RESEARCH ARTICLE
Retrieval of Paddy Crop Nutrient Content at Plot Scale Using Optimal Synthetic Bands of High Spectral and Spatial Resolution Satellite Imagery
Shreedevi Moharana1•Subashisa Dutta2
Received: 6 November 2020 / Accepted: 10 December 2021 / Published online: 4 February 2022 ÓIndian Society of Remote Sensing 2022
Abstract
The main aim of the present study is to introduce Hyperion narrow band information into high spatial broad band imagery so that it will be helpful for the estimation of crop parameters at small field scale from multispectral data. Here, we are targeting to retrieve the nitrogen content of summer paddy at plot scale. Our findings concluded that paddy crop heterogeneity can be assessed by implementing narrow band index regression model designed for paddy crop from multispectral LISS IV data. Moreover, the proposed models to construct the synthetic narrow bands in green (GS), red (RS) and NIR (NS) regions of the spectrum for high-resolution satellite imagery (LISS IV) are also presented here {GSqLISS IV ¼0:032GqIHþ17:296;RSqLISS IV ¼0:072RqIHþ16:025;NSqLISS IV ¼0:087NqIHþ24:923}. It showed the spatial variability of nitrogen content ranged from 3.37–6.04% obtained by leaf nitrogen concentration (LNC: R705, R717, R491) index regression model and 3.82–5.96% by simple ratio (SR: R533, R565) index regression model over a rice agriculture system. Thus, the details of spatial variability at crop field level over a rice agriculture system can be assessed from multispectral LISS IV data with 5.6 m spatial resolution through the synthetic broad bands. The results conclude that narrow band-based index regression model derived from Hyperion bands by implementing band average method can effectively evaluate the nitrogen content distribution of croplands subjected to varying field heterogeneity. It confirmed that the proposed methodology can be applied for the variability mapping of crop parameters at plot-scale level within the fields from space platform, which can be beneficial in achieving operational precision farming more efficiently.
Keywords Paddy Nutrient contentPlot scaleHeterogeneity MultispectralHyperion
Introduction
Paddy crop yield is turning into progressively the foremost information for effective crop management in precision agriculture. Therefore, knowledge on spatial and also the temporal variability is extremely necessary to create well- organized, sustainable and environment friendly farm practices accompanied by efficient inputs management targeting improved crop yield or quantity. In developing
countries like India, implementation of remote sensing technologies (Mondal & Basu,2009) provides much-nee- ded detailed information for crop monitoring which could guide farmers to make right decisions at right time for fertilizer and water management at crop field level. Satel- lite-based measurements especially from aerial-based multispectral remote sensing data can be utilized to map crop nutritional stress, water status and other crop param- eters associated with field-based approaches (Berni et al., 2009; Darvishzadeh et al.,2008; Kar et al.,2016; Mariotto et al., 2013; Marshall et al.,2016).
Regarding field-scale variability management, imple- mentation of conventional sampling methods is insufficient to manage the crop-yield influencing factors like biophys- ical, chemical and soil characteristics (Dimassi et al.,2014;
Pezzuolo et al.,2017). Therefore, adoption of hyperspectral remote sensing is essential to retrieve crop parameters in
& Shreedevi Moharana
1 Department of Civil Engineering, Indian Institute of Technology Hyderabad, Hyderabad, Telangana, India
2 Department of Civil Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, India https://doi.org/10.1007/s12524-021-01479-3(0123456789().,-volV)(0123456789().,- volV)
details. The most essential paddy crop parameters, chlorophyll and nitrogen content that measure crop growth need to be determined so that it can be further used to identify the complex nature of its field variability (Wang et al., 2014). Moreover, Gianelle and Guastella (2007) reported that most of the crop parameters have spectral signature at a certain wavelength of the spectrum that can be captured and identified by satellite sensors with a number of very narrow wave bands (Marshall & Thenk- abail,2015; Verrelst et al.,2015).
Potential applications of hyperspectral remote sensing in agriculture have been reported by researchers mainly in the field of discrimination of crops and their genotypes, esti- mation of different biophysical and biochemical parame- ters through empirical and physical modelling, abiotic and biotic stresses assessment in India as well abroad (Sahoo et al., 2015). Thenkabail et al. (2004) discussed on the performance of hyperspectral wavebands for different types of vegetation studies, crop characteristics and their applications. Moreover, by implementing principal com- ponent analysis (PCA) and stepwise discriminant analysis (SDA) on ground-based hyperspectral measurements, Sahoo et al. (2013) described how to discriminate wheat crop cultivars using hyperspectral reflectance data (350–2500 nm at 10 nm interval) and Manjunath et al.
