Hyperspectral measurement from EO-1 satellite

105

Study Site and Field Survey

For this research work, the experimental station of RRLRRS, Assam, India (Study site 1) centred at latitude 26°15'15.08"N and longitude 91°33'50.88"E was selected as the study site.

Field campaign was carried out to get detail information on winter rice cultivation practice from farmers of the area. The information regarding rice varieties, fertilizer application and dozes, crop growth status, crop yield and topography variation were collected. The detailed description of study area is reported in Chapter 3.

Data Required: Hyperspectral Reflectance Measurements

106

temporal resolution was very low, hence getting temporal data for a certain area was very difficult. An image providing critical growth information of paddy was acquired i.e. during its reproductive phase. The Hyperion image covering the farm area in Assam (Study site 1) was acquired on 3^rd October, 2014 at 3.30 GST. The scene characteristics of the acquired Hyperion image is enumerated in Chapter 3. The acquired Hyperion image was radiometrically corrected but it required careful processing to nullify the sensor noise. It was processed by using remote sensing (RS) image processing software ENVI 4.5. Pre-processing was necessary not only for elimination of sensor noise during acquisition but also for the reduction of data dimensionality in order to reduce computational complexity. Pre-processing steps like rescaling, abnormal pixels stripping, cross-track illumination correction, and elimination of bad columns prior to atmospheric correction were applied to the dataset. The details of pre-processing is discussed in Chapter 3.

Finally, spatial subset was selected from the whole scene for the experimental study site.

Plant measurements

Plant biophysical parameters were measured for 24 rice varieties during their booting stage from each experimental plot. Just after each canopy spectral measurement, plant samples (7- 15 plants/plot) were selected randomly to determine the Chl and N content. Plant leaves were removed from its stem for the measurement by standard procedures. Leaf chlorophyll content was estimated by 80% acetone extraction method. After recording the fresh weight of each leaf sample, the leaf pigments were extracted in 80% acetone and leaf was pulverized completely. The sample was centrifuged at 3000 rpm for 15 mins to precipitate the cell debris.

Then the supernatant was separated and extract of the sample was collected in a test tube. The absorbance (A) of the same was measured using spectrophotometer at 645 nm and 663 nm.

Chlorophyll a, chlorophyll b and total chlorophyll contents of each sample were computed from the equations described below.

Using Arnon's following equations:

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w) (v/1000 )]

A (2.6 ) A (12.7 [ ) g (mg a

Chl ^-1   ₆₆₃   ₆₄₅   (6.1)

w) (v/1000 )]

A (4.68 )

A (22.9 [ ) g (mg b

Chl ^-1   ₆₄₅   ₆₆₃   (6.2)

w) (v/1000 )]

A (8.02 )

A (20.3 [ l Chlorophyl

Total   ₆₄₅   ₆₆₃   (6.3)

or

b Chl a Chl Chl

Total   (6.4)

where,

sample the

of volume total

v

sample the

of weight w





The micro-Kjeldahl method was used for quantitative determination of nitrogen concentration in paddy leaf. Initially plant leaves were separated from their stem and kept in oven to dry at 70°Cfor 72 hrs to derive a constant weight of each sample and then leaves in each sample were properly crushed. 0.5 g of the each dried sample was taken for chemical analysis. The leaf nitrogen content of sample was calculated from the amount of ammonia present in it. The amount of ammonia present was determined by titration with sulphuric acid solution with a methyl orange pH indicator. Percentage of nitrogen was calculated using the following formula,

leaves collected

the of weight Sample

SO H of Normality required

SO H of Volume (0.014

)

Nitrogen(%  ^{2 4}  ^{2 4}

 (6.5)

Spectral Index approach-estimating Biophysical variables from Space platform

Spectral indices are the true indicators of the biophysical parameters of agricultural crop (Chen et al., 2010; Heiskanen et al., 2013). Specifically ratio based index and normalized difference spectral index produce effective results in different plant variables by using ground based and airborne reflectance spectra (Eitel et al., 2007; Hansen and Schjoerring, 2003; Zhao et al., 2007). Inoue et al. (2012) has reported that both reflectance and derivative spectra index have worked well in evaluating plant physiological variables. For the present study, indices TH-2211_126104014

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specifically aimed at leaf Chl and N content estimation of different crops were applied to paddy crop to quantify the Chl and N concentrations.

