Scientia Agricultura Sinica ›› 2022, Vol. 55 ›› Issue (19): 3738-3750.doi: 10.3864/j.issn.0578-1752.2022.19.005


Prediction of Soil Organic Carbon Content in Jiangxi Province by Vis-NIR Spectroscopy Based on the CARS-BPNN Model

WU Jun1(),GUO DaQian3,LI Guo2,4,GUO Xi1,2(),ZHONG Liang1,ZHU Qing1,GUO JiaXin1,YE YingCong1   

  1. 1College of Land Resources and Environment, Jiangxi Agricultural University/Key Laboratory of Poyang Lake Watershed Agricultural Resources and Ecology of Jiangxi Province, Nanchang 330045
    2Ecological Restoration and Innovation Research Institute of Jiangxi Province, Nanchang 330045
    3The National Land and Space Survey and Planning Research Institute of Jiangxi Province, Nanchang 330045
    4912 Brigade, Geological Bureau of Jiangxi Province, Nanchang 330045
  • Received:2021-12-07 Accepted:2022-03-30 Online:2022-10-01 Published:2022-10-10
  • Contact: Xi GUO;


【Objective】 This study explored the roles of spectral variable selection and stratified calibration based on soil type in visible-near-infrared (Vis-NIR) spectroscopy for predicting soil organic carbon (SOC) content on a large spatial scale. 【Method】 A total of 490 samples were collected in Jiangxi province (Southeast China) and used for modeling with partial least squares regression (PLSR), support vector machine (SVM), random forests (RF), and back-propagation neural network (BPNN). The competitive adaptive reweighted sampling (CARS) procedure was used to select the feature bands of different soil types and total samples (i.e., sum of red soils and paddy soils). The prediction accuracy of models incorporating full bands or feature bands was evaluated for the different soil types. Further, the prediction accuracy of these models based on their global and stratification calibration was compared for the total samples. 【Result】 (1) The feature bands of red soils were 484, 683-714, and 2 219-2 227 nm, while those of paddy soils were 484, 689-702, and 2 146-2 156 nm. The CARS-BPNN model showed the best prediction performance for red soils (validation set R2 = 0.82), being 0.07 higher than that of BPNN with full bands. The CARS-RF model also had the best prediction performance for paddy soils (validation set R2 = 0.83), being 0.13 higher than that of RF with full bands. (2) Based on the stratified calibration, the best prediction performance was obtained using the CARS-BPNN model (validation set R2 = 0.82), which was 0.06 higher than that of the model based on global calibration. 【Conclusion】 The CARS-BPNN model combined with stratified calibration based on soil type could accurately predict SOC content in the study area.

Key words: soil organic carbon, competitive adaptive reweighted sampling, stratified calibration, random forest, back propagation neural network

Fig. 1

Location of the study area and distribution of sampling points"

Fig. 2

Technical process"

Table 1

Descriptive statistical characteristics of soil organic carbon content in Jiangxi Province"

Type of soil
Type of sample
Number of samples
Standard deviation (g·kg-1)
Coefficient of variation
490 4.12 34.11 16.75 6.21 0.37
367 4.63 34.11 16.89 6.10 0.36
123 4.12 29.58 16.33 6.52 0.40
Red soil
242 4.44 28.89 16.17 5.91 0.37
182 4.44 28.89 16.29 5.89 0.36
60 4.44 27.20 15.83 5.99 0.38
Paddy soil
248 4.12 34.11 17.32 6.45 0.37
186 4.63 34.11 17.53 6.28 0.36
62 4.12 30.92 16.71 6.95 0.42

Fig. 3

Spectral curves of red soil and paddy soil"


The process and results of CARS algorithm for selecting characteristic bands a: Changes in the number of waveband variables; b: Variation of RMSECV; c: Path of variable regression coefficients; d-f: Characteristic bands of red soil, paddy soil and total soil"

Table 2

Inversion accuracy of organic carbon content in different soil types"