(2011) were able to discriminate among different types of pulses grown in India. Further, the estimation of biophys- ical parameter leaf area index (LAI) of potato crop under different irrigation treatments was reported in Ray et al.
(2006). Moreover, Moharana and Dutta (2016) demon- strated the estimation of essential crop growth parameters from spaceborne satellite imagery. The critical wave- lengths at which the effect of nitrogen fertilizer applica- tions or crop varietal on spectral reflectance response was found significant were observed to be very close to narrow bands in near infrared, red and green regions of the spec- trum. Chlorophyll and nitrogen content was estimated using different index models from Hyperion imagery. It was revealed that the SR index model, which gave the best results in destructive estimation of nitrogen content for in situ hyperspectral data, did not quite produce satisfactory result from Hyperion imagery. The modified LNC index model which followed a nonlinear relationship performed better than that of the established Tian et al. (2011) model as far as estimated nitrogen content from Hyperion imagery was concerned (Moharana & Dutta, 2016). Additionally, exploiting in situ hyperspectral measurements, MTCI, OASVI, Gitelson, mSR, LNC indices produced well acceptable results for the assessment of chlorophyll content from spaceborne Hyperion imagery (Moharana & Dutta, 2016). Additionally, crop stress has been identified by different researchers such as potato late blight disease (Ray et al., 2011), yellow mosaic virus infection (Das et al.,
2013), nitrogen stress (Ranjan et al.,2012) and water stress (Bandyopadhyay et al.,2014; Moharana & Dutta,2019).
To overcome these difficulties and limitations of Hyperion data like a very low temporal and spatial reso- lution, present study introduced a new methodology to incorporate critical hyperspectral narrow bands into mul- tispectral high spatial resolution LISS IV data through index-based fusion approach. It mainly emphasizes to retrieve optimum set of narrow bands from Hyperion sensor to visualize the spatial distribution of crop nutrient, especially at field scale. This study demonstrated that the proposed methodology was efficient to differentiate nitro- gen content variability at plot scale within the rice field using hyperspectral image which will add insights for paddy crop growth and yield.
Materials and Methodology
Study Area and Field SurveyThis study was conducted in the farmers’ agricultural farm fields in the district of Cuttack, Odisha, India (20°19024.84‘‘N, 85°32041.94’’E), with summer rice culti- vation during pre-monsoon period (Fig.1). The cultivated cropland was purely predominated by rice, and the field was under paddy cultivation already for several years prior to this study. This area experiences a tropical wet and dry climate where temperatures may exceed 45°C in summer and may fall below 28 °C in winter. Temperature is con- siderably lower during the rainy season, averaging around 30°C. The area receives most of its rainfall from the South West Monsoon during the months of July to October.
Thunderstorms are common during summer. It experiences an average annual rainfall of nearly 1440 mm. It is situated at an average elevation of 34 m above the mean sea level (MSL).
A field campaign was conducted in the study site during the pre-monsoon period to collect detailed information regarding the paddy crop varieties being grown, application of fertilizers and mode of irrigation used in the rice agri- cultural farmland. According to the farmers, several high- yielding rice varieties such as Khandagiri, Konark, Lalata, Laxmisagar, Naveen, No 303, Purusotum, Pratikhya and Surendra were grown during the season. Organic manure mainly green leaf manure was spread evenly on the upper surface of the soil and ploughed in to get it well mixed in the soil. Chemical fertilizer, urea (CO(NH2)2.), was applied in three splits during the tillering, panicle initiation and heading stages. Assured and timely supply of irrigation water is the most important factor for summer rice which has a considerable influence on the crop yield.
Satellite Data Collection and Pre-Processing
For the present study, multispectral and hyperspectral satellite images of the study site in Cuttack, State of Orissa, India, were collected. Additionally, field survey and an interactive campaign with farmers were also carried out to study the pre-monsoon seasonal summer rice agriculture system in Orissa, India. The Hyperion satellite image data, covering the study site in Cuttack, Odisha, acquired on 8th March 2016, were used for hyperspectral resolution simu- lation to generate spatial distribution of paddy crop parameters from multispectral data. Crop studies, using optical satellite data, are generally not possible during monsoon season (July–October) due to the presence of cloud cover in the sky. Therefore, Resourcesat-2 (IRS-P6) completely cloud-free images were not available during this period. Hence, IRS-P6, LISS IV sensor data passing through the study site, acquired on 22nd February 2016, was ordered for this work from National Remote Sensing Centre, India (https://www.nrsc.gov.in).
