The role of near-infrared sensors to measure water relationships in crops and plants

References

• Idso, S. B. (1982) Non-water-stressed baselines: A key to measuring and interpreting plant water stress. Agric. Meteorol. 27: 59–70.
   Google Scholar
• Jones, H. G. (2004) Irrigation scheduling: Advantages and pitfalls of plant-based methods. J. Exp. Bot. 55(407): 2427–2436.
   PubMed Web of Science ®Google Scholar
• Jones, H. G. (2014) The use of indirect or proxy markers in plant physiology. Plant, Cell Environ. 37: 1270–1272.
   PubMed Web of Science ®Google Scholar
• Fischer, R. A. and Edmeades, G. O. (2010) Breeding and cereal yield progress. Crop Sci. 50: 85–98.
   Web of Science ®Google Scholar
• Ray, D. K., Mueller, N. D., West, P. C., and Foley, J. A. (2013) Yield trends are insufficient to double global crop production by 2050. PLoS One 8. doi: 10.1371/journal.pone.0066428.
   Web of Science ®Google Scholar
• Cifre, J., Bota, J., Escalona, J. M., Medrano, H., and Flexas, J. (2005) Physiological tools for irrigation scheduling in grapevine (Vitis vinifera L.): an open gate to improve water-use efficiency? Agric. Ecosyst. Environ. 106: 159–170.
   Web of Science ®Google Scholar
• Menesatti, P., Pallottino, F., Antonucci, F., Roccuzzo, G., Intrigliolo, F., and Costa, C. (2012) Non-destructive proximal sensing for early detection of citrus nutrient and water stress. Adv. Citrus Nutr. 7: 113–123.
   Google Scholar
• Dry, P. R., Loveys, B. R., McCarthy, M. G., and Stoll, M. (2001) Strategic irrigation management in Australian vineyards. J. Int Science de la Vigne et du Vin. 35: 129–139.
   Google Scholar
• Jones, H. G. (2007) Monitoring plant and soil water status: established and novel method revisited and their relvance to studies in drought tolerance. J. Exp. Bot. 58(407): 119–130.
   PubMed Web of Science ®Google Scholar
• Clarke, T. R. (1997) An empirical approach for detecting crop water stress using multispectral airborne sensors. HortTechnology 7(1): 9–16.
   Google Scholar
• Araus, J. L. and Cairns, J. E. (2014) Field high-throughput phenotyping: The new crop breeding frontier. Trends Plant Sci. 19: 52–61.
   PubMed Web of Science ®Google Scholar
• Tuberosa, R. (2012) Phenotyping for drought tolerance of crops in the genomics era. Front. Physiol. 3: 1–26.
   PubMed Web of Science ®Google Scholar
• Deery, D., Jimenez-Berni, J., Jones, H. G., Sirault, X., and Furbanks, R. (2014) Proximal remote sensing buggies and potential applications for field-based phenotyping. Agronomy 5: 349–379.
   Google Scholar
• White, J. W., Andrade-Sanchez, P., Gore, M. A., Bronson, K. F., Coffelt, T. A., Conley, M. M., Feldmann, K. A., French, A. N., Heun, J. T., and Hunsaker, D. J. (2012) Field-based phenomics for plant genetics research. Field Crop. Res. 133: 101–112.
   Web of Science ®Google Scholar
• Behrens, T., Müller, J., and Diepenbrock, W. (2007) Optimizing a diode array VIS/NIR spectrometer system to detect plant stress in the field. J. Agron. Crop Sci. 193(4): 292–304.
   Web of Science ®Google Scholar
• Gutiérrez-Rodríguez, M., Reynolds, M. P., Escalante-Estrada, J. A., and Rodríguez-González, M. T. (2004) Association between canopy reflectance indices and yield and physiological traits in bread wheat under drought and well-irrigated conditions. Aust. J. Agric. Res. 55: 1139–1147.
   Google Scholar
• Gutierrez, M., Reynolds, M. P., Raun, W. R., Stone, M. L., and Klatt, A. R. (2010) Spectral water indices for assessing yield in elite bread wheat genotypes under well-irrigated, water-stressed, and high-temperature conditions. Crop Sci. 50: 197–214.
   Web of Science ®Google Scholar
• Bartzanas, T., Katsoulas, N., Elvanidi, A., Ferentinos, K. P., and Kittas, C. (2015) Remote sensing for crop water stress detection in greenhouses. 10th European Conference on Precision Agriculture, ECPA, Volcani Centre, Israel, pp. 669–676.