Type of soil
训练集 Training set 验证集 Validation set
R2 RMSE (g·kg-1) RPD R2 RMSE (g·kg-1) RPD
PLSR R 0.80 2.61 2.25 0.74 3.03 1.96
P 0.76 3.07 2.04 0.73 3.59 1.92
SVM R 0.84 2.38 2.47 0.76 2.91 2.05
P 0.77 2.99 2.10 0.76 3.41 2.02
RF R 0.88 2.05 2.86 0.74 3.01 1.97
P 0.76 3.06 2.05 0.70 3.78 1.82
BPNN R 0.86 2.19 2.69 0.75 2.95 2.01
P 0.80 2.81 2.23 0.77 3.32 2.08
CARS-PLSR R 0.83 2.41 2.44 0.81 2.61 2.28
P 0.81 2.74 2.29 0.79 3.12 2.21
CARS-SVM R 0.83 2.45 2.40 0.78 2.79 2.13
P 0.79 2.87 2.18 0.77 3.31 2.08
CARS-RF R 0.89 1.97 2.98 0.80 2.68 2.21
P 0.85 2.46 2.55 0.83 2.85 2.42
CARS-BPNN R 0.85 2.28 2.58 0.82 2.50 2.38
P 0.82 2.69 2.33 0.81 2.99 2.31

Fig. 5

Comparison between measured and estimated values of organic carbon content in red soil and paddy soil under different models of validation set"

Table 3

Inversion accuracy of soil organic carbon content based on global and classification modeling"

训练集 Training set 验证集 Validation set
R2 RMSE (g·kg-1) RPD R2 RMSE (g·kg-1) RPD
PLSR G 0.71 3.26 1.87 0.67 3.73 1.75
C 0.78 2.85 2.14 0.73 3.33 1.96
SVM G 0.79 2.78 2.19 0.73 3.40 1.91
C 0.80 2.70 2.26 0.76 3.17 2.05
RF G 0.84 2.45 2.49 0.61 4.08 1.59
C 0.82 2.61 2.34 0.72 3.42 1.90
BPNN G 0.79 2.78 2.19 0.61 4.05 1.60
C 0.83 2.52 2.42 0.76 3.14 2.07
CARS-PLSR G 0.80 2.74 2.22 0.76 3.20 2.03
C 0.82 2.58 2.36 0.80 2.88 2.26
CARS-SVR G 0.79 2.78 2.19 0.74 3.32 1.95
C 0.81 2.67 2.28 0.77 3.07 2.12
CARS-RF G 0.85 2.33 2.61 0.72 3.46 1.88
C 0.87 2.23 2.73 0.82 2.77 2.35
CARS-BPNN G 0.80 2.70 2.26 0.76 3.18 2.04
C 0.83 2.49 2.44 0.82 2.75 2.36

Fig. 6

Comparison between measured and estimated values of organic carbon content in global and classification models under different validation set"

