1. College of Resources and Environment, Jilin Agricultural University, Changchun 130118, China
2. Key Laboratory of Soil Resource Sustainable Utilization for Commodity Grain Bases of Jilin Province, Jilin Agricultural University, Changchun 130118, China
3. Key Laboratory of Straw Comprehensive Utilization and Black Soil Conservation, Ministry of Education, Changchun 130118, China
4. The Monitoring Center of Soil and Water Conservation, Songliao Water Resources Commission, Changchun 130021, China
5. College of Resources and Environment, China Agricultural University, Beijing 100083, China
Abstract:As an important part of the soil, the soil organic matter (SOM) can reflect soil fertility and quality. Compared with the traditional SOM measurement method, UAV hyperspectral images can quickly and real-time obtain the SOM content at the field-scale, which is of great significance for precision fertilization and sustainable utilization in the black soil region of Northeast China. In order to explore the difference in estimating the accuracy of SOM under crop cover by linear and nonlinear models based on hyperspectral data, the soil samples at the jointing stage and silking stage and UAV hyperspectral images were collected from the experimental corn field in the black soil region of Northeast China as the study area. The correlation between soil spectral reflectance and SOM content under crop cover was analyzed, and the spectral indices were calculated according to their response band. With the fertilizer rates and spectral indices as independent variables, multiple stepwise linear regression models (SMLR), support vector machine (SVM), random forest (RF) and eXtreme gradient boosting (XGBoost) models were established by screening characteristic variables, and the accuracies of the models were verified and compared (select R2 and RMSE as evaluation indicators). The results showed that the response band of SOM content under crop cover was 450~640 nm. Long-term application of chemical fertilizers had a significant effect on SOM content, and introducing it into the model as a covariate significantly improved the estimation accuracy of SOM. The test accuracies of the four models were: XGBoost>RF>SMLR>SVM, and the estimation result of XGBoost at the jointing stage was the best (R2 and RMSE of modeling set were 0.516, 0.253, and those of the verification set were 0.590, 0.222, respectively). Therefore, UAV hyperspectral technology can rapidly estimate SOM content in maize fields at field-scale, and the XGBoost model is a preferable option for estimating SOM content under crop cover conditions.
夏晨真,姜艳艳,张星宇,沙 野,崔 帅,米国华,高 强,张 月. 基于无人机高光谱影像的黑土区玉米农田土壤有机质估算[J]. 光谱学与光谱分析, 2023, 43(08): 2617-2626.
XIA Chen-zhen, JIANG Yan-yan, ZHANG Xing-yu, SHA Ye, CUI Shuai, MI Guo-hua, GAO Qiang, ZHANG Yue. Estimation of Soil Organic Matter in Maize Field of Black Soil Area Based on UAV Hyperspectral Image. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2617-2626.
[1] WANG Li-gang, YANG Li, HE Mei, et al(王立刚, 杨 黎, 贺 美, 等). Soil and Fertilizer Sciences in China(中国土壤与肥料), 2016, (6): 1.
[2] Dotto A C, Dalmolin R S D, Caten A T, et al. Geoderma, 2018, 314: 262.
[3] LIU Huan-jun, PAN Yue, DOU Xin, et al(刘焕军, 潘 越, 窦 欣, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2018, 34(1): 127.
[4] WANG Da-ming, QIN Kai, LI Zhi-zhong, et al(汪大明, 秦 凯, 李志忠, 等). Earth Science(地球科学), 2018, 4 3(6): 2184.
[5] MAO Xin, LIU Lin-jing, LI Chang-an, et al(毛 欣, 刘林敬, 李长安, 等). Earth Science(地球科学), 2017, 42(10): 1750.
[6] ZHOU Wei, XIE Li-juan, YANG Han(周 伟, 谢利娟, 杨 晗). Chinese Journal of Soil Science(土壤通报), 2021, 52(3): 564.
[7] ZHAO Ming-song, LIU Bin-yin, LU Hong-liang, et al(赵明松,刘斌寅,卢宏亮,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报),2019,35(20):102.
[8] ZHANG Gan-lin,SHI Zhou,ZHU A-xing, et al(张甘霖, 史 舟, 朱阿兴, 等). Acta Pedologica Sinica(土壤学报), 2020, 57(5): 1060.
[9] Guo L, Fu P, Shi T Z, et al. Soil and Tillage Research, 2020, 196: 104477.
[10] Xu S X, Wang M Y, Shi X Z. Geoderma, 2020, 370: 114358.
[11] Al-Abbas A H, Swain P H, Baumgarder M F. Soil Science, 1972, 114(6): 477.
[12] Krishnan P, Alexander J D, Butler B J, et al. Soil Society of American Journal, 1980, 44(6): 1280.
[13] TAO Pei-feng, WANG Jian-hua, LI Zhi-zhong, et al(陶培峰, 王建华, 李志忠, 等). Geology and Resources(地质与资源), 2020, 29(1): 68.
[14] LIU Tian-lin, ZHU Xi-cun, BAI Xue-yuan, et al(刘恬琳, 朱西存, 白雪源, 等). Smart Agriculture(智慧农业(中英文)), 2020, 2(3): 129.
[15] Khanal S, Fulton J, Klopfenstein A, et al. Computers and Electronics in Agriculture, 2018, 153: 213.
[16] Nunez-Ramirez F, Santillano Cazares J, Roque Diaz L G, et al. Terra Latinoamericana, 2019, 37:7.
[17] WANG Xi, LI Yu-huan, WANG Rui-yan, et al(王 曦, 李玉环, 王瑞燕, 等). Chinese Journal of Applied Ecology(应用生态学报), 2020, 31(7): 2399.
