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

Volumn 144, Issue , 2018, Pages 325-340

  Teluguntla, P ^a,b   Thenkabail, P ^a   Oliphant, Adam ^a   Xiong, Jun ^b   Gumma, Murali Krishna ^c   Congalton, Russell G ^d   Yadav, Kamini ^d   Huete, Alfredo ^e  

^a U S GEOLOGICAL SURVEY   (United States)

^b NASA AMES RESEARCH CENTER   (United States)

^c INTERNATIONAL CROPS RESEARCH INSTITUTE FOR THE SEMI ARID TROPICS ICRISAT   (India)

^d UNIVERSITY OF NEW HAMPSHIRE   (United States)

^e UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

Author keywords

Australia; China; Cropland mapping; Google Earth Engine; Landsat; Machine learning algorithm; Random forest

Indexed keywords

CLOUD COMPUTING; COMPUTING POWER; CROPS; DATA HANDLING; DECISION TREES; ENGINES; KNOWLEDGE BASED SYSTEMS; LEARNING ALGORITHMS; MACHINE LEARNING; MAPPING; PRODUCTIVITY; SAMPLING;

AUSTRALIA; CHINA; CLOUD COMPUTING PLATFORMS; CROPLAND MAPPING; FOREST MACHINES; GOOGLE EARTH ENGINE; GOOGLE EARTHS; LANDSAT; MACHINE LEARNING ALGORITHMS; RANDOM FORESTS;

LANDSAT;

ACCURACY ASSESSMENT; AGRICULTURAL LAND; ALGORITHM; LANDSAT; MACHINE LEARNING; MAPPING; PIXEL; REMOTE SENSING; SPATIAL RESOLUTION; WORLD WIDE WEB;

AUSTRALIA; CHINA;

EID: 85051136400     PISSN: 09242716     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.isprsjprs.2018.07.017     Document Type: Article

References (93)

