Hyperspectral Estimation Model Construction and Accuracy Comparison of Soil Organic Matter Content

doi:10.12133/j.smartag.2020.2.3.201912-SA004

Abstract

Abstract:

Soil organic matter (SOM) is an important source of crop growth, its content can reflect soil fertility status. In order to realize the fast and real-time estimation of the SOM, based on hyperspectral data, a rapid estimation model of SOM content in orchards was established. A total of 100 brown soil samples were collected from the apple orchard of Qixia county, Yantai city, Shandong province. After drying and grinding, the hyper-spectrum of the soil was measured in the laboratory using ASD FieldSpec. The spectral data was preprocessed by the method of moving average, and the spectral reflectance features of orchard soil were analyzed to study the correlation between spectral reflectance and its soil organic matter content. In order to enhance the correlation between relevant spectral parameters and soil indexes, the original data were processed by using the multivariate scattering correction, the first derivative and the first derivative of MSC. After the sensitive wavelengths of soil organic matter content were selected and the spectral indexes were constructed. Multiple linear regression models (MLR), support vector machines (SVM) and random forest (RF) models were respectively established. The estimation accuracy of the orchard soil organic matter estimation model was measured by the determination coefficient (R²), root mean square error (RMSE) and relative analysis error (RPD). The sensitive wavelengths of soil organic matter content selected were 678, 709, 1931, 1939, 1996 and 2201 nm. The spectral parameters were constructed using the selected wavelengths, which were NDSI_{(678, 709)}, NDSI_{(678, 1931)}, NDSI_{(678, 2201)}, NDSI_{(709, 1939)}, and NDSI_{(1939, 2201)}. These models established include MLR, SVM and RF model. The RF model had the best precision. The calibration sample R² was 0.8804, the RMSE was 0.1423 and RPD reached 2.25; the R²of the verification model was 0.7466, the RMSE was 0.1266, and the RPD was 1.79. The results showed that the fitting effect of the hyperspectral inversion model based on RF regression analysis was better than that based on MLR analysis and SVM regression analysis. As a promising and effective method, RF can play a vital role in predicting soil organic matter. The results can help understanding the distribution of soil nutrients, guiding farmers to apply fertilizer reasonably and improving the efficiency of orchard production and management.

Key words: hyperspectral, soil organic matter, multiple linear regression, support vector machine, random forest, model

CLC Number:

S127

LIU Tianlin, ZHU Xicun, BAI Xueyuan, PENG Yufeng, LI Meixuan, TIAN Zhongyu, JIANG Yuanmao, YANG Guijun. Hyperspectral Estimation Model Construction and Accuracy Comparison of Soil Organic Matter Content[J]. Smart Agriculture, 2020, 2(3): 129-138.