[1] ROSSEL R V, WALVOORT D J J, MCBRATNEY A B, JANIK L J, SKJEMSTAD J O. Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma, 2006, 131(1/2): 59-75.
doi: 10.1016/j.geoderma.2005.03.007
[2] KUANG B, MOUAZEN A M. Calibration of visible and near infrared spectroscopy for soil analysis at the field scale on three European farms. European Journal of Soil Science, 2011, 62(4) : 629-636.
doi: 10.1111/j.1365-2389.2011.01358.x
[3] ROSSEL R V, BEHRENS T. Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma, 2010, 158(1/2): 46-54.
doi: 10.1016/j.geoderma.2009.12.025
[4] 史舟, 王乾龙, 彭杰, 纪文君, 刘焕军, 李曦. 中国主要土壤高光谱反射特性分类与有机质光谱预测模型. 中国科学, 2014, 44(5): 978-988.
SHI Z, WANG Q L, PENG J, JI W J, LIU H J, LI X. Development of a national VNIR soil-spectral library for soil classification and prediction of organic matter concentrations. Science China, 2014, 44(5): 978-988. (in Chinese)
[5] MOUAZEN A M, KUANG B, DE BAERDEMAEKER J, RAMON H. 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. Geoderma, 2010, 158(1/2): 23-31.
doi: 10.1016/j.geoderma.2010.03.001
[6] DING J, YANG A, WANG J, SAGAN V, YU D. Machine-learning- based quantitative estimation of soil organic carbon content by VIS/NIR spectroscopy. PeerJ, 2018, 6: e5714.
doi: 10.7717/peerj.5714
[7] CHENG H, WANG J, DU Y. Combining multivariate method and spectral variable selection for soil total nitrogen estimation by Vis-NIR spectroscopy. Archives of Agronomy and Soil Science, 2021, 67(12): 1665-1678.
doi: 10.1080/03650340.2020.1802013
[8] XU S, ZHAO Y, WANG M, SHI X. Comparison of multivariate methods for estimating selected soil properties from intact soil cores of paddy fields by Vis-NIR spectroscopy. Geoderma, 2018, 310: 29-43.
doi: 10.1016/j.geoderma.2017.09.013
[9] 纪文君, 史舟, 周清, 周炼清. 几种不同类型土壤的VIS-NIR 光谱特性及有机质响应波段. 红外与毫米波学报, 2012, 31(3): 277-282.
doi: 10.3724/SP.J.1010.2012.00277
JI W J, SHI Z, ZHOU Q, ZHOU L Q. VIS-NIR reflectance spectroscopy of the organic matter in several types of soils. Journal of Infrared and Millimeter Waves, 2012, 31(3): 277-282. (in Chinese)
doi: 10.3724/SP.J.1010.2012.00277
[10] VOHLAND M, LUDWIG M, HARBICH M, EMMERLING C, THIELE-BRUHN S. Using variable selection and wavelets to exploit the full potential of visible-near infrared spectra for predicting soil properties. Journal of Near Infrared Spectroscopy, 2016, 24(3): 255-269.
doi: 10.1255/jnirs.1233
[11] 朱亚星, 于雷, 洪永胜, 章涛, 朱强, 李思缔, 郭力, 刘家胜. 土壤有机质高光谱特征与波长变量优选方法. 中国农业科学, 2017, 50(22): 4325-4337.
ZHU Y X, YU L, HONG Y S, ZHANG T, ZHU Q, LI S D, GUO L, LIU J S. Hyperspectral features and wavelength variables selection methods of soil organic matter. Scientia Agricultura Sinica, 2017, 50(22): 4325-4337. (in Chinese)
[12] YANG M, XU D, CHEN S, LI H, SHI Z. Evaluation of machine learning approaches to predict soil organic matter and pH using Vis-NIR spectra. Sensors, 2019, 19(2): 263.
doi: 10.3390/s19020263
[13] KAWAMURA K, TSUJIMOTO Y, NISHIGAKI T, ANDRIAMANANJARA A, RABENARIVO M, ASAI H, RAZAFIMBELO T. Laboratory visible and near-infrared spectroscopy with genetic algorithm-based partial least squares regression for assessing the soil phosphorus content of upland and lowland rice fields in Madagascar. Remote Sensing, 2019, 11(5): 506.
doi: 10.3390/rs11050506
[14] LIU J, DONG Z, XIA J, WANG H, MENG T, ZHANG R, XIE J. Estimation of soil organic matter content based on CARS algorithm coupled with random forest. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2021, 258: 119823.
doi: 10.1016/j.saa.2021.119823
[15] 于雷, 洪永胜, 周勇, 朱强, 徐良, 李冀云, 聂艳. 高光谱估算土壤有机质含量的波长变量筛选方法. 农业工程学报, 2016, 32(13): 95-102.
YU L, HONG Y S, ZHOU Y, ZHU Q, XU L, LI J Y, NIE Y. Wavelength variable selection methods for estimation of soil organic matter content using hyperspectral technique. Transactions of the Chinese Society of Agricultural Engineering, 2016, 32(13): 95-102. (in Chinese)
