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Mapping Leaf Area Index at Various Rice Growth Stages in Southern India Using Airborne Hyperspectral Remote Sensing

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26 December 2023

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26 December 2023

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Abstract
Globally, rice is one of the most important staple food crops. The most significant metric for evaluating the rice growth and productivity is the Leaf Area Index (LAI), which can be effectively monitored using remote sensing data. Hyperspectral remote sensing provides contiguous bands at narrow wavelengths for mapping LAI at various rice phenological stages and it is functionally related to canopy spectral reflectance. Hyperspectral signatures for different phases of rice crop growth was recorded using Airborne Visible Near-Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG) along with corresponding ground based observations. Ground based hyperspectral canopy spectral reflectance measurements was recorded with FieldSpec 3 Hi-Res spectroradiometer (ASD Inc., USA; spectral range: 350-2500 nm) and LAI data from 132 farmer’s fields in Southern India. Among 29 hyperspectral vegetation indices tested, eight were found promising for mapping rice LAI at various phenological stages. Among all the growth stages, elongation stage was the most accurately estimated using vegetation indices that exhibited significant correlation with the airborne hyperspectral reflectance. The validation of hyperspectral vegetation indices revealed that the best fit model for estimating rice LAI was mND705 (red-edge, blue and NIR bands) at seedling and elongation, SAVI (red and NIR bands) at tillering and WDRVI (red and NIR bands) at booting stage.
Keywords: 
hyperspectral remote sensing
;  
vegetation indices
;  
canopy reflectance
;  
leaf area
Subject: 
Biology and Life Sciences  -   Agricultural Science and Agronomy

1. Introduction

Global rice production was 513.68 mt, of which, India contributes to 132 mt after China, and plays an important role in the global rice economy [1]. India ranks second in consumption which attributes to the adequate supply of rice leading to food security. Remote sensing technology has gained attention as it provides rapid and macro-scale observations using spectral data of the crop canopy to ameliorate the crop growth parameter estimation [2,3,4]. The reflectance curve in the red and blue region displays a valley when the plant canopy is healthy as it significantly absorbs blue and red light and reflect green light [5]. Thus, higher reflectance in the near infrared region (NIR) was closely associated with internal cell structure, biomass, vegetation cover, leaf water content, and LAI, whereas red-edge region exhibits strong absorption because of leaf chlorophyll content, and reflection is due to mesophyll cells [6].
The LAI of crop is a key variable for estimating the foliage cover and photosynthetic processes, ultimately aids in forecasting of the crop growth and yield [7]. The estimation of LAI includes direct and indirect measurement methods. The direct measurement of LAI includes destructive sampling and laboratory evaluation which is time-consuming, labor-intensive, discontinuous and is not applicable for large-scale investigations [8,9]. Therefore, numerous research efforts have been emphasized on the indirect measurement of LAI using remote sensing technology due to its non-destructive and rapid monitoring potential [10,11].
Among various vegetation indices (VI) and neural network approaches, the scaled normalized difference vegetation index (NDVI) methodology was the most efficient way to retrieve LAI [12]. However, it is sensitive to atmospheric processes, soil conditions and saturates at low LAI [13]. Thus, other VIs viz., modified red-edge normalized difference vegetation index (mND705) [14], soil adjusted vegetation index (SAVI) [15], triangular vegetation index [16], and renormalized difference vegetation index (RDVI) [17] were developed to enhance the precision of LAI estimation. The VIs with red-edge bands are better predictors of LAI [18,19,20,21]. Recently, hyperspectral remote sensing has drawn the emphasis on LAI estimation because it provides continuous spectral coverage and achieves a spectral resolution of <10 nm in the range of 400-2500 nm [4,22]. Mapping the distribution of LAI in winter wheat for various growth phases was performed using UAV-based hyperspectral data [9] and AVIRIS-NG data [23]. Many studies in rice revealed that the VIs were positively correlated with biomass during pre-heading stages and negatively during post-heading stages [24,25].
To maximize the effectiveness of LAI estimation, identification of significant wave bands in the hyperspectral data is essential for retrieving more specific VIs [26]. Further, the combination of these wave bands under diverse environmental conditions and cultural practices provides appropriate information for the characterization of LAI with the crop phenological stages [27]. In this study, we examined the utility of different VIs through hyperspectral measurements from airborne AVIRIS-NG to map LAI at various phenological stages of rice.

2. Materials and Methods

2.1. Study Area

The study area is Banaganapalle mandal, Kurnool district, Andhra Pradesh, India in which rice is predominantly cultivated in both wet and dry seasons. The wet season plantings were carried out during July-August and dry season plantings during November-December. Rice is mostly transplanted and in few cases direct seeding was noticed in the study area. The soils are predominantly vertisols, and rice was grown following the recommended agronomic practices. Farmers mostly cultivate rice varieties viz., BPT 5204, NDLR-7, RP Bio-226 and BPT 2270 of medium to long (130-160 days) crop growth duration.

