WATER STRESS ASSESSMENT IN MAIZE CROP USING FIELD HYPERSPECTRAL DATA

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1 WATER STRESS ASSESSMENT IN MAIZE CROP USING FIELD HYPERSPECTRAL DATA V.S.Manivasagam 1 and R.Nagarajan 2 1 Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Powai, Mumbai 476, India, vsmanivasagam@gmail.com 2 Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Powai, Mumbai 476, India, rn@iitb.ac.in KEY WORDS: Water stress, Maize, Hyperspectral, Spectroradiometer, Principal components ABSTRACT: Water stress is the major growth limiting factor in rainfall dependent agriculture regions. The effect of water stress displayed in the crop canopy can be effectively monitored by the field based observations. This study focus on the relationship between the crop water stress and spectral signature of the maize crop measured in the Hyper-spectral mode during different growth stage. Maize crop was grown under controlled irrigation and plants were exposed to known water stress condition by limiting water supply. Spectral measurements were made before and after irrigation, using Spectroradiometer (GER 15) having spectral range of 35 to 15nm with view angle of 8. The observations were made during early vegetative, vegetative and flowering stages of crop growth. Water stress displayed by the leaves from green to dry leaf conditions were also observed. Principal component analysis was performed in identifying the best spectral region for discriminating the water stressed and normal irrigated crop spectra. It is found that 84-87nm and 4-45nm were the most appropriate bandwidth for discriminating water stress at different growth stages. Water stressed leaves could be effectively discriminated in the nm and 62-7nm region. These spectral signatures can also be used in hyperspectral image classification for Hyperion data. Field spectral bandwidth of 84-87nm (indicates the crop water stress) coincides with Hyperion bands and Hence, these Hyperion bands can be exploited for regional crop water stress studies. Further these results can be efficiently used for crop yield prediction under stressed condition. The accuracy of yield prediction is very high, when the observation data are during flowering stage. 1. INTRODUCTION Water stress is one among the major limiting factors in rainfed agriculture resulted in reduced crop growth and productivity (Ghassemi-Golezani et al. 29). Maize (Zea mays L) is one of the most versatile cereal crops having wider growth adaptability under varied agro-climatic conditions and also has the highest yield potential among the cereals. It is the important cereal crop in India after rice and wheat. The area and production of maize in India have grown significantly in the last forty years. Maize production has been increased from 7 million tons in 198 to 21 million tons in 21. Water stress is observed as a major production constraint factor of maize crop for rainfed farming regions in India (Prasanna, 212). Maize is highly sensitive to water stress and it s the effects incudes the stunted growth and biomass, delayed maturity and low crop productivity. Water stress in the maize crop during the initial growth stage is influenced by surface soil moisture and during this period the maize requires very less water for survival. Relationships between water availability and crop yield are more significant in the late vegetative growth stage. Flowering stage has been found to be the most sensitive stage to water shortage, leading to reductions in crop growth, biomass production and finally the yield (Pandey et al., 2; Cakir, 24; Farré and Faci, 26). Studies report that the decrease in yield varies by 1 76% depending on the water stress intensity and the sensitivity of the crop and its growth stage (Aguilar et al., 27). The greatest potential yield reduction occurs during the silking process. Stress during the reproductive period can substantially reduce final grain yield by 35-5%. Hence, providing supplementary irrigation at critical growth stages during maize cultivation is crucial to achieve the high yield. Crop response to water stress can be measured using proxy values called indicators. The indicators are in morphological and physiological adaptations. Leaf growth can be used as an indicator of suboptimal plant water

2 status. The visible symptoms of water stress as growth stages progress are (i) curling of leaves to reduce the water loss through transpiration, (ii) leaf wilting and drying of leaf tips, (iii) stunted crop growth and reduced crop yield and (iv) finally the death of the plant. Monitoring crop growth response to water availability at farm level and regional scale are essential for potential crop yield assessment. The continuous retrieval of biophysical information on crops from the multispectral satellite data has constraints in spatial, spectral and temporal data. Broad band multispectral data records very few spectral variations and most of band information are redundant (Thenkabail et al., 2). Spectral characteristics of plants are influenced by canopy structure, size, greenness and leaf water content which were not prominently captured by multispectral data. The continuous narrow band spectral information of hyperspectral data can able to differentiate the spectrally similar object conditions from the crop canopy (Bajwa et al., 24; Genc et al., 213). Hence, an attempt has been made in understanding crop water stress conditions exhibited by the soil moisture, leaf morphology using the field hyperspectral measures during the different growth stage of maize. Thus, with the above background the study has been conducted to understand the spectral behavior of maize owing to growth stage and water availability deploying non-destructive approach. The study is also focusing on identification of spectral band for maize crop under water stress condition. 2. MATERIALS AND METHODS Field experiment has been conducted in Aurangabad district, one of the major maize growing areas during kharif season in the Central Peninsular India (Figure 1). The region receives average rainfall of 734 mm and the temperature varies from 6 C (minimum) to 46 C (maximum) during peak summer. In order to understand the water stress impact on crop, Maize crop was grown under controlled irrigation and plants were exposed to known water stress condition by limiting the water supply. The field spectra were recorded from various growth stage of maize crop grown during summer season (Figure 2). The 512-channel GER15 spectroradiometer (Spectra Vista) with a range of nm was used to record reflectance. Spectral measurements were recorded before and after irrigation, observed within 8 o field of view. Uniform conditions were maintained in terms of sunlight condition, plant geometry, and sensor distance from target. While taking readings, the spectroradiometer has been calibrated with a white spectrum to minimize the effect of change in sun illumination. The readings were averaged to produce a single reflectance spectrum. The observations were made during early-vegetative, vegetative, VT stage (Tassels fully emerged) and reproductive stages of crop growth (Figure 3). Water stress displayed by the leaves from green to dry leaf conditions was also observed (Figure 4). Figure 1. Location map of Study area

