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Vegetation Stress Detection with Hyperspectral Remote Sensing for a Winning Agribusiness


Affiliations
1 ESRI India, Mathura Road, New Delhi., India
2 Indian Statistical Institute, Agricultural and Ecological Research Unit (AERU), Kolkata., India
3 Remote Sensing Department, Birla Institute of Technology, Mesra, Ranchi., India
4 ESRI India, MCIA, Mathura Road, New Delhi., India
     

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The subject agribusiness has drawn an enormous attention by organized sector in national and multinational level with the recent move of the Government of India to allow Foreign Direct Investment (FDI) in the retailing sector. Technology driven efforts are very much important in this changed scenario to increase market efficiency reducing inventories, waste, and costs. Earth Observation Satellite (EOS) imagery driven Remote Sensing (RS) and Geographical Information System (GIS) technology can be utilized as a high-end Spatial Decision Support System (SDSS) to extract the different aspects of agriculture like land-use land-cover (LULC) condition, soil properties like moisture estimation, moisture conservation, crop identification, identification of suitable farming site for suitable crop, acreage estimation, crop monitoring, damage monitoring, and complete supply chain monitoring includes crop vehicle tracking integrating Global Positioning System (GPS). Increased availability of narrow band hyperspectral imagery from Hyperion sensor has prompted to explore hyeprspectral imagery to estimate the vegetation biophysical parameters and leaf biochemical used to detect nutritional and water stress condition. This paper summarizes the use of hyperspectral remote sensing for vegetation monitoring through biochemical and biophysical parameter estimation, discussing the potential for detecting water stress. Central to this objective is our primary research question: Can remote sensing play a key role to monitor agri-crop health to enhance the agribusiness efficiency?.

Keywords

Vegetation Stress, Hyperspectral Remote Sensing, Vegetation Index, Agricultural Monitoring, Agribusiness
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  • Adams, M. L., Philpot, W. D., & Norvell, W. A. (1999). Yellowness Index: an Application of Spectral Second Derivatives to Estimate Chlorosis of Leaves in Stressed Vegetation. International Journal of Remote Sensing, 20, 3663-3675.

  • Carter, G. A. (1994). Ratios of Leaf Reflectances in Narrow Wavebands as Indicators of Plant Stress. International Journal of Remote Sensing, 15, 697-704.

  • Ceccato, P., Flasse, S., Tarantola, S., Jacquemoud, S., & Gregoire, J.M. (2001). Detecting Vegetation Leaf Water Content Using Reflectance in the Optical Domain. Remote Sensing of Environment, 77, 22-33.

  • Champagne, C., Pattey, E., Bannari, A., & Stratchan, I.B. (2001). Mapping Crop Water Status: Issues of Scale in the Detection of Crop Water Stress Using Hyperspectral Indices. Proceedings of the 8th International Symposium on Physical Measurements and Signatures in Remote Sensing, Aussois, France. 79-84.

  • Chen, Y., & Barak, P. (1982). Iron Nutrition of Plants in Calcareous soils. Adv. Agron, 35, 217-240.

  • Curran, P. J., Dungan, J. L., & Gholz, H. L. (1990). Exploring the Relationship Between Reflectance Red Edge and Chlorophyll Content in Slash Pine. Tree Physiology, 7, 33-48.

  • Dash, J., & Curran, P. J. (2004). The MERIS Terrestrial Chlorophyll Index. International Journal of Remote Sensing, 25, 5403-5413.

  • Datt, B. (1999). A New Reflectance Index for Remote Sensing of Chlorophyll Content in Higher Plants: Tests Using Eucalyptus Leaves. Journal of Plant Physiology, 154, 30-36.

  • Daughtry, C.S.T. (2001). Discriminating Crop Residues from Soil by Short-wave Infrared Reflectance. Agronomy Journal, 93, 125-131.

  • Daughtry, C.S.T., Hunt, E.R., Jr., & McMurtrey J.E. III. (2004). Assessing Crop Residue Cover Using Shortwave Infrared Reflectance. Remote Sensing of Environment, 90, 126-134.

  • Fernandez-Escobar, R., Moreno, R., & Garcia-Creus, M., (1999). Seasonal Changes of Mineral Nutrients in Olive Leaves During the Alternate-Bearing Cycle. Scientia Horticulturae, 82, 24-45.

  • Fourty, T., Baret, F., Jacquemoud, S., Schmuck, G., & Verdebout, J. (1996). Leaf Optical Properties with Explicit Description of Its Biochemical Composition: Direct and Inverse Problems. Remote Sensing of Environment, 56, 104-117.

