Investigating within-field variability of rice from high resolution satellite imagery in Qixing farm county, northeast China

ISPRS International Journal of Geo-Information

Volumn 4, Issue 1, 2015, Pages 236-261

  Zhao, Quanying ^a   Lenz Wiedemann, Victoria I S ^a   Yuan, Fei ^a,b   Jiang, Rongfeng ^c   Miao, Yuxin ^a,c   Zhang, Fusuo ^c   Bareth, Georg ^a  

^a UNIVERSITY OF COLOGNE   (Germany)

^b MINNESOTA STATE UNIVERSITY   (United States)

^c CHINA AGRICULTURAL UNIVERSITY   (China)

Author keywords

Agronomic variable; Expert classification; FORMOSAT 2; Multi data Approach (MDA); Northeast China; Rice; Sanjiang Plain; Within field variability

Indexed keywords

EID: 84948953088     PISSN: None     EISSN: 22209964     Source Type: Journal    
DOI: 10.3390/ijgi4010236     Document Type: Article

References (73)

1. Food and Agriculture Organization of the United Nations. FAO Statistical Database. Available online: http://www.fao.org/docrep/015/i2490e/i2490e03d.pdf (accessed on 13 June 2014).
2. Yan, X., Ohara, T., Akimoto, H. Development of region-specific emission factors and estimation of methane emission from rice fields in the East, Southeast and South Asian countries. Glob. Change Biol. 2003, 9, 237-254.
3. Smith, P., Martino, D., Cai, Z., Gwary, D., Janzen, H., Kumar, P., McCarl, B., Ogle, S., OMara, F., Rice, C., et al. Greenhouse gas mitigation in agriculture. Philos. Trans. R. Soc. B 2008, 363, 789-813.
4. Global Methane Initiative. Global Methane Emissions and Mitigation Opportunities. Available online: http://www.globalmethane.org (accessed on 26 August 2014).
5. Tan, Z.X., Lal, R., Wiebe, K.D. Global soil nutrient depletion and yield reduction. J. Sustain. Agr. 2005, 26, 123-146.
6. Kumar, M., Monteith, J.L. Remote sensing of crop growth. In Plants and the Daylight Spectrum; Smith, H., Ed., Academic Press: London, UK, 1981; pp. 133-144.
7. Moulin, S., Bondeau, A., Delecolle, R. Combining agricultural crop models and satellite observations: From field to regional scales. Int. J. Remote Sens. 1998, 19, 1021-1036.
8. Hatfield, J.L., Gitelson, A.A., Schepers, J.S., Walthall, C.L. Application of spectral remote sensing for agronomic decisions. Agron. J. 2008, 100, S117-S131.
9. Gitelson, A.A., Peng, Y., Arkebauer, T.J., Schepers, J. Relationships between gross primary production, green LAI, and canopy chlorophyll content in maize: Implications for remote sensing of primary production. Remote Sens. Environ. 2014, 144, 65-72.
10. 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. Glob. Biogeochem. Cycle 2002, 16, 10.1029/2001GB001425.
11. Xiao, X., Boles, S., Liu, J., Zhuang, D., Frolking, S., Li, C., Salas, W., Moore, B., III. Mapping paddy rice agriculture in southern China using multi-Temporal MODIS images. Remote Sens. Environ. 2005, 95, 480-492.
12. Kuenzer, C., Knauer, K. Remote sensing of rice crop areas. Int. J. Remote Sens. 2013, 34, 2101-2139.
13. Waldhoff, G., Curdt, C., Hoffmeister, D., Bareth, G. Analysis of multitemporal and multisensor remote sensing data for crop rotation mapping. In Proceedings of the 2012 International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Melbourne, Australia, 25 August-1 September 2012; pp. 177-182.
14. Rozenstein, O., Karnieli, A. Comparison of methods for land-use classification incorporating remote sensing and GIS inputs. Appl. Geogr. 2011, 31, 533-544.
15. Chang, K., Shen, Y., Lo, J. Predicting rice yield using canopy reflectance measured at booting stage. Agron. J. 2005, 97, 872-878.
16. Wang, Y., Chang, K., Chen, R., Lo, J., Shen, Y. Large-Area rice yield forecasting using satellite imageries. Int. J. Appl. Earth Obs. 2010, 12, 27-35.