3 Principal Component Analysis (PCA) was performed to identify suitable spectral bandwidth for water stress discrimination of each growth stage (Table 1). PCA is one of the widely used statistical methods to analyze the factors which governs most of the variance for the input datasets (Ruiliang and Gong, 2; Kumar et al., 213). PCA technique reduces the data volume by formulating a new set of uncorrelated variables also called as Principal Components (PC). The top ten bandwidths of first two principal components (PC) consisting of the highest factor loadings, were identified as the suitable spectral bandwidths for discrimination showing maximum variability. The initial preprocessing of spectral data has been carried out using FSF Post Processing tool (Ver ). PCA analyze was performed using MATLAB and spectral bands obtained were considered as suitable region for stress discrimination. Table 1. Maize water stress experimental observations Date of Observation Growth stage Irrigation Observation Before After Leaf Plant 17-Apr-15 Early Vegetative - Y Healthy leaf - 19-Apr-15 Early Vegetative Y - Stressed leaf - 7-May-15 Vegetative Y - - Stressed Plant 9-May-15 Vegetative - Y - Healthy plant 23-May-15 VT Stage Y - - Stressed Plant 26-May-15 VT stage - Y - Stress recovered plant 9-Jun-15 Reproductive stage - Y - Stress survived plant Figure 2. Growth stages of Maize plants at experimental plot (A) Healthy early vegetative (B) Stress early vegetative stage (C) Healthy vegetative (D) Stress vegetative (E) Stress VT stage (F) Stress recovered VT stage (G) Stress survived reproductive stage (a) Green leaf (b) Green stress leaf (c) Drying leaf (d) Dried leaf

4 Reflectance (%) Reflectance (%) Healthy Early Vegetative Stress Early Vegetative Healthy Vegetative Stress Vegetative Healthy VT stage Stress VT stage Reproductive Figure 3. Spectral reflectance of maize under healthy and stress condition at different growth stages.5.5 Green leaf Green stress leaf Drying leaf Dried leaf Figure 4. Spectral reflectance of maize leaf under different water stress condition

5 5 (A) PC 1 PC 2 5 (B) PC 1 PC 2 5 (C) PC 1 PC 2 5 (D) PC 1 PC 2. 5 (E) PC 1 PC 2 5 (F) PC 1 PC 2 Figure 5. Results of principal component analysis: (A) Healthy growth stage comparison (B) Stressed growth Stage comparison (C) PCA of early vegetative stage(d) PCA of vegetative stage (E) PCA of Tasseling and Cob emergence (F) Water Stress Maize leaf comparison