2.2. Sampling Sites and Field Data Collection

Ground-truth surveying of rice fields was carried out during fourth week of February, 2018 (20-24 February) and the AVIRIS NG images were acquired on 26 February, 2018 which has spectral range of 380-2510 nm with spatial resolution of 4-8 m. Farmers’ fields were surveyed based on the stratified random sampling procedure. A total of 132 fields in Banaganapalle were selected randomly during the dry season for rice LAI estimation at various phenological phases (15.723°N to 15.979°N latitude; 77.926°E to 78.186°E longitude) (Appendix A1, Figure 1). The dates of sowing and transplanting were recorded through farmer’s interaction and rice growth stages observed at the time of data collection. The plantings varied considerably and lasted for three months from second week of November, 2017 to first week of February, 2018. The harvesting was done for a period of two months i.e., from first week of March to second week of May, 2018. Field measurements of in-situ LAI was recorded from the surveyed fields individually during or near the AVIRIS-NG airborne data acquisition.

2.3. Sampling of LAI

Field surveys were conducted from 132 farmer’s fields for ground-truth data collection (Figure 2). As the study area is a traditional rice growing belt, the crop was observed at different phenological stages. Accordingly, the fields were categorized into seven phenological stages (Table 1) as seedling (planted in first to second week of February), tillering (planted in fourth week of January), elongation (planted during second week of January), booting (planted in first week of January), heading (planted during fourth week of December), flowering (planted during second week of December), and maturity (planted during second week of November) (Figure 3). The surveyed fields were segregated separately for calibration (78 fields) and validation (54 fields) to estimate LAI using remote sensing (Table 1). Five plants were selected randomly from each surveyed field for non-destructive LAI measurement using SunScan plant canopy analyzer (Delta-T devices Ltd, UK) representing each phenological stage.

2.4. Canopy Spectral Reflectance Measurements

Canopy spectral reflectance measurements from rice were recorded with FieldSpec 3 Hi-Res spectroradiometer (ASD Inc., USA; spectral range: 350-2500 nm). All the spectral measurements were made during 20-24 February, 2018 between 11:00 and 14:00 hr Indian standard time (GMT + 5.30 hrs) under clear sky. The radiometer was configured to record 30 scans for each sample, and 15-25 plants from each field were sampled at the time of data recording. The pistol grip of radiometer was positioned at 1 m above the crop canopy, centered over the rice hills, with 25° FOV foreoptic to ensure the reflectance from a single desired plant. Prior to field measurements, the spectral radiometer was warmed-up for about 20 min to avoid the spectral steps at detector overlap wavelength regions, which occur due to different warm-up rates for the three spectroradiometer arrays [28]. The sampling interval was 1.4 nm at 350-1000 nm range, and 2 nm at 1000-2500 nm range. The spectral resolution (full width half maximum) was 3 nm at 700 nm, 6.5 nm at 1400 nm, and 8.5 nm at 2100 nm. Finally, the collected spectral data was exported to RS2 software and interpolated using ASD ViewSpecPro software in the post processing to produce values at each nanometer [29]. The spectral reflectance data from 1300-1400 nm and 1800-1950 nm were not considered for analysis due to noise caused by atmospheric water absorption in the raw spectrum.

2.5. AVIRIS-NG Airborne Data Acquisition

The present study is part of the collaborative program between Indian Space Research Organization (ISRO), India and Jet Propulsion Laboratory (JPL), NASA; Department of Science & Technology, Government of India and many other research organizations including the Indian Council of Agricultural Research (ICAR). AVIRIS-NG data covers a wavelength range of 380-2510 nm in VNIR and SWIR spectral channels, with a contiguous spectral band of 425 at a 5 nm interval. Spatially, it has a varying resolution of 4-8 m with a flying altitude of 4-8 km for a swath of 4-6 km. The AVIRIS-NG radiance data were orthorectified using input geometry files (IGM) present with the data containing the original line number and sample number for georeferencing and later atmospheric correction was carried out.

2.6. Data Processing

AVIRIS-NG data Endmember spectra was collected using ENVI software for the selected locations in farmers field. Correlation analysis was performed for spectral reflectance and LAI. Highly correlated spectral bands were used to calculate the new band ratios and estimate the relevant vegetation indices. The regression analysis was performed between vegetation indices and LAI. Descriptions and formulas of VIs used in the study are listed in Table 2.

2.7. Data Analysis

The mean reflectance values of LAI at different phenological stages was obtained from different rice fields (area ranged from 0.5 to 10.0 ha) to find out the sensitive wavebands for each phenological stage. The descriptive statistics of LAI and vegetation indices at different phenological stages were performed, and means along with standard deviation were presented. Coefficient of determination (R2) and root mean square error (RMSE) were used to evaluate the prediction accuracy of the regression models [52], as they are the indicators of how well regression models (best-fit function) capture the relationship between LAI and vegetation indices. Regression analysis was performed between LAI and vegetation indices for calibration and validation of the data and R2 and RMSE were presented. Statistical analysis was performed using SAS Institute Inc. [53].

3. Results

3.1. Reflectance Spectra of Rice Canopy from Hand held Hyperspectral Radiometry

The ground based mean canopy reflectance spectra of rice at different phenological stages was recorded and presented in Figure 4a. It was found that the spectral reflectance of rice showed variation at various phenological stages in ultraviolet (350-400 nm), visible (400-750 nm) and NIR regions (750-1100 nm) with maximum reflectance in the NIR region. In UV region, the canopy reflectance was maximum at tillering followed by seedling stage and the lowest was observed during flowering and maturity. Within the visible region, green region (490-560 nm) had higher reflectance compared to violet-blue (400-425 nm) and red region (640-685 nm). The reflectance in visible region increased as the crop advanced from seedling to tillering and reduced from elongation to flowering, with the lowest reflectance at heading. The reflectance in the NIR region varied steadily with crop phenological stage, indicating a distinct variation in the spectral pattern that can be leveraged to identify the crop phenological stage.