17. Van Niel, T.G., McVicar, T.R. Current and potential uses of optical remote sensing in rice-based irrigation systems: A review. Crop Pasture Sci. 2004, 55, 155-185.
18. Duggin, M.J., Piwinski, D. Recorded radiance indices for vegetation monitoring using NOAA AVHRR data; atmospheric and other effects in multitemporal data sets. Appl. Opt. 1984, 23, 2620-2623.
19. McCloy, K.R., Smith, F.R., Robinson, M.R. Monitoring rice areas using LANDSAT MSS data. Int. J. Remote Sens. 1987, 8, 741-749.
20. Okamoto, K., Yamakawa, S., Kawashima, H. Estimation of flood damage to rice production in North Korea in 1995. Int. J. Remote Sens. 1998, 19, 365-371.
21. Che, N., Price, J.C. Survey of radiometric calibration results and methods for visible and near infrared channels of NOAA-7,-9, and-11 AVHRRs. Remote Sens. Environ. 1992, 41, 19-27.
22. Fang, H., Wu, B., Liu, H., Huang, X. Using NOAA AVHRR and Landsat TM to estimate rice area year-by-year. Int. J. Remote Sens. 1998, 19, 521-525.
23. Kim, H.O., Yeom, J.M. Multi-Temporal spectral analysis of rice fields in South Korea using MODIS and RapidEye satellite imagery. J. Astron. Space Sci. 2012, 29, 407-411.
24. Son, N.T., Chen, C.F., Chen, C.R., Chang, L.Y., Duc, H.C., Nguyen, L.D. Prediction of rice crop yield using MODIS EVI-LAI data in the Mekong Delta, Vietnam. Int. J. Remote Sens. 2013, 34, 7275-7292.
25. Huang, J., Wang, X., Li, X., Tian, H., Pan, Z. Remotely sensed rice yield prediction using multi-Temporal NDVI data derived from NOAAs-AVHRR. PLoS One 2013, 8, e70816.
26. Peng, D., Huang, J., Li, C., Liu, L., Huang, W., Wang, F., Yang, X. Modelling paddy rice yield using MODIS data. Agric. For. Meteorol. 2014, 184, 107-116.
27. Chang, C.H., Liu, C.C., Tseng, P.Y. Emissions inventory for rice straw open burning in Taiwan based on burned area classification and mapping using FORMOSAT-2 satellite imagery. Aerosol Air Qual. Res. 2013, 13, 474-487.
28. Kim, H.O., Yeom, J.M. Effect of red-edge and texture features for object-based paddy rice crop classification using RapidEye multi-spectral satellite image data. Int. J. Remote Sens. 2014, 35, 7046-7068.
29. MacDonald, R.B., Hall, F.G. Global crop forecasting. Science 1980, 208, 670-679.
30. 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. 2006, 100, 95-113.
31. Duveiller, G., López-Lozano, R., Seguini, L., Bojanowski, J.S., Baruth, B. Optical remote sensing requirements for operational crop monitoring and yield forecasting in Europe. In Proceedings of 2012 nel-3 OLCI/SLSTR and MERIS/(A) ATSR Workshop, Frascati, Italy, 15-19 October 2012.
32. Xie, Y., Sha, Z., Yu, M. Remote sensing imagery in vegetation mapping: A review. J. Plant Ecol. 2008, 1, 9-23.
33. Mulla, D.J. Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosyst. Eng. 2013, 114, 358-371.
34. Tucker, C.J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 1979, 8, 127-150.
35. Bannari, A., Morin, D., Bonn, F., Huett, A.R. A review of vegetation indices. Remote Sens. Rev. 1995, 13, 95-120.
36. Jackson, R.D., Slater, P.N., Pinter, P.J., Jr. Discrimination of growth and water stress in wheat by various vegetation indices through clear and turbid atmospheres. Remote Sens. Environ. 1983, 13, 187-208.
37. Hansen, P.M., Schjoerring, J.K. Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sens. Environ. 2003, 86, 542-553.
38. Yao, Y., Miao, Y., Huang, S., Gao, L., Ma, X., Zhao, G., Jiang, R., Chen, X., Zhang, F., Yu, K., et al. Active canopy sensor-based precision N management strategy for rice. Agron. Sustain. Dev. 2012, 32, 925-933.
