Evaluation of soil and vegetation salinity in crops lands using reflectance spectroscopy. Study cases : cotton crops and tomato plants Goldshleger Naftaly -Soil erosion research station Tel Aviv University-Alexandra Chudnovsky Seventh Workshop On Global Food Security-Support Analysis Data

Poor quality of irrigation water Low Groundwater level Poor drainage Groundwater level rises Artzim Pressure Low amount of precipitation Causes of Salinity

UV I n f r a - r e d FIR/TIR NI R MI R % Reflectance Wavelengths (  m ) Visible range Spectral reflectance curves of vegetation under stress wilting

The problem Crops are damaged as a result of soil salination and its very important to intentify the damage in real time. It is important to identify and to map areas affected by soil salinity. Missing of low cost spatial fast tool for mapping and salting soils and crops.

GPS Trimble 5700 BY Using remote sensing tool can identify salinity areas Combination of remote sensing tools active and passive allows to obtain a good spatial salinity of surface and sub-soil vegetation

Comparison of our spectrum data to the laboratory spectrum data for Gypsum and Halite 1450nm 1480nm 1550nm 1750nm 2200nm 1950nm Important absorption in relation to air-bone sensor Halite Spectrum

Main objectives Characterization of soil qualities' in different salinity environments and linking salty content of tomato leaves and cotton as a basis for other agricultural crops. Soil salinity assessment based on the reflectance spectrum of cotton and tomato crops. Building generic models for soils and vegetation salinity Created the basis for relationship between salinity in the soil and crops using airborne and satellites sensors.

explanation In this study we investigated the influence of using the whole spectral region as well as selecting individual wavelengths to generate an optimal PLS model to predict salinity in the tomato vegetation.

Material and methods Research area

Conventional Methods Conventional methods of monitoring changes in soil salinity are based on field observation and laboratory analyses: EC (electrical conductivity) of the soil solution also of the ESP (exchangeable sodium percentage) and pH and Na and Cl content. This method is time-consuming, expensive and Spatially limited..

Methods leaves measurements Chemistry : - Na and Cl content of tomato plants - Na and Cl content in Soil -Na and Cl content of cotton - Na and Cl content in the soil vicinity Na (%) and Cl (Meq/l) for the soil samples, and Na (%) and Cl (Meq/l) for the tomato and cotton leaves. Spectral measurements: - Vegetation, Soil surface. Using a portable spectrometer type of ASD (Analytical Spectral Device) as a platform for spectral reflectance measurements.

Spectral measurements The spectral measurements is done by Spectrometer(ASD) ASD מסוג, בת

Model In order to determine the salinity content we use complex statistical methods based on multivariate regression type : When Y is a chemical measurement (Na and Cl), A- coefficient for return specific spectral wavelength and X spectral data. Here, we are looking at the relationship between the measured reflectance(Depended) and Chemical measurements(In depended). The final product is a development model that's predict salinity from the spectral measurement.

Two stages: a model and prediction

Chemometric Approach Modeling Reference method: Na, Cl in tomato leaves NIR Spectral Measurements of soil Calibration/Validation: 56 samples External Set: 16 samples Establishing the PLS model: Cross- Validation (Leave-one- out) Prediction: Assessing the prediction accuracy Tomato Leaves: General Methodology Flowchart of data division for calibration and prediction sets:

Chemometric Approach Modeling Reference method: Na, Cl in soil NIR Spectral Measurements of cotton crops Calibration Set: 33 samples One Data Set : Flowchart of data division for calibration and prediction sets: Cotton crops: General Methodology Establishing the PLS model: Cross-Validation (Leave-one-out) Reference method: Na, Cl in cotton crops NIR Spectral Measurements of soil Chemometric Approach Modeling Model 1 Model 2

Martens’ Uncertainty Test We first ran our models on the whole wavelength region, and thereafter we applied Martens' uncertainty test, algorithm in Unscrambler software. We tested the model by taking out each sample and testing it in the model.

Spectral Pre-processing Derivatives, Continuum removal are intuitive ways of dealing with systematic variations in the spectra, decrease noises and the effect of the physical qualities..