   Google Scholar
• Govender, M., Dye, P. J., Weiersbye, I. M., Witkowski, E. T. F., and Ahmed, F. (2009). Review of commonly used remote sensing and ground based technologies to measure plant water stress. Available at: www.wrc.org.za.
   Google Scholar
• Blackburn, G. A. (2007) Hyperspectral remote sensing of plant pigments. J. Exp. Bot. 58: 855–867.
   PubMed Web of Science ®Google Scholar
• Eitel, J. U. H., Gessler, P. E., Smith, A. M. S., and Robberecht, R. (2006) Suitability of existing and novel spectral indices to remotely detect water stress in Populus spp. For. Ecol. Manage. 229: 170–182.
   Web of Science ®Google Scholar
• Howell, T. A. (2001) Enhancing water use efficiency in irrigated agriculture. Agron. J. 93: 281–289.
   Web of Science ®Google Scholar
• Hong, M., Zeng, W., Ma, T., Lei, G., Zha, Y., Fang, Y., Wu, J., and Huang, J. (2017) Determination of growth stage-specific crop coefficients (Kc) of sunflowers (Helianthus annuus L.) under salt stress. Water 9: 1–17.
   Google Scholar
• Feret, J.-B., Francois, C., Gitelson, A., Asner, G. P., Barry, K. M., Panigada, C., and Jacquemoud, S. (2011) Optimizing spectral indices and chemometric analysis of leaf chemical properties using radiative transfer modeling. Remote Sens.Environ. 115: 2742–2750.
   Web of Science ®Google Scholar
• Glenn, E. P., Neale, C. M. U., Hunsaker, D. J., and Nagler, P. L. (2011) Vegetation index-based crop coefficients to estimate evapotranspiration by remote sensing in agricultural and natural ecosystems. Hydrol. Process. 25(26): 4050–4062.
   Web of Science ®Google Scholar
• Thenkabail, P. S., Gumma, M. K., Teluguntla, P., and Mohammed, I. A. (2014) Hyperspectral remote sensing of vegetation and agricultural crops. Photogramm. Eng. Remote Sens. 80: 697–709.
   Web of Science ®Google Scholar
• Bastianassen, W. G. M., Molden, D. J., and Makin, I. (2000) Remote sensing for irrigated agriculture: examples from research and possible applications. Agric. Water Manag. 46: 137–155.
   Web of Science ®Google Scholar
• Turner, N. C. (1988) Measurement of plant water status by the pressure chamber technique. Irrig. Sci. 9: 289–308.
   Web of Science ®Google Scholar
• Chaerle, L., Leinonen, I., Jones, H. G., and Van Der Straeten, D. (2007) Monitoring and screening plant populations with combined thermal and chlorophyll fluorescence imaging. J. Exp. Bot. 58: 773–784.
   PubMed Web of Science ®Google Scholar
• Pou, A., Diago, M. P., Medrano, H., Baluja, J., and Tardaguila, J. (2014) Validation of thermal indices for water status identification in grapevine. Agric. Water Manag. 134: 60–72.
   Web of Science ®Google Scholar
• Yu, G.-R., Miwa, T., Nakayama, K., Matsuoka, N., and Kon, H. (2000) A proposal for universal formulas for estimating leaf water status of herbaceous and woody plants based on spectral reflectance properties. Plant Soil 227: 47–58.
   Web of Science ®Google Scholar
• Elsayed, S., Mistele, B., and Schmidhalter, U. (2011) Can changes in leaf water potential be assessed spectrally? Fun. Plant Bio. 38(6): 523–533.
   Web of Science ®Google Scholar
• Elsayed, S., Rischbeck, P., and Schmidhalter, U. (2015) Comparing the performance of active and passive reflectance sensors to assess the normalized relative canopy temperature and grain yield of drought-stressed barley cultivars. Field Crops Res. 177: 148–160.
   Web of Science ®Google Scholar
• Tucker, C. J. (1980). Remote sensing of leaf water content in the near infrared. Remote Sens. Environ. 10: 23–32.
   Web of Science ®Google Scholar
• Hunt, E. R. and Rock, B. N. (1989) Detection of changes in leaf water content using near and middle infrared reflectances. Remote Sens. Environ. 30: 43–54.
   Web of Science ®Google Scholar
• Cho, M. A., Skidmore, A., Corsi, F., van Wieren, S. E., and Sobhan, I. (2007) Estimation of green grass/herb biomass from airborne hyperspectral imagery using spectral indices and partial least squares regression. Int. J. Appl. Earth Obs. Geoinf. 9: 414–424.
   Web of Science ®Google Scholar
• Carter, G. A. (1991) Primary and secondary effects of water content on spectral reflectance of leaves. Am. J. Bot. 78: 916–924.
   Web of Science ®Google Scholar
• Hansen, P. M. and Schjoerring, J. K. (2003) Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sens. Environ. 86: 542–553.
   Web of Science ®Google Scholar
• Zarco-Tejada, P. J., Berjon, A., Lopez-Lozano, R., Miller, J. R., Martin, P., Cachorro, V., and de Frutos, A. (2005) Assessing vineyard condition with hyperspectral indices: leaf and canopy reflectance simulation in a row-structured discontinuous canopy. Remote Sens. Environ. 99: 271–287.
   Web of Science ®Google Scholar
• Musick, H. B. and Pelletier, R. E. (1986) Response of some thematic mapper band ratios to variation in soil water content. ISPRS J. Photogr. Remote Sens. 52: 1661–1668.
   Google Scholar
• Musick, H.B and Pelletier, R. E. (1988). Response to soil moisture of spectral indexes derived from bidirectional reflectance in thematic mapper wavebands. Remote Sens. Environ. 25: 167–184.
   Web of Science ®Google Scholar
• Ceccato, P., Flasse, S., Tarantola, S, Jacquemond, S., and Gregoire, J. M. (2011) Detection of vegetation leaf water content using reflectance in the optical domain. Remote Sens. Environ. 77: 22–33.
   Web of Science ®Google Scholar
• Foutry, T. and Baret, F. (1997) Vegetation water and dry matter contents estimated from top of the atmosphere reflectance data: a simulation study. Remote Sens. Environ. 61:34–45.
   Web of Science ®Google Scholar
• Penuelas, J., Filella, I., Lloret, P., Munoz, F., and Vilajeliu, M. (1995) Reflectance assessment of mite effects on apple trees. Int. J. Remote Sens. 16: 2727–2733.
   Web of Science ®Google Scholar
• Gao, B.-C. (1996) NDWI – a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ. 58: 257–266.
   Web of Science ®Google Scholar
• Penuelas, J., Filella, I., Biel, C., Serrano, L., and Save, R. (1993) The reflectance at the 950–970 nm region as an indicator of plant water status. Int. J. Remote Sens. 14: 1887–1905.
   Web of Science ®Google Scholar
• Carter, G. A. and Knapp, A. K. (2001) Leaf optical properties in higher plants: linking spectral characteristics to stress and chlorophyll concentration. Am. J. Bot. 88: 677–684.
   PubMed Web of Science ®Google Scholar
• Sankaran, S., Khot, L. R., Zúniga-Espinosa, C., Jarolmasjed, S., Sathuvalli, V. R., Vandemarkd, G. J., Miklas, P. N., Carter, A. H., Pumphrey, M. O., Knowles, N. R., and Pavek, M. J. (2015) Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review. Europ. J. Agron. 70: 112–123.
   Web of Science ®Google Scholar
• Lelong, C. C. D., Pinet, P. C., and Poilvé, H. (1998) Hyperspectral imaging and stress mapping in agriculture: A case study on wheat in Beauce (France). Remote Sens. Environ. 66(2): 179–191.
   Web of Science ®Google Scholar
• Tian, Y., Zhu, Y., and Cao, W. (2005) Monitoring leaf photosynthesis with canopy spectral reflectance in rice. Photosynthetica 43: 481–489.
   Web of Science ®Google Scholar
• Yousfi, S., Kellas, N., Saidi, L., Benlakehal, Z., Chaou, L., Siad, D., Herda, F., Karrou, M., Vergara, O., Gracia, A., Araus, J. L., and Serret, M. D.(2016) Comparative performance of remote sensing methods in assessing wheat performance under Mediterranean conditions. Agri. Water Manag. 164: 137–147.
   Web of Science ®Google Scholar
• Hunt, E. R., Jr., Daughtry, C. S. T., and Li, L. (2016) Feasibility of estimating leaf water content using spectral indices from WorldView-3′s near-infrared and shortwave infrared bands. Int. J. Remote Sens. 37 (2): 388–402.