3.2. Reflectance Spectra of Rice Canopy from AVIRIS-NG

The air borne spectral reflectance showed marked variation similar to ground-truth spectra at different phenological stages, as shown in Figure 4b. The variations in spectral reflectance were observed in UV, visible and NIR regions with increased reflectance in NIR region and reduced reflectance in both UV and visible regions. The findings of canopy reflectance observed in ground-truth spectra were similar and consistent with the air borne spectral reflectance at different phenological stages. In UV region, maximum and minimum reflectance was noticed in tillering and maturity stages, respectively. Within visible region, the reflectance variations were clearly observed in the green region compared to violet-blue and red region, with the highest reflectance in tillering and lowest in elongation stage. In NIR region, the reflectance pattern exhibited increased trend from seedling to booting and reduced trend from flowering to maturity stage. From these results, it is evident that various crop growth phases of rice can be differentiated using airborne hyperspectral reflectance spectra.

3.3. Rice LAI at Different Phenological Stages

LAI measurements at different phenological stages were presented in Table 3. As the crop advances from seedling to maturity, variations in LAI were obvious with changing phenological stages. LAI increased from seedling to heading, and reached peak at flowering and reduced thereafter (1.21 at seedling to 3.61 at heading and flowering). The range of LAI was maximum at booting (1.70 to 4.00) followed by tillering (0.75 to 2.60) and elongation stage (1.60 to 3.00). The minimal variations in LAI were observed at maturity stage (1.60 to 2.50).

3.4. Relationship of Vegetation Indices to Rice Phenological Stages

The vegetation indices used in the study showed marked variations with canopy growth from seedling to maturity stage. We used 29 vegetation indices to estimate rice LAI at varied phenological stages (Table 4). The relationship of different VIs with growth stages from seedling to heading were established. The temporal variations of these VIs increased from seedling to heading and later decreased till crop maturity. Results showed that NDVI (0.54-0.91), SAVI (0.82-1.36), MSR (0.91- 3.54) and VOG (1.06-2.11) performed better. Similarly, other VIs viz., WDRVI, OSAVI and GNDVI also performed better from seedling to heading with the values ranging between 0.50-1.27, 0.38-0.74 and 0.41-0.78, respectively. The SR (3.78-20.79), TVI (7.15-17.95) and RVI (3.83-21.75) showed relatively higher values, whereas MTVI 2 (0.17-0.47), mND705 (0.16-0.74) and RDVI (0.27-0.56) exhibited lower values from seedling to heading stage.

3.5. Evaluation of Vegetation Indices for Rice LAI Estimation

The LAI data collected using hand held canopy analyzer during ground truthing showed higher LAI till heading stage, which was also found true when compared with the LAI estimated using AVIRIS-NG. These vegetation indices were subjected to regression analysis to build models for estimating LAI at each phenological stage. The regression analysis (R2 and RMSE) was performed between LAI and VIs. The indices that are statistically significant at each phenological stage were represented in Table 5. The results showed that vegetation indices had significant relationship with LAI till booting stage only. Thereafter, at heading and flowering stages, the relationship was non-significant. Among the VIs, at seedling stage, SR (R2 = 0.66**, RMSE = 0.28), MSR (R2 = 0.65***, RMSE = 0.28), RVI (R2 = 0.65***, RMSE = 0.28) and WDRVI (R2 = 0.64***, RMSE = 0.28) captured LAI better than the other VIs. However, SAVI (R2 = 0.60**, RMSE = 0.29) and NDVI (R2 = 0.59**, RMSE = 0.30) exhibited slightly lower significant relationship with LAI at the same phenological stage. At tillering, though VOG2 (R2 = 0.52**, RMSE = 0.38) and PRI (R2 = 0.47**, RMSE = 0.41) had strong positive relationship, it was not considered as the best fit, since no relationship with LAI was observed at later phenological stages. The mND705 had exhibited strong relationship with LAI with better values (R2 = 0.44**, RMSE = 0.42), followed by NDVI (R2 = 0.42**, RMSE = 0.42) and SAVI (R2 = 0.42**, RMSE = 0.42). The DD (R2 = 0.30**, RMSE = 0.46) and GNDVI (R2 = 0.32*, RMSE = 0.46) had weak relationship at all phenological stages.
Interestingly, REP (R2 = 0.74**, RMSE = 0.29) and MSR705 (R2 = 0.70**, RMSE = 0.31) had captured LAI better than other VIs during elongation stage, but failed for rest of the phenological stages. The best-fit models of LAI and VI for elongation stage were NDVI (R2 = 0.69**, RMSE = 0.31), SAVI (R2 = 0.69**, RMSE = 0.31), WDRVI (R2 = 0.68**, RMSE = 0.32) and MSR (R2 = 0.66**, RMSE = 0.33), while the least fit model was with mND705 (R2 = 0.57*, RMSE = 0.37). At booting stage, WDRVI (R2 = 0.67**, RMSE = 0.49), NDVI (R2 = 0.65**, RMSE = 0.52), SAVI (R2 = 0.65**, RMSE = 0.51) and MSR (R2 = 0.64**, RMSE = 0.52) had strong relationship between LAI and VI whereas, RVI (R2 = 0.53*, RMSE = 0.59) had weak relationship at booting stage. Some of the other VIs viz., RVSI, OSAVI, RDVI, MTVI2, TVI, mND705 and PLS were good at seedling stage; SIPI, VOG, MTCI and DD at tillering; SIPI, PRI, VOG and MTCI at elongation stage; SIPI, MSR705, OSAVI, RDVI, MTVI2 and DD at booting stage (Table 5). The indices that had varied responses with different phenological stages was not considered for further validation.

3.6. Validation of VIs for Estimation of LAI

The best fit VIs were validated using independent data sets of 54 field locations. As the crop growth advanced from seedling to flowering, the LAI increased. Thereafter, it decreased till maturity stage. The maximum deviations in LAI were noticed in the booting stage ranging from 2.50-4.10. The minimal variations were observed in the seedling stage i.e., from 0.60-1.31 (Table 6, Figure 5). Further, the R2 and RMSE of the validation dataset at four phenological stages were calculated for all the best-fit models, but only the significant ones are represented in Table 7. Among several VIs that were found statistically significant, only 8 VIs were identified to estimate LAI at all the phenological stages except booting stage. For booting stage, only 2 VIs were found significant. The R2 and RMSE values differed for each phenological stage and vegetation indices. Similar to the calibration data, VIs showed differential response at each stage with fluctuating R2 values and the best prediction was observed at elongation stage.
At seedling stage, strong correlation between LAI and mND705 (R2 = 0.52**) was observed that indicated the best fit model followed by MSR and RVI (R2 = 0.51**). Other VIs viz., SR, WDRVI, SAVI, NDVI and GNDVI have significant correlation and have R2 values ranging from 0.45 to 0.50. At tillering stage, SAVI (R2 = 0.56**) followed by MSR (R2 = 0.48*) proved best fit with field LAI. Other VIs which are statistically significant include GNDVI (R2 = 0.45*), mND705 (R2 = 0.42*), SR (R2 = 0.40*), WDRVI (R2 = 0.37*), RVI (R2 = 0.38*) and NDVI (R2 = 0.34*). The best estimation of LAI was noticed at elongation stage (R2 = 0.63- 0.88). The best fit model with strong correlation was noticed between LAI and mND705 which had maximum R2 (0.88**) followed by GNDVI (0.83**). The performance of VIs viz., WDRVI, NDVI and RVI were better (R2 = > 0.70*) compared to MSR, SR and SAVI (R2 = <0.70*) at elongation stage. At booting stage, only two VIs viz., WDRVI (0.62*) and SAVI (0.59*) found strongly correlated with LAI and rest of the VIs were non-significant. The satellite images of the best-performing VIs for LAI estimation of the selected fields viz., mND705, SAVI and WDRVI were depicted in Figure 6 respectively.
Our results showed that RMSE values also differed with phenological stages. The lower RMSE values were observed at seedling (0.17-0.18) and elongation stages (0.15-0.27) compared to tillering (0.28-0.34) and booting (0.39-0.60). The models with least RMSE value will predict better. Thus, seedling stage can be estimated better followed by elongation stage. The estimation of LAI with VIs at booting stage was not significantly correlated when compared to other three phenological stages. Hence, most of the indices performed better at later phenological stages (elongation stage, the canopy was fully exposed) compared to early stages (seedling and tillering).