The Hyperion Level-1 radiometric product contains 242 spectral narrow bands covering a spectral range of 400–2500 nm at 10 nm bandwidth. The spatial resolution is 30 m (ground sample) with a swath width of 7.7 km and covers an area of 7.79100 square km per image with high radiometric accuracy (16-bit quantization). Radiometri- cally corrected Hyperion L1R dataset is used for the cur- rent analysis. The temporal resolution of this sensor is very
poor; hence, getting temporal data for a certain area at a specific time is very difficult. The data are allowed for various pre-processing steps by using remote sensing (RS) image processing software ENVI 4.5 which includes rescaling, abnormal pixels stripping, cross-track illumina- tion correction, and elimination of bad bands prior to atmospheric correction. Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) algorithm implemented in the FLAASH ENVI module was applied to filter out target reflectance from the mixed signal. Fur- thermore, multispectral IRS-P6, LISS IV sensor data were acquired on 22nd February, was used for the analysis fol- lowed by standard pre-processing steps. The LISS IV sensor has three bands in different regions of EMR B2 EMR (b2: Green, b3: Red, b4: NIR). In this study, atmo- spheric correction is done by FLAASH module coupled with ENVI 4.5.
Methodology for Field-Scale Extraction of Crop Parameters
The methodology of the present study utilizes the hyper- spectral and high-resolution multispectral data to achieve the objectives. The primary concern is (i) to identify the critical bands which are sensitive especially for paddy crop nutrient estimation not all the vegetation indices related to crop variables. This has been discussed in detailed for rice chlorophyll and nitrogen estimation from space platform Fig. 1 (a) Map of India and satellite image of Hyperion for (b) Study site—Cuttack, Odisha: the study area showing summer rice agriculture system and surrounding areas
by Moharana and Dutta (2016) and considered as a refer- ence to the optimal band selection for further analysis.
Secondary part is (ii) to develop index-based fusion model by incorporating these sensitive bands from high-resolution multispectral LISS IV data. Moreover, this can be made possible by implementing a new method ‘‘Band Average Concept’’. It is confirmed that only those indices performed well in mapping of crop nutrient variability in which sig- nificant critical bands resulted from the adopted approaches are implemented, rather than all the chlorophyll and nitrogen-related vegetation indices. Furthermore, detailed methodology of the same has been discussed in the fol- lowing sub-sections.
Selection of Optimal Narrowband Vegetation Indices Sensitive to Paddy Crop
The observed ground-based hyperspectral data were uti- lized to establish the relation between spectral reflectance and paddy crop parameters. This was further investigated by Moharana and Dutta (2016) and was observed that there is a statistically significant correlation between the spectral reflectance and biophysical parameters of the agricultural crop. In this study, the vegetation index predictive models were established to achieve spatial variation in nutrient nitrogen of paddy crop. The critical bands sensitive to paddy crop from different narrow band vegetation indices are selected from Moharana and Dutta (2016) for further analysis.
Index-based Fusion Approach of Hyperspectral and Multispectral Data
An effort has been made to derive narrow band vegetation indices by adopting fusion of Hyperion narrow band with LISS IV broad band. The hyperspectral bands of Hyperion L1R Hyperion data are of 10 nm bandwidth, whereas LISS IV data have three bands: b2 (0.52–0.59lm, green), b3 (0.62–0.68lm, red) and b4 (0.77–0.86lm, NIR). This fusion approach was further implemented to retrieve nitrogen content at plot scale within the rice field.
Derivation of Multispectral Bands (Synthetic Bands) from Hyperion Data
A new methodology is introduced to get synthetic multi- spectral narrowband, ‘‘Band Average Concept’’. The crit- ical bands, sensitive to paddy crop, were selected for different narrow band vegetation indices such as simple ratio (SR), leaf nitrogen concentration (LNC), Optimised Soil-Adjusted Vegetation Index (OSAVI), Gitelson, mod- ified simple ratio (mSR) and MERIS Terrestrial Chloro- phyll Index (MTCI). All the broad band equivalents were
expressed in terms of narrow bands of the Hyperion image of the study site acquired on 8th March, 2016. These broad band equivalents were tested for the existence of relation- ship with that of critical narrow bands. The relationship is stated in the following expressions
qGC ¼b1GqIHþa1 ð1Þ
qRC ¼b2RqIHþa2 ð2Þ
qNC ¼b3NqIHþa3 ð3Þ
whereqIH ¼1nPn
i¼1
qi.
qIH = broad band equivalent to integrated narrow bands of Hyperion image.
qi = broad band reflectance at i (i at a step size of 10 nm).
a,b = linear coefficients.
andGqIH,RqIH,NqIH are the three spectral (LISS IV) broad bands equivalent to integrated Hyperion narrow bands calculated for green, red and NIR regions of the spectrum, respectively, and qGC,qRC,qNC are the critical narrow bands of green, red and NIR regions of the spec- trum, respectively.