[18] SUN Xiao-lin, ZHAO Yu-guo, LIU Feng, et al(孙孝林, 赵玉国, 刘 峰, 等). Chinese Journal of Soil Science(土壤通报), 2013, 44(3): 752.
[19] Jenny H. Factors of Soil Formation: A System of Quantitative Pedology. New York: McGraw-Hill, 1941.
[20] LI Cheng, WANG Rang-hui, LI Zhao-zhe, et al(李 成, 王让会, 李兆哲, 等). Environmental Science(环境科学), 2021, 42(5): 2432.
[21] ZHAO Qing-yue, XU Shi-jie, ZHANG Wu-shuai, et al(赵晴月, 许世杰, 张务帅, 等). Scientia Agricultura Sinica(中国农业科学), 2020, 53(15): 3120.
[22] Keskin H, Grunwald S, Harris W G. Geoderma, 2019, 339: 40.
[23] WU Cai-wu, ZHANG Yue-cong, XIA Jian-xin(吴才武, 张月丛, 夏建新). Acta Pedologica Sinica(土壤学报), 2016, 53(6): 1568.
[24] Mosleh Z, Salehi M H, Jafari A, et al. Environmental Monitoring and Assessment, 2016, 188(3): 195.
[25] Zeng C Y, Zhu A X, Liu F, et al. Ecological Indicators, 2017, 72: 297.
[26] Guo S X, Zhu A X, Meng L K, et al. International Journal of Applied Earth Observation and Geoinformation, 2016, 49: 126.
[27] Guo L, Sun X R, Fu P, et al. Geoderma, 2021, 398: 115118.
[28] XU Ming-gang, YU Rong, SUN Xiao-feng, et al(徐明岗, 于 荣, 孙小凤, 等). Journal of Plant Nutrition and Fertilizers(植物营养与肥料学报), 2006, (4): 459.
[29] XU Zhi-qiang, DAI Ji-guang, YU Xiang-hua, et al(徐志强, 代继光, 于向华, 等). Chinese Journal of Soil Science(土壤通报), 2008, (4): 766.
[30] Yue J B, Feng H K, Yang G J, et al. Remote Sensing, 2018, 10(1): 66.
[31] Saman A, Arie P, Selam A, et al. Computers and Electronics in Agriculture, 2018, 148: 250.
[32] Mojtaba Z, Shamsollah A, Azam J, et al. Geoderma, 2018, 338: 445.
[33] Rodrigues E, Gomes á, Gaspar A R, et al. Renewable and Sustainable Energy Reviews, 2018, 94: 959.
[34] Gu X H, Wang Y C, Sun Q, et al. Computers and Electronics in Agriculture, 2019, 167: 105053.
[35] GE Xiang-yu, DING Jian-li, WANG Jing-zhe, et al(葛翔宇, 丁建丽, 王敬哲, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2020, 40(2): 602.
[36] FENG Guo-zhong, WANG Yin, YAN Li, et al(冯国忠, 王 寅, 焉 莉, 等). Acta Pedologica Sinica(土壤学报), 2017, 54(2): 444.
[37] BAO Shi-dan(鲍士旦). Soil Agrochemical Analysis(土壤农化分析). Beijing: China Agriculture Press(北京: 中国农业出版社), 2000. 172.
[38] Huete A, Didan K, Miura T, et al. Remote Sensing of Environment, 2002, 83(1-2): 195.
[39] Gitelson A A, Kaufman Y J, Stark R, et al. Remote Sensing of Environment, 2002, 80(1): 76.
[40] Mistele B, Gutser R, Schmidhalter U, et al. Validation of Field-Scaled Spectral Measurements of the Nitrogen Status in Winter Wheat 7th International Conference on Precision Agriculture and other Precision Resources Management. 2004. 1187.
[41] Gamon J A, Penuelas J, Field C B. Remote Sensing of Environment, 1992, 41: 35.
[42] Sylvain J, Nathalie G, Julien M, et al. Remote Sensing of Environment, 2017, 198: 173.
[43] Wu C Y, Niu Z, Tang Q, et al. Agricultural and Forest Meteorology, 2008, 148(8): 1230.
[44] Maccioni A, Agati G, Mazzinghi P. Journal of Photochemistry & Photobiology B: Biology, 2001, 61(1): 52.
[45] Dash J, Curran P J. International Journal of Remote Sensing, 2004, 25(23): 5403.
[46] Chen J M. Canadian Journal of Remote Sensing, 2014, 22(3): 229.
[47] Chen P F, Haboudane D, Tremblay N, et al. Remote Sensing of Environment, 2010, 114(9): 1987.
[48] Gitelson A A, Merzlyak M N. Journal of Plant Physiology, 1996, 148(3): 494.
[49] Gitelson A A, Viña A, Ciganda V, et al. Geophysical Research Letters, 2005, 32(8): L08403, doi: 10.1029/2005GL022688.
[50] Daughtry C S T, Walthall C L, Kim M S, et al. Remote Sensing of Environment, 2000, 74(2): 229.
[51] Zarco-Tejada P J, Miller J R, Morales A, et al. Remote Sensing of Environment, 2004, 90(4): 463.
[52] Haboudane D, Tremblay N, Miller J R, et al. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46: 423.
[53] Haboudane D, Miller J R, Pattey E, et al. Remote Sensing of Environment, 2004, 90(3): 337.
[54] Chen T Q, Guestrin C. XGBoost: A Scalable Tree Boosting System Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016. arXiv: 1603.02754[CS LG].
[55] XIE Jia-gui, HOU Yun-peng, YIN Cai-xia, et al(谢佳贵, 侯云鹏, 尹彩侠, 等). Journal of Plant Nutrition and Fertilizers(植物营养与肥料学报), 2014, 20(5): 1110.