4. Discussion

This study aims to estimate the rice LAI at different phenological stages viz., seedling, tillering, elongation, booting, heading, flowering and maturity using AVIRIS-NG hyperspectral data and ground measured LAI data. The results indicated that the relationship of LAI with vegetation indices at different phenological stages was distinct (Table 5). Rice plant undergoes distinct changes in the canopy at different phenological stages during the growing season. As the rice plants grow from seedling to booting, the growth flourishes. Once the plant enters the reproductive stage (heading stage), the panicles start emerging, bringing changes in the canopy architecture. Our findings revealed that LAI increased upto heading and thereafter decreased (Table 3). The similar changes in LAI at different growth phases of rice has been reported earlier [54,55].
The reflectance data of both ground-based hyperspectral radiometry and AVIRIS-NG showed marked variations between phenological stages and identical trend was noticed in both the ground based and airborne hyperspectral data (Figure 4). The results further sowed that the spectral reflectance increased and reached peak value at booting and reduced thereafter at ripening phase. The canopy reflectance is governed by biophysical characteristics of vegetation, architecture of the canopy, atmospheric absorption, and scattering, direction of incidence radiation and soil backgrounds [56]. As higher photosynthetic activity takes place during vegetative stage of rice, the plants absorb more photosynthetically active radiation from the visible region [57], and variations in the reflectance in visible region was low during this stage when compared to heading stage, where the panicles start emerging on the top of canopy (Figure 4b). Contrarily, in NIR region, the canopy reflectance of rice from ripening stages (heading to maturity) tend to be higher compared to vegetative stages (Figure 4b). During early vegetative stage of rice, the canopy reflectance was reduced due to slow growth and increased exposure to soil and water. As the crop advances to reproductive stage, the growth hastens and increased reflectance was noticed from elongation to flowering, probably due to increased leaf area and reduced exposure of soil [58,59]. Spectral reflectance in the visible region is mainly influenced by pigment absorption, while the reflectance in NIR region is determined by canopy structure and the changes in leaf orientation from horizontal to vertical at critical stages owing to overlapping of leaves, chlorotic and necrotic turning during senescence [60,61,62,63].
The vegetation indices tested in this study were calculated based on the ratio or difference between NIR and visible reflectance, having distinct response at different phenological stages. These vegetation indices have been used in the past for estimating biophysical parameters viz., LAI, chlorophyll and biomass in several crops [24,64,65]. Our findings revealed that there was significant correlation between LAI and VIs at different phenological stages (Table 5) and these results are in accordance with previous studies [66,67]. The ground measured LAI was best fitted with the VIs at elongation stage (R2 = 0.51-0.74, p<0.05) and tillering (R2 = 0.30-0.52, p<0.05) followed by booting stage (R2 = 0.52 to 0.67, p<0.05), suggesting that rice LAI can be predicted better at elongation stage. This shows that the 51-74% variability in vegetation indices could be explained by LAI, while the remaining 26-49% variation could probably attribute to different agronomic management practices viz., crop variety, fertilizer management, planting time etc. Validation of LAI with vegetation indices at different phenological stages showed that the performance was low at seedling (R2 = 0.45-0.52), tillering (R2 = 0.34-0.56) and booting (R2 = 0.10-0.62), whereas better performance was observed at elongation stage (R2 = 0.63-0.88). The LAI of rice crop at vegetative stages was upto 3.0 and indices viz., mND705, SR, MSR, RVI, WDRVI, SAVI, NDVI and GNDVI better predicted LAI across all the stages from seedling to booting with few exceptions (Table 7). Whereas, from heading to maturity, there was no relationship between LAI and VIs.
The best performing VIs for LAI have bands at green, red, blue and NIR regions. These findings agreed with the earlier reports by Thenkabail et al. [67], Darvishzadeh et al. [68] and Herrmann et al. [69] suggesting that the red-edge and NIR bands are important for LAI assessment and possess strong influence to strengthen the relationship between spectral reflectance and LAI. At seedling and elongation stages, the mND705 accurately predicted the LAI which consists of the combination of red-edge, blue and NIR bands suggesting that blue bands can also have influence in LAI estimation [70,71,72]. This index effectively reduces the impact of differences in leaf surface reflectance and improves the sensitivity of pigment content estimation [33]. At tillering stage, SAVI with red and NIR bands predicted LAI better and similar reports were observed by He et al. [73]. This could be because, in contrast to the NDVI, SAVI increases the linearity between the index and LAI and normalizes the soil-induced changes from spectral vegetation indices without affecting the vegetation measures [15]. At booting stage, WDRVI with red and NIR bands predicted better LAI with strong relationship and explained 62% of variation in LAI. This might be due to the higher sensitivity of WDRVI to moderate-to-high LAI (between 2 and 6) that aid in monitoring the vegetation status and precision farming [74]. Earlier studies also established the WDRVI vegetation index for accurate estimation of LAI in maize, soybean, wheat and potato [75,76]. The distinct variation of VIs for LAI at different phenological stages was likely due to the leaf angle within the canopy rather than individual leaf reflectance properties [65,77].

5. Conclusions

In this study, eight hyperspectral vegetation indices were found promising for estimating rice LAI at different phenological stages. Among all the stages, LAI at elongation stage was the best predicted using AVIRIS-NG airborne hyperspectral sensors. The findings revealed different vegetation indices for different phenological stages. Red-edge, Blue and NIR bands were found sensitive for seedling and elongation stages, whereas, Red and NIR bands for tillering and booting stages. The results revealed that the best suitable hyperspectral vegetation indices for mapping and estimating LAI in rice were mND705 at seedling and elongation stages, SAVI at tillering and WDRVI at booting stage. The feasibility of using AVIRIS NG airborne hyperspectral sensors with very high spatial resolution for precise mapping of LAI from rice crop is demonstrated in the present study.

Author Contributions

Conceptualization, M.P.; methodology, K.A.G. and U.S.S.; software, G.S.S.; validation, M.T.; formal analysis, M.T., U.S.S. and G.S.K.; investigation, M.P., K.A.G. and N.R.K.; resources, M.P.; data curation, M.T., U.S.S., G.S.K. and P.C.; writing – original draft preparation, U.S.S. and P.C.; writing – review and editing, M.P., K.A.G. and N.R.K.; visualization, M.P.; supervision, M.P.; project administration, V.K.S.; funding acquisition, M.P. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by Department of Science and Technology, Government of India, Network project Big Data Analytics – Hyperspectral Data (BDA-HSRS), Grant Number BDID/01/23/2014-HSRS18.

Acknowledgments

We gratefully acknowledge the farmers for their cooperation during field surveys and collecting ground-truth data. We thankfully acknowledge the support of staff and facilities under the Indian Council of Agricultural Research (ICAR) - National Innovations in Climate Resilient Agriculture (NICRA) project in analyzing the field samples and statistical analysis.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix

Preprints 94514 g00a1
Figure A1. Map of India representing the study area.
Figure A1. Map of India representing the study area.
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Figure 1. FCC of the study area and the sampling sites.
Figure 1. FCC of the study area and the sampling sites.
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Figure 2. Ground-truth data collection in the study area.
Figure 2. Ground-truth data collection in the study area.
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Figure 3. Rice crop at various growth phases (a) Seedling; (b) Tillering; (c) Elongation; (d) Heading; (e) Flowering; (f) Maturity.
Figure 3. Rice crop at various growth phases (a) Seedling; (b) Tillering; (c) Elongation; (d) Heading; (e) Flowering; (f) Maturity.
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Figure 4. Hyperspectral signatures for different crop growth stages of rice recorded using (a) ground based measurements and (b) AVIRIS-NG.
Figure 4. Hyperspectral signatures for different crop growth stages of rice recorded using (a) ground based measurements and (b) AVIRIS-NG.
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Figure 5. LAI of rice fields used for calibration and validation of data.
Figure 5. LAI of rice fields used for calibration and validation of data.
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Figure 6. Estimated Leaf Area Index (LAI) of rice fields with (a) mND705; (b) SAVI; (c) WDRVI.
Figure 6. Estimated Leaf Area Index (LAI) of rice fields with (a) mND705; (b) SAVI; (c) WDRVI.
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Table 1. Details of rice fields sampled at different phenological stages.
Table 1. Details of rice fields sampled at different phenological stages.
Crop growth
stage		 Field samples considered for calibration Field samples considered for validation
Seedling 14 12
Tillering 19 12
Elongation 9 7
Booting 11 7
Heading 10 6
Flowering 8 5
Maturity 7 5
Total 78 54
Table 2. Hyperspectral vegetation indices used in the study.
Table 2. Hyperspectral vegetation indices used in the study.
Spectral Vegetation Index Formula Reference
VOG 1 R 740 R 720 [30]
MTCI R 850 − R 730 / R 730 − R 675 [31]
VOG 2 R 734 − R 747 / R 715 + R 726 [30]
MSR R 800 / R 670 − 1 R 800 / R 670 + 1 [32]
mND705 R 750 − R 705 R 750 + R 705 − 2 * R 445 [33]
DD R 749 − R 720 − R 701 − R 672 [34]
GNDVI R 750 − R 550 / R 750 + R 550 [35]
OSAVI ( 1 + 0.16 ) R 800 − R 670 / R 800 + R 670 + 0.16 [36]
RDVI R 800 − R 670 R 800 + R 670 [17]
SR R 800 R 670 [37]
MTVI 2 1.5 * 1.2 * R 800 − R 550 − 2.5 * R 670 − R 550 2 * R 800 + 1 2 − 6 * R 800 − 5 * R 670 − 0.5 [13]
SAVI R 800 − R 670 R 800 + R 670 + 0.5 ( 1 + 0.5 ) [15]
NDVI R 810 − R 680 / R 810 + R 680 [38]
RVI R 810 / R 680 [39]
EVI 1 2.5 * R 860 − R 645 / 1 + R 860 + 6 * R 645 − 7.5 * R 470 [40]
DVI R 810 − R 680 [39]
PRI R 570 − R 531 / R 570 + R 531 [41]
TVI 120 * R 750 − R 550 − 200 * R 670 − R 550 2 [16]
DDn 2 * R 710 − R 660 − R 760 [42]
MSR705 R 750 − R 445 R 705 − R 445 [33]
MNLI 1 + 0.5 * R 810 2 − R 680 R 810 2 + R 680 + 0.5 [43]
SIPI R 800 − R 445 R 800 − R 680 [44]
WI R 900 R 970 [45]
RVSI R 714 − R 752 2 − R 733 [46]
Slaidi S * R 1050 − R 1250 R 1050 + R 1250 * R 1555 where S=5 [47]
NDWI R 858 − R 2130 / R 858 + R 2130 [48]
NDII R 819 − R 1600 R 819 + R 1600 [49]
REP R 700 + 40 * R 670 + R 780 2 − R 700 / R 740 − R 700 [50]
WDRVI 0.2 * R 800 − R 670 0.2 * R 800 + R 670 + 1 − 0.2 1 + 0.2 [51]