  • Gamon, J.A., Penuelas, J., & Field, C.B. (1992). A Narrow-Waveband Spectral Index that Tracks Diurnal Changes in Photosynthetic Efficiency. Remote Sensing of Environment, 41, 35-44.

  • Gamon, J.A., Serrano, L., & Surfus, J.S. (1997). The Photochemical Reflectance Index: an Optical Indicator of Photosynthetic Radiation Use Efficiency Across Species, Functional Types and Nutrient Levels. Oecologia, 112, 492-501.

  • Gao, B.C. (1995). Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space. Proceedings of SPIE, 2480, 225-236.

  • Gitelson, A. A., & Merzlyak, M. N. (1996). Signature Analysis of Leaf Reflectance Spectra: Algorithm Development for Remote Sensing of Chlorophyll. Journal of Plant Physiology, 148, 494-500.

  • Gitelson, A. A., & Merzlyak, M.N. (1994). Spectral Reflectance Changes Associated with Autumn Senescence of Aesculus Hippocastanum L. and Acer Platanoides L. Leaves. Spectral Features and Relation to Chlorophyll Estimation. Journal of Plant Physiology, 143, 286292.

  • Gitelson, A.A., Merzlyak, M.N., & Chivkunova., O.B. (2001). Optical Properties and Nondestructive Estimation of Anthocyanin Content in Plant Leaves. Photochemistry and Photobiology, 71, 38-45.

  • Gitelson, A.A., Zur, Y., Chivkunova, O.B., & Merzlyak, M.N. (2002). Assessing Carotenoid Content in Plant Leaves with Reflectance Spectroscopy. Photochemistry and Photobiology, 75, 272-281.

  • Gutierrez-Rosales, G., Garrido-Fernandez, F.J., Galliardo-Guerrero, L., & Gandul-Rojas, B. (1992). Action of Chlorophylls on the Stability of Virgin Olive Oil. J. Am. Oil Chem. Soc., 69.

  • Hardisky, M. A., Klemas, V., & Smart, R.M. (1983). The Influences of Soil Salinity, Growth Form, and Leaf Moisture on the Spectral Reflectance of Spartina Alterniflora Canopies. Photogrammetric Engineering and Remote Sensing, 49, 77-83.

  • Horler, D. N. H., Dockray, M., & Barber, J. (1983). The Red Edge of Plant Leaf Reflectance. International Journal of Remote Sensing, 4, 273-288.

  • Huete, A.R., H. Liu, K. Batchily, & W. van Leeuwen, 1997. A Comparison of Vegetation Indices over a Global Set of TM Images for EOS-MODIS. Remote Sensing of Environment, 59(3), 440-451.

  • Hunt Jr., E.R. & Rock, B.N. (1989). Detection of Changes in Leaf Water Content Using Near- and Middle-infrared Reflectances. Remote Sensing of Environment, 30, 43-54.

  • Jackson, T.L., Chen, D., Cosh, M., Li, F., Anderson, M., C. Walthall, P., Hunt, E.R. (2004). Vegetation Water Content Mapping Using Landsatdata Derived Normalized Difference Water Index for Corn and Soybeans. Remote Sensing of Environment, 92, 475-482.

  • Jolley, V.D., & Brown, J.C. (1994). Genetically Controlled Uptake and Use of Iron by Plants, In Manthey, J.A., Crowley, D.E., Luster, D.G. (Eds.). Biochemistry of metal Micronutrients in the Rhizosphere, Lewis Publishers, Boca Raton, 251-266.

  • Kaufman, Y.J., & Tanre, D. (1996). Strategy for Direct and Indirect Methods for Correcting the Aerosol Effect on Remote Sensing: from AVHRR to EOSMODIS. Remote Sensing of Environment, 55, 65-79.

  • Marschner, H., Romheld, V., & Kissel, M. (1986). Different Strategies in Higher Plants in Mobilization and Uptake of Iron. J. Plant Nutr., 9, 695-713.

  • Melillo, J.M., Aber, J.D., & Muratore, J.F. (1982). Nitrogen and Lignin Control of Hardwood Leaf Litter Decomposition Dynamics. Ecology, 63, 621-626.

  • Merzlyak, M. N., Gitelson, A. A., Chivkunova, O. B., & Rakitin, V. Y. (1999). Non-Destructive Optical Detection of Pigment Changes During Leaf Senescence and Fruit Ripening. Physiologia Plantarum, 106, 135-141.

  • Penuelas, J., Baret, F., & Filella, I. (1995). Semi-Empirical Indices to Assess Carotenoids/Chlorophyll-a Ratio from Leaf Spectral Reflectance. Photosynthetica, 31, 221-230.

