Sensitivity of spectral vegetation indices for monitoring water stress in tomato plants

Abstract Innovations in irrigation water management are required to optimize agricultural water use in water stressed regions of the world, and physiological response of plants to water stress is an important criterion. Remotely sensed plant stress indicators, based on the visible and near-infrared spectral regions, provide an alternative to traditional field measurements of plant stress parameters, as this provides information about the spatial and temporal variability of crops and soil. The present study is a proof of concept on the feasibility of using narrow-band hyperspectral derived indices for monitoring water stress in tomato plants (Solanum Lycopersicum L.). Spectral reflectance data were acquired from tomato plants, with five different irrigation regimes namely 100, 80, 60, 40, and 20% of plant available water, in a completely randomized design. Also, plant water stress indicators including canopy temperature (Tc) and relative leaf water content (RWC), as well as volumetric soil moisture content (SMC) were concurrently measured with spectral data acquisition. Normalized Difference Vegetation Index (NDVI), Renormalized Difference Vegetation Index (RDVI), Optimized Soil Adjusted Vegetation Index (OSAVI), Photochemical Reflectance Index centered at 570 nm (PRI 570 ), normalized PRI (PRI norm ), Water Index (WI), and Normalized Water Index (NWI) were computed from the spectral data. The relationships between canopy reflectance and water stress indicators were analyzed at different water stress levels. The result showed that the PRI centered at 550 nm wavelength (PRI 550 ), WI, OSAVI, and WI/NDVI were the most sensitive indices to distinguish water stress levels in tomato plants. This study provides an insight into the feasibility of using spectral vegetation indices to monitor water stress in tomato crops for precision irrigation water management.

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

[2]  Jan U.H. Eitel,et al.  Response of high frequency Photochemical Reflectance Index (PRI) measurements to environmental conditions in wheat , 2016 .

[3]  Chandra A. Madramootoo,et al.  Thresholds for Irrigation Management of Processing Tomatoes Using Soil Moisture Sensors in Southwestern Ontario , 2013 .

[4]  M. S. Moran,et al.  Canopy temperature variability as an indicator of crop water stress severity , 2006, Irrigation Science.

[5]  R. Tamborrino,et al.  Discrimination of tomato plants under different irrigation regimes: analysis of hyperspectral sensor data , 2015 .

[6]  A. Elvanidi,et al.  Reflectance indices for the detection of water stress in greenhouse tomato (Solanum lycopersicum) , 2016 .

[7]  Paul D. Colaizzi,et al.  A crop water stress index and time threshold for automatic irrigation scheduling of grain sorghum , 2012 .

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

[9]  J. Flexas,et al.  UAVs challenge to assess water stress for sustainable agriculture , 2015 .

[10]  P. Zarco-Tejada,et al.  Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera , 2012 .

[11]  C. Madramootoo,et al.  Crop reflectance indices for mapping water stress in greenhouse grown bell pepper , 2019, Agricultural Water Management.

[12]  N. R. Patel,et al.  Monitoring of water stress in wheat using multispectral indices derived from Landsat-TM , 2016 .

[13]  John A. Gamon,et al.  Reviews and Syntheses: optical sampling of the flux tower footprint , 2015 .

[14]  Gengxing Zhao,et al.  Improving the Accuracy of the Hyperspectral Model for Apple Canopy Water Content Prediction using the Equidistant Sampling Method , 2017, Scientific Reports.

[15]  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.

[16]  P. Zarco-Tejada,et al.  A PRI-based water stress index combining structural and chlorophyll effects: Assessment using diurnal narrow-band airborne imagery and the CWSI thermal index , 2013 .

[17]  Stefano Amaducci,et al.  Fluorescence, PRI and canopy temperature for water stress detection in cereal crops , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[18]  Ray D. Jackson,et al.  Remote sensing for agricultural water management and crop yield prediction , 1977 .

[19]  A. Elvanidi,et al.  Crop water status assessment in controlled environment using crop reflectance and temperature measurements , 2017, Precision Agriculture.

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

[21]  Ni Guo,et al.  Determining the Canopy Water Stress for Spring Wheat Using Canopy Hyperspectral Reflectance Data in Loess Plateau Semiarid Regions , 2015 .

[22]  M. Kacira,et al.  Crop reflectance monitoring as a tool for water stress detection in greenhouses: A review , 2016 .

[23]  Chandra A. Madramootoo,et al.  Recent advances in crop water stress detection , 2017, Comput. Electron. Agric..

[24]  Y. Erdem,et al.  DETERMINATION OF WATER STRESS INDEX IN SUNFLOWER / DETERMINACIÓN DEL ÍNDICE DE ESTRÉS HÍDRICO PARA GIRASOL / DÉTERMINATION DE L’INDEX DU STRESS DÛ AU MANQUE D’HUMIDITÉ POUR LE TOURNESOL , 2002 .

[25]  Eyüp Selim Köksal,et al.  Hyperspectral reflectance data processing through cluster and principal component analysis for estimating irrigation and yield related indicators , 2011 .

[26]  R. Sahoo,et al.  Characterization of water stress and prediction of yield of wheat using spectral indices under varied water and nitrogen management practices , 2014 .

[27]  H. Jones.  Irrigation scheduling: advantages and pitfalls of plant-based methods. , 2004, Journal of experimental botany.

[28]  Stefano Amaducci,et al.  Assessing canopy PRI from airborne imagery to map water stress in maize , 2013 .

[29]  N. Katsoulas,et al.  The effect of covering material on the yield, quality and chemical composition of greenhouse-grown tomato fruit. , 2019, Journal of the science of food and agriculture.

[30]  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.

[31]  J. Roujean,et al.  Estimating PAR absorbed by vegetation from bidirectional reflectance measurements , 1995 .

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

[33]  J. Peñuelas,et al.  Estimation of plant water concentration by the reflectance Water Index WI (R900/R970) , 1997 .

[34]  P. Johnstone,et al.  Processing Tomato Yield and Fruit Quality Improved with Potassium Fertigation , 2005 .