Remote sensing of regional crop transpiration of winter wheat based on MODIS data and FAO-56 crop coefficient method

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1 This article was downloaded by: [Institute of Geographic Sciences & Natural Resources Research] On: 13 August 2013, At: 23:12 Publisher: Taylor & Francis Informa Ltd Registered in England and Wales Registered Number: Registered office: Mortimer House, Mortimer Street, London W1T 3JH, UK Intelligent Automation & Soft Computing Publication details, including instructions for authors and subscription information: Remote sensing of regional crop transpiration of winter wheat based on MODIS data and FAO-56 crop coefficient method Heli Li a b c, Yi Luo b, Chunjiang Zhao c & Guijun Yang c a State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China b Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China c Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China Published online: 13 Aug To cite this article: Intelligent Automation & Soft Computing (2013): Remote sensing of regional crop transpiration of winter wheat based on MODIS data and FAO-56 crop coefficient method, Intelligent Automation & Soft Computing, DOI: / To link to this article: PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the Content ) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at

2 Intelligent Automation and Soft Computing, REMOTE SENSING OF REGIONAL CROP TRANSPIRATION OF WINTER WHEAT BASED ON MODIS DATA AND FAO-56 CROP COEFFICIENT METHOD HELI LI 1,2,3,YI LUO 2 *, CHUNJIANG ZHAO 3, AND GUIJUN YANG 3 1 State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China 2 Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China 3 Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China ABSTRACT Crop evapotranspiration is one of the most important parameters of farmland water cycle, which consists of crop transpiration (T c ) and soil evaporation. As the efficient component for crop production, T c and its accurate determination, especially on a regional scale, is very critical for scientific design of irrigation scheduling and high-efficiency utilization of water resources. In this work, the T c of winter wheat over an irrigation area located in the lower Yellow River of China was estimated by combining MODIS data and FAO-56 crop coefficient method. Specifically, the relationships between the single crop coefficient (K c ), basal crop coefficient (K cb ) and canopy vegetation indices were investigated and compared based on field data. Then, the actual K cb map of winter wheat over the study area was estimated with MODIS-derived soil adjusted vegetation index (SAVI) using the relationship obtained from above field investigations. Finally, the T c of winter wheat over the area was determined as the product of K cb and reference crop evapotranspiration (ET 0 ). ET 0 was calculated from meteorological data, and then were spatially interpolated to obtain the regional map matching with the remotely sensed K cb. It was found that compared with K c, K cb was much more closely related to the vegetation indices of NDVI, SAVI, and EVI, even in the presence of nitrogen and water stress, with the coefficients of determination (R 2 ) being 0, 7 and 8 respectively (n ¼ 195) which could be even higher without the water-stress points that had not reached the severity to make obvious changes in canopies. Results also demonstrated that it was feasible to utilize the K cb -SAVI relationship to derive the K cb of winter wheat over a large area by means of satellite remote sensing, and that it was effective to determine regional crop T c using the above approach. It would be useful in practical application due to the advantages of easy operation and separating soil evaporation effectively. Key Words: Transpiration; MODIS Data; FAO-56 Crop Coefficient Method; Winter Wheat 1. INTRODUCTION Crop evapotranspiration (ET c ) consists of crop transpiration (T c ) and soil evaporation (E s ), and is very important to farmland water cycle. Many methods have been developed for its accurate determination, such as lysimeters, energy balance, eddy covariance and soil water balance, etc. However, difficulties still exist in the effective separation of T c from ET c, especially on a regional scale. This is very crucial for the scientific design of irrigation scheduling and the high-efficiency utilization of agricultural water resources, since T c is the only efficient part for crop production while E s is commonly regarded as invalid *Corresponding author. luoyi.cas@hotmail.com q 2013 TSI w Press

