Fusion of moderate resolution earth observations for operational crop Type mapping

Remote Sensing

Volumn 10, Issue 7, 2018, Pages

  Torbick, Nathan ^a   Huang, Xiaodong ^a   Ziniti, Beth ^a   Johnson, David ^b   Masek, Jeff ^c   Reba, Michele ^d  

^a Applied GeoSolutions   (United States)

^b Research and Development Division   (United States)

^c NASA GODDARD SPACE FLIGHT CENTER   (United States)

^d AGRICULTURAL RESEARCH SERVICE   (United States)

Author keywords

Classification; Crop type; Food security; Fusion; HLS; Sentinel 1

Indexed keywords

CLASSIFICATION (OF INFORMATION); DECISION TREES; FOOD SUPPLY; FUSION REACTIONS; MAPPING; OBSERVATORIES; SPACE-BASED RADAR;

EARTH OBSERVATIONS; FOOD SECURITY; MODERATE RESOLUTION; OPERATIONAL LAND IMAGER; RANDOM FOREST CLASSIFIER; RESEARCH APPLICATIONS; SENTINEL-1; TEMPORAL FREQUENCY;

CROPS;

EID: 85050487962     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs10071058     Document Type: Article

References (44)

1. Kussul, N.; Lavreniuk, M.; Skakun, S.; Shelestov, A. Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data. IEEE Geosci. Remote Sens. Lett. 2017, 14, 778-782
2. Whitcraft, A.; Becker-Reshef, I.; Justice, C. Agricultural growing season calendars derived from MODIS surface reflectance. Int. J. Digit. Earth 2014, 8, 173-197
3. Becker-Reshef, I.; Vermote, E.; Lindeman, M.; Justice, C. A Generalized Regression-based Model for ForecastingWinter Wheat Yields in Kansas and Ukraine Using MODIS Data. Remote Sens. Environ. 2010, 114, 1312-1323
4. Franch, B.; Vermote, E.; Becker-Reshef, I.; Claverie, M.; Huang, J.; Zhang, J.; Justice, C.; Sobrino, J. Improving the timeliness of winter wheat production forecast in the United States of America, Ukraine and China using MODIS data and NCAR Growing Degree Day information. Remote Sens. Environ. 2015, 161, 131-148
5. Johnson, D. A comprehensive assessment of the correlations between field crop yields and commonly used MODIS products. Int. J. Appl. Earth Obs. Geoinform. 2016, 52, 65-81
6. Xiong, J.; Thenkabail, P.S.; Tilton, J.C.; Gumma, M.K.; Teluguntla, P.; Oliphant, A. 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. Remote Sens. 2017, 9, 1065
7. Song, X.P.; Potapov, P.V.; Krylov, A.; King, L.; Di Bella, C.M.; Hudson, A.; Khan, A.; Adusei, B.; Stehman, S.V.; Hansen, M.C. National-scale soybean mapping and area estimation in the United States using medium resolution satellite imagery and field survey. Remote Sens. Environ. 2017, 190, 383-395
8. 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. Int J. Photogramm. Remote Sens. 2009, 64, 434-449
9. Bouvet, A.; LeToan, T.; Lam-Dao, N. Monitoring of the rice cropping system in the Mekong Delta using ENVISAT/ASAR dual polarization data. IEEE Trans. Geosci. Remote Sens. 2011, 47, 517-526
10. Liao, C.; Wang, J.; Shang, J.; Huang, X.; Liu, J.; Huffman, T. Sensitivity study of Radarsat-2 polarimetric SAR to crop height and fractional vegetation cover of corn and wheat. Int. J. Remote Sens. 2018, 39, 1475-1490
11. Huang, X.; Wang, J.; Shang, J.; Liao, C.; Liu, J. Application of polarization signature to land cover scattering mechanism analysis and classification using multi-temporal C-band polarimetric RADARSAT-2 imagery. Remote Sens. Environ. 2018, 193, 11-28
12. Torbick, N.; Chowdhury, D.; Salas, W.; Qi, J. Monitoring rice agriculture across Myanmar using time series Sentinel-1 assisted by Landsat-8 and PALSAR-2. Remote Sens. 2017, 9, 119
13. Clauss, K.; Ottinger, M.; Kuenzer, C. Mapping rice areas with Sentinel-1 time series and superpixel segmentation. Int. J. Remote Sens. 2018, 39, 1399-1420
14. Nelson, A.; Setiyono, T.; Rala, A.B.; Quicho, E.D.; Raviz, J.V.; Abonete, P.J.; Maunahan, A.A.; Garcia, C.A.; Bhatti, H.Z.M.; Villano, L.S.; et al. Towards an Operational SAR-Based Rice Monitoring System in Asia: Examples from 13 Demonstration Sites across Asia in the RIICE Project. Remote Sens. 2014, 6, 10773-10812
15. Steventon, M.; Ward, S.; Dyke, G.; Sobue, S.; Oyoshi, K. Asian Rice Crop Estimation and Monitoring Component of GEOGLAM (Asia-RiCE) 2017/Phase 2 Implementation Report; JAXA (Japan Aerospace Exploration Agency): Tokyo, Japan, 2018
16. Small, D. Flattening Gamma: Radiometric Terrain Correction for SAR Imagery. IEEE Trans. Geosci. Remote Sens. 2011, 49, 3081-3093
17. Vermote, E.; Justice, C.; Claverie, M.; Franch, B. Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sens. Environ. 2016, 185, 46-56
18. 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. IEEE Geosci. Remote Sens. Soc. 2006, 3, 68-72
