty-First Century: Current Achievements and Future Possibilities,. s.l.: Land Resources: Monitoring, Modelling, and Mapping, Remote Sensing Handbook. CRC Press.
Tucker, C.J., 1978, Red and Photographic Infrared Linear Combinations for Monitoring Vegetation, Remote Sensing of Environment, 8(2), PP. 127-150.
Wang, D., Wan, B., Qiu, P., Su, Y., Guo, Q., & Wu, X., 2018, Artificial mangrove species mapping using pléiades-1: An evaluation of pixel-based and object-based classifications with selected machine learning algorithms, Remote Sensing, 10(2), PP. 294.
Weiss, M., & Jacob, F. & Duveillerc, G., 2020, Remote Sensing for Agricultural Applications: A Meta-Review, Remote Sensing of Environment., 236, P. 111402.
Wilson, E.H. & Sader, S.A., 2002, Detection of Forest Harvest Type Using Multiple Dates of Landsat TM Imagery, Remote Sensing of Environment, 80(3), PP. 385-396.
Xiong, J., Thenkabai, P.S., Tilton, J.C., Gumma, M.K., Teluguntla, P., Oliphant, A., Congalton, R.G., Yadav, K. & N. Gorelick, 2017b. Nominal 30-m Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine, Remote Sensing, 9(10), P. 1065.
Xiong, J., Thenkabail, P., Gumma, M., Teluguntla, P., Poehnelt, J., Congalton, R., Kamini, Y., Thau, D., 2017a, Automated Cropland Mapping of Continental Africa Using Google Earth Engine Cloud Computing, ISPRS Journal of Photogrammetry and Remote Sensing, 126, PP. 225-244.
Zhou, K., Ming, D., Lv, X., Fang, J., & Wang, M. ,2019, CNN-based land cover classification combining stratified segmentation and fusion of point cloud and very high-spatial resolution remote sensing image data, Remote Sensing, 11(17), PP. 2065.