Ground-Based Hyperspectral Remote Sensing for Estimating Water Stress in Tomato Growth in Sandy Loam and Silty Loam Soils

Drought and water scarcity due to global warming, climate change, and social development have been the most death-defying threat to global agriculture production for the optimization of water and food security. Reflectance indices obtained by an Analytical Spectral Device (ASD) Spec 4 hyperspectral spectrometer from tomato growth in two soil texture types exposed to four water stress levels (70–100% FC, 60–70% FC, 50–60% FC, and 40–50% FC) was deployed to schedule irrigation and management of crops’ water stress. The treatments were replicated four times in a randomized complete block design (RCBD) in a 2 × 4 factorial experiment. Water stress treatments were monitored with Time Domain Reflectometer (TDR) every 12 h before and after irrigation to maintain soil water content at the desired (FC%). Soil electrical conductivity (Ec) was measured daily throughout the growth cycle of tomatoes in both soil types. Ec was revealing a strong correlation with water stress at R2 above 0.95 p < 0.001. Yield was measured at the end of the end of the growing season. The results revealed that yield had a high correlation with water stress at R2 = 0.9758 and 0.9816 p < 0.01 for sandy loam and silty loam soils, respectively. Leaf temperature (LT °C), relative leaf water content (RLWC), leaf chlorophyll content (LCC), Leaf area index (LAI), were measured at each growth stage at the same time spectral reflectance data were measured throughout the growth period. Spectral reflectance indices used were grouped into three: (1) greenness vegetative indices; (2) water overtone vegetation indices; (3) Photochemical Reflectance Index centered at 570 nm (PRI570), and normalized PRI (PRInorm). These reflectance indices were strongly correlated with all four water stress indicators and yield. The results revealed that NDVI, RDVI, WI, NDWI, NDWI1640, PRI570, and PRInorm were the most sensitive indices for estimating crop water stress at each growth stage in both sandy loam and silty loam soils at R2 above 0.35. This study recounts the depth of 858 to 1640 nm band absorption to water stress estimation, comparing it to other band depths to give an insight into the usefulness of ground-based hyperspectral reflectance indices for assessing crop water stress at different growth stages in different soil types.

[1]  V. Cantore,et al.  The Application of Ground-Based and Satellite Remote Sensing for Estimation of Bio-Physiological Parameters of Wheat Grown Under Different Water Regimes , 2020, Water.

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

[3]  M. A. Aziz The effect of Si on minimizing the implications of water stress on tomato plants , 2020 .

[4]  F. Zhang,et al.  Estimation of vegetation water content using hyperspectral vegetation indices: a comparison of crop water indicators in response to water stress treatments for summer maize , 2019, BMC Ecology.

[5]  Jack Watson,et al.  Leaf Thickness and Electrical Capacitance as Measures of Plant Water Status , 2017 .

[6]  A. P. Annan,et al.  Electromagnetic determination of soil water content: Measurements in coaxial transmission lines , 1980 .

[7]  J. Nagy,et al.  Relationships between stomatal behaviour, spectral traits and water use and productivity of green peas (Pisum sativum L.) in dry seasons , 2015, Acta Physiologiae Plantarum.

[8]  J. Dolata,et al.  Down-regulation of CBP80 gene expression as a strategy to engineer a drought-tolerant potato. , 2013, Plant biotechnology journal.

[9]  L. Helyes,et al.  Effect of water supply on the water use-related physiological traits and yield of snap beans in dry seasons , 2018, Irrigation Science.

[10]  Chandra A. Madramootoo,et al.  Sensitivity of spectral vegetation indices for monitoring water stress in tomato plants , 2019, Comput. Electron. Agric..

[11]  Jana Zinkernagel,et al.  New technologies and practical approaches to improve irrigation management of open field vegetable crops , 2020 .

[12]  Jia Lu,et al.  Yield, fruit quality and water use efficiency of tomato for processing under regulated deficit irrigation: A meta-analysis , 2019, Agricultural Water Management.

[13]  J. Peñuelas,et al.  The reflectance at the 950–970 nm region as an indicator of plant water status , 1993 .

[14]  A. Viña,et al.  Remote estimation of canopy chlorophyll content in crops , 2005 .