1. ABARES, 2016. Australian Crop Report Series, Australian Bureau of Agricultural and Resource Economics and Sciences, Canberra, February. (accessed March 2016).
2. Arsanjani, J.J., Tayyebi, A., Vaz, E., GlobeLand30 as an alternative fine-scale global land cover map: challenges, possibilities, and implications for developing countries. Habitat Int. 55 (2016), 25–31.
3. Belgiu, M., Drăguţ L., Random forest in remote sensing: a review of applications and future directions. ISPRS J. Photogramm. Remote Sens. 114 (2016), 24–31.
4. Biggs, T., Thenkabail, P., Gumma, M., Scott, C., Parthasaradhi, G., Turral, H., Irrigated area mapping in heterogeneous landscapes with MODIS time series, ground truth and census data, Krishna Basin, India. Int. J. Remote Sens. 27 (2006), 4245–4426.
5. Biradar, C.M., Thenkabail, P.S., Noojipady, P., Li, Y., Dheeravath, V., Turral, H., Velpuri, M., Gumma, M.K., Gangalakunta, O.R.P., Cai, X.L., A global map of rainfed cropland areas (GMRCA) at the end of last millennium using remote sensing. Int. J. Appl. Earth Observ. Geoinformat. 11 (2009), 114–129.
6. Boryan, C., Yang, Z., Mueller, R., Craig, M., Monitoring US agriculture: the US department of agriculture, national agricultural statistics service, cropland data layer program. Geocarto Int. 26:5 (2011), 341–358.
7. Breiman, L., Random forests. Mach. Learn. 45 (2001), 5–32.
8. Carroll, M.L., Townshend, J.R., DiMiceli, C.M., Noojipady, P., Sohlberg, R.A., A new global raster water mask at 250 m resolution. Int. J. Digital Earth 2:4 (2009), 291–308.
9. Chan, J.C.W., Paelinckx, D., Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery. Remote Sens. Environ. 112:6 (2008), 2999–3011.
10. Chander, G., Markham, B.L., Helder, D.L., Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sens. Environ. 113 (2009), 893–903.
11. Chen, C., Liaw, A., Breiman, L., 2004. Using Random Forest to Learn Imbalanced Data, Technical Report 666.. University of California, Berkeley.
12. Chen, G., Thill, J.C., Anantsuksomsri, S., Tontisirin, N., Tao, R., Stand age estimation of rubber (Hevea brasiliensis) plantations using an integrated pixel-and object-based tree growth model and annual Landsat time series. ISPRS J. Photogramm. Remote Sens. 144 (2018), 94–104.
13. Chen, J., Chen, J., Liao, A., Cao, X., Chen, L., Chen, X., He, C., Han, G., Peng, S., Lu, M., Global land cover mapping at 30m resolution: a POK-based operational approach. ISPRS J. Photogramm. Remote Sens. 103 (2015), 7–27.
14. Cohen, W.B., Goward, S.N., Landsat's role in ecological applications of remote sensing. Bioscience 54 (2004), 535–545.
15. Congalton, R., Assessing positional and thematic accuracies of maps generated from remotely sensed data. Remote Sensing Handbook” Three Volume set Remotely Sensed Data Characterization, Classification, and Accuracies, 2015, 583–602.
16. Congalton, R.G., A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens. Environ. 37 (1991), 35–46.
17. Congalton, R.G., 19 accuracy and error analysis of global and local maps: lessons learned and future considerations. Remote Sens. Glob. Croplands Food Secur., 2009, 441.
18. Congalton, R.G., Green, K., Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. 2008, CRC Press.
19. Congalton, G., R., Yadav, K., McDonnell, K., Poehnelt, J., Stevens, B., Gumma, K.M., Teluguntla, P., Thenkabail, P.S., 2017. NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 Validation Global 30 m V001 [Data set]. NASA EOSDIS Land Processes DAAC. http://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30VAL.001.
20. Defourny P., Schouten, L., Bartalev, S., Bontemps, S., Caccetta, P., de Witt, A., di Bella, C., Gerard, B., Giri, C., Gond, V., Hazeu, G., Heinimann, A., Herold, M., Jaffrain, G., Latifovic, R., Ling, H., Mayaux, P., Muncher, S., Nonguierma, A., Stibig, H.-J., Van Bogaert, E., Vancutsem, C., Bicheron, P., Leroy, M., Arino, O., 2009. Accuracy assessment of a 300-m global land cover map: the GlobCover experience. In: 33rd International Symposium on Remote Sensing of Environment (ISRSE), Stresa, Italy, May 2009.
