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Crop Type Discrimination and Health Assessment using Hyperspectral Imaging


Affiliations
1 Agriculture and Land Eco-system Division, Earth, Ocean, Atmosphere, Planetary Sciences and Applications Area, Space Applications Centre (ISRO), Ahmedabad 380 015, India
2 National Remote Sensing Centre (ISRO), Hyderabad 500 037, India
3 M.G. Science Institute, Ahmedabad 380 009, India
4 Environmental and Resource Sciences, University of California, Davis, CA 95616, United States
 

Advancements in hyperspectral remote sensing technology have opened new avenues to explore innovative ways to map crops in terms of area and health. To study precise mapping of agriculture and horticulture crops along with biophysical and biochemical constituents at field scale, an airborne AVIRIS-NG hyperspectral imaging has been conducted in various agro-climatic regions representing diverse agricultural types of India. Crop classification with available and developed algorithms has been applied over homogeneous and heterogeneous agriculture and horticulture cropped areas. The spectral angle mapper and maximum likelihood algorithms showed classification accuracy of 77%–94% for AVIRI-NG and 42%–55% for LISS IV. The customized deep neural network and maximum noise function (MNF)-based classification schemes showed an accuracy of 93% and 86% for mapping of agriculture and horticulture crops respectively. The forward and inversion of canopy radiative transfer model protocol was developed for retrieval of crop parameters such as leaf area index (LAI) and chlorophyll content (Cab) using AVIRIS-NG narrow bands. The retrieved LAI and Cab showed 19%–27% and 23%–29% deviation from measured mean for homogeneous and heterogeneous agricultural areas respectively. Red edge position index-based empirical model and multivariate linear regression of multiple indices showed maximum correlation of 0.62 and 0.93 respectively, to map leaf nitrogen content. Water condition index was developed using vegetation and water indices to distinguish crop water-based abiotic stress. Wheat yellow rust disease has been identified at field scale using absorption band depth analysis at 662–702 and 2155–2175 nm, and further applied to AVIRIS-NG data to detect biotic stress at spatial scale. This study establishes that such missions have the potential to boost accurate mapping of economically valuable minor crops and generate health indicators to distinguish biotic and abiotic stresses at field scale.

Keywords

Assessment, Biotic and Abiotic Stress, Crop Classification, Health, Hyperspectral Imaging.
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  • Thenkabail, P. S., Enclona, E. A., Ashton, M. S. and Van Der Meer, V., Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications. Remote Sensing Environ., 2004, 91, 354–376.

  • Meerdink, S. K. et al., Linking seasonal foliar traits to VSWIRTIR spectroscopy across California ecosystems. Remote Sensing Environ., 2016, 186, 322–338.

  • Gitelson, A., In Hyperspectral Remote Sensing of Vegetation (eds Thenkabail, P. S., Lyon, G. J. and Huete, A.), CRC Press-Taylor and Francis Group, Boca Raton, FL, USA, 2011, pp. 141–166.

  • Ozdogan, M. and Woodcock, C. E., Resolution dependent error in remote sensing of cultivated areas. Remote Sensing Environ., 2006, 103, 203–217.

  • Asner, G. P. and Martin, R. E., Spectral and chemical analysis of tropical forests: scaling from leaf to canopy levels. Remote Sensing Environ., 2008, 112, 3958–3970.

  • Jacquemoud, S., Baret, F., Andrieu, B., Danson, F. M. and Jaggard, K., Extraction of vegetation biophysical parameters by Inversion of the PROSPECT + SAIL models on sugar beet canopy reflectance data. Application to TM and AVIRIS sensors. Remote Sensing Environ., 1995, 52, 63–172.

  • Zarco-Tajada, P. J., Guillin-Climent, M. L., Hernandez-Clemente, R. and Catalina, A., Estimating leaf carotenoid content in vineyards using high resolution hyperspectral imaging acquired from an unmanned aerial vehicle (UAV). Agric. For. Met., 2013, 17, 281–294.

