Estimating Leaf Area Index of Crops Based on Hyperspectral Compact Airborne Spectrographic Imager (CASI) Data
TANG Jian-min1, LIAO Qin-hong1*, LIU Yi-qing1, YANG Gui-jun2, FENG Hai-kuan2, WANG Ji-hua2
1. College of Life Science and Forestry, Chongqing University of Art and Science, Chongqing 402160, China 2. Beijing Agriculture Information Technology Research Center, Beijing 100097, China
Abstract：The fast estimation of leaf area index (LAI) is significant for learning the crops growth, monitoring the disease and insect, and assessing the yield of crops. This study used the hyperspectral compact airborne spectrographic imager (CASI) data of Zhangye city, in Heihe River basin, on July 7, 2012, and extracted the spectral reflectance accurately. The potential of broadband and red-edge vegetation index for estimating the LAI of crops was comparatively investigated by combined with the field measured data. On this basis, the sensitive wavebands for estimating the LAI of crops were selected and two new spectral indexes (NDSI and RSI) were constructed, subsequently, the spatial distribution of LAI in study area was analyzed. The result showed that broadband vegetation index NDVI had good effect for estimating the LAI when the vegetation coverage is relatively lower, the R2 and RMSE of estimation model were 0.52, 0.45 (p<0.01), respectively. For red-edge vegetation index, CIred edge took the different crop types into account fully, thus it gained the same estimation accuracy with NDVI. NDSI(569.00, 654.80) and RSI(597.60, 654.80) were constructed by using waveband combination algorithm, which has superior estimation results than NDVI and CIred edge. The R2 of estimation model used NDSI(569.00, 654.80) was 0.77(p<0.000 1), it mainly used the wavebands near the green peak and red valley of vegetation spectrum. The spatial distribution map of LAI was made according to the functional relationship between the NDSI(569.00, 654.80) and LAI. After analyzing this map, the LAI values were lower in the northwest of study area, this indicated that more fertilizer should be increased in this area. This study can provide technical support for the agricultural administrative department to learn the growth of crops quickly and make a suitable fertilization strategy.
Key words：Hyperspectral data of CASI;Leaf area index;Vegetation index;Waveband combination;Spatial distribution
唐建民1，廖钦洪1*，刘奕清1，杨贵军2，冯海宽2，王纪华2 . 基于CASI高光谱数据的作物叶面积指数估算 [J]. 光谱学与光谱分析, 2015, 35(05): 1351-1356.
TANG Jian-min1, LIAO Qin-hong1*, LIU Yi-qing1, YANG Gui-jun2, FENG Hai-kuan2, WANG Ji-hua2 . Estimating Leaf Area Index of Crops Based on Hyperspectral Compact Airborne Spectrographic Imager (CASI) Data . SPECTROSCOPY AND SPECTRAL ANALYSIS, 2015, 35(05): 1351-1356.
[1] Nguy-Robertson A L, Peng Y, Gitelson A A, et al. Agricultural and Forest Meteorology, 2014, 192-193: 140. [2] Via A, Gitelson A A, Nguy-Robertson A L, et al. Remote Sensing of Environment, 2011, 115(12): 3468. [3] WANG Dong-wei, WANG Jin-di, LIANG Shun-lin, et al(王东伟，王锦地，梁顺林，等). Science China: Earth Sciences(中国科学：地球科学), 2010, 40(1): 73. [4] ZHANG Jing-cheng, GU Xiao-he, WANG Ji-hua, et al(张竞成，顾晓鹤，王纪华，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2010, 26(7): 186. [5] LI Xin-chuan, XU Xin-gang, BAO Yan-song, et al(李鑫川，徐新刚，鲍艳松，等). Scientia Agricultura Sinica(中国农业科学), 2012, 45(17): 3486. [6] Stagakis S, Markos N, Sykioti O, et al. Remote Sensing of Environment, 2010, 114(5): 977. [7] SUN Xiao, TAN Bing-xiang(孙 晓，谭炳香). Guangdong Agricultural Sciences(广东农业科学), 2012, 14: 189. [8] Gómez J A, Zarco-Tejada P J, García-Morillo J, et al. Agronomy Journal, 2011, 103(3): 644. [9] GAO Hai-liang, GU Xing-fa, YU Tao, et al(高海亮，顾行发，余 涛，等). Remote Sensing Information(遥感信息), 2010, 4(22): 110. [10] Carlson T N, Ripley D A. Remote Sensing of Environment, 1997, 62(3): 241. [11] Chen J, Cihlar J. Remote Sensing of Environment, 1996, 55(2): 153. [12] Huete A, Liu H Q, Batchily K, et al. Remote Sensing of Environment, 1997, 59(3): 440. [13] Gitelson A A, Merzlyak M N. Journal of Plant Physiology, 1996, 148(3-4): 494. [14] Li F, Miao Y X, Feng G H, et al. Field Crops Research, 2014, 157: 111. [15] Ju C H, Tian Y C, Yao X, et al. Pedosphere, 2010, 20(5): 633. [16] Tucker C J. Remote Sensing of Environment, 1979, 8(2): 127. [17] Gitelson A A, Via A, Ciganda V, et al. Geophysical Research Letters, 2005, 32(8): L08403. [18] Haboudane D, Tremblay N, Miller J R, et al. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(2): 423. [19] Daughtry C S T, Walthall C L, Kim M S, et al. Remote Sensing of Environment, 2000, 74(2): 229. [20] Haboudane D, Miller J R, Tremblay N, et al. Remote Sensing of Environment, 2002, 81(2-3): 416. [21] Dash J, Curran P J. International Journal of Remote Sensing, 2004, 25(23): 5403. [22] le Maire G, Franois C, Dufrene E. Remote Sensing of Environment, 2004, 89(1): 1. [23] Gitelson A A, Gritz Y, Merzlyak M N. Journal of Plant Physiology, 2003, 160(3): 271. [24] Gitelson A A, Keydan G P, Merzlyak M N. Geophysical Research Letters, 2006, 33(11): L11402. [25] Li F, Mistele B, Hu Y C, et al. Agricultural and Forest Meteorology, 2013, 180: 44. [26] Zhao D H, Huang L M, Li J L, et al. ISPRS Journal of Photogrammetry and Remote Sensing, 2007, 62(1): 25. [27] Blackburn G A. International Journal of Remote Sensing, 1998, 19(4): 657.
