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Table 5.3 : Discriminated and mixed rice genotypes from clustering
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combinations having least correlation indicate least redundancy of information.
Table 5.4 : Waveband combinations having least correlation between narrow bands (N3-N1) Name of the variety
(N3-N1)
Spectral band 1 (nm)
Spectral band 2 (nm)
r2
Gautam
559 729 0.000488
489 740 0.000260
510 789 0.000674
609 799 0.000581
IR-64
559 769 0.000456
519 789 0.000512
559 809 0.000451
559 799 0.000581
689 729 0.000456
IET-18558
459 809 0.005889
519 779 0.00788
499 729 0.001128
629 799 0.002433
IET-19601
489 819 0.000636
649 839 0.000104
649 849 0.000135
659 959 0.000636
Chandrama
519 769 0.000512
659 849 0.000456
489 749 0.000512
649 969 0.000451
To select the significant wavelengths from band-band analysis, frequency of occurrence of wavelengths were evaluated (Figure 5.9) and a threshold frequency >3 was employed to discriminate the rice genotypes. The significant spectral wavebands were found in between Green, Red, and NIR regions of the spectrum in discriminating the rice genotypes with nitrogen applications (Table 5.6). Our findings show that wavelengths in green region (519, 549, 559 nm) showed high reflectance due to heavy chlorophyll absorption in the green region.
These findings were also similar to green band peak (550nm) noticed by Thenkabail et al.
(2004). The pre-maxima absorption band (650 nm) cited by Jain et al. (2007) and the red edge
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centred in the range of 700–720 nm (Daughtry et al., 2000) were found similar to the results (649,729 nm) obtained in the present study. The proposed methodology identified the narrow band reflectance characteristics at wavelengths (789,799,809,819 nm) that have the potential to discriminate the rice species in an effective way. This significant wavelengths matched with the findings (810,830 nm) obtained by Inoue et al. (2008) that are sensitive to nitrogen. Hence, these wavelengths have an advantage to identify pure rice spectra and to classify the rice genotypes prior to nitrogen applications accurately. Furthermore, these wavelengths may be used for developing three or four band indices which will help to distinguish the rice varieties prior to nitrogen applications.
Table 5.5 : Waveband combinations having least correlation between narrow bands (N2-N1) Name of the variety
(N2-N1)
Spectral band 1 (nm)
Spectral band 2 (nm)
r2
IR-64
499 789 0.002243
539 799 0.003165
639 749 0.00488
539 799 0.007225
IET-18558
549 799 0.007449
549 809 0.008279
519 809 0.002298
579 789 0.009315
649 819 0.003345
K.Hansa
510 819 0.002720
549 839 0.006801
549 849 0.000998
Chandrama
659 729 0.000456
549 769 0.000512
549 809 0.000451
649 819 0.000581
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Table 5.6 : Significant waveband in discriminating rice species prior to nitrogen application
Spectral range Wavelengths (nm)
Green 519, 549, 559
Red 649
Red edge 729
NIR 789,799,809,819
Conclusions
The hyperspectral measurements were taken from eight rice species that were subjected to three nitrogen treatments and 24 rice species with no nitrogen application. It was attempted to discriminate the rice genotypes along with their treatments through clustering technique and it was revealed that rice varieties were significantly distinguished from each other. Thus, utilization of waveform classification followed by clustering technique performed successfully for the present study. Noise in the hyperspectral data was eliminated resulting in Figure 5.9 : Frequency of occurrence of wavebands selected from band–band analysis
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accurate spectral signatures of rice genotypes. Thus, a better spectral library for the rice genotypes with application of varying nitrogen treatments and with no nitrogen treatment at all were achieved. Furthermore, a few varieties were mixed representing one cluster in which the fine difference among the rice varieties could not be determined. The significant wavelengths for this discrimination were found in green (519, 549, 559 nm), red (649 nm), red edge (729 nm) and NIR (789, 799, 809, 819 nm) regions of the spectrum. These optimal narrow bands can be useful to develop three band or four band indices specially designed for paddy crop that will provide the most essential information in regards to precision rice farming. However, it requires to be validated with more number of field hyperspectral data sets for practical applicability.
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6 Estimation and Variability Study of Crop Parameters of Rice from Hyperspectral Imagery
Introduction
Nitrogen (N) is one of the most important crop elements closely related to its growth. It influences the crop chlorophyll content (Chl) and regulates its photosynthesis rate, thereby impacting the grain yield of rice crop. Photosynthetic pigments like chlorophyll-a, chlorophyll-b and carotenoids are strongly related to crop biophysical conditions, which influence crop productivity. Moreover, leaf nitrogen content is a strong factor influencing (Kergoat et al., 2008) both optimum canopy light use efficiency and canopy photosynthesis rate. Besides enhancing the crop productivity, nitrogen is also responsible for surface and groundwater contamination and atmospheric pollution (Chhabra et al., 2010; Manjunath et al., 2006). Therefore, it is very important to increase rice productivity for augmenting growth, while at the same time taking care of its adverse environmental impacts through precision nitrogen fertilizer management practices and recent advances in hyperspectral remote sensing techniques (Crutzen et al., 1986; Neue, 1993).
Previous studies have demonstrated the potential use of hyperspectral remote sensing for the assessment of biophysical variables (Goel et al., 2003) and biochemical components (Inoue et al., 2012; Main et al., 2011) of plant, indicating the plant’s nutrient status. Chlorophyll content (Daughtry et al., 2000; Main et al., 2011; Schlemmer et al., 2013) and nitrogen content
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(Lee et al., 2007; Tian et al., 2011; Zhu et al., 2007) at both leaf and canopy level can be extracted non-destructively by employing vegetation indices (VIs) from hyperspectral data.
Importantly, the narrow band indices were developed and tested for leaf and canopy level estimation of Chl and N content, rather than mapping these essential parameters from space platform for a regional or global scale. Literatures discussed in Chapter 2 have confirmed that, they have the potential for a wide varieties of leaf species, plants, cash crops and agricultural crops, however there are no such indices specifically aimed for Chl and N content estimation of paddy crop. Moreover, very few literatures have supported the testing of indices for paddy crop and have also proposed one or two indices for estimation of these parameters from in- situ data (Tian et al., 2011). However, these are not robust indices as paddy cultivation varies with topography and climatic conditions. On the other hand, agricultural applications for precision farming, particularly for rice, are time specific, require accurate supply of nutrients and demand practical feasibility (Haboudane et al., 2002; Miao et al., 2009; Peng et al., 2010).
Thus, they are more critical than other remote sensing applications. Treating a large crop region as a uniform area and applying farming inputs uniformly all over the field without considering within the field variation will result in inefficient use of inputs and loss of productivity. Thus, information on spatial variability of the field is an essential requisite to make efficient utilization of inputs for increasing farm profitability. Thus, advanced algorithms need to be developed to fully make use of hyperspectral image to get the field variability requisites for precision farming.
This study evaluated the potential of published Chl and N related VIs to map spatial variation of Chl and N, especially for rice agriculture system in India. Firstly, the performance of chlorophyll related indices to retrieve chlorophyll content in paddy leaf was examined.
Furthermore, published indices related to nitrogen specifically tested for rice were considered to estimate nitrogen content in rice leaf. Secondly, relationships between the indices and rice plant parameters from ground based hyperspectral measurements were established. Different vegetation index models were developed to generate classified Chl and N map showing their spatial variation in a rice agriculture area from a high dimensional hyperspectral Hyperion data. Finally, crop field variation showing heterogeneity in the field was established from space platform. This study can be used as source for extracting different types of information for precision rice agriculture.
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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.