39. Yu, K., Li, F., Gnyp, M.L., Miao, Y., Bareth, G., Chen, X. Remotely detecting canopy nitrogen concentration and uptake of paddy rice in the Northeast China Plain. ISPRS J. Photogramm. Remote Sens. 2013, 78, 102-115.
40. Hall, F.G., Huemmrich, K.F., Goward, S.N. Use of narrow-band spectra to estimate the fraction of absorbed photosynthetically active radiation. Remote Sens. Environ. 1990, 32, 47-54.
41. Baret, F., Hagolle, O., Geiger, B., Bicheron, P., Miras, B., Huc, M., Berthelot, B., Niño, F., Weiss, M., Samain, O., et al. LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION: Part 1: Principles of the algorithm. Remote Sens. Environ. 2007, 110, 275-286.
42. Bouman, B.A.M. Linking physical remote sensing models with crop growth simulation models, applied for sugar beet. Int. J. Remote Sens. 1992, 13, 2565-2581.
43. Prasad, A.K., Chai, L., Singh, R.P., Kafatos, M. Crop yield estimation model for Iowa using remote sensing and surface parameters. Int. J. Appl. Earth Obs. 2006, 8, 26-33.
44. Bhatti, A.U., Mulla, D.J., Frazier, B.E. Estimation of soil properties and wheat yields on complex eroded hills using geostatistics and thematic mapper images. Remote Sens. Environ. 1991, 37, 181-191.
45. Zhang, N., Wang, M., Wang, N. Precision agriculture-A worldwide overview. Comput. Electron. Agr. 2002, 36, 113-132.
46. Thenkabail, P.S. Biophysical and yield information for precision farming from near-real-Time and historical Landsat TM images. Int. J. Remote Sens. 2003, 24, 2879-2904.
47. Donlon, C., Berruti, B., Buongiorno, A., Ferreira, M.H., Féménias, P., Frerick, J., Goryl, P., Klein, U., Laur, H., Mavrocordatos, C., et al. The global monitoring for environment and security (GMES) sentinel-3 mission. Remote Sens. Environ. 2012, 120, 37-57.
48. Duveiller, G., Baret, F., Defourny, P. Remotely sensed green area index for winter wheat crop monitoring: 10-Year assessment at regional scale over a fragmented landscape. Agr. For. Meteorol. 2012, 166, 156-168.
49. Duveiller, G., Defourny, P. A conceptual framework to define the spatial resolution requirements for agricultural monitoring using remote sensing. Remote Sens. Environ. 2010, 114, 2637-2650.
50. Ouyang, W., Xu, Y., Hao, F., Wang, X., Chen, S., Lin, C. Effect of long-Term agricultural cultivation and land use conversion on soil nutrient contents in the Sanjiang Plain. Catena 2013, 104, 243-250.
51. Dhaliwal, J.S., Benbasat, I. The use and effects of knowledge-based system explanations: Theoretical foundations and a framework for empirical evaluation. Inf. Syst. Res. 1996, 7, 342-362.
52. Saastamoinen, J. Atmospheric correction for the troposphere and stratosphere in radio ranging satellites. In The Use of Artificial Satellites for Geodesy; Henriksen, S.W., Chovitz, B.H., Mancini, A., Eds., American Geophysical Union: Washington D.C., WA, USA, 1972; pp. 247-251.
53. Kaufman, Y.J., Sendra, C. Algorithm for automatic atmospheric corrections to visible and near-IR satellite imagery. Int. J. Remote Sens. 1988, 9, 1357-1381.
54. Courault, D., Seguin, B., Olioso, A. Review to estimate evapotranspiration from remote sensing data: Some examples from the simplified relationship to the use of mesoscale atmospheric models. In Proceedings of the 2003 Workshop on Remote Sensing of ET for Large Regions, Montpellier, France, 17 September 2003; pp.1-18.
55. Hadjimitsis, D.G., Papadavid, G., Agapiou, A., Themistocleous, K., Hadjimitsis, M.G., Retalis, A., Michaelides, S., Chrysoulakis, N., Toulios, L., Clayton, C.R.I. Atmospheric correction for satellite remotely sensed data intended for agricultural applications: Impact on vegetation indices. Nat. Hazard Earth Syst. 2010, 10, 89-95.
