An alternative method using digital cameras for continuous monitoring of crop status

Agricultural and Forest Meteorology

Volumn 154-155, Issue , 2012, Pages 113-

  Sakamoto, Toshihiro ^a,b   Gitelson, Anatoly A ^b   Nguy Robertson, Anthony L ^b   Arkebauer, Timothy J ^c   Wardlow, Brian D ^b   Suyker, Andrew E ^b   Verma, Shashi B ^b   Shibayama, Michio ^a  

^a NATIONAL INSTITUTE FOR AGRO ENVIRONMENTAL SCIENCES   (Japan)

^b UNIVERSITY OF NEBRASKA LINCOLN   (United States)

^c UNIVERSITY OF NEBRASKA LINCOLN   (United States)

Author keywords

Crop phenology; Exposure value; MODIS; Nighttime flash images; Radiometer; Vegetation index

Indexed keywords

ABOVEGROUND BIOMASS; ACCURACY ASSESSMENT; AGRICULTURAL ECOSYSTEM; AGRONOMY; ATMOSPHERE-BIOSPHERE INTERACTION; BRIGHTNESS TEMPERATURE; CARBON FLUX; CHLOROPHYLL; COST-BENEFIT ANALYSIS; CROP PRODUCTION; CROP YIELD; DIGITAL IMAGE; FIELD SURVEY; GROWTH RATE; INFRARED IMAGERY; LEAF AREA INDEX; MAIZE; MODIS; MONITORING; NDVI; PHENOLOGY; PHYSIOLOGICAL RESPONSE; RADIOMETER; REMOTE SENSING; SATELLITE IMAGERY; SOYBEAN; SPECTRAL REFLECTANCE; TIME SERIES;

GLYCINE MAX; ZEA MAYS;

EID: 84858138280     PISSN: 01681923     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.agrformet.2011.10.014     Document Type: Article

References (48)