  • Green, R. O. et al., Imaging spectroscopy and the airborne visible/ infrared imaging spectrometer (AVIRIS). Remote Sensing Environ., 1998, 65, 227–248.

  • Turner, D. P., Ollinger, S., Smith, M.-L., Krankina, O. and Gregory, M., Scaling net primary production to a MODIS footprint in support of Earth observing system product validation. Int. J. Remote Sensing, 2004, 25, 1961–1979.

  • Bremner, J. M., Determination of nitrogen in soil using Kjeldahl method. J. Agric. Sci., 1960, 55, 11–38.

  • Aguilar, M. A., Saldaña, M. M. and Aguilar, F. J., GeoEye-1 and WorldView-2 pan-sharpened imagery for object-based classification in urban environments. Int. J. Remote Sensing, 2012, 34, 2583–2606.

  • Yonezawa, C., Maximum likelihood classification combined with spectral angle mapper algorithm for high resolution satellite imagery. Int. J. Remote Sensing, 2007, 28, 3729–3737.

  • Paola, J. D. and Schowengerdt, R. A., A review and analysis of backpropagationneural networks for classification of remotely sensed multi-spectral imagery. Int. J. Remote Sensing, 1995, 16, 3033–3058.

  • Vapnik, V. N., The Nature of Statistical Learning Theory, Springer Verlag, New York, USA, 1995.

  • Melgani, F. and Bruzzone, L., Classification of hyperspectral remote sensing images with support vector machine. IEEE Trans. Geosci. Remote Sensing, 2004, 8, 1778–1790.

  • Krebel, U., Pairwise classification and support vector machines. In Advances in Kernel Methods-Support Vector Learning (eds Schӧlkopf, B., Burges, C. J. C. and Smola, A. J.), The MIT Press, Cambridge, MA, USA, 1999, pp. 255–268.

  • Green, A. A., Berman, M., Switzer, P. and Craig, M. D., A transformation for ordering multispectral data in terms of image quality with implications for noise removal. IEEE Trans. Geosci. Remote Sensing, 1988, 26, 65–74.

  • Singh, A. and Harrison, A., Standardized principal components. Int. J. Remote Sensing, 1985, 6, 883–896.

  • Friedl, M. A. and Brodley, C. E., Decision tree classification of land cover from remotely sensed data. Remote Sensing Environ., 1997, 61, 399–409.

  • Clark, R. N. et al., Imaging spectroscopy: earth and planetary remote sensing with the USGS tetracorder and expert systems. J. Geophys. Res., 2003, 108, 5131; doi:10.1029/2002JE001847.

  • Chen, Y., Lin, Z., Zhao, X., Wang, G. and Gu, Y., Deep learningbased classification of hyperspectral data. IEEE J. Appl. Earth Obs. Remote Sensing, 2014, 6, 2094–2107.

  • Kussul, N., Lavreniuk, M., Skakun, S. and Shelestov, A., Deep learning classification of land cover and crop types using remote sensing data. IEEE Geosci. Remote Sensing Lett., 2017, doi:10.1109/LGRS.

  • Jacquemoud, S. et al., PROSPECT + SAIL models: a review of use for vegetation characterization. Remote Sensing Environ., 2009, 113, S56–S66.

  • Barker, D. M., Huang, W., Guo, Y. R., Bourgeois, A. J. and Xiao, Q. N., A three-dimensional variational data assimilation system for MM5: implementation and initial results. Mon. Weather Rev., 2004, 132, 897–914.

  • Rouse, J. W., Has, R. H., Schell, J. A., Deering, D. W. and Harlan, J. C., Monitoring the vernal advancement of retrodegradation of natural vegetation, NASA/GSFC, Type III, Final report, Greenbelt, MD, 1974, p. 371; Rouse, J. W., Haas, R. S., Schell, J. A. and Deering, D. W., Monitoring vegetation systems in the Great Plains with ERTS. In Proceedings, 3rd Earth Resources Technology Satellite Symposium, 1973, vol. 1, pp. 48–623.