3 2 Intelligent Automation and Soft Computing water consumption. The FAO-56 paper [1] developed a methodology to determine ET c by adopting a single crop coefficient K c (i.e., the ratio between ET c and the reference crop evapotranspiration ET 0 )or by the dual crop coefficients of K cb (i.e., the basal crop coefficient, a ratio of T c to ET 0 )andk e (i.e., the soil evaporation coefficient, a ratio of E s to ET 0 ). This methodology, especially the dual crop coefficient approach, has been widely used to derive ET c in many sites of the world because of its easy operation, high accuracy and strong practicability, as well as the ability to divide ET c into T c and E s effectively [2 6]. Many studies [2, 5, 7 11] at field level have proved that the determination procedures of K c, K cb and K e described in FAO56 paper [1] have high reliability and generality, based on the given information such as local meteorological conditions, cultural practices, soil moisture, leaf area index (LAI) and plant height. For a regional application, it would be faced with the difficulties to access the required data over a large area. An alternative way to determine K c or K cb is needed for regional ET c or T c estimation using the FAO-56 crop coefficient method. Since both crop coefficient and canopy reflectance is effected by such factors as vegetation coverage, LAI, greenness, etc. [4, 6, 12], it may be possible to estimate K c or K cb using canopy vegetation indices by means of remote sensing. Some limited studies [4, 13 16]) have been conducted to investigate the relationships between K c or K cb and the vegetation indices of forest, grass or well-irrigated crops, using the data from ground measurements, satellite platforms or model simulations. Further, more test work is still needed both at the field level (especially in the presence of environmental stress) and the practical regional application. In this study, winter wheat, one of the best-known grain crops, was taken as the object. First, we investigated and compared the relationships between K c, K cb and the vegetation indices which were commonly involved in above-mentioned literatures, based on field data with nitrogen (N) and water stress. And then, the feasibility of estimating regional T c by combining satellite remote sensing data and crop coefficient method was demonstrated using a concrete case over an area of hm 2 (see below). It is hoped this work can provide helpful information for future related studies. 2. MATERIALS AND METHODS 2.1 Study area Panzhuang irrigation district (PID) of hm 2, located in Shandong province and the northern shore of lower Yellow River, China, was selected for this study (Figure 1). The Yucheng Comprehensive Experimental Station (YCES) of Chinese Academy of Sciences was set in this study area. The PID has an annual mean temperature of 13.18C and average annual rainfall of mm, with a sub-humid warm temperate continental monsoon climate. A total of eight counties were covered in the PID, with each having a weather station. In addition, there was also a meteorological station installed at the YCES. Winter wheat is commonly sown in mid-october and harvest around 10 June of the coming year. 2.2 Field data collection In order to analyze and compare the relationships between K c, K cb and the vegetation indices, the data collected from the field experiments at YCES in 2009/2010 wheat season were used here. The experiments consisted of five nitrogen (N) treatments (i.e., from 0 to 280 kg N/hm 2,withanincrementof 70 kg N/hm 2 ), including fifteen plots. In each plot, an irrigation of 50 mm was applied after winter wheat turning green. Due to the low rainfall in 2009/2010 wheat season (especially in the rapid growth period

4 Remote Sensing of Regional Crop Transpiration of Winter Wheat 3 Figure 1. Location of Panzhuang irrigation district (PID, hm 2 ) and the meteorological stations in this area. after revival), water stress was measured in several high-n plots through the grain filling period. For more detailed information on the field experiments, please refer to [17]. Crop K c and K cb as well as the T c at YCES were determined according to the procedures described in FAO- 56 paper [1]. The information required in the calculation process, including daily meteorological data, management practices through whole season, weekly soil moisture, plant height and leaf area index at a ten-day interval, were all collected in this experimental site. For detailed descriptions about the measurement methods and the instruments used, please refer to [17]. The vegetation indices commonly used in previous related literatures [4, 13 16], like normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI) and enhanced vegetation index (EVI), were derived from weekly field spectral measurements through revival to maturity period, using a field spectroradiometer (Analytical Spectral Devices, USA). Other details on canopy reflectance measurements were given in [17]. For the calculation formulas of NDVI, SAVI and EVI, please see the above literatures and were not presented here. The near infrared, red and blue bands were chosen here according to the wavelength ranges of Moderate-resolution imaging spectroradiometer (MODIS). 2.3 Regional data collection Over the PID, MODIS images with good quality through 2008/2009 wheat season were acquired to derive vegetation indices after preprocessing. Then using the maximum value composite method, the composited vegetation index images of specific periods were obtained. In this work, the composited vegetation index images of middle-october, early-april, late-april, early-may, and early-june were used (see below). Regional ground surveys with numerous points distributed evenly in the PID were conducted three times through the 2008/2009 wheat season, respectively at mid-october (with 63 points), late-april and early-june (with 52 points). Such information as cropping structure, crop growth conditions (e.g. soil and water supply), field management measures, crop biophysical and spectral reflectance characteristics, etc., were collected to provide reference materials for remote sensing monitoring. Regional meteorological data, which were used to determine ET 0, were collected from the weather stations set in the eight counties within the PID. In addition, the statistical data of wheat area in 2008/2009 season were provided by the local statistic bureau and used as references in this work.

5 4 Intelligent Automation and Soft Computing 3. RESULTS AND DISCUSSIONS 3.1 Relationships between K c,k cb and canopy vegetation indices of winter wheat The canopy vegetation indices (i.e., NDVI, SAVI, and EVI) and the values of K c and K cb on the corresponding dates of spectral measurements were presented in Figure 2. The data points were from null N to high N treatments (for details see above), with some degree of water stress existing in several high-n plots during the grain filling stage (i.e., 19 May to 5 June). Large scatters were found in the relationships between K c and the vegetation indices (the coefficients of determination R 2 ¼ , n ¼ 195). This may be mainly due to that K c was not only related to crop transpiration but also included the effects of soil evaporation, which could fluctuate largely with the soil moisture dynamics in a short period. In contrast, the K cb, which was dominated by the factors of crop transpiration, had significant relationships with NDVI, SAVI, and EVI (R 2 ¼ 0, 7 and 8 respectively, n ¼ 195) in the presence of nitrogen and water stress. Note that the relations of K cb to NDVI, SAVI, and EVI would be not sensitive to the water stress that had not reached the severity to make obvious changes in canopies (as the case in this study). 3.2 Extracting cultivation area map of winter wheat over the PID Multi-temporal MODIS-NDVI images in middle-october, early-april and early-june, were used to extract the cultivation area of winter wheat and its spatial distribution in the PID. In this area, mid-october is the sowing time of winter wheat, while cotton is in the late season and other vegetations (e.g. trees) remaining green at this period; at early-april, winter wheat is in the rapid growth stage with a nearly full green coverage, while cotton is being sown and other vegetations (e.g. trees) at this period just begin to bud; in early-june, winter wheat has reached maturity or being harvested, while cotton is in the middle season and other K c (=K cb +K e ) K cb 1.4 y = 290x R 2 = NDVI y = 549ln(x) R 2 = K c (=K cb +K e ) K cb 1.4 y = ln(x) R 2 = EVI y = ln(x) R 2 = 845 K c (=K cb +K e ) K cb 1.4 y = x R 2 = SAVI NDVI EVI y = 230ln(x) R 2 = 720 SAVI Figure 2. Relationships between crop coefficients (i.e., K c and K cb ) and canopy vegetation indices (i.e., NDVI, SAVI and EVI) of winter wheat. Solid circles indicated the data points with water stress.

6 Remote Sensing of Regional Crop Transpiration of Winter Wheat 5 Figure 3. Cultivation area map of winter wheat in 2008/2009 season over the PID. Also, the main diversion canal from Yellow River and the cropping structure from regional surveys of 63 points were given here for comparison. vegetations have developed a large green coverage. Based on these three-period images, the cultivation area map of winter wheat over the PID was derived, as shown in Figure 3. For comparison, the cropping structure from regional surveys and the main diversion canal from Yellow River were also given in this figure. Results indicated that the extracted wheat acreage and its spatial distribution agreed well with the cropping structure information obtained from ground surveys. Irrigation played a significant role in wheat production of this area, whereas significant differences existed among different sites in the chances to access the irrigation water due to their geographic locations. So in the sites far away from the canal with weak irrigation conditions, cotton was planted. Further, the extracted wheat area was quantitatively compared with the data provided by local statistic bureau, as presented in Table I. It showed that the extracted results had a good accuracy in magnitude, while errors in its spatial distribution were inevitable due to mixed pixels with a spatial resolution of 250 m. 3.3 Remote sensing of the K cb of winter wheat over the PID In this work, the regional K cb of winter wheat over the PID was estimated from MODIS images using the relationship between K cb and SAVI on the basis of above analysis, with a spatial resolution of 250 m. The K cb -EVI relation was not used here due to the relatively low spatial resolution of MOIDS-derived EVI data (i.e., 500 m). The investigated period was from late-april to early-may (i.e., 27 April to 4 May of 2009), when winter wheat was at the end of the vegetative phase and the onset of the reproductive stage. The satellite SAVI data of this period was first corrected according to the ground-based canopy reflectance

7 6 Intelligent Automation and Soft Computing Table I. Comparison of winter wheat acreage extracted from multi-temporal MODIS data and that provided by local statistic bureau. Area name Information provided by local statistic bureau (hm 2 ) Information extracted from multi-temporal MODIS Absolute errors (hm 2 ) data (hm 2 ) Dechengqu 15,495 15,500 5 Lingxian 56,647 56, Ningjin 31,807 31, Qihe 69,462 69, Pingyuan 41,457 41, Xiajin 16,263 16, Wucheng 22,260 22,263 3 Yucheng 48,391 48, PID (total above) 301, , Absolute error was equal to the difference between the wheat area extracted from MODIS data and that provided by the local statistic bureau. The positive values meant overestimation and the negative values meant underestimation. information collected by a regional survey in the same period, with an aim to minimize the effect caused by mixed pixels. The estimated K cb map was given in Figure 4. Results showed that the K cb range of winter wheat over the PID was 7 3. Compared with the maximum K cb value of 1.10 in this area determined Figure 4. Actual K cb map of winter wheat in the period from 27 April to 4 May of 2008/2009 season in the PID, which was estimated using the average SAVI of this period with MODIS images. Also, the main diversion canal from Yellow River was shown for comparison.