1. Adamsen F.J., Pinter P.J., Barnes E.M., LaMorte R.L., Wall G.W., Leavitt S.W., Kimball B.A. Measuring wheat senescence with a digital camera. Crop Science 1999, 39(3):719-724.
2. Ahrends H.E., Etzold S., Kutsch W.L., Stoeckli R., Bruegger R., Jeanneret F., Wanner H., Buchmann N., Eugster W. Tree phenology and carbon dioxide fluxes: use of digital photography at for process-based interpretation the ecosystem scale. Climate Research 2009, 39(3):261-274.
3. Betancourt J., et al. Implementing a US national phenology network. Eos Transactions on AGU 2005, 86(51):539.
4. Brown M.E., de Beurs K.M. Evaluation of multi-sensor semi-arid crop season parameters based on NDVI and rainfall. Remote Sensing of Environment 2008, 112(5):2261-2271.
5. Delbart N., Kergoat L., Le Toan T., Lhermitte J., Picard G. Determination of phenological dates in boreal regions using normalized difference water index. Remote Sensing of Environment 2005, 97(1):26-38.
6. Demarez V., Duthoit S., Baret F., Weiss M., Dedieu G. Estimation of leaf area and clumping indexes of crops with hemispherical photographs. Agricultural and Forest Meteorology 2008, 148(4):644-655.
7. Falkowski M.J., Gessler P.E., Morgan P., Hudak A.T., Smith A. Characterizing and mapping forest fire fuels using ASTER imagery and gradient modeling. Forest Ecology and Management 2005, 217(2-3):129-146.
8. Gamon J.A. Integrating remote sensing and flux measurements. Flux Letter 2010, 3(1):7-10.
9. Garrity S.R., Vierling L.A., Bickford K. A simple filtered photodiode instrument for continuous measurement of narrowband NDVI and PRI over vegetated canopies. Agricultural and Forest Meteorology 2010, 150(3):489-496.
10. Gitelson A.A. Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. Journal of Plant Physiology 2004, 161(2):165-173.
11. Gitelson A.A., Kaufman Y.J., Stark R., Rundquist D. Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment 2002, 80(1):76-87.
12. Gitelson A.A., Vina A., Ciganda V., Rundquist D.C., Arkebauer T.J. Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters 2005, 32(8.).
13. 2 flux measurements for estimation of canopy PAR absorption. Global Change Biology 2002, (8):563-574.
14. Huete A., Didan K., Miura T., Rodriguez E.P., Gao X., Ferreira L.G. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 2002, 83(1-2):195-213.
15. Hunt E.R., Hively W.D., Fujikawa S., Linden D., Daughtry C.S., McCarty G. Acquisition of NIR-green-blue digital photographs from unmanned aircraft for crop monitoring. Remote Sensing 2010, 2(1):290-305.
16. Islam A.S., Bala S.K. Assessment of potato phenological characteristics using MODIS-derived NDVI and LAI information. Geoscience & Remote Sensing 2008, 45(4):454-470.
17. Jordan C.F. Derivation of leaf-area index from quality of light on forest floor. Ecology 1969, 50(4):663-666.
18. Lukina E.V., Stone M.L., Rann W.R. Estimating vegetation coverage in wheat using digital images. Journal of Plant Nutrition 1999, 22(2):341-350.
19. Matsuda M., Ozawa S., Hosaka Y., Kaneda K., Yamashita H. Estimation of plant growth in paddy field based on proximal remote sensing-measurement of leaf nitrogen contents by using digital camera. Journal of The Remote Sensing Society of Japan 2003, 23(5):506-515.
20. Meyer G.E., Hindman T.W., Laksmi K. Machine Vision Detection Parameters for Plant Species Identification 1999, 327-335. SPIE, Boston, MA, USA. G.E. Meyer, J.A. DeShazer (Eds.).
21. Motohka T., Nasahara K.N., Oguma H., Tsuchida S. Applicability of green-red vegetation index for remote sensing of vegetation phenology. Remote Sensing 2010, 2(10):2369-2387.
22. Nagai S., Nasahara K.N., Muraoka H., Akiyama T., Tsuchida S. Field experiments to test the use of the normalized-difference vegetation index for phenology detection. Agricultural and Forest Meteorology 2010, 150(2):152-160.
23. Nishida K. Phenological eyes network (PEN)-a validation network for remote sensing of the terrestrial ecosystems. Asia Flux Newsletter 2007, (21):9-13.
24. Perez A.J., Lopez F., Benlloch J.V., Christensen S. Colour and shape analysis techniques for weed detection in cereal fields. Computers and Electronics in Agriculture 2000, 25(3):197-212.
25. Reed B.C., Brown J.F., Vanderzee D., Loveland T.R., Merchant J.W., Ohlen D.O. Measuring phenological variability from satellite imagery. Journal of Vegetation Science 1994, 5(5):703-714.
26. Richardson A.D., et al. Use of digital webcam images to track spring green-up in a deciduous broadleaf forest. Oecologia 2007, 152(2):323-334.
27. Rouse, J., 1974. Monitoring vegetation systems in the Great Plains with ERTS.