Mathematically, expression for Normalized Difference Spectral Index (NDSI) and Ratio Spectral Index (RSI) can be written as

y) x)/(x - (y y)

NDSI(x,   (6.6)

(x/y) y)

RSI(x,  (6.7)

Here, x and y are the reflectances (Ri and Rj) or first derivative (Di and Dj) values at i and j nm over the whole hyperspectra (Inoue et al., 2008).

Spectral indices were established using combinations of whole hyperspectral bands or some specific wavelengths helpful for the estimation of plant variables (Maccioni et al., 2001; Tian et al., 2011; Xu et al., 2010). There are indices available for estimation of biophysical parameters of different crops (Main et al., 2011; Marshall and Thenkabail, 2015), trees and forests, but there are no spectral indices specifically developed for paddy crop, as it is a very dynamic crop that varies with different topographical positions and climatic zones. Instead, paddy crop was tested with field hyperspectral data using previously published indices those were not meant for it. Therefore, an attempt has been made to estimate Chl and N content from hyperspectral imagery using the indices tested for rice and other well performed Chl and N aimed indices. For the present study, ten selected spectral indices that were found effective for the assessment of Chl and N content at leaf or canopy level for different species, were considered for Chl and N estimation in rice. These indices are given in Table 6.1 and Table 6.2.

Table 6.1 : Vegetation indices examined for nitrogen content estimation

Index Equation Related to Reference Tested species

Simple Ratio

Index

R

⁵³³

R

⁵⁶⁵ N Tian et al.

(2011)

Rice (Oryza sativa L.) LNC Index R705



R717 R491



N Tian et al.

(2011)

Rice (Oryza sativa L.)

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Table 6.2 : Vegetation indices examined for chlorophyll content estimation

Index Equation Related to Reference Tested species

Simple ratio Index N Tian et al. (2011) Rice (Oryza sativa L.)

mND705 Chl Sims and Gamon

(2002)

Thin leaves (herbaceous), High dry mass leaves (sclerophyllous), Succulent leaves, Pubescent leaves, Waxy leaves, Grasses

Maccioni Chl Maccioni et al.

(2001)

Four different plants (i)croton, Codiaeum variegatum (ii) spotted eleagnus, Eleagnus pungens Maculata (iii) Japanese pittosporum, Pittosporum tobira (iv) Benjamin fig, Ficus benjamina Starlight

PRIc N Gamon et al.(1992) Sunflower

MTCI (MERIS Terrestrial

chlorophyll Index) Chl Dash and Curran

(2004)

Douglas fir (Pseudotsuga enziesii) and bigleaf maple (Acer macrophyllum)

LNC R705



R717R491



N Tian et al. (2011) Rice (Oryza sativa L.)

Datt Chl Datt (1999) Eucalyptus leaves

OSAVI

(Optimised Soil-Adjusted Vegetation Index)

Chl Wu et al. (2008) Wheat and Corn

Triticum aestivum L.; Zea mays L.

Gitelson Chl Gitelson et al.

(2003)

Higher plant leaves Norway maple (Acer platanoides L.); horse chestnut (Aesculus hippocastanum L.); flush beech leaves (Fagus sylvatica L.); wild vine shrub (Parthenocissus tricuspidata L.)

mSR Chl+LAI Chen (1996)

Boreal forests plants (e.g Pine (Pinus banksiana) and Black Spruce (Picea mariana))

565 533 R R



R₇₅₀R₇₀₅

 

R₇₅₀R₇₀₅2R₄₄₅





R₇₈₀R₇₁₀

 

R₇₈₀R₆₈₀





R₅₇₀R₅₃₉

 

R₅₂₈R₅₃₉





R₅₇₄R₇₀₉

 

R₇₀₉R₆₈₁





R₈₅₀R₇₁₀

 

R₈₅₀R₆₈₀





10.16



R₇₅₀R₇₀₅

 

R₇₅₀R₇₀₅0.16





R₇₅₀R₈₀₀ R₆₉₅R₇₄₀



1



R750 R705



1 sqrt

 

R750 R705



1



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Results and Discussions