- Chinese Core Journal
- Chinese Science Citation Database (CSCD) Source journal
- Journal of Citation Report of Chinese S&T Journals (Core Edition)
| Citation: |
ZHONG Hesen, LI Wei, ZHANG Zeyu, et al. Assessment for soil nutrient content prediction model based on visible-near infrared spectroscopy in the typical regions of Guangdong Province[J]. Journal of South China Agricultural University, 2024, 45(2): 218-226. DOI: 10.7671/j.issn.1001-411X.202301020
|
Visible-near-infrared spectroscopy (VNIRS) can non-invasively and rapidly predict soil nutrient contents based on models developed using data of some representative soil samples, but soil nutrient content prediction models for different regions of Guangdong Province are still lacking. In this study, representative agricultural soil samples were collected from Guangdong Province, and models were developed based on the traditional soil chemical analyses and VNIRS spectral signatures. The objective was to assess the predictive ability of the models for soil total and available nutrients so as to provide a scientific basis for rapid soil nutrient content estimation and soil quality evaluation in Guangdong Province.
A total of 514 representative soil samples were collected from the east (Meizhou), west (Zhanjiang), north (Shaoguan), and northwest regions (Zhaoqing) and the Pearl River Delta (Huizhou and Zhuhai) in Guangdong Province, and the contents of soil organic matter, total nitrogen, dissolved organic carbon, available nitrogen, and available phosphorus were analyzed. In addition, the VNIRS spectral signatures of soil samples were obtained between 400 and 2 490 nm. Models were developed and calibrated using partial least squares regression combined with principal component analysis, and the models were reversely validated and assessed for their predictive abilities.
There were significant differences in the contents of soil organic matter, total nitrogen, dissolved organic carbon, available nitrogen and available phosphorus and VNIRS spectral signatures among the soils from different regions. The models for soil organic matter and total nitrogen showed good prediction performances, with validation determination coefficients of 0.831 1 for soil organic matter in the northwest region and 0.789 8 for total nitrogen in the Pearl River Delta. The models for dissolved organic carbon, available nitrogen, and available phosphorus showed very different prediction performances among regions, with much better performances for available nitrogen and available phosphorus in the northwest region and Pearl River Delta compared with other regions. Model validation showed good correlations between the predicted and measured values of soil organic matter and total nitrogen, with the highest coefficients of determination (R2) being up to 0.69 and 0.65, respectively. The predicted and measured values of available nitrogen in the northwest region and Pearl River Delta also showed good correlations, with R2 of 0.63 and 0.62, respectively. However, model validation showed generally poor correlations between the predicted and measured values of dissolved organic carbon and available phosphorus.
VNIRS technology can predict soil organic matter and nutrient contents to differentiate soils from different regions at the provincial scale. Soil VNIRS spectral signature can be used as an important index for soil classification and soil quality evaluation. VNIRS technology shows good performance in the predictions of soil organic matter and total nitrogen contents, but its performance in the predictions of dissolved organic carbon, available nitrogen, and available phosphorus contents is element- and region-dependent. Therefore, future efforts should be focused on spectral range selection and model optimization.
| [1] |
赵其国, 孙波, 张桃林. 土壤质量与持续环境Ⅰ: 土壤质量的定义及评价方法[J]. 土壤, 1997(3): 113-120.
|
| [2] |
张甘霖, 朱永官, 傅伯杰. 城市土壤质量演变及其生态环境效应[J]. 生态学报, 2003, 23(3): 539-546.
|
| [3] |
XIAO R, SU S, ZHANG Z, et al. Dynamics of soil sealing and soil landscape patterns under rapid urbanization[J]. Catena, 2013, 109: 1-12. doi: 10.1016/j.catena.2013.05.004
|
| [4] |
江恩赐, 陈林, 颜继忠, 等. 智能高光谱成像融合CARS特征波段筛选快速检测硫熏牛膝SO2含量[J]. 中国中药杂志, 2022, 47(7): 1864-1870.
|
| [5] |
王巧华, 马逸霄, 付丹丹. 基于光谱技术的禽蛋内部品质无损检测研究进展[J]. 华中农业大学学报, 2021, 40(6): 220-230.