21. DeFries, R., Chan, J.C.-W., Multiple criteria for evaluating machine learning algorithms for land cover classification from satellite data. Remote Sens. Environ. 74 (2000), 503–515.
22. Dheeravath, V., Thenkabail, P.S., Chandrakantha, G., Noojipady, P., Reddy, G.P.O., Biradar, C.M., Velpuri, M., et al. Irrigated areas of India derived using MODIS 500 m time series for the years 2001–2003. ISPRS J. Photogramm. Remote Sens. 65:1 (2010), 42–59.
23. Duro, D.C., Franklin, S.E., Dubé M.G., A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Remote Sens. Environ. 118 (2012), 259–272.
24. Erickson, T., 2014. Multi-source Geospatial Data Analysis with Google Earth Engine, Fall Meeting 2014, abstract #IN53E-05. American Geophysical Union.
25. Fischer, G., van Velthuizen, H., Nachtergaele, F.O., 2000. Global Agroecological Zones Assessment: Methodology and Results (IIASA Interim Report IR-00-064).
26. Foley, J.A., Ramankutty, N., Brauman, K.A., Cassidy, E.S., Gerber, J.S., Johnston, M., Mueller, N.D., O'Connell, C., Ray, D.K., West, P.C., Solutions for a cultivated planet. Nature 478 (2011), 337–342.
27. Friedl, M.A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N., Sibley, A., Huang, X., MODIS Collection 5 global land cover: algorithm refinements and characterization of new datasets. Remote Sens. Environ. 114 (2010), 168–182.
28. Fritz, S., See, L., McCallum, I., You, L., Bun, A., Moltchanova, E., Havlik, P., et al. Mapping global cropland and field size. Global change biology 21:5 (2015), 1980–1992.
29. Funk, C.C., Brown, M.E., Declining global per capita agricultural production and warming oceans threaten food security. Food Secur. 1 (2009), 271–289.
30. Gislason, P.O., Benediktsson, J.A., Sveinsson, J.R., Random forests for land cover classification. Pattern Recogn. Lett. 27:4 (2006), 294–300.
31. Gong, P., Wang, J., Yu, L., Zhao, Y., Zhao, Y., Liang, L., Niu, Z., Huang, X., Fu, H., Liu, S., Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data. Int. J. Remote Sens. 34 (2013), 2607–2654.
32. Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., Moore, R., Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202 (2017), 18–27.
33. Gumma, M.K., Nelson, A., Thenkabail, P.S., Singh, A.N., Mapping rice areas of South Asia using MODIS multitemporal data. J. Appl. Remote Sens., 5, 2011 053547-053547-053526.
34. Gumma, M.K., Thenkabail, P.S., Teluguntla, P., Rao, M.N., Mohammed, I.A., Whitbread, A.M., Mapping rice-fallow cropland areas for short-season grain legumes intensification in South Asia using MODIS 250 m time-series data. Int. J. Digital Earth 9 (2016), 981–1003.
35. Gumma, M.K., Thenkabail, P.S., Maunahan, A., Islam, S., Nelson, A., Mapping seasonal rice cropland extent and area in the high cropping intensity environment of Bangladesh using MODIS 500 m data for the year 2010. ISPRS J. Photogramm. Remote Sens. 91 (2014), 98–113.
36. Gumma, M.K., Thenkabail, P.S., Deevi, K.C., Mohammed, I.A., Teluguntla, P., Oliphant, A., Whitbread, A.M., et al. Mapping cropland fallow areas in myanmar to scale up sustainable intensification of pulse crops in the farming system. GISci. Remote Sens., 2018, 1–24.
37. Gutman, G., Byrnes, R., Masek, I., Covington, S., Justice, C., Franks, S., Headley, R., Towards monitoring changes at a Globa the Global Land S. Photogramm. Eng. Remote Sens. 74 (2008), 6–10.
38. Han, W., Yang, Z., Di, L., Mueller, R., CropScape: a Web service based application for exploring and disseminating US conterminous geospatial cropland data products for decision support. Comput. Electron. Agric. 84 (2012), 111–123.
39. Hansen, M.C., Potapov, P.V., Moore, R., Hancher, M., Turubanova, S., Tyukavina, A., Kommareddy, A., et al. High-resolution global maps of 21st-century forest cover change. Science 342:6160 (2013), 850–853.