References

1	DOTTO A C, DALMOLIN R S D, CATEN A T, et al. A systematic study on the application of scatter-corrective and spectral-derivative preprocessing for multivariate prediction of soil organic carbon by Vis-NIR spectra[J]. Geoderma, 2018, 314: 262-274.
2	冷疏影, 李秀彬. 土地质量指标体系国际研究的新进展[J]. 地理学报, 1999(2): 83-91.
2	LENG S, LI X. New progress of international research on land quality index system [J]. Journal of Geography, 1999(2): 83-91.
3	姚阔, 郭旭东, 周冬, 等. 高光谱遥感土地质量指标信息提取研究进展[J]. 地理与地理信息科学, 2014, 30(6): 7-12.
3	YAO K, GUO X, ZHOU D, et al. Advances in researches on hyperspectral remote sensing information extraction of land quality indicators[J]. Geography and Geo-Information Science, 2014, 30(6): 7-12.
4	傅伯杰. 我国生态系统研究的发展趋势与优先领域[J]. 地理研究, 2010, 29(3): 383-396.
4	FU B. Trends and priority areas in ecosystem research of China[J]. Geographical Research, 2010, 29(3): 383-396.
5	包青岭, 丁建丽, 王敬哲, 等. 基于随机森林算法的土壤有机质含量高光谱检测[J]. 干旱区地理, 2019, 42(6): 1404-1414.
5	BAO Q, DING J, WANG J, et al. Hyperspectral detection of soil organic matter content based on random forest algorithm[J]. Arid Land Geography, 2019, 42(6): 1404-1414.
6	李冠稳, 高小红, 肖能文, 等. 基于sCARS-RF算法的高光谱估算土壤有机质含量[J]. 发光学报, 2019, 40(8): 1030-1039.
6	LI G, GAO X, XIAO N, et al. Estimation soil organic matter contents with hyperspectra based on sCARS and RF algorithms[J]. Chinese Journal of Luminescence, 2019, 40(8): 1030-1039.
7	GEORGE K J, KUMAR S, RAJ R A. Soil organic carbon prediction using visible-near infrared reflectance spectroscopy employing artificial neural network modeling[J]. Current Science, 2020, 119(2): 377-381.
8	WEI L, YUAN Z, WANG Z, et al. Hyperspectral inversion of soil organic matter content based on a combined spectral index model[J]. Sensors, 2020, 20(10): 2777.
9	沈兰芝, 高懋芳, 闫敬文, 等. 基于SVR和PLSR的土壤有机质高光谱估测模型研究[J]. 中国农业信息, 2019, 31(1): 58-71.
9	SHEN L, GAO M, YAN J, et al. Estimation model of soil organic matter based on SVR and PLSR[J]. China Agricultural Informatics, 2019, 31(1): 58-71.
10	国佳欣, 赵小敏, 郭熙, 等. 基于PLSR-BP复合模型的红壤有机质含量反演研究[J]. 土壤学报, 2020, 57(3): 636-645.
10	GUO J, ZHAO X, GUO X, et al. Inversion of organic matter content in red soil based on PLSR-BP composite model[J]. Acta Pedologica Sinica, 2020, 57(3): 636-645.
11	张智韬, 劳聪聪, 王海峰, 等. 基于FOD和SVMDA-RF的土壤有机质含量高光谱预测[J]. 农业机械学报, 2020, 51(1): 156-167.
11	ZHANG Z, LAO C, WANG H, et al. Estimation of Desert Soil Organic Matter through hyperspectra based on Fractional-Order Derivatives and SVMDA-RF[J]. Transactions of the CSAM, 2020, 51(1): 156-167.
12	王延仓, 杨秀峰, 赵起超, 等. 二进制小波技术定量反演北方潮土土壤有机质含量[J]. 光谱学与光谱分析, 2019, 39(9): 2855-2861.
12	WANG Y, YANG X, ZHAO Q, et al. Quantitative inversion of soil organic matter content in Northern Alluvial soil based on binary wavelet transform[J]. Spectroscopy and Spectral Analysis, 2019, 39(9): 2855-2861.
13	王崇红, 陈冬生, 王燕. 烟台市苹果产业布局及结构优化建议[J]. 中国果树, 2019(6): 101-105.
13	WANG C, CHEN D, WANG Y. Suggestions on optimization of apple industry layout and structure in Yantai city [J]. China Fruits, 2019(6): 101-105
14	鲍士旦. 土壤农化分析[M]. 北京: 中国农业出版社, 1999.
14	BAO S. Soil agrochemical analysis [M]. Beijing: China Agricultural Press, 1999.
15	洪永胜, 于雷, 耿雷, 等. 应用DS算法消除室内几何测试条件对土壤高光谱数据波动性的影响[J]. 华中师范大学学报(自然科学版), 2016, 50(2): 303-308.
15	HONG Y, YU L, GENG L, et al. Using direct standardization algorithm to eliminate the effect of laboratory geometric parameters on soil hyperspectral data fluctuate characteristic[J]. Journal of Central China Normal University (Natural Sciences), 2016, 50(2): 303-308.
16	O'ROURKE S M, HOLDEN N M. Determination of soil organic matter and carbon fractions in forest top soils using spectral data acquired from visible-near infrared hyperspectral images[J]. Soil Science Society of America Journal, 2012, 76: 586-596.
17	周萍, 王润生, 阎柏琨, 等. 高光谱遥感土壤有机质信息提取研究[J]. 地理科学进展, 2008(5): 27-34.
17	ZHOU P, WANG R, YAN B, et al. Extraction of soil organic matter information by hyperspectral remote sensing[J]. Progress in Geography, 2008(5): 27-34.
18	洪永胜, 朱亚星, 苏学平, 等. 高光谱技术联合归一化光谱指数估算土壤有机质含量[J]. 光谱学与光谱分析, 2017, 37(11): 3537-3542.
18	HONG Y, ZHU Y, SU X, et al. Estimation of soil organic matter content using hyperspectral techniques combined with normalized difference spectral index[J]. Spectroscopy and Spectral Analysis, 2017, 37(11): 3537-3542.
19	崔霞, 宋清洁, 张瑶瑶, 等. 基于高光谱数据的高寒草地土壤有机碳预测模型研究[J]. 草业学报, 2017, 26(10): 20-29.
19	CUI X, SONG Q, ZHANG Y, et al. Estimation of soil organic carbon content in alpine grassland using hyperspectral data[J]. Acta Prataculturae Sinica, 2017, 26(10): 20-29.
20	于雷, 洪永胜, 周勇, 等. 高光谱估算土壤有机质含量的波长变量筛选方法[J]. 农业工程学报, 2016, 32(13): 95-102.
20	YU L, HONG Y, ZHOU Y, et al. Wavelength variable selection methods for estimation of soil organic matter content using hyperspectral technique[J]. Transactions of the CSAE, 2016, 32(13): 95-102.
21	韩兆迎, 朱西存, 刘庆, 等. 黄河三角洲土壤有机质含量的高光谱反演[J]. 植物营养与肥料学报, 2014, 20(6): 1545-1552.
21	HAN Z, ZHU X, LIU Q, et al. Hyperspectral inversion models for soil organic matter content in the Yellow River Delta[J]. Journal of Plant Nutrition and Fertilizers, 2014, 20(6): 1545-1552.
22	GAO L, ZHU X, HAN Z, et al. Spectroscopy-based soil organic matter estimation in brown forest soil areas of the Shandong Peninsula, China[J]. Pedosphere, 2019, 29(6): 810-818.
23	CAO S, ZHU X, LI C, et al. Estimating total nitrogen content in brown soil of orchard based on hyperspectrum[J]. Open Journal of Soil Science, 2017, 7: 203-215.

