Application of remote sensing methods in agriculture

Communications in Biometry and Crop Science

Volumn 11, Issue 1, 2016, Pages 31-50

  Wójtowicz, Marek ^a   Wójtowicz, Andrzej ^b   Piekarczyk, Jan ^c  

^a PLANT BREEDING AND ACCLIMATIZATION INSTITUTE NATIONAL RESEARCH INSTITUTE   (Poland)

^b INSTITUTE OF PLANT PROTECTION NATIONAL RESEARCH INSTITUTE   (Poland)

^c ADAM MICKIEWICZ UNIVERSITY   (Poland)

Author keywords

Agronomy; Crop irrigation; Plant protection; Remote sensing; Vegetation indices

Indexed keywords

EID: 84951076523     PISSN: None     EISSN: 18960782     Source Type: Journal    
DOI: None     Document Type: Article

References (99)

1. Anderson G.L., Everitt J.H, Richardson A.J., Escobar D.E. (1993). Using satellite data to map False Broomweed (Ericamerria austrotexana) infestations on south Texas rangelands. Weed Technology 7, 865–871
2. Aguera F., Carvajal F., Perez M. (2011). Measuring sunflower nitrogen status from an unmanned aerial vehicle-based system and an on the ground device. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XXXVIII-1/C22 UAV-g 2011, Conference on Unmanned Aerial Vehicle in Geomatics, Zurich, Switzerland.
3. Apan A., Held A., Phinn S., Markley J. (2004). Detecting sugarcane 'orange rust' disease using EO-1 Hyperion hyperspectral imagery. International Journal of Remote Sensing 25, 489–498
4. Ashourloo D., Mobasheri M.R., Huete A. (2014). Developing two spectral disease indices for detection of wheat leaf rust (Pucciniatriticina). Remote Sensing 6, 4723–4740
5. Ayala-Silva T., Beyl C.A. (2005). Changes in spectral reflectance of wheat leaves in response to specific macronutrient deficiency. Advances in Space Research 35, 305–317
6. Backes M., Jacobi J. (2006). Classification of weed patches in QuickBird images: verification by ground truth data. EARSel – European Association of Remote Sensing Laboratories eProceedings, EARSel, Warsaw, Poland. Available at: http://www.eproceedings.org/static/vol05_2/05_2_backes1.html.
7. Bagheri N., Ahmadi H., Alavipanah S.K., Omid M. (2012). Soil-line vegetation indices for corn nitrogen content prediction. International Agrophysics 26, 103–108
8. Baluja J., Diago M.P., Balda P., Zorer R., Meggio F., Morales F., Tardaguila J. (2012). Assessment of vineyard water status variability by thermal and multispectral imagery using an unmanned aerial vehicle (UAV). Irrigation Science 30, 511–522
9. Baranowski P., Jędryczka M., Mazurek W., Babula-Skowronska D., Siedliska A., Kaczmarek J. (2015). Hyperspectral and thermal imaging of oilseed rape (Brassica napus) response to fungal species of the genus Alternaria. PLoSONE 10(3), e0122913
10. Basnyat P., McConkey B. (2001). Using remote sensing information to identify in-field crop productivity. http://paridss.usask.ca/precisionfarm/factsheets/fs17.pdf.
11. Bausch W.C., Khosla R. (2010). QuickBird satellite versus ground-based multi-spectral data for estimating nitrogen status of irrigated maize. Precision Agriculture 11, 274–290
12. Burks T.F., Shearer S.A., Payne F.A. (2000). Classification of weed species using color texture features and discriminant analysis. Transactions of the American Society of Agricultural Engineers 43, 441–448
13. Burks T.F., Shearer S.A., Green J.D., Heath J.R. (2002). Influence of weed maturity levels on species classification using machine vision. Weed Science 50, 802–811
14. Burks T.F., Shearer S.A., Heath J.R., Donohue K.D. (2005). Evaluation of neural network classifiers for weed species discrimination. Biosystems Engineering 91, 293–304