40. Irons, J.R., Dwyer, J.L., Barsi, J.A., The next Landsat satellite: the Landsat data continuity mission. Remote Sens. Environ. 122 (2012), 11–21.
41. Johnson, D.M., Mueller, R., The 2009 cropland data layer. PE&RS Photogramm. Eng. Remote Sens. 76:11 (2010), 1201–1205.
42. Kumar, A., Hubert-Moy, L., Buvaneshwari, S., Sekhar, M., Ruiz, L., Bandyopadhyay, S., Corgne, S., Irrigation history estimation using multitemporal Landsat satellite Images: application to an intensive groundwater irrigated agricultural watershed in India. Remote Sens., 10(6), 2018, 893.
43. Landsat 8 OLI and TIRS. Available online: https://www.lta.cr.usgs.gov/L8 (accessed on 4 December 2017).
44. Lawrence, R.L., Wood, S.D., Sheley, R.L., Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (RandomForest). Remote Sens. Environ. 100 (2006), 356–362.
45. Liang, D., Zuo, Y., Huang, L., Zhao, J., Teng, L., Yang, F., Evaluation of the consistency of MODIS land cover product (MCD12Q1) based on Chinese 30 m globeland30 datasets: a case study in Anhui Province, China. ISPRS Int. J. Geo-Inf. 4 (2015), 2519–2541.
46. Loveland, T., Reed, B., Brown, J., Ohlen, D., Zhu, Z., Yang, L., Merchant, J., Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data. Int. J. Remote Sens. 21 (2000), 1303–1330.
47. Lymburner, L., Tan, P., Mueller, N., Thackway, R., Lewis, A., Thankappan, M., Randall, L., Islam, A., Senarath, U., 2011. The national dynamic land cover dataset (DLCD), record 2011/31. In.
48. Masek, J.G., Vermote, E.F., Saleous, N.E., Wolfe, R., Hall, F.G., Huemmrich, K.F., Gao, F., Kutler, J., Lim, T.-K., A Landsat surface reflectance dataset for North America, 1990–2000. Geosci. Remote Sens. Lett. IEEE 3 (2006), 68–72.
49. Marshall, M., Thenkabail, P., Advantage of hyperspectral EO-1 Hyperion over multispectral IKONOS, GeoEye-1, WorldView-2, Landsat ETM+, and MODIS vegetation indices in crop biomass estimation. ISPRS J. Photogramm. Remote Sens. 108 (2015), 205–218.
50. Matejicek, L., Kopackova, V., Changes in croplands as a result of large scale mining and the associated impact on food security studied using time-series Landsat images. Remote Sens. 2:6 (2010), 1463–1480.
51. Mountrakis, G., Im, J., Ogole, C., Support vector machines in remote sensing: a review. ISPRS J. Photogramm. Remote Sens. 66 (2011), 247–259.
52. Na, X., Zhang, S., Li, X., Yu, H., Liu, C., Improved land cover mapping using random forests combined with landsat thematic mapper imagery and ancillary geographic data. Photogramm. Eng. Remote Sens. 76 (2010), 833–840.
53. Nhamo, L., van Dijk, R., Magidi, J., Wiberg, D., Tshikolomo, K., Improving the accuracy of remotely sensed irrigated areas using post-classification enhancement through UAV capability. Remote Sens., 10(5), 2018, 712.
54. Ozdogan, M., Gutman, G., A new methodology to map irrigated areas using multi-temporal MODIS and ancillary data: An application example in the continental US. Remote Sens. Environ. 112:9 (2008), 3520–3537.
55. Ozdogan, M., Woodcock, C.E., Resolution dependent errors in remote sensing of cultivated areas. Remote Sens. Environ. 103 (2006), 203–217.
56. Pan, Z., Huang, J., Zhou, Q., Wang, L., Cheng, Y., Zhang, H., Blackburn, G.A., Yan, J., Liu, J., Mapping crop phenology using NDVI time-series derived from HJ-1 A/B data. Int. J. Appl. Earth Observ. Geoinformat. 34 (2015), 188–197.
57. Pantazi, X., Moshou, D., Alexandridis, T., Whetton, R., Mouazen, A., Wheat yield prediction using machine learning and advanced sensing techniques. Comput. Electron. Agric. 121 (2016), 57–65.
58. Pelletier, C., Valero, S., Inglada, J., Champion, N., Dedieu, G., Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas. Remote Sens. Environ. 187 (2016), 156–168.
59. 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 Sens. Environ. 115:6 (2011), 1301–1316.