  • Peñuelas, J., Gamon, J. A., Fredeen, A. L., Merino, J. and Field, C. B., Reflectance indices associated with physiological changes in nitrogen and water limited sunflower leaves. Remote Sensing Environ., 1994, 48, 135–146.

  • Hardisky, M. A., Klemas, V. and Smart, R. M., The influences of soil salinity, growth form, and leaf moisture on the spectral reflectance of Spartina alterniflora canopies. Photogramm. Eng. Remote Sensing, 1983, 49, 77–83.

  • Gao, B.-C., NDWI – a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing Environ., 1996, 58, 257–266.

  • Chen, D., Huang, J. and Jackson, T. T., Vegetation water content estimation for corn and soybeans using spectral indices derived from MODIS near- and short-wave infrared bands. Remote Sensing Environ., 2005, 98, 225–236.

  • Ashourloo, D., Mobasheri, M. R. and Huete, A., Developing two spectral disease indices for detection of wheat leaf rust (Puccinia triticina). Remote Sensing, 2014, 6, 4723–4740.

  • Bolstad, P. V. and Lillesand, T. M., Semi-automated training approaches for spectral class definition. Int. J. Remote Sensing, 1991, 13, 3157–3166.

  • Clark, M. L., Comparison of simulated hyperspectral HyspIRI and multispectral Landsat 8 and Sentinel-2 imagery for multi-seasonal, regional land-cover mapping. Remote Sensing Environ., 2017, 200, 311–325.

  • Gitelson, A. A., Kaufman, Y. J., Stark, R. and Rundquist, D., Novel algorithm for remote estimation of vegetation fraction. Remote Sensing Environ., 2002, 80, 76–87.

  • Gamon, J. A., Penuelas, J. and Field, C. B., A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sensing Environ., 1992, 41, 35–44.

  • Serrano, L., Penuelas, J. and Ustin, S. L., Remote sensing of nitrogen and lignin in mediterranean vegetation from AVIRIS data: decomposing biochemical from structural signals. Remote Sensing Environ., 2002, 81, 355–364.

  • Gitelson, A. A., Zur, Y., Chivkunova, O. B. and Merzlyak, M. N., Assessing carotenoid content in plant leaves with reflectance spectroscopy. Photochem. Photobiol., 2002, 75, 272–281.

  • Birth, G. S. and McVey, G. R., Measuring colour of growing turf with a reflectance spectrometer. Agron. J., 1968, 60, 640–649.

  • Huete, A. R., Liu, H., Batchily, K. and van Leeuwen, W., A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sensing Environ., 1997, 59, 440–451.

  • Kaufman, Y. J. and Tanre, D., Strategy for direct and indirect methods for correcting the aerosol effect on remote sensing: from AVHRR to EOS-MODIS. Remote Sensing Environ., 1996, 55, 65–79.

  • Sims, D. A. and Gamon, J. A., Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing Environ., 2002, 81, 337–354.

  • Vogelmann, J. E., Rock, B. N. and Moss, D. M., Red edge spectral measurements from sugar maple leaves. Int. J. Remote Sensing, 1993, 14, 1563–1575.

  • Curran, P. J., Windham, W. R. and Gholz, H. L., Exploring the relationship between reflectance red edge and chlorophyll concentration in slash pine leaves. Tree Physiol., 1993, 15, 203–206.

  • Sandholt, I., Rasmussen, K. and Anderson, J., A simple interpretation of the surface temperature/vegetation index space for assessment of the surface moisture status. Remote Sensing Environ., 2002, 79, 213–224.

  • Mirik, M., Michels, G. J., Kassymzhanova-Mirik, S. and Elliott, N. C., Reflectance characteristics of Russian wheat aphid (Hemiptera: Aphididae) stress and abundance in winter wheat. Comp. Electron. Agric., 2007, 57, 123–134.