19. Zhu, Z.;Wang, S.;Woodcock, C.E. Improvement and expansion of the Fmask algorithm: Cloud, cloud shadow, and snow detection for Landsats 4-7, 8, and Sentinel 2 images. Remote Sens. Environ. 2015, 159, 269-277
20. Claverie, M.; Vermote, E.; Franch, B.; He, T.; Hagolle, O.; Kadiri, M.; Masek, J. Evaluation of Medium Spatial Resolution BRDF-Adjustment Techniques Using Multi-Angular SPOT4 (Take5) Acquisitions. Remote Sens. 2015, 7, 12057-12075
21. Roy, D.P.; Zhang, H.K.; Ju, J.; Gomez-Dans, J.L.; Lewis, P.E.; Schaaf, C.B.; Sun, Q.; Li, J.; Huang, H.; Kovalskyy, V. A general method to normalize Landsat reflectance data to nadir BRDF adjusted reflectance. Remote Sens. Environ. 2016, 176, 255-271
22. Gao, F.; Masek, J.G.; Wolfe, R.E. Automated registration and orthorectification package for Landsat and Landsat-like data processing. J. Appl. Remote Sens. 2009, 3, 033515
23. Rouse, J.W.; Haas, R.H.; Schell, J.A.; Deering, D.W. Monitoring vegetation systems in the Great Plains with ERTS. In Proceedings of the Third ERTS Symposium, Washington, DC, USA, 10 December 1974
24. Tucker, C.J. Red and photographic infrared linear combinations formonitoring vegetation. Remote Sens. Environ. 1979, 8, 127-150
25. Xiao, X.; Boles, S.; Liu, J.; Zhuang, D.; Liu, M. Characterization of forest types in Northeastern China, using multi-temporal SPOT-4 VEGETATION sensor data. Remote Sens. Environ. 2002, 82, 335-348
26. Hagen, S.; Heilman, P.; Marsett, R.; Torbick, N.; Salas, W.; van Ravensway, J.; Qi, J. Mapping total vegetation cover across western rangelands with MODIS data. Rangel. Ecol. Manag. 2012, 65, 456-467
27. Breiman, L. Random Forests. Mach. Learn. 2001, 45, 5-32
28. Wilkes, P.; Jones, S.D.; Suarez, L.; Mellor, A.; Woodgate, W.; Soto-Berelov, M.; Haywood, A.; Skidmore, A.K. Mapping forest canopy height over large areas by upscaling ALS estimates with freely available satellite data. Remote Sens. 2015, 7, 12563-12587
29. Song, W.; Dolon, J.M.; Cline, D.; Xiong, G. Leanring-based algal bloom event recognition for oceanographic decision support system using remote sensing data. Remote Sens. 2015, 7, 13564-13585
30. Torbick, N.; Corbiere, M. Mapping urban sprawl and impervious surfaces in the northeast United States for the past four decades. GISci. Remote Sens. 2015, 52, 746-764
31. Karlson, M.; Ostwald, M.; Reese, H.; Sanou, J.; Tankoano, B.; Mattsson, E. Mapping tree canopy cover and aboveground biomass in Sudano-Sahelian woodlands using Landsat 8 and random forest. Remote Sens. 2015, 7, 10017-10041
32. Hastie, T.; Tibshirani, R.; Friedman, J.H. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed.; Springer: New York, NY, USA, 2009
33. Strobl, C.; Boulesteix, A.-L.; Zeileis, A.; Hothorn, T. Bias in random forest variable importance measures: Illustrations, sources and a solution. BMC Bioinform. 2007, 8, 25
34. Savitzky, A.; Golay, M.J.E. Smoothing and Differentiation of Data by Simplified Least Squares Procedures. Anal. Chem. 1964, 36, 1627-1639
35. Whittaker, E.T.; Robinson, G. The Calculus of Observations. Trans. Fac. Act. 1924, 10, 1924-1925
36. Atzberger, C.; Eilers, P.H.C. A time series for monitoring vegetation activity and phenology at 10-daily time steps covering large parts of South America. Int. J. Dig. Earth 2011, 4, 365-386
37. Atkinson, P.M.; Jeganathan, C.; Dash, J.; Atzberger, C. Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology. Remote Sens. Environ. 2012, 123, 400-417
38. Eilers, P.H.C. A Perfect Smoother. Anal. Chem. 2003, 75, 3631-3636
39. Jönsson, P.; Eklundh, L. TIMESAT-A program for analyzing time-series of satellite sensor data. Comput. Geosci. Vol. 2004, 30, 833-845
40. 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. 2011, 26, 341-358
41. Foga, S.; Scaramuzza, P.; Guo, S.; Zhu, Z.; Dilley, R.; Beckmann, T.; Schmidt, G.; Dwyer, J.; Hughes, M.; Laue, B. Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sens. Environ. Vol. 2017, 194, 379-390
42. Qiu, S.; He, B.; Zhu, Z.; Quan, X. Improving Fmask cloud and cloud shadow detection in mountainous area for Landsats 4-8 images. Remote Sens. Environ. 2017, 199, 107-119
43. Huang, X.; Wang, J.; Shang, J. An integrated surface parameter inversion scheme over agricultural fields at early growing stages by means of C-band polarimetric RADARSAT-2 imagery. IEEE Trans. Geosci. Remote Sens. 2017, 54, 2510-2528
44. Canisius, F.; Shang, J.; Liu, J.; Huang, X.; Ma, B.; Jiao, X.; Geng, X.; Kovacs, J.; Walters, D. Tracking crop phenological development using multi-temporal polarimetric Radarsat-2 data. Remote Sens. Environ. Vol. 2018, 210, 508-518