For this, LISS IV image representing the study area dominated with paddy crop cultivation, a linear relation- ship was established between multispectral broad band (so called synthetic band;SqLISS IV) and broad band equivalent to integrated narrow bands of Hyperion image (qIH).
The relationship is represented in the following expressions,
GSqLISS IV ¼bGGqIHþaG ð4Þ
RSqLISS IV ¼bRRqIHþaR ð5Þ
NSqLISS IV ¼bNNqIHþaN ð6Þ
where a and b are linear coefficients, and GSqLISS IV, RSqLISS IV, NSqLISS IV are the three multispectral synthetic bands derived for Hyperion narrow bands, andGqIH,RqIH, NqIH are the three broad bands equivalent to integrated narrow bands in green, red and NIR regions of the spec- trum, respectively.
The overall methodology adopted in this study is schematically depicted in a flowchart (Fig. 2).
Results and Discussion
Due to non-availability of Hyperion data with appropriate temporal resolution, predominantly it is not possible to monitor the rice crop parameters covering all the stages of rice crop age period. Secondly, it is a difficult task to find the paddy crop parameters like chlorophyll and nitrogen at field level from space platform. Therefore, an effort has been made to estimate the parameters from multispectral
Fig. 2 Complete sequence to map nitrogen content variability at plot scale
data at plot scale within the crop field. To do so, index- based fusion approach for multispectral and hyperspectral data was adopted by incorporating band average method.
Band Average Method
It was found that some of the critical bands were highly correlated with the broad bands equivalent to the integrated narrow bands, whereas others failed to reciprocate much.
Therefore, a higher linear coefficient of determination (R2[0.95) between critical bands and broad bands equivalent to integrated narrow bands derived from Hyperion bands was considered (Fig.3). By adopting band average concept method, bands like 533, 565, 681, 705, 717, 750 and 800 nm were found to be significant amongst
all the critical bands. Moreover, the functional relation- ships between multispectral band and hyperspectral band were derived as shown below.
GSqLISS IV ¼0:032GqIHþ17:296 ð7Þ
RSqLISS IV ¼0:072RqIHþ16:025 ð8Þ
NSqLISS IV ¼0:087NqIHþ24:923 ð9Þ
However, the foremost concern is that by adopting this index fusion approach, all the vegetation indices could not be used to estimate paddy crop biophysical or biochemical parameters. It was confirmed from above analysis that solely those indices in which significant critical bands resulted from band average methodology were considered.
Fig. 3 Correlation between critical bands and broad bands equivalent to integrated narrow bands, green: (a,b), red: (c,d), NIR: (e,f)
In this context, only two indices, LNC and SR indices, were confirmed for nitrogen prediction (Table1).
Variability Mapping of Nitrogen at Plot Scale from Space Platform
The spatial variability of nitrogen content of rice agricul- ture system was determined by employing both the LNC and SR index models, obtained from band average method.
Based on the critical bands obtained from the above- mentioned concepts, LNC index model exhibited nitrogen content variation from 3.37 to 6.04% (Fig.4). It illustrates considerable spatial variability of nitrogen content at plot scale within the field for the rice crop shown in five plots. It was observed that the variation obtained from LNC index model has a good agreement with observed nitrogen con- tent that ranged from 1.12 to 3.92%.
Table 1 Proposed regression models for plot-scale rice nitrogen nutrient estimation
Index Fit-equation Related to Tested species
SR y¼29:178x25:294 N Rice (Oryza sativaL.)
LNC y¼ 306:34x2þ372:74x109:56 N Rice (Oryza sativaL.)
Fig. 4 Spatial variability of nitrogen content at plot scale from LNC model using (b,c) band average method of the (a) Study site—Cuttack, Odisha
Furthermore, nitrogen content variability was tried to be mapped from LISS IV image by using SR index model (Fig.5). Figure5 exhibits the nitrogen variability indicat- ing the spatial distribution of crop field from space plat- form. The rice nitrogen content spatial distribution over rice plots ranged from 3.82 to 5.96%. An interesting feature was noticed that there was no variability in the fifth rice plot compared to the other four plots. However, the LNC index model was able to predict the spatial variability of paddy crop nitrogen in that plot. This may have happened because the combinations of green narrow bands in the SR index only may not be sufficient to find out the induced
variability during pre-monsoon period for summer rice crop. Further, it is interesting to note that the present result, which is showing estimated nitrogen content variation in paddy crop in the range of 3.37–6.04%, exhibits great similarity with the nitrogen content obtained by previous researcher Liang et al., (2018) from the reflectance spectra of crop using the inversion models for Precision Agricul- ture in Beijing, China, where it varied from 1.5 to 5%.
Despite several influencing factors like radiation intensity, observation geometry and the distribution of leaf inclina- tions in the field, the obtained results of nitrogen content variation in the present study are in a good agreement with Fig. 5 Spatial variability of nitrogen content at plot scale from SR model using (b,c) band average method (a) Study site—Cuttack, Odisha
the Liang et al. (2018) range. Additionally, the degree of spatial variability of nitrogen derived from LNC index model was found to be significant as compared to SR index model in summer rice. The above findings indicate that the properties concerning the spatial variability of rice nitrogen content at plot scale within the paddy fields derived from LISS IV image reflect the heterogeneity in rice agriculture system evidently and provide a better choice of fertilizer selection by taking variability into account for precision rice farming.
Statistical Analysis of the Index-Based Fusion Approach
The overall functional range of nitrogen content was cat- egorized into several classes between 0 and 6% to visualize their plot-wise spatial distribution (% of total area) obtained from band average method. The spatial distribu- tion of LNC and SR models at plot scale is represented in Fig.6. Here, both the models were found to be functional in predicting nitrogen content from band average method, whereas LNC model exhibited more variability as com- pared to SR model. This may be due to the phenological behaviour of paddy crop in the particular region of the spectrum. Besides these, plot-wise statistics of nitrogen content of rice derived from (a) LNC (b) SR models employing band average method is presented here (Table 2). It indicates that variation of mean value of nitrogen content of each plot is ranged from 4.34 to 5.60%.
In case of SR index model, plot-wise mean value of
nitrogen content was found to be high, mostly greater than 5%, representing a minor spatial variability in comparison to LNC model. By introducing this methodology, the synthetic bands of high-resolution satellite imagery from different available space sensors can also be derived such as Sentinel 1, Sentinel 2A, Sentinel 2B and SPOT. How- ever, it requires strong ground validated dataset for implementation in case of various agricultural crops in different growth stages for specific topographic locations.
Conclusion
This study demonstrated the introduction of Hyperion narrow band information into high spatial broad band imagery. The results confirmed that paddy crop hetero- geneity can be assessed by implementing narrow index regression models designed for paddy crop from multi- spectral high-resolution LISS IV data. It showed the mean spatial variability of nitrogen nutrient content ranged from 4.34 to 5.35% obtained from LNC regression model and 4.52–5.60% by using SR index regression model over a rice agriculture system. Moreover, the spatial variability at crop field level over a rice agriculture system can be esti- mated from multispectral high-resolution LISS IV data rather than hyperspectral data. Furthermore, this technique has the potential to significantly narrow down the nitrogen variability at small field scale thereby the performance of precision rice agriculture, which may ensure better avail- ability of nutrients through proper nutrient management as Fig. 6 Distribution of area
under different classes of nitrogen (N) derived from fused product of LNC and SR models using band average method
Table 2 Plot-wise statistics of nitrogen derived from (a) LNC (b) SR models employing band average method
Nitrogen Content (%)
plot1 plot2 plot3 plot4 plot5
(a) LNC
Band average method Mean 5.35 5.6 5.24 4.58 4.34
SD 0.112 0.103 0.12 0.206 0.158
(b) SR
Band average method Mean 5.48 4.52 5.12 5.43 5.6
SD 0.121 0.182 0.157 0.126 0.101
well as irrigation management. Hence, it can be concluded that the proposed methodology can be applied for the variability mapping of crop parameters at plot-scale level within the field to overcome the limitations of Hyperion image. It can also be further used for operational precision farming more efficiently; however, it requires strong ground observation data.
Acknowledgements We wish to draw the attention of the Editor that the manuscript has been read and approved by the named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We confirm that the contents of this manuscript have not been copyrighted, published, or submitted elsewhere. We further confirm that we have given due consideration to the protection of intellectual property associated with this work and that there are no impediments to publication, including the timing of publication, with respect to intellectual property. We understand that the Corresponding Author (Shreedevi Moharana) is responsible for communicating with the other authors.
Authors’ Contributions SM (Corresponding author) contributed to Conceptualization, Methodology, Geospatial analysis, Software, Validation, Investigation, Writing—original draft; SD contributed to Writing—Review and editing, Supervision.
Funding Not applicable.
Declarations
Conflict of interest The authors declare no conflicts of interest.
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