  • Penuelas, J., Filella, I., Biel, C., Serrano, L., & Save, R. (1995). The Reflectance at the 950-970 nm Region as an Indicator of Plant Water Status. International Journal of Remote Sensing, 14, 1887-1905.

  • Rouse, J.W., Haas, R.H., Schell, J.A., & Deering, D.W. (1973). Monitoring Vegetation Systems in the Great Plains with ERTS. Third ERTS Symposium, NASA SP-351 I, 309-317.

  • Sellers, P.J. (1985). Canopy Reflectance, Photosynthesis and Transpiration. International Journal of Remote Sensing, 6, 1335-1372.

  • Serrano, L., Penuelas, J., & Ustin, S.L. (2002). Remote Sensing of Nitrogen and Lignin in Mediterranean Vegetation from AVIRIS Data: Decomposing Biochemical from Structural Signals. Remote Sensing of Environment, 81, 355-364.

  • Sims, D.A. & Gamon, J.A. (2002). Relationships between Leaf Pigment Content and Spectral Reflectance across a Wide Range of Species, Leaf Structures and Developmental Stages. Remote Sensing of Environment, 81, 337-354.

  • Tagliavini, M., Rombola, A.D., (2001). Iron Deficiency and Chlorosis in Orchard and Vineyard Ecosystems, European Journal of Agronomy, 15, 71-92.

  • Tucker, C.J., (1979). Red and Photographic Infrared Linear Combinations for Monitoring Vegetation. Remote Sensing of the Environment, 8, 127-150.

  • Vogelmann, J. E., Rock, B. N. & Moss, D. M., (1993). Red Edge Spectral Measurements from Sugar Maple Leaves. International Journal of Remote Sensing, 14:1563-1575.

  • Wallace, A. (1991). Rational Approaches to Control of Iron Deficiency Other than Plant Breeding and Choice of Resistant Cultivars. Plant Soil, 130, 281-288.

  • Zarco-Tejada P.J., Sepulcre-Canto G., (2007). Remote Sensing of Vegetation Biophysical Parameters for Detecting Stress Condition and Land Cover Changes. Estudios de la Zona No Saturada del Suelo J.V. Giraldez Cervera y F.J. JimĂ©nez Hornero, 8, 37-44

  • Zarco-Tejada, P.J., Miller, J.R., Mohammed, G.H., Noland, T.L., & Sampson, P.H., (2001). Scaling-up and Model Inversion Methods with Narrow-band Optical Indices for Chlorophyll Content Estimation in Closed Forest Canopies with Hyperspectral data. IEEE Trans. on Geoscience and Remote Sensing, 39(7), 1491-1507.


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  • Vegetation Stress Detection with Hyperspectral Remote Sensing for a Winning Agribusiness

Abstract Views: 797  |  PDF Views: 2

Authors

Partha Pratim Ghosh
ESRI India, Mathura Road, New Delhi., India
Pabitra Banik
Indian Statistical Institute, Agricultural and Ecological Research Unit (AERU), Kolkata., India
Nilanchal Patel
Remote Sensing Department, Birla Institute of Technology, Mesra, Ranchi., India
Deb Jyoti Pal
ESRI India, MCIA, Mathura Road, New Delhi., India

Abstract


The subject agribusiness has drawn an enormous attention by organized sector in national and multinational level with the recent move of the Government of India to allow Foreign Direct Investment (FDI) in the retailing sector. Technology driven efforts are very much important in this changed scenario to increase market efficiency reducing inventories, waste, and costs. Earth Observation Satellite (EOS) imagery driven Remote Sensing (RS) and Geographical Information System (GIS) technology can be utilized as a high-end Spatial Decision Support System (SDSS) to extract the different aspects of agriculture like land-use land-cover (LULC) condition, soil properties like moisture estimation, moisture conservation, crop identification, identification of suitable farming site for suitable crop, acreage estimation, crop monitoring, damage monitoring, and complete supply chain monitoring includes crop vehicle tracking integrating Global Positioning System (GPS). Increased availability of narrow band hyperspectral imagery from Hyperion sensor has prompted to explore hyeprspectral imagery to estimate the vegetation biophysical parameters and leaf biochemical used to detect nutritional and water stress condition. This paper summarizes the use of hyperspectral remote sensing for vegetation monitoring through biochemical and biophysical parameter estimation, discussing the potential for detecting water stress. Central to this objective is our primary research question: Can remote sensing play a key role to monitor agri-crop health to enhance the agribusiness efficiency?.

Keywords


Vegetation Stress, Hyperspectral Remote Sensing, Vegetation Index, Agricultural Monitoring, Agribusiness

References