  • Xu X., Li, J., Wu, C. and Plaza, A., Regional clustering-based spatial preprocessing for hyperspectral unmixing. Remote Sensing Environ., 2018, 204, 333–346.


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  • Crop Type Discrimination and Health Assessment using Hyperspectral Imaging

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Authors

Rahul Nigam
Agriculture and Land Eco-system Division, Earth, Ocean, Atmosphere, Planetary Sciences and Applications Area, Space Applications Centre (ISRO), Ahmedabad 380 015, India
Rojalin Tripathy
Agriculture and Land Eco-system Division, Earth, Ocean, Atmosphere, Planetary Sciences and Applications Area, Space Applications Centre (ISRO), Ahmedabad 380 015, India
Sujay Dutta
Agriculture and Land Eco-system Division, Earth, Ocean, Atmosphere, Planetary Sciences and Applications Area, Space Applications Centre (ISRO), Ahmedabad 380 015, India
Nita Bhagia
Agriculture and Land Eco-system Division, Earth, Ocean, Atmosphere, Planetary Sciences and Applications Area, Space Applications Centre (ISRO), Ahmedabad 380 015, India
Rohit Nagori
Agriculture and Land Eco-system Division, Earth, Ocean, Atmosphere, Planetary Sciences and Applications Area, Space Applications Centre (ISRO), Ahmedabad 380 015, India
K. Chandrasekar
National Remote Sensing Centre (ISRO), Hyderabad 500 037, India
Rajsi Kot
M.G. Science Institute, Ahmedabad 380 009, India
Bimal K. Bhattacharya
Agriculture and Land Eco-system Division, Earth, Ocean, Atmosphere, Planetary Sciences and Applications Area, Space Applications Centre (ISRO), Ahmedabad 380 015, India
Susan Ustin
Environmental and Resource Sciences, University of California, Davis, CA 95616, United States

Abstract


Advancements in hyperspectral remote sensing technology have opened new avenues to explore innovative ways to map crops in terms of area and health. To study precise mapping of agriculture and horticulture crops along with biophysical and biochemical constituents at field scale, an airborne AVIRIS-NG hyperspectral imaging has been conducted in various agro-climatic regions representing diverse agricultural types of India. Crop classification with available and developed algorithms has been applied over homogeneous and heterogeneous agriculture and horticulture cropped areas. The spectral angle mapper and maximum likelihood algorithms showed classification accuracy of 77%–94% for AVIRI-NG and 42%–55% for LISS IV. The customized deep neural network and maximum noise function (MNF)-based classification schemes showed an accuracy of 93% and 86% for mapping of agriculture and horticulture crops respectively. The forward and inversion of canopy radiative transfer model protocol was developed for retrieval of crop parameters such as leaf area index (LAI) and chlorophyll content (Cab) using AVIRIS-NG narrow bands. The retrieved LAI and Cab showed 19%–27% and 23%–29% deviation from measured mean for homogeneous and heterogeneous agricultural areas respectively. Red edge position index-based empirical model and multivariate linear regression of multiple indices showed maximum correlation of 0.62 and 0.93 respectively, to map leaf nitrogen content. Water condition index was developed using vegetation and water indices to distinguish crop water-based abiotic stress. Wheat yellow rust disease has been identified at field scale using absorption band depth analysis at 662–702 and 2155–2175 nm, and further applied to AVIRIS-NG data to detect biotic stress at spatial scale. This study establishes that such missions have the potential to boost accurate mapping of economically valuable minor crops and generate health indicators to distinguish biotic and abiotic stresses at field scale.

Keywords


Assessment, Biotic and Abiotic Stress, Crop Classification, Health, Hyperspectral Imaging.

References





DOI: https://doi.org/10.18520/cs%2Fv116%2Fi7%2F1108-1123