[1]	HE Feng, WU Huarui, SHI Yangming, ZHU Huaji. Recognition Method of Facility Cucumber Farming Behaviours Based on Improved SlowFast Model [J]. Smart Agriculture, 2024, 6(3): 118-127.
[2]	HE Qing, JI Jie, FENG Wei, ZHAO Lijun, ZHANG Bohan. Adaptive Time Horizon MPC Path Tracking Control Method for Mowing Robot [J]. Smart Agriculture, 2024, 6(3): 82-93.
[3]	ZHANG Yue, LI Weijia, HAN Zhiping, ZHANG Kun, LIU Jiawen, HENKE Michael. Three-Dimensional Dynamic Growth and Yield Simulation Model of Daylily Plants [J]. Smart Agriculture, 2024, 6(2): 140-153.
[4]	XU Ruifeng, WANG Yaohua, DING Wenyong, YU Junqi, YAN Maocang, CHEN Chen. Shrimp Diseases Detection Method Based on Improved YOLOv8 and Multiple Features [J]. Smart Agriculture, 2024, 6(2): 62-71.
[5]	WANG Tong, WANG Chunshan, LI Jiuxi, ZHU Huaji, MIAO Yisheng, WU Huarui. Agricultural Disease Named Entity Recognition with Pointer Network Based on RoFormer Pre-trained Model [J]. Smart Agriculture, 2024, 6(2): 85-94.
[6]	ZHANG Jing, ZHAO Zexuan, ZHAO Yanru, BU Hongchao, WU Xingyu. Oilseed Rape Sclerotinia in Hyperspectral Images Segmentation Method Based on Bi-GRU and Spatial-Spectral Information Fusion [J]. Smart Agriculture, 2024, 6(2): 40-48.
[7]	SHEN Yanyan, ZHAO Yutao, CHEN Gengshen, LYU Zhengang, ZHAO Feng, YANG Wanneng, MENG Ran. Identification and Severity Classification of Typical Maize Foliar Diseases Based on Hyperspectral Data [J]. Smart Agriculture, 2024, 6(2): 28-39.
[8]	ZHANG Jianhua, YAO Qiong, ZHOU Guomin, WU Wendi, XIU Xiaojie, WANG Jian. Intelligent Identification of Crop Agronomic Traits and Morphological Structure Phenotypes: A Review [J]. Smart Agriculture, 2024, 6(2): 14-27.
[9]	GUO Wang, YANG Yusen, WU Huarui, ZHU Huaji, MIAO Yisheng, GU Jingqiu. Big Models in Agriculture: Key Technologies, Application and Future Directions [J]. Smart Agriculture, 2024, 6(2): 1-13.
[10]	HONG Yujiao, ZHANG Shuo, LI Li. Research Progresses of Crop Growth Monitoring Based on Synthetic Aperture Radar Data [J]. Smart Agriculture, 2024, 6(1): 46-62.
[11]	YANG Feng, YAO Xiaotong. Lightweighted Wheat Leaf Diseases and Pests Detection Model Based on Improved YOLOv8 [J]. Smart Agriculture, 2024, 6(1): 147-157.
[12]	LIU Zhiyong, WEN Changkai, XIAO Yuejin, FU Weiqiang, WANG Hao, MENG Zhijun. Path Tracking Control Algorithm of Tractor-Implement [J]. Smart Agriculture, 2023, 5(4): 58-67.
[13]	LU Bang, DONG Wanjing, DING Youchun, SUN Yang, LI Haopeng, ZHANG Chaoyu. An Rapeseed Unmanned Seeding System Based on Cloud-Terminal High Precision Maps [J]. Smart Agriculture, 2023, 5(4): 33-44.
[14]	PAN Weiting, SUN Mengli, YUN Yan, LIU Ping. Identification Method of Wheat Grain Phenotype Based on Deep Learning of ImCascade R-CNN [J]. Smart Agriculture, 2023, 5(3): 110-120.
[15]	WANG Jingyong, ZHANG Mingzhen, LING Huarong, WANG Ziting, GAI Jingyao. A Hyperspectral Image-Based Method for Estimating Water and Chlorophyll Contents in Maize Leaves under Drought Stress [J]. Smart Agriculture, 2023, 5(3): 142-153.