15. Calderon R., Navas-Cortes J.A., Lucena C., Zarco-Tejada P.J. (2013). High-resolution airborne hyperspectral and thermal imagery for early detection of Verticillium wilt of olive using fluorescence, temperature and narrow-band spectral indices. Remote Sensing of Environment 139, 231–245
16. Calvao T., Palmeirim J.M. (2004). Mapping Mediterranean scrub with satellite imagery: biomass estimation and spectral behaviour. International Journal of Remote Sensing 25, 3113–3126
17. Casa, R., Jones, H.G. (2005). LAI retrieval from multiangular image classification and inversion of a ray tracing model. Remote Sensing of Environment 98, 414–428
18. Ceccato P., Gobron N., Flasse S., Pinty B., Tarantola S. (2002). Designing a spectral index to estimate vegetation water content from remote sensing data: Part 1 Theoretical approach. Remote Sensing of Environment 82, 198–207
19. Chang J., Clay D.A, Dalsted K., Clay S., O’Neill M. (2003). Corn (Zea mays L.) Yield prediction using multispectral and multidate reflectance. Agronomy Journal 95, 1447–1453
20. Chen X., Ma J., Qiao H., Cheng D., Xu Y., Zhao Y. (2007). Detecting infestation of take-all disease in wheat using Landsat Thematic Mapper imagery. International Journal of Remote Sensing 28, 5183–5189
21. Cheng T., Riano D., Koltunov A., Whiting M.L., Ustin S.L., Rodriguez J. (2013). Detection of diurnal variation in orchard canopy water content using MODIS/ASTER airborne simulator (MASTER) data. Remote Sensing of Environment 132, 1–12
22. Cheng T., Riano D., Ustin S.L. (2014). Detecting diurnal and seasonal variation in canopy water content of nut tree orchards from airborne imaging spectroscopy data using continuous wavelet analysis. Remote Sensing of Environment 143, 39–53
23. Colombo R., Merom M., Marchesi A., Busetto L., Rossini M., Giardino C. (2008). Estimation of leaf and canopy water content in poplar plantations by means of hyperspectral indices and inverse modeling. Remote Sensing of Environment 112, 1820–1834
24. Champagne C.M., Staenz K., Bannari A., McNairn H., Jean-Claude D. (2003). Validation of a hyperspectral curve-fitting model for the estimation of plant water content of agricultural canopies. Remote Sensing of Environment 87, 295–309
25. Dąbrowska-Zielińska K., Ciołkosz A., Budzyńska M., Kowalik W. (2008). Monitorowanie wzrostu i plonowania zboż metodami teledetekcji. Problemy Inżynierii Rolniczej 4, 45–54
26. Deguise J. C., Staenz K., Lefebvre J. (1999). A hyperspectral data: weed detection. Fourth International Airborne Remote sensing Conference and Exhibition/21st Canadian Symposium on Remote Sensing, Ottawa, Ontario, Canada, 21-24 June.
27. Doraiswamy P.C., Hatfield J.L., Jackson T.J., Akhmedov B., Prueger J., Stern A. (2004). Crop condition and yield simulations using Landsat and MODIS. Remote Sensing of Environment 92, 548–559
28. El-Magd I.A., Tanton T.W. (2003). Improvements in land use mapping for irrigated agriculture from satellite sensor data using a multi-stage maximum likelihood classification. International Journal of Remote Sensing 24, 4197–4206
29. Fensholt R., Sandholt I. (2003). Derivation of a shortwave infrared water stress index from MODIS near- and shortwave infrared data in a semiarid environment. Remote Sensing of Environment 87, 111–121
30. Franke J., Menz G. (2007). Multi-temporal wheat disease detection by multi-spectral remote sensing. Precision Agriculture 8, 161–172