|
| [6] |
宋亮, 刘善军, 虞茉莉, 等. 基于可见−近红外和热红外光谱联合分析的煤和矸石分类方法研究[J]. 光谱学与光谱分析, 2017, 37(2): 416-422.
|
| [7] |
BADARÓ A T, MORIMITSU F L, FERREIRA A R, et al. Identification of fiber added to semolina by near infrared (NIR) spectral techniques[J]. Food Chemistry, 2019, 289: 195-203. doi: 10.1016/j.foodchem.2019.03.057
|
| [8] |
孙园园, 蔡怡聪, 谢黎虹, 等. 近红外光谱分析技术在稻米品质测定和遗传分析中应用研究概述[J]. 中国稻米, 2016, 22(6): 1-3.
|
| [9] |
MENDES DE OLIVEIRA D, FONTES L M, PASQUINI C. Comparing laser induced breakdown spectroscopy, near infrared spectroscopy, and their integration for simultaneous multi-elemental determination of micro-and macronutrients in vegetable samples[J]. Analytica Chimica Acta, 2019, 1062: 28-36. doi: 10.1016/j.aca.2019.02.043
|
| [10] |
ELLE O, RICHTER R, VOHLAND M, et al. Fine root lignin content is well predictable with near-infrared spectroscopy[J]. Scientific Reports, 2019, 9: 6396. doi: 10.1038/s41598-018-37186-2
|
| [11] |
BELLON-MAUREL V, MCBRATNEY A. Near-infrared (NIR) and mid-infrared (MIR) spectroscopic techniques for assessing the amount of carbon stock in soils: Critical review and research perspectives[J]. Soil Biology and Biochemistry, 2011, 43(7): 1398-1410. doi: 10.1016/j.soilbio.2011.02.019
|
| [12] |
刘燕德, 熊松盛, 刘德力. 近红外光谱技术在土壤成分检测中的研究进展[J]. 光谱学与光谱分析, 2014, 34(10): 2639-2644.
|
| [13] |
VELASQUEZ E, LAVELLE P, BARRIOS E, et al. Evaluating soil quality in tropical agroecosystems of Colombia using NIRS[J]. Soil Biology and Biochemistry, 2005, 37(5): 889-898. doi: 10.1016/j.soilbio.2004.09.009
|
| [14] |
DE SANTANA F B, DE SOUZA A M, POPPI R J. Visible and near infrared spectroscopy coupled to random forest to quantify some soil quality parameters[J]. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2018, 191: 454-462. doi: 10.1016/j.saa.2017.10.052
|
| [15] |
JOUQUET P, HENRY-DES-TUREAUX T, MATHIEU J, et al. Utilization of near infrared reflectance spectroscopy (NIRS) to quantify the impact of earthworms on soil and carbon erosion in steep slope ecosystem: A study case in Northern Vietnam[J]. Catena, 2010, 81(2): 113-116. doi: 10.1016/j.catena.2010.01.010
|
| [16] |
张欣跃, 赵玉国, 刘峰, 等. 基于可见−近红外光谱与化学属性的土壤来源地判别[J]. 土壤学报, 2019, 56(5): 1060-1071.
|
| [17] |
MOUAZEN A M, KUANG B, DE BAERDEMAEKER J, et al. Comparison among principal component, partial least squares and back propagation neural network analyses for accuracy of measurement of selected soil properties with visible and near infrared spectroscopy[J]. Geoderma, 2010, 158(1/2): 23-31. doi: 10.1016/j.geoderma.2010.03.001
|
| [18] |
李伟, 张书慧, 张倩, 等. 近红外光谱法快速测定土壤碱解氮、速效磷和速效钾含量[J]. 农业工程学报, 2007, 112(1): 55-59.
|
| [19] |
赵明松, 谢毅, 陆龙妹, 等. 基于高光谱特征指数的土壤有机质含量建模[J]. 土壤学报, 2021, 58(1): 42-54.
|
| [20] |
陈秋宇, 杨仁敏, 朱长明. 基于 VIS-NIR 光谱的互花米草入侵湿地土壤有机碳预测研究[J]. 土壤学报, 2021, 58(3): 694-703.