28. 2 uptake of a desiccation-tolerant moss. International Journal of Plant Sciences 2006, 167(4):751-758.
29. Ryu Y., et al. Testing the performance of a novel spectral reflectance sensor, built with light emitting diodes (LEDs), to monitor ecosystem metabolism, structure and function. Agricultural and Forest Meteorology 2010, 150(12):1597-1606.
30. Sakamoto T., Gitelson A.A., Wardlow B.D., Arkebauer T.J., Shashi B.V., Suyker A.E., Shibayama M. Application of day and night digital photographs for estimating maize biophysical characteristics. Precision Agriculture 2011, 10.1007/s11119-011-9246-1.
31. Sakamoto T., Shibayama M., Kimura A., Takada E. Assessment of digital camera-derived vegetation indices in quantitative monitoring of seasonal rice growth. ISPRS. Journal of Photogrammetry and Remote Sensing 2011, 66(6):872-882.
32. Sakamoto T., Shibayama M., Takada E., Inoue A., Morita K., Takahashi W., Miura S., Kimura A. Detecting seasonal changes in crop community structure using day and night digital images. Photogrammetric Engineering and Remote Sensing 2010, 76(6):713-726.
33. Sakamoto T., Wardlow B.D., Gitelson A.A. Detecting spatiotemporal changes of corn developmental stages in the US corn belt using MODIS WDRVI data. IEEE Transactions on Geoscience and Remote Sensing 2011, 49(6):1926-1936.
34. Sakamoto T., Wardlow B.D., Gitelson A.A., Verma S.B., Suyker A.E., Arkebauer T.J. A two-step filtering approach for detecting maize and soybean phenology with time-series MODIS data. Remote Sensing of Environment 2010, 114(10):2146-2159.
35. Sakamoto T., Yokozawa M., Toritani H., Shibayama M., Ishitsuka N., Ohno H. A crop phenology detection method using time-series MODIS data. Remote Sensing of Environment 2005, 96(3-4):366-374.
36. Schwartz M.D., Reed B.C., White M.A. Assessing satellite-derived start-of-season measures in the conterminous USA. International Journal of Climatology 2002, 22(14):1793-1805.
37. Shibayama M., Sakamoto T., Homma K., Okadai S., Yamamoto H. Daytime and nighttime field spectral imagery of ripening paddy rice for determining leaf greenness and 1000-grain weight. Plant Production Science 2009, 12(3):307-318.
38. Shibayama M., Sakamoto T., Takada E., Inoue A., Morita K., Takahashi W., Kimura A. Continuous monitoring of visible and near-infrared band reflectance from a rice paddy for determining nitrogen uptake using digital cameras. Plant Production Science 2009, 12(3):293-306.
39. Shibayama M., Sakamoto T., Takada E., Inoue A., Morita K., Takahashi W., Kimura A. Estimating paddy rice leaf area index with fixed point continuous observation of near infrared reflectance using a calibrated digital camera. Plant Production Science 2011, 14(1):30-46.
40. Tucker C.J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment 1979, 8(2):127-150.
41. Verma S.B., Dobermann A., Cassman K.G., Walters D.T., Knops J.M., Arkebauer T.J., Suyker A.E., Burba G.G., Amos B., Yang H.S., Ginting D., Hubbard K.G., Gitelson A.A., Walter-Shea E.A. Annual carbon dioxide exchange in irrigated and rainfed maize-based agroecosystems. Agricultural and Forest Meteorology 2005, 131(1-2):77-96.
42. Viña A., Gitelson A.A New developments in the remote estimation of the fraction of absorbed photosynthetically active radiation in crops. Geophysical Research Letters 2005, 32:L17403. 10.1029/2005GL023647.
43. Vina A., Gitelson A.A., Rundquist D.C., Keydan G., Leavitt B., Schepers J. Remote sensing-monitoring maize (Zea mays L.) phenology with remote sensing. Agronomy Journal 2004, 96(4):1139-1147.
44. Wardlow B.D., Kastens J.H., Egbert S.L. Using USDA crop progress data for the evaluation of greenup onset date calculated from MODIS 250-m data. Photogrammetric Engineering and Remote Sensing 2006, 72(11):1225-1234.
45. White M.A., Thornton P.E., Running S.W. A continental phenology model for monitoring vegetation responses to interannual climatic variability. Global Biogeochemical Cycles 1997, 11(2):217-234.
46. Woebbecke D.M., Meyer G.E., Vonbargen K., Mortensen D.A. Color indexes for weed identification under various soil, residue, and lighting conditions. Transactions of the ASAE 1995, 38(1):259-269.
47. Xiao X.M., Holinger D., Aber J., Goltz M., Davidson E.A., Zhang Q.Y., Moore B. Satellite-based modeling of gross primary production in an evergreen needle leaf forest. Remote Sensing of Environment 2004, 89(4):519-534.
48. Zhang X.Y., Friedl M.A., Schaaf C.B., Strahler A.H., Hodges J.C.F., Gao F., Reed B.C., Huete A. Monitoring vegetation phenology using MODIS. Remote Sensing of Environment 2003, 84(3):471-475.