60. Peña, J.M., Gutiérrez, P.A., Hervás-Martínez, C., Six, J., Plant, R.E., López-Granados, F., Object-based image classification of summer crops with machine learning methods. Remote Sens. 6:6 (2014), 5019–5041.
61. Pervez, M.S., Budde, M., Rowland, J., Mapping irrigated areas in Afghanistan over the past decade using MODIS NDVI. Remote Sens. Environ. 149 (2014), 155–165.
62. Pittman, K., Hansen, M.C., Becker-Reshef, I., Potapov, P.V., Justice, C.O., Estimating global cropland extent with multi-year MODIS data. Remote Sensing 2 (2010), 1844–1863.
63. Portmann, F.T., Siebert, S., Döll, P., MIRCA2000—global monthly irrigated and rainfed crop areas around the year 2000: a new high-resolution data set for agricultural and hydrological modeling. Glob. Biogeochem. Cycles, 24, 2010.
64. Roy, D.P., Wulder, M., Loveland, T.R., Woodcock, C., Allen, R., Anderson, M., Helder, D., Irons, J., Johnson, D., Kennedy, R., Landsat-8: science and product vision for terrestrial global change research. Remote Sens. Environ. 145 (2014), 154–172.
65. Salmon, J.M., Friedl, M.A., Frolking, S., Wisser, D., Douglas, E.M., Global rain-fed, irrigated, and paddy croplands: a new high resolution map derived from remote sensing, crop inventories and climate data. Int. J. Appl. Earth Observ. Geoinformat. 38 (2015), 321–334.
66. Samberg, L.H., Gerber, J.S., Ramankutty, N., Herrero, M., West, P.C., Subnational distribution of average farm size and smallholder contributions to global food production. Environ. Res. Lett., 11, 2016, 124010.
67. Shao, Y., Lunetta, R.S., Comparison of support vector machine, neural network, and CART algorithms for the land-cover classification using limited training data points. ISPRS J. Photogramm. Remote Sens. 70 (2012), 78–87.
68. Siebert, S., Döll, P., Quantifying blue and green virtual water contents in global crop production as well as potential production losses without irrigation. J. Hydrol. 384 (2010), 198–217.
69. Sun, Y., Kamel, M.S., Wong, A.K., Wang, Y., Cost-sensitive boosting for classification of imbalanced data. Pattern Recogn. 40 (2007), 3358–3378.
70. Tatsumi, K., Yamashiki, Y., Torres, M.A.C., Taipe, C.L.R., Crop classification of upland fields using Random forest of time-series Landsat 7 ETM+ data. Comput. Electron. Agric. 115 (2015), 171–179.
71. Teluguntla, P., Ryu, D., George, B., Walker, J.P., Malano, H.M., Mapping flooded rice paddies using time series of MODIS imagery in the Krishna River Basin, India. Remote Sens. 7 (2015), 8858–8888.
72. Teluguntla, P., Thenkabail, P.S., Xiong, J., Gumma, M.K., Giri, C., Milesi, C., Ozdogan, M., Congalton, R.G., Tilton, J., Sankey, T.T., 2015b. Global Food Security Support Analysis Data at Nominal 1 km (GFSAD1km) Derived from Remote Sensing in Support of Food Security in the Twenty-First Century: current Achievements and Future Possibilities. Land Resources Monitoring, Modeling, and Mapping with Remote Sensing. CRC Press, pp. 131–159.
73. Teluguntla, P., Thenkabail, P.S., Xiong, J., Gumma, M.K., Congalton, R.G., Oliphant, A., Poehnelt, J., Yadav, K., Rao, M., Massey, R., Spectral matching techniques (SMTs) and automated cropland classification algorithms (ACCAs) for mapping croplands of Australia using MODIS 250-m time-series (2000–2015) data. Int. J. Digital Earth, 2017, 1–34.
74. Teluguntla, P., Thenkabail, P.S., Xiong, J., Gumma, M.K., G., Congalton, R., Oliphant, J., A., Sankey, T., Poehnelt, J., Yadav, K., Massey, R., Phalke, A., Smith, C., 2017b. NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 Australia, New Zealand, China, Mongolia 30 m V001 [Data set]. NASA EOSDIS Land Processes DAAC. http://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30AUNZCNMOCE.001.
75. Thenkabail, P., GangadharaRao, P., Biggs, T., Krishna, M., Turral, H., Spectral matching techniques to determine historical land-use/land-cover (LULC) and irrigated areas using time-series 0.1-degree AVHRR Pathfinder datasets. Photogramm. Eng. Remote Sens. 73 (2007), 1029–1040.