|
| [21] |
QIAO Y, ZHANG S. Near-infrared spectroscopy technology for soil nutrients detection based on LS-SVM[C]//International Conference on Computer and Computing Technologies in Agriculture. Berlin, Heidelberg: Springer, 2012: 325-335.
|
| [22] |
陈滢伊, 司友涛, 鲍勇, 等. 隔离降雨对亚热带米槠天然林土壤可溶性有机质数量及光谱学特征的影响[J]. 应用生态学报, 2019, 30(9): 2964-2972.
|
| [23] |
LIU S, ZHU Y, LIU L, et al. Cation-induced coagulation of aquatic plant-derived dissolved organic matter: Investigation by EEM-PARAFAC and FT-IR spectroscopy[J]. Environmental Pollution, 2018, 234: 726-734. doi: 10.1016/j.envpol.2017.11.076
|
| [24] |
刘雪梅, 柳建设. 基于MC-UVE的土壤碱解氮和速效钾近红外光谱检测[J]. 农业机械学报, 2013, 44(3): 88-91.
|
| [25] |
贾生尧, 杨祥龙, 李光, 等. 近红外光谱技术结合递归偏最小二乘算法对土壤速效磷与速效钾含量测定研究[J]. 光谱学与光谱分析, 2015, 35(9): 2516-2520.
|
| [26] |
方向, 金秀, 朱娟娟, 等. 基于可见−近红外光谱预处理建模的土壤速效氮含量预测[J]. 浙江农业学报, 2019, 31(9): 1523-1530.
|
| [27] |
汪六三, 鲁翠萍, 王儒敬, 等. 土壤碱解氮含量可见/近红外光谱预测模型优化[J]. 发光学报, 2018, 39(7): 1016-1023.
|
| [28] |
何电源. 华南热带土壤养分含量状态及肥力评价[J]. 土壤学报, 1983, 20(2): 154-166.
|
| [29] |
孟鑫鑫, 于雷, 周勇, 等. 基于可见近红外和中红外近地面光谱数据融合的土壤有机碳含量反演[J]. 土壤通报, 2022, 53(2): 301-307.
|
| [30] |
SHI Z, WANG Q L, PENG J, et al. Development of a national VNIR soil-spectral library for soil classification and prediction of organic matter concentrations[J]. Science China Earth Sciences, 2014, 57(7): 1671-1680. doi: 10.1007/s11430-013-4808-x
|
| [31] |
TZIOLAS N, TSAKIRIDIS N, OGEN Y, et al. An integrated methodology using open soil spectral libraries and Earth Observation data for soil organic carbon estimations in support of soil-related SDGs[J]. Remote Sensing of Environment, 2020, 244: 111793. doi: 10.1016/j.rse.2020.111793
|
| [32] |
鲁如坤. 土壤农业化学分析方法[M]. 北京: 中国农业科技出版社, 1999: 146-191.
|
| [33] |
刘磊, 沈润平, 丁国香. 基于高光谱的土壤有机质含量估算研究[J]. 光谱学与光谱分析, 2011, 31(3): 762-766.
|
| [34] |
张雪莲, 李晓娜, 武菊英, 等. 不同类型土壤总氮的近红外光谱技术测定研究[J]. 光谱学与光谱分析, 2010, 30(4): 906-910.
|
| [35] |
王昶. 近红外光谱快速评估土壤和有机肥质量研究[D]. 南京: 南京农业大学, 2014.
|
| [36] |
BEN-DOR E, HELLER D, CHUDNOVSKY A. A novel method of classifying soil profiles in the field using optical means[J]. Soil Science Society of America Journal, 2008, 72(4): 1113-1123. doi: 10.2136/sssaj2006.0059
|
| [37] |
曾招兵, 曾思坚, 汤建东, 等. 广东省耕地土壤有效磷时空变化特征及影响因素分析[J]. 生态环境学报, 2014, 23(3): 444-451.
|
| [38] |
王文俊, 李志伟, 王璨, 等. 高光谱成像的褐土土壤速效钾含量预测[J]. 光谱学与光谱分析, 2019, 39(5): 1579-1585.
|
Supported by: Beijing Renhe Information Technology Co., Ltd. 
DownLoad: