Detecting water stress effects on fruit quality in orchards with time-series PRI airborne imagery

article A methodology for the assessment of fruit quality in crops subjected to different irrigation regimes is presented. Highspatialresolutionmultispectralandthermalairborneimagerywereusedtomonitorcrowntemperatureand the Photochemical Reflectance Index (PRI) over three commercial orchards comprising peach, nectarine and orange fruit trees during 2008. Irrigation regimes included sustained and regulated deficit irrigation strategies, leading to high variability of fruit quality at harvest. Stem water potential was used to monitor individual tree water status on each study site. Leaf samples were collected for destructive sampling of xanthophyll pigments to assess the relationship between the xanthophyll epoxidation state (EPS) and PRI at leaf and airborne-canopy level. At harvest, fruit size, Total Soluble Solids (TSS) and Tritatable Acidity (TA) were measured to characterize fruit quality. A statistically significant relationship between EPS and PRI was found at the leaf (r 2 =0.81) and

[1]  O. Björkman,et al.  Leaf Xanthophyll content and composition in sun and shade determined by HPLC , 1990, Photosynthesis Research.

[2]  W. Oechel,et al.  Parallel adjustments in vegetation greenness and ecosystem CO2 exchange in response to drought in a Southern California chaparral ecosystem , 2006 .

[3]  P. Zarco-Tejada,et al.  Modelling PRI for water stress detection using radiative transfer models , 2009 .

[4]  F. Baret,et al.  Potentials and limits of vegetation indices for LAI and APAR assessment , 1991 .

[5]  Pablo J. Zarco-Tejada,et al.  Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Javier Abadía,et al.  Fe Resupply to Fe-deficient Sugar Beet Plants Leads to Rapid Changes in the Violaxanthin Cycle and other Photosynthetic Characteristics without Significant de novo Chlorophyll Synthesis , 2004, Photosynthesis Research.

[7]  Philip N. Slater,et al.  Discrimination of growth and water stress in wheat by various vegetation indices through clear and turbid atmospheres , 1983 .

[8]  John R. Miller,et al.  Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture , 2002 .

[9]  Pablo J. Zarco-Tejada,et al.  Assessing Canopy PRI for Water Stress detection with Diurnal Airborne Imagery , 2008 .

[10]  D. Lamb,et al.  Using remote sensing to predict grape phenolics and colour at harvest in a Cabernet Sauvignon vineyard: Timing observations against vine phenology and optimising image resolution , 2008 .

[11]  J. Peñuelas,et al.  Relationship between photosynthetic radiation-use efficiency of barley canopies and the photochemical reflectance index (PRI) , 1996 .

[12]  Craig M. Trotter,et al.  Estimating photosynthetic light-use efficiency using the photochemical reflectance index: variations among species. , 2004, Functional plant biology : FPB.

[13]  Pablo J. Zarco-Tejada,et al.  Monitoring yield and fruit quality parameters in open-canopy tree crops under water stress. Implications for ASTER , 2007 .

[14]  D. Whitehead,et al.  The photochemical reflectance index as a measure of photosynthetic light use efficiency for plants with varying foliar nitrogen contents , 2002 .

[15]  H. Jones,et al.  Combining thermal and visible imagery for estimating canopy temperature and identifying plant stress. , 2004, Journal of experimental botany.

[16]  S. Idso,et al.  Normalizing the stress-degree-day parameter for environmental variability☆ , 1981 .

[17]  A. Huete,et al.  A Modified Soil Adjusted Vegetation Index , 1994 .

[18]  G.,et al.  Estimation of Total Above-Ground Phytomass Production Using Remotely Sensed Data * , 2002 .

[19]  T. A. Black,et al.  Remote sensing of photosynthetic-light-use efficiency of boreal forest , 2000 .

[20]  Rasmus Fensholt,et al.  Remote Sensing , 2008, Encyclopedia of GIS.

[21]  M. Umeda,et al.  Assessment of the water status of mandarin and peach canopies using visible multispectral imagery , 2008 .

[22]  D. C. Williams,et al.  REMOTE SENSING OF VINEYARD MANAGEMENT ZONES: IMPLICATIONS FOR WINE QUALITY , 2001 .

[23]  Pablo J. Zarco-Tejada,et al.  Discriminating irrigated and rainfed olive orchards with thermal ASTER imagery and DART 3D simulation. , 2009 .

[24]  J. Girona,et al.  Peach tree response to single and combined regulated deficit irrigation regimes under shallow soils , 2003 .

[25]  C. Crisosto,et al.  Segregation of peach and nectarine (Prunus persica (L.) Batsch) cultivars according to their organoleptic characteristics , 2006 .

[26]  P. Newton,et al.  Initial observations of increased requirements for light-energy dissipation in ryegrass (Lolium perenne) when source / sink ratios become high at a naturally grazed free air CO2 enrichment (FACE) site. , 2006, Functional plant biology : FPB.

[27]  Josep Peñuelas,et al.  Assessing forest structure and function from spectral transmittance measurements: a case study in a Mediterranean holm oak forest. , 2005, Tree physiology.

[28]  P. North,et al.  Remote sensing of canopy light use efficiency using the photochemical reflectance index , 2001 .

[29]  D. Chalmers,et al.  Control of peach tree growth and productivity by regulated water supply, tree density, and summer pruning [Trickle irrigation] , 1981 .

[30]  B. Myers.  Water stress integral-a link between short-term stress and long-term growth. , 1988, Tree physiology.

[31]  Ismael Moya,et al.  Photochemistry, remotely sensed physiological reflectance index and de-epoxidation state of the xanthophyll cycle in Quercus coccifera under intense drought , 2008, Oecologia.

[32]  Hiroyuki Oguma,et al.  Seasonal changes in the relationship between photochemical reflectance index and photosynthetic light use efficiency of Japanese larch needles , 2006 .

[33]  T. A. Black,et al.  A MODIS-derived photochemical reflectance index to detect inter-annual variations in the photosynthetic light-use efficiency of a boreal deciduous forest , 2005 .

[34]  J. Castel,et al.  Regulated deficit irrigation in `Clementina de Nules' citrus trees. I. Yield and fruit quality effects , 1999 .

[35]  B. Clothier,et al.  Plant Water Status and Fruit Quality in `Braeburn' Apples , 1994 .

[36]  T. Winkel,et al.  The Photochemical Reflectance Index (PRI) as a water-stress index , 2002 .

[37]  Jordi Marsal,et al.  Effects of water deficit during stage II of peach fruit development and postharvest on fruit quality and ethylene production , 2003 .

[38]  Uwe Rascher,et al.  Assessing photosynthetic efficiency in an experimental mangrove canopy using remote sensing and chlorophyll fluorescence , 2005, Trees.

[39]  C. Ginestar,et al.  Responses of young clementine citrus trees to water stress during different phenological periods , 1996 .

[40]  C. Field,et al.  A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency , 1992 .

[41]  J. Gamon,et al.  The photochemical reflectance index: an optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels , 1997, Oecologia.

[42]  J. Girona.  REGULATED DEFICIT IRRIGATION IN PEACH. A GLOBAL ANALYSIS , 2002 .

[43]  Jordi Marsal,et al.  Relationship between Leaf Water Potential and Gas Exchange Activity at Different Phenological Stages and Fruit Loads in Peach Trees , 1997 .

[44]  F. Veihmeyer.  Some factors affecting the irrigation requirements of deciduous orchards , 1927 .

[45]  C. V. M. Bartona,et al.  Remote sensing of canopy light use efficiency using the photochemical reflectance index Model and sensitivity analysis , 2000 .

[46]  E. Fereres,et al.  Deficit irrigation for reducing agricultural water use. , 2006, Journal of experimental botany.

[47]  Sherwood B. Idso,et al.  Non-water-stressed baselines: A key to measuring and interpreting plant water stress , 1982 .

[48]  Peter R. J. North,et al.  Three-dimensional forest light interaction model using a Monte Carlo method , 1996, IEEE Trans. Geosci. Remote. Sens..

[49]  Roberta E. Martin,et al.  Substrate age and precipitation effects on Hawaiian forest canopies from spaceborne imaging spectroscopy , 2005 .

[50]  Carlos H. Crisosto,et al.  Irrigation Regimes Affect Fruit Soluble Solids Concentration and Rate of Water Loss of `O'Henry' Peaches , 1994 .

[51]  R. Nemani,et al.  Mapping vineyard leaf area with multispectral satellite imagery , 2003 .

[52]  F. J. García-Haro,et al.  A generalized soil-adjusted vegetation index , 2002 .

[53]  A. García-Luís,et al.  Increasing fruit size in Citrus. Thinning and stimulation of fruit growth , 2000, Plant Growth Regulation.

[54]  Caroline J. Nichol,et al.  Remote sensing of photosynthetic-light-use efficiency of a Siberian boreal forest , 2002 .

[55]  L. Barton,et al.  Iron chelation in plants and soil microorganisms , 1994 .

[56]  S. Idso.  Humidity measurement by infrared thermometry , 1982 .

[57]  S. Idso,et al.  Canopy temperature as a crop water stress indicator , 1981 .

[58]  G. Rondeaux,et al.  Optimization of soil-adjusted vegetation indices , 1996 .

[59]  John A. Gamon,et al.  Mapping carbon and water vapor fluxes in a chaparral ecosystem using vegetation indices derived from AVIRIS , 2006 .

[60]  J. Abadía,et al.  Iron and plant pigments , 1993 .

[61]  Pablo J. Zarco-Tejada,et al.  Simple reflectance indices track heat and water stress-induced changes in steady-state chlorophyll fluorescence at the canopy scale , 2005 .

[62]  Y. Cohen,et al.  Estimation of leaf water potential by thermal imagery and spatial analysis. , 2005, Journal of experimental botany.

[63]  F. Baret,et al.  PROSPECT: A model of leaf optical properties spectra , 1990 .

[64]  M. Génard,et al.  Modeling the response of peach fruit growth to water stress. , 1996, Tree physiology.

[65]  Ismael Moya,et al.  A new instrument for passive remote sensing: 2. Measurement of leaf and canopy reflectance changes at 531 nm and their relationship with photosynthesis and chlorophyll fluorescence , 2004 .

[66]  Elizabeth Pattey,et al.  Impact of nitrogen and environmental conditions on corn as detected by hyperspectral reflectance , 2002 .

[67]  A. Huete.  A soil-adjusted vegetation index (SAVI) , 1988 .

[68]  Patricia Sánchez-de-Miguel,et al.  Water relations between leaf water potential, photosynthesis and agronomic vine response as a tool for establishing thresholds in irrigation scheduling , 2007 .