31. Franklin P., Pearlstine L., Dewitt B., Smith S., Watts A., Ifju P. (2006) Autonomus unmanned aerial vehicle (UAV) for ecological research. http://www.wec.ufl.edu/coop/print/posters/2006watts.pdf.
32. Gago J., Martorell S., Tomas M., Pou A., Millan B., Ramon J., Ruiz M., Sanchez R., Galmes J., Conesa M.A., Cuxart J., Tardaguila J., Ribas-Carbo M., Flexas J., Medrano H., Escalona, J.M. (2013). High-resolution aerial thermal imagery for plant water status assessment in vineyards using a multicopter-RPAS. First Conference of the International Society for Atmospheric Research using Remotely-piloted Aircraft, (ISARRA), Palma de Mallorca (Spain).
33. Galvao L.S., Roberts D.A., Formaggio A.R., Numata I., Breunig F.M. (2009). View angle effects on the discrimination of soybean varieties and on the relationships between vegetation indices and yield using off-nadir Hyperion data. Remote Sensing of Environment 113, 846–856
34. Gao B.C. (1996). NDWI – A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment 58, 257–266
35. Garcia-Ruiz F., Sankaran S., Maja J.M., Lee W.S., Rasmussen J., Ehsani R. (2013). Comparison of two aerial imaging platforms for identification of Huanglongbing-infected citrus trees. Computers and Electronics in Agriculture 91, 106–115
36. Genc H., Genc L., Turhan H., Smith S. E., Nation J. L. (2008). Vegetation indices as indicators of damage by the sunn pest (Hemiptera: Scutelleridae) to field frown wheat. African Journal of Biotechnology 7, 173–180
37. Gitelson A.A., Kaufman Y.J., Stark R., Rundquist D. (2002). Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment 80, 147–155
38. Glaser J., Casas J., Copenhaver K., Mueller S. (2009). Development of a broad landscape monitoring system using hyperspectral imagery to detect pest infestation. Proceedings of the First Workshop on Hyperspectral Image and Signal Processing - Evolution in RemoteSensing (WHISPERS), Grenoble, France.
39. Goel P.K., Prasher S.O., Patel R.M., Smith D.L., Di Tommaso A. (2001). Use of airborne multi– spectral imagery for weed detection in field crops. https://weedecology.css.cornell.edu/pubs/ASAE2002paper.pdf.
40. Goel P.K., Prasher S.O., Landry J.-A., Patel R.M., Bonnell R.B., Viau A.A., Miller J.R. (2003). Potential of airborne hyperspectral remote sensing to detect nitrogen deficiency and weed infestation in corn. Computers and Electronics in Agriculture 38, 99–124
41. Hatfield J.L., Prueger J.H. (2010). Value of using different vegetative indices to quantify agricultural crop characteristics at different growth stages under varying management practices. Remote Sensing 2, 562–578
42. Heege H.J., Reusch S., Thiessen E. (2008). Prospects and results for optical systems for sitespecific on-the-go control of nitrogen-top-dressing in Germany. Precision Agriculture 9, 115–131
43. Huete A.R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment 25, 295–309
44. Huete A., Justice C. (1999). MODIS Vegetation Index (MOD 13) Algorithm Theoretical Basis Document, Greenbelt: NASA Goddard Space Flight Center, http://modarch.gsfc.nasa.gov/MODIS/LAND/ #vegetation-indices, 129 pp.
45. Hunt E.R., Rock B.N., Nobel P.S. (1987). Measurement of leaf relative water content by infrared reflectance. Remote Sensing of Environment 22, 429–435
46. Hunt Jr.E.R., Doraiswamy P.C., McMurtrey J.E., Daughtry C.S.T., Perry E.M. (2013). A visible band index for remote sensing leaf chlorophyll content at the canopy scale. International Journal of Applied Earth Observation and Geoinformation 21, 103–112
47. Jackson R.D. (1986). Remote sensing of biotic and abiotic plant stress. Annual Review of Phytopathology 24, 265–286
48. Jackson R.D., Idso S.B., Reginato R.J., Pinter P.J. (1981). Canopy temperature as a crop water stress indicator. Water Resources Research 17, 1133–1138
49. Jia L., Yu Z., Li F., Gnyp M., Koppe W., Bareth G., Miao Y., Chen X., Zhang F. (2011). Nitrogen status estimation of winter wheat by using an Ikonos satellite image in the north china plain. Computer and computing technologis in agriculture V. 5 th IFIP TC5/SIG 5,1 Conference, CCTA 2011 Beijing, Cina, October 2011 Proceedings, Part II.
50. Kumar J., Vashisth A., Sehgal V.K., Gupta V.K. (2010). Identification of aphide infestation in mustard by hyperspectral remote sensing. Journal of Agricultural Physics 10, 53–60
51. Laliberte A., Rango A., Slaughter A. (2006). Unmanned Aerial Vehicle (UAVs) for rangeland remote sensing. Proc. 3rd Annual Symposium research insights in semiarid ecosystems RISE. USDA-ARS Walnut Experimental Watershed.
52. Lamb D.W., Weedon, M. M., Rew L. J. (1999). Evaluating the accuracy of mapping weeds in seedling crops using airborne digital imaging: Avena spp. in seedling triticale. Weed Research 39, 481–492
53. Lamb D.W., Brown R.B. (2001). Remote sensing and mapping of weeds in crops. Journal of Agricultural Engineering Research 78, 117–125
54. Launay M., Guerif M. (2005). Assimilating remote sensing data into a crop model to improve predictive performance for spatial applications. Agriculture, Ecosystems and Environment 111, 321–339
55. Li F., Gnyp M.L., Jia L., Miao Y., Yu Z., Koppe W., Bareth G., Chen X., Zhang F. (2008a). Estimating N status of winter wheat using a handheld spectrometer in the North China Plain. Field Crop Research 106, 77–85
56. Li J., Liu Q., Liu Q., Tang Y., Xiao Q. (2008b). A patch spectral purification method to extract field patch average parameters from moderate resolution data. International Journal of Remote Sensing 29, 4993–5011
57. Mewes T. (2010). The impact of the spectral dimension of hyperspectral datasets on plant disease detection. PhD dissertation, Bonn, Germany.
58. Mirik M., Michels G.J., Kassymzhanova-Mirik S., Elliott N.C. (2007). Reflectance characteristics of Russian wheat aphid (Hemiptera: Aphididae) stress and abundance in winter wheat. Computers and Electronics in Agriculture 57, 123–134
59. Mogensen V.O., Jensen C.R., Mortensen G., Thage J.H., Koribidis J., Ahmed A. (1996). Spectral reflectance index as an indicator of drought of field grown oilseed rape (Brassica napus L.). European Journal of Agronomy 5, 125–135
60. Moran M.S., Clarke T.R., Inoue Y., Vidal A. (1994). Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index. Remote Sensing Environment 49, 246–263
61. Nebiker S., Annen A., Scherrer M., Oesch D. (2008). A light-weight multispectral sensor for micro UAV – opportunities for very high resolution airborne remote sensing. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B1 Beijing, 1193-1200
62. Nieuwenhuizen A.T., Tang L., Hofstee J.W., Muller J., van Henten E.J. (2007). Color based detection of volunteer potatoes as weeds in sugar beet fields using machine vision. Precision Agriculture 8, 267–278
63. Panuelas J., Filella I., Biel C., Serrano L. (1993). The reflectance at the 950–970 nm region as an indicator of plant water status. International Journal of Remote Sensing 4, 1887–1905
64. Pena J.M., Torres-Sanchez J., Serrano-Perez A., de Castro A.I., Lopez-Granados F. (2015). Quantifying efficacy and limits of unmanned aerial vehicle (UAV) technology for weed seedling detection as affected by sensor resolution. Sensors 15, 5609–5626
65. Pena-Barragan J.M., Lopez-Granados F., Jurado-Exposito M., Garcia-Torres L. (2006). Mapping Ridolfia segetum patches in sunflower crop using remote sensing. Weed Research 47, 164–172
66. Peters A.J, Reed B.C., Eve M.D., McDaniel K.C. (1992). Remote-sensing of Broom Snakeweed (Gutierrezia sarthrae) with NOAA-10 spectral image processing. Weed Technology 6, 1015–1020
67. Piekarczyk J. (2011a). Szacowanie plonow roślin uprawnych na podstawie naziemnych pomiarow spektralnych. Teledetekcja Środowiska 46, 23–28
68. Piekarczyk J., Sulewska H., Szymańska G. (2011b). Winteroilseed-rape yield estimates from hyperspectral radiometer measurements. Quaestiones Geographicae 30, 77–84
69. Piekarczyk J., Wojtowicz M., Wojtowicz A. (2004). Wpływ nawożenia azotowego i odmian na charakterystyki spektralne łanu rzepaku ozimego. Rośliny Oleiste – Oilseed Crops XXV(1), 280–291
70. Pinter P., Hatfield J., Schepers J., Barnes E., Moran M., Daughtry C., Upchurch D. (2003). Remote Sensing for Crop Management. Photogrammetric Engineering and Remote Sensing 647–664
71. Portz G., Molin J.P., Jasper J. (2012). Active crop sensor to detect variability of nitrogen supply and biomass on sugarcane fields. Precision Agriculture 13, 33–44
72. Prasad A.K., Chai L., Singh R.P., Kafatos M. (2006). Crop yield estimation model for Iowa using remote sensing and surface parameters. International Journal of Applied Earth Observation and Geoinformation 8, 26–33
73. Quemada M., Gabriel J.L., Zarco-Tejada P. (2014). Airborne hyperspectral images and ground-level optical sensors as assessment tools for maize nitrogen fertilization. Remote Sensing 6, 2940–2962
74. Ranjitha G., Srinivasan M.R., Rajesh A. (2014). Detection and estimation of damage caused by Thrips Thrips tabaci (Lind) of cotton using hyperspectral radiometer. Agrotechnology 3, 1–5
75. Riedell W.E., Blackmer T.M. (1999). Leaf reflectance spectra of cereal aphid-damaged wheat. Crop Science 39, 1835–1840
76. Rock B.N., Vogelmann J.E., Williams D.L., Vogelmann A.F., Hoshizaki T. (1986). Remote detection of forest damage. Bioscience 36, 439–445
77. Rouse J.W., Haas R.H., Schell J.A., Deering D.W. (1974). Monitoring vegetation systems in the Great Plains with ERTS. Proceedings, 3rd Earth Resource Technology Satellite (ERTS), Symposium 1, 48–62
78. Shrestha D.S., Steward B.L. (2005). Shape and size analysis of corn plant canopies for plant population and spacing sensing. Applied Engineering in Agriculture 21, 295–303
79. Singh I., Srivastava I.A., Chandna P., Gupta R. (2006). Crop sensors for efficient nitrogen management in sugarcane: Potential and constraints. Sugar Tech 8, 299–302
80. Slaughter D.C., Giles D.K., Downey D. (2007). Autonomous robotic weed control systems: a review. Computers and Electronics in Agriculture 61, 63–78
81. Soderstron M., Borjesson T., Pettersson C.G., Nissen K., Hagner O. (2010). Prediction of protein content in malting barley using proximal and remote sensing. Precision Agriculture 11, 587–599
82. Stafford J.V. (2000). Implementing precision agriculture in the 21st century. Journal of Agricultural Engineering Research 76, 267–275
83. Stroppiana D., Boschetti M., Brivio P.A., Bocchi S. (2009). Plant nitrogen concentration in paddy rice from field canopy hyperspectral radiometry. Field Crop Research 111, 119–129
84. Swain K.C., Zaman Q.U. (2012). Rice crop monitoring with unmanned helicopter remote sensing images. In: Fatoyinbo L. (Ed.) Remote Sensing of Biomass - Principles and Applications, InTech.
85. Taghvaeian S., Chavez J.L., Altenhofen J., Trout T., DeJonge K. (2013). Remote sensing for evaluating crop water stress at field scale using infrared thermography: potential and limitations. Hydrology Days, Fort Collins, USA, 73–83
86. Thenkabail P.S., Smith R.B., De-Pauw E. (2002). Evaluation of narrowband and broadband vegetation indices for determining optimal hyperspectral wavebands for agricultural crop characterization. Photogrammetric Engineering 68, 607–621
87. Tremblay N., Wang Z., Ma B.L., Belec C., Vigneault P. (2009). A comparison of crop data measured by two commercial sensors for variable-rate nitrogen application. Precision Agriculture 10, 145–161
88. Ullah E., Shepherd R.C.H., Baxter J.T., Peterson J.A. (1989). Mapping flowering Patterson's Curse (Echium plantagineum) around Lake Hume, Northeastern Victoria, using Landsat TM data. Plant Protection Quarterly 4, 155–157
89. Van Evert F.K., Van Der Heijden G., Lotz L.A.P., Polder G., Lamaker A., De Jong A., Kuyper M.C., Groendijk E.J.K., Neeteson J.J., Van der Zalm T. (2006). A mobile field robot with vision-based detection of volunteer potato plants in a corn crop. Weed Technology 20, 853–861
90. Vidal A., Pinglo F., Durand H., Devaux-Ros C., Maillet A. (1994). Evaluation of a temporal fire risk index in mediterranean forests from NOAA thermal IR. Remote Sensing of Environment 49, 296–303
91. Wallace J.F., Caccetta P.A., Kiiveri H.T. (2004). Recent developments in analysis of spatial and temporal data for landscape qualities and monitoring. Australian Ecology 29, 100–107
92. Walsh O.S., Klatt A.R., Solie J.B., Godsey C.B., Raun W.R. (2012). Use of soil moisture data for refined GreenSeeker sensor based nitrogen recommendations in winter wheat (Triticum aestivum L.). Precision Agriculture 14, 343–356
93. Wojtowicz A., Wojtowicz M., Piekarczyk J. (2005). Zastosowanie teledetekcji do monitorowania i oceny produktywności plantacji rzepaku. Rośliny Oleiste - Oilseed Crops XXVI/1, 269–276
94. Yang Z., Rao M.N., Elliott N.C., Kindler D., Popham T.W. (2005). Using ground based multispectral radiometry to detect stress in wheat caused by greenbug (Homoptera: Aphididae) infestation. Computers and Electronics in Agriculture 47, 121–135
95. Zarco-Tejada P.L.J., Rueda C.A., Ustin S.L. (2003). Water content estimation in vegetation with MODIS reflectance data and model inversion methods. Remote Sensing of Environment 85, 109–124
96. Zebarth B.J., Rees H., Tremblay N., Fournier P., Leblon B. (2003). Mapping spatial variation in potato nitrogen status using the N Sensor. Acta Horticulturae 627, 267–273
97. Zhang M., Qin Z., Liu X., Ustin S.L. (2003). Detection of stress in tomatoes induced by late blight disease in California, USA, using hyperspectral remote sensing. International Journal of Applied Earth Observation and Geoinformation 4, 295–310
98. Zhu Y., Yao X., Tian Y., Liu X., Cao W. (2008). Analysis of common canopy vegetation indices for indicating leaf nitrogen accumulations in wheat and rice. International Journal of Applied Earth Observation and Geoinformation 10, 1–10
99. Zillmann E., Graeff S., Link J., Batchelor W.D., Claupein W. (2006). Assessment of cereal nitrogen requirements derived by optical on-the-go sensors on heterogeneous soils. Agronomy Journal 98, 682–690.