Abstract
The accumulative moisture stress of potato crop was detected using Field Spectroradiometer data. Different degree of stress was created by manipulating the amount and interval between two consecutive irrigations. The daily soil moisture was simulated using Hydrus-1D and validated (r = 0.805 and RMSE = 0.015). The ambient soil moisture <65 % of available soil moisture was considered as moisture stress which was further integrated over period to estimate the accumulative moisture stress. The accumulated stress was regressed with several linear moisture indices and band-depth-maxima at both weak (914–1095 and 1091–1303 nm) and strong (1250–1674 and 1851–2301 nm) water absorption regions. Although there was high correlation between stress and band-depth-maxima (r > 0.9), at higher level of accumulative stress beyond 5.0, the values get saturated except for 1091–1303 nm. Hence, the band-depth-maxima at 1091–1303 nm is sensitive to wider range of stress and may be used for targeted irrigation technique to maintain safe irrigation zone.
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References
Chaerle, L., & Van Der Straeten, D. (2000). Imaging techniques and the early detection of plant stress. Trends in Plant Science, 5(11), 495–500.
Chuvieco, E., & Huete, A. (2010). Fundamental of satellite remote sensing (pp. 302–310). Boca Raton: CRC Press.
Clevers, J. G. P. W., Kooistra, L., & Schaepman, M. E. (2008). Using spectral information from the NIR water absorption features for the retrieval of canopy water content. International Journal of Applied Earth Observation and Geo-Information, 10, 388–397.
Curran, P. J., Dungan, J. L., & Peterson, D. L. (2001). Estimating the foliar biochemical concentration of leaves with reflectance spectrometry: Testing the Kokaly and Clark methodologies. Remote Sensing of Environment, 76, 349–359.
Curwen, D. (1993). Water management. In R. C. Rowe (Ed.), Potato health management. Wooster: The American Phytopathological Society, ASP Press.
Dutt, B. (1999). Remote sensing of water content in eucalyptus leaves. Australian Journal of Botany, 47(6), 909–923.
Efetha, A. (2011). Irrigation scheduling for potato in southern Alberta. Agri-facts. Agdex 258/561-1. www.agriculture.alberta.ca.
Gates, D. M., Keegan, H. J., Schleter, J. C., & Weidner, V. P. (1965). Spectral properties of plants. Applied Optics, 4, 11–20.
Hunt, E. R., & Rock, B. N. (1989). Detection of changes in leaf water content using near and middle-infrared reflectance. Remote Sensing of Environment, 30, 43–54.
Knipling, E. B. (1970). Physical and physiological basis for the reflectance of visible and near infrared radiation from vegetation. Remote Sensing of Environment, 1, 155–159.
Kokaly, R. F. (2001). Investigating a physical basis for spectroscopic estimates of leaf nitrogen concentration. Remote Sensing of Environment, 75, 153–161.
McFeeters, S. K. (1996). The use of Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7), 1425–1432.
Pu, R., Ge, S., Kelly, N. M., & Gong, P. (2003). Spectral absorption features as indicators of water status in coast live Oak (Quercus agrifolia) leaves. International Journal of Remote Sensing, 24, 1799–1810.
Savitzky, A., & Golay, M. J. E. (1964). Soothing and differentiation of data by simplified least squares procedures. Analytical Chemistry, 36, 1627–1639.
Sims, D. A., & Gamon, J. A. (2003). Estimation of vegetation water content and photosynthetic tissue area from spectral reflectance: A comparison of indices based on liquid water and chlorophyll absorption features. Remote Sensing of Environment, 84, 526–537.
Šimůnek J, Huag K, van Genuchten MTH (1998). The HYDRUS code for simulating the one-dimensional movement of water, heat, and multiple solutes in variably-saturated media. Version 6.0. Research Report No. 144, U.S. Salinity Laboratory, USDA, ARS. Riverside, California.
Simunek, J., Sejna, M., Saito, H., Sakai, M., van Genuchten M.T.H. (2012). The HYDRUS-1D Software Package for Simulating the One-Dimensional Movement of Water, Heat, and Multiple Solutes in Variably-Saturated Media, Riverside, California.
Uto, K., & Kosugi, Y. (2012). Hyperspectral manipulation for the water stress evaluation of plants. Comtemporary Materials, III-1, 18–25.
Woolley, J. T. (1971). Reflectance and transmittance of light by leaves. Plant Physiology, 47, 656–662.
Acknowledgments
The authors are thankful to the anonymous reviewers for providing valuable suggestions to improve the quality of the research paper. Thanks are due to Dr. J. R. Sharma, CGM, Regional Remote Sensing Centres, for his continuous support and encouragements during the investigation. The authors wishes to put into record the field support received from Dr Biswapati Mandal and Dr Prasanta Kumar Bandyopadhyay, BCKV.
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Dutta, D., Das, P.K., Paul, S. et al. Spectral Response of Potato Crop to Accumulative Moisture Stress Estimated from Hydrus-1D Simulated Daily Soil Moisture During Tuber Bulking Stage. J Indian Soc Remote Sens 44, 363–371 (2016). https://doi.org/10.1007/s12524-015-0503-z
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DOI: https://doi.org/10.1007/s12524-015-0503-z