[15]  Quazi K. Hassan,et al.  Remote sensing of agricultural drought monitoring: A state of art review , 2016 .

[16]  Josep Peñuelas,et al.  The photochemical reflectance index (PRI) and the remote sensing of leaf, canopy and ecosystem radiation use efficiencies: A review and meta-analysis , 2011 .

[17]  L. Helyes,et al.  Effect of irrigation on yield parameters and antioxidant profiles of processing cherry tomato , 2014, Central European Journal of Biology.

[18]  Timothy J. Arkebauer,et al.  Leaf Radiative Properties and the Leaf Energy Budget , 2005 .

[19]  Chunlei Chen,et al.  One-step reverse transcription loop-mediated isothermal amplification assay for rapid detection of melon yellow spot virus , 2015, European Journal of Plant Pathology.

[20]  Qin Zhang,et al.  Automatic irrigation scheduling of apple trees using theoretical crop water stress index with an innovative dynamic threshold , 2015, Comput. Electron. Agric..

[21]  Hiba Shaghaleh,et al.  Impact of alternative wetting and soil drying and soil clay content on the morphological and physiological traits of rice roots and their relationships to yield and nutrient use-efficiency , 2019, Agricultural Water Management.

[22]  Josep Peñuelas,et al.  A remotely sensed pigment index reveals photosynthetic phenology in evergreen conifers , 2016, Proceedings of the National Academy of Sciences.

[23]  Walter C. Bausch,et al.  Estimating corn nitrogen status using ground-based and satellite multispectral data , 2004, SPIE Optics + Photonics.

[24]  David Pommerenke,et al.  Measurement of Dielectric Constant and Cross-Sectional Variations of a Wire , 2018, IEEE Transactions on Instrumentation and Measurement.

[25]  Giuseppina Monti,et al.  TDR-based monitoring of rising damp through the embedding of wire-like sensing elements in building structures , 2017 .

[26]  Y. Dian,et al.  Hyperspectral sensing of photosynthesis, stomatal conductance, and transpiration for citrus tree under drought condition , 2021, bioRxiv.

[27]  Li Wang,et al.  Tomato yield and water use efficiency change with various soil moisture and potassium levels during different growth stages , 2019, PloS one.

[28]  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.

[29]  Chaolei Zheng,et al.  Best hyperspectral indices for tracing leaf water status as determined from leaf dehydration experiments , 2015 .

[30]  Wenting Han,et al.  Mapping Maize Water Stress Based on UAV Multispectral Remote Sensing , 2019, Remote. Sens..

[31]  Indirect method for measurement of leaf area and leaf area index of soilless cucumber crop , 2018 .

[32]  Xu Xu,et al.  Effects of water stress on processing tomatoes yield, quality and water use efficiency with plastic mulched drip irrigation in sandy soil of the Hetao Irrigation District , 2017 .

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

[34]  T. S. F. Silva,et al.  Accuracy and limitations for spectroscopic prediction of leaf traits in seasonally dry tropical environments , 2020, Remote Sensing of Environment.

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

[36]  Determination of soil water content using time domain reflectometer (TDR) for clayey soil , 2018 .

[37]  Josep Peñuelas,et al.  Photochemical Reflectance Index (PRI) for Detecting Responses of Diurnal and Seasonal Photosynthetic Activity to Experimental Drought and Warming in a Mediterranean Shrubland , 2017, Remote. Sens..

[38]  Thomas Udelhoven,et al.  Challenges and Future Perspectives of Multi-/Hyperspectral Thermal Infrared Remote Sensing for Crop Water-Stress Detection: A Review , 2019, Remote. Sens..

[39]  B. Gao NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space , 1996 .

[40]  Saleh Taghvaeian,et al.  Infrared Thermometry to Estimate Crop Water Stress Index and Water Use of Irrigated Maize in Northeastern Colorado , 2012, Remote. Sens..

[41]  Anatoly A. Gitelson,et al.  MODIS-based corn grain yield estimation model incorporating crop phenology information , 2013 .

[42]  Uwe Spank,et al.  Comparison of satellite- and ground-based NDVI above different land-use types , 2009 .