56. Dai, X., Khorram, S. The effects of image misregistration on the accuracy of remotely sensed change detection. IEEE Trans. Geosci. Remote Sens. 1998, 36, 1566-1577.
57. Moré, J., Pons, X. Preliminary considerations about the assessment and visualisation of the quality on geometric corrections of satellite imagery depending on the number of ground control points. In Proceedings of the 7th International Symposium on Spatial Data Quality, Coimbra, Portugal, 12-14 October 2011; pp. 12-14.
58. Zhao, Q., Hött, C., Lenz-Wiedemann, V.I.S., Miao, Y., Yuan, F., Zhang, F., Bareth, G. Georeferencing multi-source geospatial data using multi-Temporal TerraSAR-X imagery: A case study in Qixing Farm, northeast China. Photogramm. Fernerkun. 2015, accepted.
59. Chen, J., Huang, J., Hu, J. Mapping rice planting areas in southern China using the China Environment Satellite data. Math. Comput. Model. 2011, 54, 1037-1043.
60. Tanaka, A. Studies on characteristics of physiological function of leaf at definite position on stem of rice plant (Part 3). Relation between nitrogen metabolism and physiological function of leaf at definite position. J. Sci. Soil Manure Jpn. 1956, 26, 413-418.
61. Mae, T., Ohira, K. The remobilization of nitrogen related to leaf growth and senescence in rice plants (Oryza sativa L.). Plant Cell Physiol. 1981, 22, 1067-1074.
62. Willmott, C.J. On the validation of models. Phys. Geogr. 1981, 2, 184-194.
63. Legates, D.R., McCabe, G.J. Evaluating the use of "goodness-of-fit" measures in hydrologic and hydroclimatic model validation. Water Resour. Res. 1999, 35, 233-241.
64. Shrestha, D.P., Zinck, J.A. Land use classification in mountainous areas: Integration of image processing, digital elevation data and field knowledge (application to Nepal). Int. J. Appl. Earth Obs. 2001, 3, 78-85.
65. ONeill, A.L., Kupiec, J.A., Curran, P.J. Biochemical and reflectance variation throughout a Sitka spruce canopy. Remote Sens. Environ. 2002, 80, 134-142.
66. Blackburn, G.A. Hyperspectral remote sensing of plant pigments. J. Exp. Bot. 2007, 58, 855-867.
67. Filella, I., Serrano, L., Serra, J., Peñuelas, J. Evaluating wheat nitrogen status with canopy reflectance indices and discriminant analysis. Crop Sci. 1995, 35, 1400-1405.
68. Moran, J.A., Mitchell, A.K., Goodmanson, G., Stockburger, K.A. Differentiation among effects of nitrogen fertilization treatments on conifer seedlings by foliar reflectance: A comparison of methods. Tree Physiol. 2000, 20, 1113-1120.
69. Yu, K., Leufen, G., Hunsche, M., Noga, G., Chen, X., Bareth, G. Investigation of leaf diseases and estimation of chlorophyll concentration in seven barley varieties using fluorescence and hyperspectral indices. Remote Sens. 2013, 5, 64-86.
70. Chen, X., Chen, C., Zhou, L. Measurements and relationship analyses of chlorophyll contents and phenological growing stages in rice. Mod. Agricu. Sci. Technol. 2010, 17, 42-44.
71. Cao, S., Lu, W., Zhai, H., Sheng, S., Gong, H., Yang, T., Zhang, R. Research on the method to estimating flag leaf photosynthesis function duration at rice seedling stage by relative steady phase of chlorophyll content. Chin. J. Rice Sci. 2001, 15, 309-313. (In Chinese)
72. Gnyp, M.L., Miao, Y., Yuan, F., Ustin, S.L., Yu, K., Yao, Y., Huang, S., Bareth, G. Non-destructive estimation of paddy rice aboveground biomass at different growth stages with hyperspectral canopy remote sensing. Field Crop Res. 2014, 155, 42-55.
73. Myneni, R.B., Maggion, S., Iaquinta, J., Privette, J.L., Gobron, N., Pinty, B., Kimes, D.S., Verstraete, M.M., Williams, D.L. Optical remote sensing of vegetation: Modeling, caveats, and algorithms. Remote Sens. Environ. 1995, 51, 169-188.