Parameter such as vegetation salinity was done through correlation between the Cl and Na and between slopes at specific spectral ranges. The comparison of the method slopes was done with: Models based on Spectral Reflectance, a model based on the first derivative and, Continuum Removal (CR) Methods

Results

chemical measurements: some statistics Soil Tomato leaves Cl (meg/l) Na (meg/l) EC (dS/m)Cl (%)Na (%) AVERAGE STDEV MEDIAN MIN MAX

Predicting soil components based on spectrum cotton Problem in assessing the amount of low-sodium Wavelength calculations based on Martin test. By which the optimization of model results will be done יWavelength Results nm

Predicting soil components based on spectrum of cotton (Na) by using 10 different wavelengths the salinity in the soil can be predicted. Chemical reference was log transformed (to get normal distribution) nm

Predicting soil chemical constituents (Cl) Important wavelengths based on Marten’s test selection Cl was log transformed LV component

Important wavelengths to estimate tomato salinity Important wavelengths based on Marten’s test selection

Predicting leaf chemical constituents based repair on use of Na spectrum

Slope calculation and Data analyze The commonly used CR technique enables reflectance spectra to be normalized to a common baseline The differences in CR spectral behavior as a function of wavelength, which are indicative of changes in chemical properties, were identified

ClNa Slope Spectral Range nmR2 N= The differences in CR spectral behavior as a function of wavelength, were identified, yielding 11 spectral ranges for Cl and 10 for Na.The slopes across these spectral ranges were then calculated. Eight identical spectral ranges for Cl and for Na were found.

Partial least squares (PLS) regression models results of Slopes method and of various data preprocessing techniques to assess Cl and Na of fresh vegetation sample

Partial least squares (PLS) regression models results of Slopes method and of various data preprocessing techniques to assess Cl and Na of fresh vegetation sample (continue)

Slope methodReflectanceContinuum RemovalFirst derivative R 2 =0.77 RMSEP=0.226 RPD=1.99 N=17 R 2 =0.62 RMSEP=0.087 RPD=2.53 N=17 R 2 =0.83 RMSEP=0.224 RPD=2.56 N=15 R 2 =0.51 RMSEP=0.113 RPD=1.49 N=14 R 2 =0.77 RMSEP=0.274 RPD=2.18 N=15 R 2 =0.62 RMSEP=0.085 RPD=1.68 N=15 R 2 =0.61 RMSEP=0.135 RPD=1.67 N=15 R 2 =0.50 RMSEP=0.174 RPD=1.46 N=15 Presentation of the slope measurements efficiency

Conclusion The aim of this study was to use vegetation reflectance measurements to predict foliar Cl and Na concentration and assess salinity in the soil and in the tomato and cotton plants In our opinion these results show the possibility of monitoring for a threshold level of salinity in tomato and cotton leaves and taking remedial action Our method can be implemented to assess vegetation salinity ahead of planting, and developed as a generic tool for broader use. It was found that the model developed for concentrations of chlorine and sodium in tomato gives good results ( R 2 = 0.92 for sodium and 0.85 for chlorine and R 2 = 0.84 for sodium and 0.82 for chlorine in cotton)

Conclusion Good results are getting from the slopes analysis. This is especially noticeable in Na, which normalize the statistical information (slope, R 2, RPD), most predictive models are those based on the slope. Our results are sufficiently promising to suggest that future hyperspectral and maybe airborne and satellite remote sensing measurements of soil and vegetation salinity might be used over larger area. It will efficient to combine close remote sensing data from the soil and vegetation surface to satellite and air borne information.

Future Direction

15 cm Fiber optic 95 cm Mirror at 45÷ angle ASD Halogen Lamp Stabilizer bar Telescopic bar 9 cm Handle bar Fiber Holder (0.8cm f ) Lamp holder (1.2 cm f) Handle bar ab The 3S-HED assembly: parts and way of operation Catherization Ben-Dor et al., 2008).

S p ec tr a & Vi d e o

FDEM

Soil electric conductivity (surface and the root zone 30 cm