Sentinel-2宽波段光谱指数预测拔节后受冻冬小麦减产率

doi:10.3964/j.issn.1000-0593(2022)07-2225-08

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Abstract On the regional scale, the late frost damage to winter wheat after jointing showed a spatial difference which determines that the sub-regional measures against the frost damage should be implemented. Broad-waveband spectral indices based on the Sentinel-2 satellite were proposed in this study to predict yield reduction rates of winter wheat. It is of great significance to disaster assessment and production management decision-making. Based on the artificial frost simulation experiments, the canopy reflectance data measured by ASD FieldSpec^® 3 spectroradiometer were simulated to the Sentinel-2 wavebands using spectral resampling. And then, the nineteen published spectral indices and three new forms of wavelength random combinations (simple ratio, simple difference, and normalized difference) were used to construct the linear regression models with winter wheat yield reduction rates. In every form, broad-waveband spectral indices with the top three coefficients of determination were selected as the candidate indices. Aiming at the frost event in the Shangqiu area, all candidate indices were calculated using the Sentinel-2 reflectance data and used to predict winter wheat yield reduction rates, which were validated by the measured yields of the ground sampling points. The results indicated that: (1) With the decrease of the treatment temperatures, canopy reflectance shows a decreasing trend in the near-infrared region, but increased in the visible and short-waveband infrared regions. (2) Most of the nineteen published spectral indices were significantly (p<0.001) correlated with yield reduction rates, regardless of whether the canopy reflectance data were before or after resampling. The twelve candidate spectral indices screened out have good linear regression accuracy for predicting the yield reduction rates of winter wheat and the coefficient of determination above 0.631 in the calibration and the validation datasets. (3) The accuracy of candidate spectral indices calculated from sentinel-2 satellite data showed that the three spectral indices, including the band B9 failed to pass the significance test, and the other nine spectral indices all passed the extremely significant test. The two spectral indices (B8a-B12 and B8-B12) based on the combinations of the B8, B8a, and B12 had good accuracy. The coefficient of determination was 0.543 and 0.492, the root means square error was 8.510% and 8.971%, respectively. Further, B8a-B12 and B8-B12 were found to conform to the simple difference form, which was considered the optimal combination of the broad-waveband spectral indices predicting yield reduction rates of winter wheat. The research results revealed that the response mechanism of canopy reflectance at early spike development stage of winter wheat under different low-temperature stress, indicating that Sentinel-2 broad-waveband spectral indices have good accuracy in predicting the yield reduction rate of winter wheat. It is feasible to predict yield reduction rate at a regional scale after frost and has a guiding role in formulating frost disaster measures in different regions.

Key words：Winter wheat; Late frost damage; Spectral resampling; Sentinel-2; Remote sensing; Yield reduction rate

Received: 2021-05-18 Accepted: 2021-11-05

ZTFLH:

S127

Corresponding Authors: WU Yong-feng E-mail: wuyongfeng@caas.cn

Cite this article:

ZHAO Ai-ping,MA Jun-cheng,WU Yong-feng, et al. Predicting Yield Reduction Rates of Frost-Damaged Winter Wheat After Jointing Using Sentinel-2 Broad-Waveband Spectral Indices[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(07): 2225-2232.

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2022)07-2225-08 OR https://www.gpxygpfx.com/EN/Y2022/V42/I07/2225

[1] Zheng D X, Yang X G, Mu C Y, et al. Agricultural and Forest Meteorology, 2018, 260-261: 1.
[2] Nuttall J G, Perry E M, Delahunty A J, et al. Journal of Agronomy and Crop Science, 2019, 205(2): 220.
[3] Eileen M, Perrya C, Nuttall J G, et al. Crop and Pasture Science, 2017, 68: 516.
[4] She B, Huang J F, Zhang D Y, et al. International Journal of Agricultural and Biological Engineering, 2017, 10(3): 143.
[5] Wang X D, Dong Y S, Yue J W, et al. IEEE International Geoscience and Remote Sensing IGARSS, 2012. 6597.
[6] Alessia C, Franco M, Cassandra C, et al. Remote Sensing, 2020, 12(11): 1.
[7] HUANG Lin-sheng, JIANG Jing, HUANG Wen-jiang, et al(黄林生，江静，黄文江，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2019, 35(17): 178.
[8] OUYANG Ling, MAO De-hua, WANG Zong-ming, et al(欧阳玲，毛德华，王宗明，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2017, 33(11): 147.
[9] CHEN Ai-lian, LI Jia-yu, ZHANG Sheng-jun, et al(陈爱莲，李家峪，张圣军，等). Smart Agriculture(智慧农业), 2020, 2(3): 139.
[10] CAO Juan, ZHANG Chao, ZHANG Liang-liang, et al(曹娟，张朝，张亮亮，等). Acta Geographica Sinica(地理学报), 2020, 75(9): 1879.
[11] LIU Hong-jie, NI Yong-jing, REN De-chao, et al(刘红杰，倪永静，任德超，等). Chinese Journal of Agrometeorology(中国农业气象), 2018, 39(12): 786.
[12] Wei C W, Huang J F, Wang X Z, et al. Remote Sensing of Environment, 2017, 195(15): 56.
[13] LIAO Qin, ZHAO Hong, MA Zhi-qiang, et al(廖琴，赵虹，马志强，等). Technical Procedures for Wheat Variety Regional Trials[农作物品种（小麦）区域试验技术规程]. Beijing: China Agriculture Press(北京：中国农业出版社)，2007.
[14] FANG Xiu-feng, TAN Bing-xiang, DU Hua-qiang, et al(房秀凤，谭炳香，杜华强，等). Journal of Nanjing Forestry University·Natural Science Edition(南京林业大学学报·自然科学版)，2018, 42(5): 141.
[15] Wang L L, Qu J J. Geophysical Research Letters, 2007, 34(20): 1.
[16] DENG Lai-fei, ZHANG Fei, ZHANG Hai-wei, et al(邓来飞，张飞，张海威，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2019, 39(1): 210.
[17] Eitel J U H, Long D S, Gessler P E, et al. International Journal of Remote Sensing, 2007, 28(18): 4183.
[18] Marildo G F, Tatiana M K, Fernando L F D Q. International Journal of Remote Sensing, 2019, 41(8): 2861.