6 3. RESULTS AND DISCUSSION The suitable bands for discrimination of maize at various growth stages and water stress conditions were identified through principal component analysis and the band information for different criteria as follows: 3.1 Growth stages of Maize crop The reflectance of Maize crop during vegetative stage was higher compared to other growth stages during healthy as well as stress condition (Figure 3). PCA results of maize growth stages on healthy and stress condition resulted in 2 Principal Components shown in Figure 5A and 5B. For healthy maize growth condition, the PC1 resulted bandwidths fall in Near Infrared (NIR) region (84-87nm), which contains maximum variance information and the Blue region (42-45nm) was observed as a suitable region from PC2 result. For stress growth condition, the PC1 resulted bandwidths fall in NIR region ( nm), which contains maximum variance information and the Blue region (4-43nm) was found to be suitable bandwidth from PC2 result. Comparing PCA suitable bands for the healthy and stress condition, there is a slight forward shift in NIR bands where as in blue bands shift occurred in backward side. 3.2 Water stress and Maize crop The reflectance of Maize crop during healthy and water stress conditions were analyzed for each growth stages (Figure 3). The PCA results of maize healthy and stress condition for early vegetative, vegetative and flowering stages shown in Figure 5C, 5D and 5E respectively. During early vegetative stage, the PC1 resulted bandwidths fall in NIR region (77-78nm and 83-86nm), which contains maximum variance information and the NIR region (72-75nm) was observed as suitable region from PC2 result. For vegetative stage, the PC1 resulted bandwidths fall in NIR region (84-87nm), which contains maximum variance information and the NIR region (12-15nm) was found to be suitable bandwidth from PC2 result. The NIR region ( nm) for PC1 and Blue (4-42nm) and Green (525-54nm) regions for PC2 were observed as the suitable band for flowering (tasselling and cob emergence) stress discrimination. The NIR region was identified as the most ideal bandwidth for the healthy and stress crop discrimination in each growth stages. 3.3 Water stress in Maize leaf The reflectance of Maize leaf was higher in green leaf compare to other stage of stress exposed leaf (Figure 4).When the water stress level increase from green to dry leaf the corresponding reflectance values were increased in red region and decreased in NIR region. The PCA result of maize leaf was shown in Figure 5F. For discriminating the different stage of water stress in maize leaf, the PC1 resulted bandwidths fall in NIR region ( nm), which contains maximum variance information and the Red region (62-64nm and nm) was observed as suitable region from PC2 result. 3.4 Discussion The PCA method resulted in the best for the discrimination of maize water stress has shown in Table 2. There are 12 spectral regions were identified for maize water stress discrimination. Out of these, 4 bands were found in NIR region (72-75,77-78, 84-87, 12-15); 2 in Blue region (4-43, 42-45nm); 2 in Red region (62-64, nm) and 1 in Green region at 4-42nm. Spectral region falling in between 84-87, were found to be optimal wavelength ranges for discrimination of maize water stress. The optimal bandwidths identified for the discrimination of water stress parameters have their physiological significance. The spectral region 84-87nm is sensitive to plant water stress in full canopy condition (DeTar et al., 26; Govender et al., 29).The spectral region 62-7nm is influenced by chlorophyll b absorption (Thenkabail et al., 24). The water stressed dry maize leaf has less absorption in red region due to low chlorophyll content has been clearly observed in Figure 4. The study results are found to be a useful proxy indicator for regional crop water management studies to improve the crop production. For a large scale assessment, such information from field level is tedious and time consuming, and presently these information s generated through multispectral satellite data. To improve this assessment, the study

7 results can be used as inputs during image classification analysis (Vyas et al., 211) of hyperspectral satellite data for preparation of maps on regional scale crop water stress maps in non-destructive manner. The spectral regions identified from this experiment can be used in hyperspectral image classification for Hyperion data. Field spectral bandwidth of 84-87nm (indicates the crop water stress) is coincide with Hyperion bands and Hence, these Hyperion bands can be exploited for regional crop water stress studies. Further these results can be efficiently used for crop yield prediction under stressed condition. The accuracy of yield prediction is very high, when the observation data are during flowering stage. Table 2. Effective bandwidth for identification of water in maize using PCA Parameter Case Spectral region (nm) Water stress Growth stage Leaf appearance Healthy Plant (PC1) and (PC 2) Water stress Plant (PC1) and 4-43 (PC 2) Early vegetative 77-78, (PC1) and (PC2) Vegetative (PC1) and (PC2) Flowering (PC1) and 4-42, (PC2) Green leaf Green stress leaf Drying leaf (PC1) and 62-64, (PC2) Dried leaf 4. CONCLUSIONS This study has demonstrated the hyperspectral data characterization for Maize crop under water stress conditions at various growth stages. PCA analysis has found to be an ideal technique for identification of optimum wavelength ranges suitable for crop water stress parameters. The Blue (4-45nm) and NIR (84-87nm) regions were the best regions for discrimination of different growth stages on healthy as well as stress condition. NIR (72-75,77-78, 84-87, 12-15) region was most appropriate for discrimination of healthy and water stressed crop in each growth stage. The Red (62-64, nm) and NIR regions ( nm) were found suitable to discriminate water stress on maize leaf. Thus, the effect of water stress at different growth stages can be effectively monitored through hyperspectral remote sensing approach. The resulted spectral reflectance characteristics of maize may also be applied to hyperspectral image classification on regional level analysis. Field spectral bandwidth of 84-87nm (indicates the crop water stress) coincides with Hyperion bands and could be validated for regional crop water stress assessment. This experiment results could also be helpful for other crops having identical crop geometry in their crop stress monitoring studies. ACKNOWLEDGEMENTS The authors acknowledge the support and facilities provided by Marathwada Institute of Technology, Aurangabad and Indian Institute of Technology Bombay for carry out this field experiment. REFERENCES Aguilar, M., Borjas, F., Espinosa, M., 27. Agronomic response of maize to limited levels of water under furrow irrigation in southern Spain. Spanish Journal of Agricultural Research, 5, pp Bajwa, S., Bajcsy, P., Groves, P., Tian, L.F., 24. Hyperspectral image data mining for band selection in agricultural applications. American Society of Agricultural Engineers, 47 (3), pp Cakir, R., 24. Effect of water stress at different development stages on vegetative and reproductive growth of corn. Field Crops Research, 89, pp. 1 6.

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