Table 3. Field measured Leaf Area Index (LAI) of rice at different crop growth stages used for model building.
Table 3. Field measured Leaf Area Index (LAI) of rice at different crop growth stages used for model building.
Crop growth stage Number of fields surveyed LAI
Mean ± SD Minimum Maximum P value CV
Seedling 14 1.21 ± 0.45 0.54 2.20 0.59 37.50
Tillering 19 1.71 ± 0.54 0.75 2.60 0.68 31.66
Elongation 9 2.41 ± 0.53 1.60 3.00 0.12 21.79
Booting 11 3.00 ± 0.82 1.70 4.00 0.04 27.24
Heading 10 3.61 ± 0.47 3.10 4.30 0.15 13.00
Flowering 8 3.61 ± 0.45 2.80 4.10 0.42 12.49
Maturity 7 2.11 ± 0.35 1.60 2.50 0.38 16.48
Table 4. Vegetation indices at different crop growth stages of rice estimated from AVIRIS-NG.
Table 4. Vegetation indices at different crop growth stages of rice estimated from AVIRIS-NG.
VI Seedling Tillering Elongation Booting Heading Flowering Maturity
WI 1.80 ± 0.93 1.63 ± 0.69 1.24 ± 0.19 1.22 ± 0.18 1.18 ± 0.02 1.13 ± 0.03 1.07 ± 0.07
NDWI 0.26 ± 0.28 0.26 ± 0.20 0.14 ± 0.11 0.14 ± 0.08 0.15 ± 0.01 0.11 ± 0.03 0.04 ± 0.07
NDII 0.56 ± 0.20 0.54 ± 0.17 0.43 ± 0.11 0.44 ± 0.06 0.51 ± 0.01 0.46 ± 0.04 0.29 ± 0.14
SLAIDI 0.01 ± 0.00 0.01 ± 0.00 0.01 ± 0.00 0.01 ± 0.00 0.01 ± 0.00 0.01 ± 0.00 0.01 ± 0.00
NDVI 0.54 ± 0.14 0.64 ± 0.08 0.80 ± 0.08 0.82 ± 0.08 0.91 ± 0.02 0.84 ± 0.03 0.53 ± 0.21
OSAVI 0.38 ± 0.10 0.44 ± 0.07 0.58 ± 0.07 0.62 ± 0.08 0.74 ± 0.04 0.67 ± 0.04 0.43 ± 0.15
GNDVI 0.41 ± 0.09 0.49 ± 0.07 0.64 ± 0.08 0.67 ± 0.09 0.78 ± 0.02 0.71 ± 0.03 0.53 ± 0.11
RVSI -0.01 ± 0.00 -0.01 ± 0.00 -0.01 ± 0.00 -0.01 ± 0.01 0.01 ± 0.00 -0.00 ± 0.00 -0.01 ± 0.00
REP 735.60 ± 64.56 728.59 ± 8.77 725.35 ± 1.64 725.43 ± 2.45 729.34 ± 0.54 727.22 ± 1.09 742.50 ± 16.60
SR 3.78 ± 1.43 4.89 ± 1.31 10.64 ± 4.44 12.01 ± 4.70 20.79 ± 3.97 11.82 ± 2.88 4.28 ± 2.68
RDVI 0.27 ± 0.07 0.31 ± 0.05 0.42 ± 0.06 0.45 ± 0.06 0.56 ± 0.04 0.50 ± 0.03 0.33 ± 0.11
SAVI 0.82 ± 0.21 0.97 ± 0.13 1.20 ± 0.12 1.23 ± 0.12 1.36 ± 0.03 1.26 ± 0.05 0.80 ± 0.32
MSR 0.91 ± 0.37 1.19 ± 0.30 2.19 ± 0.70 2.40 ± 0.70 3.54 ± 0.43 2.42 ± 0.41 0.98 ± 0.65
TVI 7.15 ± 2.86 8.00 ± 2.18 12.01 ± 2.47 13.69 ± 2.33 17.95 ± 2.07 15.94 ± 1.54 9.75 ± 4.18
MNLI -0.41 ± 0.05 -0.40 ± 0.05 -0.47 ± 0.05 -0.50 ± 0.04 -0.60 ± 0.04 -0.57 ± 0.03 -0.51 ± 0.04
MTCI 0.58 ± 0.32 1.27 ± 0.49 2.80 ± 0.84 3.32 ± 1.33 6.26 ± 0.54 3.80 ± 0.83 1.69 ± 0.76
MTVI2 0.17 ± 0.06 0.20 ± 0.05 0.30 ± 0.06 0.34 ± 0.06 0.47 ± 0.06 0.40 ± 0.04 0.21 ± 0.11
PLS 1.40 ± 0.42 1.53 ± 0.33 2.07 ± 0.49 2.41 ± 0.38 3.26 ± 0.31 2.74 ± 0.37 1.99 ± 0.46
RVI 3.83 ± 1.49 4.84 ± 1.22 10.83 ± 4.54 12.71 ± 5.72 21.75 ± 3.91 11.95 ± 2.90 4.30 ± 2.73
DVI 0.14 ± 0.04 0.15 ± 0.03 0.22 ± 0.05 0.25 ± 0.04 0.35 ± 0.04 0.31 ± 0.03 0.21 ± 0.06
PRI 0.13 ± 0.03 0.09 ± 0.02 0.05 ± 0.03 0.04 ± 0.03 0.00 ± 0.01 0.04 ± 0.02 0.09 ± 0.02
WDRVI 0.50 ± 0.19 0.64 ± 0.14 0.98 ± 0.20 1.03 ± 0.20 1.27 ± 0.06 1.06 ± 0.10 0.51 ± 0.31
VOG1 1.06 ± 0.09 1.19 ± 0.12 1.53 ± 0.19 1.62 ± 0.25 2.11 ± 0.09 1.72 ± 0.15 1.26 ± 0.19
mND705 0.16 ± 0.10 0.31 ± 0.10 0.52 ± 0.09 0.56 ± 0.11 0.74 ± 0.02 0.60 ± 0.07 0.29 ± 0.16
MSR705 0.71 ± 0.09 0.74 ± 0.05 0.86 ± 0.05 0.86 ± 0.06 0.91 ± 0.01 0.88 ± 0.02 0.79 ± 0.10
SIPI 1.27 ± 0.21 1.13 ± 0.08 1.05 ± 0.03 1.04 ± 0.05 1.01 ± 0.01 1.04 ± 0.02 1.41 ± 0.30
DDN -0.20 ± 0.04 -0.21 ± 0.05 -0.32 ± 0.07 -0.37 ± 0.07 -0.54 ± 0.06 -0.46 ± 0.05 -0.41 ± 0.02
DD -0.05 ± 0.01 -0.01 ± 0.02 0.04 ± 0.03 0.06 ± 0.04 0.13 ± 0.02 0.08 ± 0.02 0.00 ± 0.04