8 Remote Sensing of Regional Crop Transpiration of Winter Wheat 7 Figure 5. Reference crop evapotranspiration (ET 0 ) over the PID of the period from 27 April to 4 May in year of Figure 6. Actual T c map of winter wheat from 27 April to 4 May in 2008/2009 season over the PID.

9 8 Intelligent Automation and Soft Computing according to FAO-56 paper [1], it was indicated that the water demand of winter wheat over the PID had not been met, especially the areas with poor irrigation conditions. 3.4 Determination of regional T c of winter wheat over the PID Regional T c of winter wheat was determined as a product of ET 0 and the K cb map. The ET 0 at the eight weather station sites within the PID were first calculated from the meteorological data using the Penman- Monteith method according to FAO-56 paper [1]. And then, the ET 0 map over the PID, with a spatial resolution matching with the remotely sensed K cb data (i.e., 250 m), was obtained by spatial interpolation (Figure 5). Over the PID, the ET 0 of the period from 27 April to 4 May in the year of 2009 had a range of mm, with a slight increase from the east to west. The estimated T c of winter wheat over the PID of this period was shown in Figure 6. Under the combined effects of meteorological factors, water conditions and crop growth status, the T c of the investigated period ranged from 31.7 to 42.4 mm. At the pixels around the YCES, the T c values were mm, which were in good consistency with the field data of mm of this period within the YCES. It was demonstrated that the above approach could perform well in regional crop T c estimation. Moreover, because of the advantages of easy operation and separating soil evaporation effectively, it would be favorable for practical application. 4. CONCLUSIONS As the efficient part of water consumption in crop production, crop T c and its accurate estimation, especially on a regional scale, is of great importance to farmland water cycle, scientific irrigation and highefficiency utilization of water resources. In this study, an approach by combining MODIS data and FAO-56 crop coefficient method was used to estimate the T c of winter wheat over the PID. First, the relationships between crop K c, K cb and the canopy vegetation indices of NDVI, SAVI and EVI were investigated and compared based on the field data. Then, the K cb map of winter wheat over the PID was derived from the MODIS-SAVI using the K cb -SAVI relation obtained from the above analysis. Finally, regional crop T c was determined as the product of K cb and ET 0. It was found that the K cb of winter wheat had significant relationships with NDVI, SAVI and EVI in the presence of nitrogen and water stress; the coefficients of determination R 2 were 0, 7 and 8 respectively (n ¼ 195), which could be even higher without the water-stress points that had not reached the severity to make obvious changes in canopies. By contrast, the relations between K c and the vegetation indices were relatively weak, which may be mainly due to the effects of fluctuating soil evaporation. Results also demonstrated that it was feasible to utilize the K cb -SAVI relationship to derive the K cb of winter wheat over a large area by means of satellite remote sensing. Further, good estimated results of T c were obtained and validated that it was effective to determine regional crop T c using the remote sensed K cb map and the ET 0 that calculated from meteorological data. Due to the advantages of easy operation and effective separation of T c from ET c, this approach would be favorable for practical application. ACKNOWLEDGEMENTS This work was supported by National Natural Science Foundation of China (Grant No , ), Beijing Natural Science Foundation (Grant No ), Postdoctoral Science Foundation of China (Grant No. 2013T60081, 2012M510010), Beijing Municipality and Beijing Academy of Agriculture and Forestry Sciences.

