Remote sensing can provide good alternatives for traditional in situ water status measurements in orchard crops, such as stem water potential (Ψ stem). However, the heterogeneity of these cropping systems causes significant differences with regards to remote sensing products within one orchard and between orchards. In this study, robust spectral indicators of Ψ stem were sought after, independent of sensor viewing geometry, orchard architecture and management. To this end, Ψ stem was monitored throughout three consecutive growing seasons in (deficit) irrigated and rainfed pear orchards and related to spectral observations of leaves, canopies and WorldView-2 imagery. On a leaf and canopy level, high correlations were observed between the shortwave infrared reflectance and in situ measured Ψ stem. Additionally, for canopy measurements, visible and near-infrared wavelengths (R 530/R 600, R 530/R 700 and R 720/R 800) showed significant correlations. Therefore, the Red-edge Normalized Difference Vegetation Index (ReNDVI) was applied on fully sunlit satellite imagery and found strongly related with Ψ stem (R 2 = 0.47; RMSE = 0.36 MPa), undoubtedly showing the potential of WorldView-2 to monitor water stress in pear orchards. The relationship between ReNDVI and Ψ stem was independent of management, irrigation setup, phenology and environmental conditions. In addition, results showed that this relation was also independent of off-nadir viewing angle and almost independent of viewing geometry, as the correlation decreased after the inclusion of fully shaded scenes. With further research focusing on issues related to viewing geometry and shadows, high spatial water status monitoring with space borne remote sensing is achievable.
References
[1]
Janssens, P.; Deckers, T.; Elsen, F.; Elsen, A.; Schoofs, H.; Verjans, W.; Vandendriessche, H. Sensitivity of root pruned ‘Conference’ pear to water deficit in a temperate climate. Agric. Water Manag 2011, 99, 58–66.
Perry, E.M.; Dezzani, R.J.; Seavert, C.F.; Pierce, F.J. Spatial variation in tree characteristics and yield in a pear orchard. Precis. Agric 2010, 11, 42–60.
[4]
Dorigo, W.A.; Zurita-Milla, R.; de Wit, A.J.W.; Brazile, J.; Singh, R.; Schaepman, M.E. A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling. Int. J. Appl. Earth Obs. Geoinf 2007, 9, 165–193.
[5]
Govender, M.; Dye, P.J.; Weiersbye, I.M.; Witkowski, E.T.F.; Ahmed, F. Review of commonly used remote sensing and ground-based technologies to measure plant water stress. Water SA 2009, 35, 741–752.
[6]
Sepulcre-Canto, G.; Zarco-Tejada, P.J.; Jimenez-Munoz, J.C.; Sobrino, J.A.; Soriano, M.A.; Fereres, E.; Vega, V.; Pastor, M. Monitoring yield and fruit quality parameters in open-canopy tree crops under water stress. Implications for ASTER. Remote Sens. Environ 2007, 107, 455–470.
[7]
Dzikiti, S.; Verreynne, J.S.; Stuckens, J.; Strever, A.; Verstraeten, W.W.; Swennen, R.; Coppin, P. Determining the water status of Satsuma mandarin trees [Citrus Unshiu Marcovitch] using spectral indices and by combining hyperspectral and physiological data. Agric. For. Meteorol 2010, 150, 369–379.
[8]
Stuckens, J.; Verstraeten, W.W.; Delalieux, S.; Swennen, R.; Coppin, P. A dorsiventral leaf radiative transfer model: Development, validation and improved model inversion techniques. Remote Sens. Environ 2009, 113, 2560–2573.
[9]
Acevedo-Opazo, C.; Tisseyre, B.; Guillaume, S.; Ojeda, H. The potential of high spatial resolution information to define within-vineyard zones related to vine water status. Precis. Agric 2008, 9, 285–302.
[10]
Serrano, L.; González-Flor, C.; Gorchs, G. Assessment of grape yield and composition using the reflectance based Water Index in Mediterranean rainfed vineyards. Remote Sens. Environ 2012, 118, 249–258.
[11]
Naor, A.; Gal, Y.; Peres, M. The inherent variability of water stress indicators in apple, nectarine and pear orchards, and the validity of a leaf-selection procedure for water potential measurements. Irrig. Sci 2006, 24, 129–135.
[12]
McCutchan, H.; Shackel, K.A. Stem-water potential as a sensitive indicator of water stress in prune trees (Prunus domestica L. cv. French). J. Am. Soc. Hortic. Sci 1992, 117, 607–611.
[13]
Suárez, L.; Zarco-Tejada, P.J.; Sepulcre-Cantó, G.; Pérez-Priego, O.; Miller, J.R.; Jiménez-Mu？oz, J.C.; Sobrino, J. Assessing canopy PRI for water stress detection with diurnal airborne imagery. Remote Sens. Environ 2008, 112, 560–575.
[14]
Stagakis, S.; González-Dugo, V.; Cid, P.; Guillén-Climent, M.L.; Zarco-Tejada, P.J. Monitoring water stress and fruit quality in an orange orchard under regulated deficit irrigation using narrow-band structural and physiological remote sensing indices. ISPRS J. Photogramm. Remote Sens 2012, 71, 47–61.
[15]
Marshall, G.J.; Dowdeswell, J.A.; Rees, W.G. The spatial and temporal effect of cloud cover on the acquisition of high quality landsat imagery in the european arctic sector. Remote Sens. Environ 1994, 50, 149–160.
[16]
Moran, M.S.; Fitzgerald, G.J.; Rango, A.; Walthall, C.L.; Barnes, E.D.; Bausch, W.C.; Clarke, T.R.; Daughtry, C.S.; Everitt, J.H.; Hatfield, J.L.; et al. Sensor development and radiometric correction for agricultural applications. Photogramm. Eng. Remote Sens 2003, 69, 705–718.
[17]
Ceccato, P.; Flasse, S.; Grégoire, J.-M. Designing a spectral index to estimate vegetation water content from remote sensing data: Part 2. Validation and applications. Remote Sens. Environ 2002, 82, 198–207.
[18]
Jackson, R.D.; Idso, S.B.; Reginato, R.J.; Pinter, P.J., Jr. Canopy temperature as a crop water stress indicator. Water Resour. Res 1981, 17, 1133–1138.
[19]
Zhao, F.; Gu, X.; Verhoef, W.; Wang, Q.; Yu, T.; Liu, Q.; Huang, H.; Qin, W.; Chen, L.; Zhao, H. A spectral directional reflectance model of row crops. Remote Sens. Environ 2010, 114, 265–285.
[20]
Haboudane, D.; Miller, J.R.; Pattey, E.; Zarco-Tejada, P.J.; Strachan, I.B. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sens. Environ 2004, 90, 337–352.
[21]
Sansavini, S.; Musacchi, S. Canopy architecture, training and pruning in the Modern European pear orchards: An overview. Acta Hort 1994, 367, 152–172.
[22]
Allen, R.G.; Pereira, L.S.; Raes, D.; Smith, M. Crop Evapotranspiration—Guidelines for Computing Crop Water Requirements—FAO Irrigation and Drainage Paper 56; Food and Agriculture Organization of the United Nations: Rome, Italy, 1998.
[23]
Mitchell, P.D.; Jerie, P.H.; Chalmers, D.J. Effects of regulated water deficits on pear tree growth, flowering, fruit growth, and yield. J. Am. Soc. Hortic. Sci 1984, 109, 604–606.
[24]
Scholander, P.F.; Bradstreet, E.D.; Hemmingsen, E.A.; Hammel, H.T. Sap pressure in vascular plants. Science 1965, 148, 339–346.
[25]
Begg, J.E.; Turner, N.C. Water potential gradients in field tobacco. Plant Physiol 1970, 46, 343–346.
[26]
Savitsky, A.; Golay, M.J.E. Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem 1964, 36, 1627–1639.
[27]
Radiometric Use of WorldView-2 Imagery. Available online: http://www.digitalglobe.com/sites/default/files/Radiometric_Use_of_WorldView-2_Imagery%20%281%29.pdf (accessed on 3 December 2013).
[28]
Adler-Golden, S.; Berk, A.; Bernstein, L.S.; Richtsmeier, S.; Acharya, P.K.; Matthew, M.W.; Anderson, G.P.; Allred, C.L.; Jeong, L.S.; Chetwynd, J.H. Flaash, a Modtran4 Atmospheric Correction Package for Hyperspectral Data Retrievals and Simulations. In Summaries of the Seventh JPL Airborne Earth Science Workshop January 12–16, 1998; Jet Propulsion Laboratory, National Aeronautics and Space Administration: Pasadena, CA, USA, 1998.
[29]
Grodecki, J.; Dial, G. Block adjustment of high-resolution satellite images described by rational polynomials. Photogramm. Eng. Remote Sens. 2003, 69, doi:10.14358/PERS.69.1.59.
[30]
Araujo, F.; Williams, L.E.; Grimes, D.W.; Matthews, M.A. A comparative study of young ‘Thompson Seedless’ grapevines under drip and furrow irrigation. I. Root and soil water distributions. Sci. Hortic 1995, 60, 235–249.
[31]
Fernandez, J.E.; Moreno, F.; Cabrera, F.; Arrue, J.L.; Mart？n-Aranda, J. Drip irrigation, soil characteristics and the root distribution and root activity of olive trees. Plant Soil 1991, 133, 239–251.
[32]
van Leeuwen, W.J.D.; Orr, B.J.; Marsh, S.E.; Herrmann, S.M. Multi-sensor NDVI data continuity: Uncertainties and implications for vegetation monitoring applications. Remote Sens. Environ 2006, 100, 67–81.
[33]
Blackburn, G.A. Hyperspectral remote sensing of plant pigments. J. Exp. Bot 2007, 58, 855–67.
[34]
Ceccato, P.; Flasse, S.; Tarantola, S.; Jacquemoud, S.; Grégoire, J.-M. Detecting vegetation leaf water content using reflectance in the optical domain. Remote Sens. Environ 2001, 77, 22–33.
[35]
Gao, B. NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ 1996, 58, 257–266.
[36]
Hunt, E.R., Jr.; Rock, B.N. Detection of changes in leaf water content using Near- and Middle-infrared reflectances. Remote Sens. Environ 1989, 30, 43–54.
[37]
Danson, F.M.; Steven, M.D.; Malthus, T.J.; Clark, J.A. High-spectral resolution data for determining leaf water content. Int. J. Remote Sens 1992, 13, 461–470.
[38]
Mogensen, V.O.; Jensen, C.R.; Mortensen, G.; Thage, J.H.; Koribidis, J.; Ahmed, A. Spectral reflectance index as an indicator of drought of field grown oilseed rape (Brassica napus L.). Eur. J. Agron 1996, 5, 125–135.
[39]
Gamon, J.A.; Serrano, L.; Surfus, J.S. The photochemical reflectance index: An optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels. Oecologia 1997, 112, 492–501.
[40]
Blackburn, G.A. Relationships between spectral reflectance and pigment concentrations in stacks of deciduous broadleaves. Remote Sens. Environ 1999, 70, 224–237.
[41]
Horler, D.N.H.; Dockray, M.; Barber, J. The red edge of plant reflectance. Int. J. Remote Sens 1983, 4, 273–288.
[42]
Stagakis, S.; Markos, N.; Sykioti, O.; Kyparissis, A. Monitoring canopy biophysical and biochemical parameters in ecosystem scale using satellite hyperspectral imagery: An application on a Phlomis fruticosa Mediterranean ecosystem using multiangular CHRIS/PROBA observations. Remote Sens. Environ 2010, 114, 977–994.
[43]
Lakso, A.N. The effects of water stress on physiological processes in fruit crops. Acta Hort 1984, 171, 275–290.
[44]
Guerfel, M.; Baccouri, O.; Boujnah, D.; Cha？bi, W.; Zarrouk, M. Impacts of water stress on gas exchange, water relations, chlorophyll content and leaf structure in the two main Tunisian olive (Olea europaea L.) cultivars. Sci. Hortic 2009, 119, 257–263.
[45]
Haas, R.H.; Schell, J.A. Monitoring the Vernal Advancement and Retrogradation (greenwave Effect) of Natural Vegetation; Remote Sensing Center, Texas A & M University: Denton, TX, USA, 1974.
[46]
Penuelas, J.; Filella, I.; Biel, C.; Serrano, L.; Savé, R. The reflectance at the 950–970 nm region as an indicator of plant water status. Int. J. Remote Sens. 1993, 14, 1887–1905.
[47]
Zarco-Tejada, P.J.; Miller, J.R.; Morales, A.; Berjón, A.; Agüera, J. Hyperspectral indices and model simulation for chlorophyll estimation in open-canopy tree crops. Remote Sens. Environ 2004, 90, 463–476.
[48]
Somers, B.; Delalieux, S.; Verstraeten, W.; Coppin, P. A conceptual framework for the simultaneous extraction of sub-pixel spatial extent and spectral characteristics of crops. Photogramm. Eng. Remote Sens 2009, 75, 57–68.
[49]
Tits, L.; Somers, B.; Stuckens, J.; Farifteh, J.; Coppin, P. Integration of in situ measured soil status and remotely sensed hyperspectral data to improve plant production system monitoring: Concept, perspectives and limitations. Remote Sens. Environ 2013, 128, 197–211.
[50]
Iordache, D.-M.; Tits, L.; Plaza, A.; Bioucas-Dias, J.; Somers, B. A dynamic unmixing framework for site specific monitoring of plant production systems. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2013. submitted.