VOG2 -0.02 ± 0.01 -0.04 ± 0.02 -0.11 ± 0.04 -0.13 ± 0.07 -0.28 ± 0.03 -0.16 ± 0.04 -0.05 ± 0.04
The values are Mean ± Standard deviation.
Table 5. Best fit models for estimating LAI using different Vegetation Indices (VI) at different crop growth stages of rice.
Table 5. Best fit models for estimating LAI using different Vegetation Indices (VI) at different crop growth stages of rice.
VI Seedling VI Tillering VI Elongation VI Booting VI Heading VI Flowering
R2 RMSE R2 RMSE R2 RMSE R2 RMSE R2 RMSE R2 RMSE
SR 0.66*** 0.28 VOG2 0.52*** 0.38 REP 0.74** 0.29 WDRVI 0.67** 0.49 NDII 0.10ns 0.47 PRI 0.44ns 0.37
MSR 0.65*** 0.28 PRI 0.47** 0.41 MSR705 0.70** 0.31 NDVI 0.65** 0.52 WI 0.08ns 0.48 REP 0.36ns 0.39
RVI 0.65*** 0.28 mND705 0.44** 0.42 NDVI 0.69** 0.31 SAVI 0.65** 0.51 SIPI 0.08ns 0.48 mND705 0.36ns 0.39
WDRVI 0.64*** 0.28 NDVI 0.42** 0.42 SAVI 0.69** 0.31 MSR 0.64** 0.52 MSR705 0.07ns 0.48 SIPI 0.35ns 0.39
SAVI 0.60** 0.29 SAVI 0.42** 0.42 WDRVI 0.68** 0.32 SIPI 0.62** 0.53 RVSI 0.05ns 0.48 MTCI 0.33ns 0.40
NDVI 0.59** 0.30 WDRVI 0.42** 0.42 MSR 0.66** 0.33 MSR705 0.60** 0.55 REP 0.03ns 0.49 VOG2 0.32ns 0.40
RVSI 0.59** 0.30 MSR 0.41** 0.42 SR 0.65** 0.33 SR 0.59** 0.55 SR 0.02ns 0.49 RVSI 0.26ns 0.42
OSAVI 0.57** 0.31 RVI 0.41** 0.43 SIPI 0.64* 0.34 mND705 0.59** 0.55 MSR 0.01ns 0.50 VOG 0.25ns 0.42
RDVI 0.56** 0.31 SIPI 0.41** 0.43 RVI 0.60* 0.36 OSAVI 0.58** 0.56 TVI 0.01ns 0.50 NDVI 0.24ns 0.42
GNDVI 0.55** 0.32 SR 0.40** 0.43 GNDVI 0.58* 0.37 GNDVI 0.54* 0.58 MNLI 0.01ns 0.50 SR 0.24ns 0.42
MTVI2 0.55** 0.32 VOG 0.40** 0.43 mND705 0.57* 0.37 RDVI 0.54* 0.58 PLS 0.01ns 0.50 SAVI 0.24ns 0.42
TVI 0.53** 0.32 MTCI 0.38** 0.44 PRI 0.55* 0.38 MTVI2 0.53* 0.59 RVI 0.01ns 0.49 MSR 0.24ns 0.42
mND705 0.52** 0.32 GNDVI 0.32* 0.46 VOG 0.54* 0.38 RVI 0.53* 0.59 DVI 0.01ns 0.50 WDRVI 0.24ns 0.42
PLS 0.48** 0.34 DD 0.30* 0.46 MTCI 0.51* 0.39 DD 0.52* 0.60 mND705 0.01ns 0.50 DD 0.24ns 0.43
* Significant at 0.05% | ** Significant at 0.01% | *** Significant at 0.001% | ns: non-significant.
Table 6. Field measured LAI of rice at different crop growth stages used for validation of models.
Table 6. Field measured LAI of rice at different crop growth stages used for validation of models.
Crop growth stage Number of fields LAI
Mean ± SD Minimum Maximum P value CV (%)
Seedling 12 0.99 ± 0.23 0.60 1.31 0.33 23.51
Tillering 12 1.78 ± 0.40 1.20 2.42 0.42 22.41
Elongation 7 2.89 ± 0.40 2.50 3.60 0.29 13.80
Booting 7 3.29 ± 0.58 2.50 4.10 0.88 17.70
Heading 6 3.63 ± 0.35 3.20 4.20 0.92 9.64
Flowering 5 3.79 ± 0.57 2.90 4.50 0.42 15.11
Maturity 5 2.24 ± 0.68 1.50 3.10 0.53 30.38
Table 7. Estimated LAI at different crop growth stages of rice (Validation of model outputs).
Table 7. Estimated LAI at different crop growth stages of rice (Validation of model outputs).
VI Seedling Tillering Elongation Booting
R2 RMSE R2 RMSE R2 RMSE R2 RMSE
mND705 0.52** 0.17 0.42* 0.32 0.88** 0.15 0.51ns 0.45
SR 0.50** 0.17 0.40* 0.32 0.66* 0.26 0.10ns 0.60
MSR 0.51** 0.17 0.48* 0.30 0.67* 0.25 0.42ns 0.49
RVI 0.51** 0.17 0.38* 0.33 0.71* 0.24 0.10ns 0.60
WDRVI 0.47* 0.18 0.37* 0.33 0.76* 0.22 0.62* 0.39
SAVI 0.49* 0.17 0.56** 0.28 0.63* 0.27 0.59* 0.41
NDVI 0.45* 0.18 0.34* 0.34 0.73* 0.23 0.54ns 0.43
GNDVI 0.49* 0.17 0.45* 0.31 0.83** 0.18 0.50ns 0.45
* Significant at 0.05% | ** Significant at 0.01% | ns: non-significant.
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