76. Thenkabail, P.S., Biradar, C.M., Noojipady, P., Dheeravath, V., Li, Y., Velpuri, M., Gumma, M., Gangalakunta, O.R.P., Turral, H., Cai, X., Global irrigated area map (GIAM), derived from remote sensing, for the end of the last millennium. Int. J. Remote Sens. 30 (2009), 3679–3733.
77. Thenkabail, P.S., Hanjra, M.A., Dheeravath, V., Gumma, M., A holistic view of global croplands and their water use for ensuring global food security in the 21st century through advanced remote sensing and non-remote sensing approaches. Remote Sens. 2:1 (2010), 211–261.
78. Thenkabail, P.S., Knox, J.W., Ozdogan, M., Gumma, M.K., Congalton, R.G., Wu, Z., Milesi, C., Finkral, A., Marshall, M., Mariotto, I., Assessing future risks to agricultural productivity, water resources and food security: how can remote sensing help?. Photogramm. Eng. Remote Sens. 78 (2012), 773–782.
79. Thenkabail, P.S., Wu, Z., An automated cropland classification algorithm (ACCA) for Tajikistan by combining Landsat, MODIS, and secondary data. Remote Sens. 4 (2012), 2890–2918.
80. Tilton, J.C., Tarabalka, Y., Montesano, P.M., Gofman, E., Best merge region-growing segmentation with integrated nonadjacent region object aggregation. IEEE Trans. Geosc. Remote Sens. 50 (2012), 4454–4467.
81. Velpuri, N.M., Thenkabail, P.S., Gumma, M.K., Biradar, C., Dheeravath, V., Noojipady, P., Yuanjie, L., Influence of resolution in irrigated area mapping and area estimation. Photogramm. Eng. Remote Sens. 75:12 (2009), 1383–1395.
82. 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. 5 (2013), 494–502.
83. Waldner, F., Canto, G.S., Defourny, P., Automated annual cropland mapping using knowledge-based temporal features. ISPRS J. Photogramm. Remote Sens. 110 (2015), 1–13.
84. Waldner, F., Fritz, S., Di Gregorio, A., Plotnikov, D., Bartalev, S., Kussul, N., Gong, P., Thenkabail, P., Hazeu, G., Klein, I., A unified cropland layer at 250 m for global agriculture monitoring. Data, 1, 2016, 3.
85. Wang, J., Zhao, Y., Li, C., Yu, L., Liu, D., Gong, P., Mapping global land cover in 2001 and 2010 with spatial-temporal consistency at 250 m resolution. ISPRS J. Photogramm. Remote Sens. 103 (2015), 38–47.
86. Xiao, X., Boles, S., Frolking, S., Li, C., Babu, J.Y., Salas, W., Moore, B., Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images. Remote Sens. Environ. 100 (2006), 95–113.
87. Xiong, J., Thenkabail, P., Tilton, J., Gumma, M., Teluguntla, P., Oliphant, A., Congalton, R., Yadav, K., Gorelick, N., 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 Sens., 9, 2017, 1065.
88. Xiong, J., Thenkabail, P.S., Gumma, M.K., Teluguntla, P., Poehnelt, J., Congalton, R.G., Yadav, K., Thau, D., Automated cropland mapping of continental Africa using Google Earth Engine cloud computing. ISPRS J. Photogramm. Remote Sens. 126 (2017), 225–244.
89. Yadav, K., Congalton, R.G., Issues with large area thematic accuracy assessment for mapping cropland extent: a tale of three continents. Remote Sens., 10(1), 2017, 53.
90. Yang, Y., Xiao, P., Feng, X., Li, H., Accuracy assessment of seven global land cover datasets over China. ISPRS J. Photogramm. Remote Sens. 125 (2017), 156–173.
91. Yu, L., Wang, J., Clinton, N., Xin, Q., Zhong, L., Chen, Y., Gong, P., FROM-GC: 30 m global cropland extent derived through multisource data integration. Int. J. Digital Earth 6 (2013), 521–533.
92. Zhong, L., Hu, L., Yu, L., Gong, P., Biging, G.S., Automated mapping of soybean and corn using phenology. ISPRS J. Photogramm. Remote Sens. 119 (2016), 151–164.
93. Zhou, Y., Xiao, X., Qin, Y., Dong, J., Zhang, G., Kou, W., Jin, C., Wang, J., Li, X., Mapping paddy rice planting area in rice-wetland coexistent areas through analysis of Landsat 8 OLI and MODIS images. Int. J. Appl. Earth Observ. Geoinformat. 46 (2016), 1–12.