   Web of Science ®Google Scholar
• Gonzalez-Dugo, V., Hernandez, P., Solis, I., and Zarco-Tejada, P. J. (2015) Using high-resolution hyperspectral and thermal airborne imagery to assess physiological condition in the context of wheat phenotyping. Remote Sens. 7(10): 13586–13605.
   Web of Science ®Google Scholar
• Facini, O., Loreti, S., Rossi, F., and Bignami, C. (2004) Canopy and leaf light reflectance features in relation to water content in apple. Acta Hort. 664: 217–224.
   Google Scholar
• Kriston-Vizi, J., Umeda, M., and Miyamoto, K. (2008) Assessment of the water status of mandarin and peach canopies using visible multispectral imagery. Biosystems Eng. 100(3): 338–345.
   Web of Science ®Google Scholar
• Mariotto, I., Thenkabail, P. S., Huete, A., Slonecker, E. T., and Platonov, A. (2013) Hyperspectral vs. multispectral crop-productivity modeling and type discrimination for the HyspIRI mission. Remote Sens. Environ. 139: 291–305.
   Web of Science ®Google Scholar
• Rodriguez-Perez, J. R., Riano, D., Carlisle, E., Ustin, S., and Smart, D. R. (2007) Evaluation of hyperspectral reflectance indexes to detect grapevine water status in vineyards. Am. J. Enol. Vit. 58: 302–317.
   Web of Science ®Google Scholar
• Serrano, L., Gonzalez-Flor, C., and Gorchs, G. (2010) Assessing vineyard water status using the reflectance based water index. Agric. Ecosyst. Environ. 139: 490–499.
   Web of Science ®Google Scholar
• Serrano, L., Gonzalez-Flor, C., and Gorchs, G. (2012) Assessment of grape yield and composition using the reflectance based water index in Mediterranean rain fed vineyards. Remote Sens. Environ. 118: 249–258.
   Web of Science ®Google Scholar
• de Bei, R., Cozzolino, D., Sullivan, W., Cynkar, W., Fuentes, S., Dambergs, R., and Tyerman, S. (2011) Non-destructive measurement of grapevine water potential using near infrared spectroscopy. Aust. J. Grape Wine Res. 17: 62–71.
   Web of Science ®Google Scholar
• Santos, A. O. and Kaye, O. (2009) Grapevine leaf water potential based upon near infrared spectroscopy. Sci. Agricola 66: 287–292.
   Web of Science ®Google Scholar
• Zúñiga, C. E., Khot, L. R., Jacoby, P., and Sankaran, S. (2016) Remote sensing based water-use efficiency evaluation in sub-surface irrigated wine grape vines. Proc. of SPIE - Int. Soc. Opt. Eng. 9866, art. no. 98660O.
   Google Scholar
• Wang, M., Ellsworth, P. Z., Zhou, J., Cousins, A. B., and Sankaran, S. (2016) Evaluation of water-use efficiency in foxtail millet (Setaria italica) using visible-near infrared and thermal spectral sensing techniques. Talanta 152: 531–539.
   PubMed Web of Science ®Google Scholar
• Beget, M. E. and Di Bella, C. M. (2007) Flooding: The effect of water depth on the spectral response of grass canopies. J. Hydrol. 335(3–4): 285–294.
   Web of Science ®Google Scholar
• Ollinger, S. V. (2011) Sources of variability in canopy reflectance and the convergent properties of plants. New Phytol. 189: 375–394.
   PubMed Web of Science ®Google Scholar
• Rapaport, T., Hochberg, U., Rachmilevitch, S., and Karnieli, A. (2014) The effect of differential growth rates across plants on spectral predictions of physiological parameters. PLoS One 9 (2). doi: 10.1371/journal.pone.0088930.
   Web of Science ®Google Scholar
• Rapaport, T., Hochberg, U., Shoshany, M., Karnieli, A., and Rachmilevitch, S. (2015). Combining leaf physiology, hyperspectral imaging and partial least squares regression (PLS-R) for grapevine water status assessment. ISPRS J. Photogr. Remote Sens. 109: 88–97.
   Web of Science ®Google Scholar
• Workman, J. and Weyer, L. (2008) Practical guide to interpretive near-infrared spectroscopy. CRC Press Taylor and Francis Group, Boca Raton.
   Google Scholar