[43]  J. L. Chávez,et al.  Improvement in estimation of soil water deficit by integrating airborne imagery data into a soil water balance model , 2017 .

[44]  Heikki Saari,et al.  Processing and Assessment of Spectrometric, Stereoscopic Imagery Collected Using a Lightweight UAV Spectral Camera for Precision Agriculture , 2013, Remote. Sens..

[45]  J. Ruíz,et al.  Antioxidant content and ascorbate metabolism in cherry tomato exocarp in relation to temperature and solar radiation , 2006 .

[46]  Susan L. Ustin,et al.  Remotely sensed estimates of crop water demand , 2004, SPIE Optics + Photonics.

[47]  M. Blanke,et al.  Effects of flooding and drought on stomatal activity, transpiration, photosynthesis, water potential and water channel activity in strawberry stolons and leaves , 2004, Plant Growth Regulation.

[48]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .

[49]  L. Helyes,et al.  Different water supply and stomatal conductance correlates with yield quantity and quality parameters , 2013 .

[50]  M. V. Folegatti,et al.  A new method for estimating the leaf area index of cucumber and tomato plants , 2003 .

[51]  Florent Mouillot,et al.  Regional Equivalent Water Thickness Modeling from Remote Sensing across a Tree Cover/LAI Gradient in Mediterranean Forests of Northern Tunisia , 2015, Remote. Sens..

[52]  T. Jackson,et al.  Vegetation water content estimation for corn and soybeans using spectral indices derived from MODIS near- and short-wave infrared bands , 2005 .

[53]  John A Gamon,et al.  Three causes of variation in the photochemical reflectance index (PRI) in evergreen conifers. , 2015, The New phytologist.

[54]  L. Helyes,et al.  Physiological Factors and their Relationship with the Productivity of Processing Tomato under Different Water Supplies , 2019, Water.

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

[56]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

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

[58]  M. Andersen,et al.  Tomato yield and water use efficiency – coupling effects between growth stage specific soil water deficits , 2015 .

[59]  Lav R. Khot,et al.  High Resolution Multispectral and Thermal Remote Sensing-Based Water Stress Assessment in Subsurface Irrigated Grapevines , 2017, Remote. Sens..

[60]  C. Patané,et al.  Effects of deficit irrigation on biomass, yield, water productivity and fruit quality of processing tomato under semi-arid Mediterranean climate conditions , 2011 .

[61]  E. Costes,et al.  Stomatal regulation of photosynthesis in apple leaves: evidence for different water-use strategies between two cultivars. , 2007, Annals of botany.

[62]  L. C. Purcell,et al.  Aerial canopy temperature differences between fast‐ and slow‐wilting soya bean genotypes , 2018 .

[63]  Kai Wang,et al.  Improved TDR Method for Quality Control of Soil-Nailing Works , 2016 .

[64]  Abdulkhaliq. A. AL-Shoaibi,et al.  Effect of water stress on growth and water use efficiency (WUE) of some wheat cultivars (Triticum durum) grown in Saudi Arabia. , 2010 .

[65]  J. Louis,et al.  REMOTE-SENSING-BASED BIOPHYSICAL MODELS FOR ESTIMATING LAI OF IRRIGATED CROPS IN MURRY DARLING BASIN , 2012 .

[66]  D. Mulla Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps , 2013 .

[67]  P.J. Zarco-Tejada,et al.  Modeling canopy water content for carbon estimates from MODIS data at land EOS validation sites , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).

[68]  S. Sánchez-Ruiz,et al.  Combining SMOS with visible and near/shortwave/thermal infrared satellite data for high resolution soil moisture estimates , 2014 .

[69]  M. J. Reigosa,et al.  Phenotypic plasticity and acclimation to water deficits in velvet-grass: a long-term greenhouse experiment. Changes in leaf morphology, photosynthesis and stress-induced metabolites , 2000 .

[70]  L. Helyes,et al.  Influence of Water Stress Levels on the Yield and Lycopene Content of Tomato , 2020, Water.

[71]  A COMPARISON OF THE GRAVIMETRIC AND TDR METHODS IN TERMS OF DETERMINING THE SOIL WATER CONTENT OF THE CORN PLANT , 2016 .