10 Remote Sensing of Regional Crop Transpiration of Winter Wheat 9 REFERENCES [1] Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration: Guidelines for computing crop water requirements. FAO Irrigation and Drainage paper No. 56. Food and agriculture organization of the United Nations, Rome, Italy, 15, [2] Fandiño, M., Cancela, J. J., Rey, B. J., Martínez, E. M., Rosa, R. G., & Pereira, L. S. (2012). Using the dual-k c approach to model evapotranspiration of Albariño vineyards (Vitis vinifera L. cv. Albariño) with consideration of active ground cover. Agricultural Water Management, 112, [3] Ray, S. S., & Dadhwal, V. K. (2001). Estimation of crop evapotranspiration of irrigation command area using remote sensing and GIS. Agricultural Water Management, 49, [4] Duchemin, B., Hadria, R., Erraki, S., Boulet, G., Maisongrande, P., Chehbouni, A., Escadafal, R., Ezzahar, J., Hoedjes, J. C. B., Kharrou, M. H., Khabba, S., Mougenot, B., Olioso, A., Rodriguez, J.-C., & Simonneaux, V. (2006). Monitoring wheat phenology and irrigation in Central Morocco: On the use of relationships between evapotranspiration, crops coefficients, leaf area index and remotely-sensed vegetation indices. Agricultural Water Management, 79, [5] Peng, S., Ding, J., Mao, Z., Xu J., & Li D. (2007). Estimation and verification of crop coefficient for water saving irrigation of late rice using the FAO-56 method. Transactions of the Chinese Society of Agricultural Engineering, 23(7), [6] DeJonge, K. C., Ascough, J. C. II, & Andales, A. A., Hansen, N. C., Garcia, L. A., & Arabi, M. (2012). Improving evapotranspiration simulations in the CERES-Maize model under limited irrigation. Agricultural Water Management, 115, [7] Liu, Y., & Pereira, L. S. (2000). Validation of FAO methods for estimating crop coefficients. Transactions of the Chinese Society of Agricultural Engineering, 16(5), [8] Fan, Y., & Cai, H. (2002). Comparison of crop water requirements computed by single crop coefficient approach and dual crop coefficient approach. Journal of Hydraulic Engineering, 3, [9] Su, M., Li, J., & Rao, M. (2005). Estimation of crop coefficients for sprinkler-irrigated winter wheat and sweet corn using a weighing lysimeter. Transactions of the Chinese Society of Agricultural Engineering, 21(8), [10] Chen, F., Cai, H., Wang, J., & Ma, H. (2006). Estimation of evapotranspiration and crop coefficients of winter wheat and summer maize in Yang ling zone. Transactions of the Chinese Society of Agricultural Engineering, 22(5), [11] Zhang, K. F., Hilton, H. W., & Greenwood, D. J., & Thompson, A. J. (2011). A rigorous approach of determining FAO56 dual crop coefficient using soil sensor measurements and inverse modeling techniques. Agricultural Water Management, 98, [12] Liu, H., & Kang, Y. (2006). Calculation of crop coefficient of winter wheat at elongation-heading stages. Transactions of the Chinese Society of Agricultural Engineering, 22(10), [13] Choudhury, B. J., Ahmed, N. U., Idso, S. B., Reginato, R. J., & Daughtry, C. S. T. (1994). Relations between evaporation coefficient and vegetation indices studied by model simulations. Remote Sensing of Environment, 50, [14] Guerschman, J. P., Van Dijk, A. I. J. M., Mattersdorf, G., Beringer, J., Hutley, L. B., Leuning, R., Pipunic, R. C., & Sherman, B.S. (2009). Scaling of potential evapotranspiration with MODIS data reproduces flux observations and catchment water balance observations across Australia. Journal of Hydrology, 369, [15] Yebra, M., Van Dijk, A., Leuning, R., Huete, A., & Guerschman, J. P. (2013). Evaluation of optical remote sensing to estimate actual evapotranspiration and canopy conductance. Remote Sensing of Environment, 129, [16] Mutiibwa, D., & Irmak, S. (2013). doi: /2012wr AVHRR-NDVI-based crop coefficients for analyzing long-term trends in evapotranspiration in relation to changing climate in the U.S. High Plains. Water Resources Research, 49. [17] Li, H., Luo, Y., Xue, X., Zhao, Y., Zhao, H., & Li, F. (2011). A comparison of harvest index estimation methods of winter wheat based on field measurements of biophysical and spectral data. Biosystems Engineering, 109(4), NOTES ON CONTRIBUTORS Heli Li is a research assistant. She received her PhD degree from the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Her research interests are agricultural ecosystem and remote sensing information applications.

11 10 Intelligent Automation and Soft Computing Yi Luo is a professor at the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China. He mainly engages in the experimental observation and modeling of eco-hydrological processes. luoyi.cas@hotmail.com Chunjiang Zhao is a professor and the leading scientist of Information Technology Application in Modern Agriculture, the National High-Tech R&D Program of China, and the director of National Engineering Research Center for Information Technology in Agriculture, Beijing, China. His main interest is precision agriculture. Guijun Yang is an associate professor at Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China. He mainly studies on quantitative remote sensing and radiative transfer modeling.