[16] 国佳欣, 朱青, 赵小敏, 郭熙, 韩逸, 徐喆. 不同土地利用类型下土壤有机碳含量的高光谱反演. 应用生态学报, 2020, 31(3): 863-871.
doi: 10.13287/j.1001-9332.202003.014
GUO J X, ZHU Q, ZHAO X M, GUO X, HAN Y, XU Z. Hyper- spectral inversion of soil organic carbon content under different land use types. Chinese Journal of Applied Ecology, 2020, 31(3): 863-871.
doi: 10.13287/j.1001-9332.202003.014
[17] 钟亮, 郭熙, 国佳欣, 徐喆, 朱青, 丁萌. 基于不同卷积神经网络模型的红壤有机质高光谱估算. 农业工程学报, 2021, 37(1): 203-212.
ZHONG L, GUO X, GUO J X, XU Z, ZHU Q, DING M. Hyperspectral estimation of organic matter in red soil using different convolutional neural network models. Transactions of the Chinese Society of Agricultural Engineering, 2021, 37(1): 203-212.
[18] YANG M, MOUAZEN A, ZHAO X, GUO X. Assessment of a soil fertility index using visible and near-infrared spectroscopy in the rice paddy region of southern China. European Journal of Soil Science, 2020, 71(4): 615-626.
doi: 10.1111/ejss.12907
[19] SHI Z, JI W, VISCARRA ROSSEL R A, CHEN S, ZHOU Y. Prediction of soil organic matter using a spatially constrained local partial least squares regression and the Chinese vis-NIR spectral library. European Journal of Soil Science, 2015, 66(4): 679-687.
doi: 10.1111/ejss.12272
[20] 赵小敏, 杨梅花. 江西省红壤地区主要土壤类型的高光谱特性研究. 土壤学报, 2018, 55(1): 31-42.
ZHAO X M, YANG M H. Hyper-spectral characteristics of major types of soils in red soil region of Jiangxi province, China. Acta Pedologica Sinica, 2018, 55(1): 31-42. (in Chinese)
[21] LIU S, SHEN H, CHEN S, ZHAO X, BISWAS A, JIA X, FANG J. Estimating forest soil organic carbon content using vis-NIR spectroscopy: Implications for large-scale soil carbon spectroscopic assessment. Geoderma, 2019, 348: 37-44.
doi: 10.1016/j.geoderma.2019.04.003
[22] LIU Y, SHI Z, ZHANG G, CHEN Y, LI S, HONG Y, LIU Y. Application of spectrally derived soil type as ancillary data to improve the estimation of soil organic carbon by using the Chinese soil vis-NIR spectral library. Remote Sensing, 2018, 10(11): 1747.
doi: 10.3390/rs10111747
[23] 唐海涛, 孟祥添, 苏循新, 马涛, 刘焕军, 鲍依临, 张美薇, 张新乐, 霍海志. 基于CARS 算法的不同类型土壤有机质高光谱预测. 农业工程学报, 2021, 37(2): 105-113.
TANG H T, MENG X T, SU X X, MA T, LIU H J, BAO Y L, ZHANG M W, ZHANG X Y, HUO H Z. Hyperspectral prediction on soil organic matter of different types using CARS algorithm. Transactions of the Chinese Society of Agricultural Engineering, 2021, 37(2): 105-113. (in Chinese)
[24] 李冠稳, 高小红, 肖能文, 肖云飞. 基于sCARS-RF算法的高光谱估算土壤有机质含量. 发光学报, 2019, 40(8): 1030-1039.
doi: 10.3788/fgxb20194008.1030
LI G W, GAO X H, XIAO N W, XIAO Y F. Estimation soil organic matter contents with hyperspectra based on sCARS and RF algorithms. Chinese Journal of Luminescence, 2019, 40(8): 1030-1039. (in Chinese)
doi: 10.3788/fgxb20194008.1030
[25] VOHLAND M, LUDWIN M, THIELE-BRUHN S, LUDWIG B. Determination of soil properties with visible to near-and mid-infrared spectroscopy: Effects of spectral variable selection. Geoderma, 2014, 223: 88-96.
[26] HONG Y, CHEN S, LIU Y, ZHANG Y, YU L, CHEN Y, LIU Y. Combination of fractional order derivative and memory-based learning algorithm to improve the estimation accuracy of soil organic matter by visible and near-infrared spectroscopy. Catena, 2019, 174: 104-116.
doi: 10.1016/j.catena.2018.10.051
[27] VISCARRA ROSSEL R A, HICKS W S. Soil organic carbon and its fractions estimated by visible-near infrared transfer functions. European Journal of Soil Science, 2015, 66(3): 438-450.
doi: 10.1111/ejss.12237
[28] KAWAMURA K, TSUJIMOTO Y, RABENARIVO M, ASAI H, ANDRIAMANANJARA A, RAKOTOSON T. Vis-NIR spectroscopy and PLS regression with waveband selection for estimating the total C and N of paddy soils in Madagascar. Remote Sensing, 2017, 9(10): 1081.
doi: 10.3390/rs9101081
[29] DOTTO A C, DALMOLIN R S D, TEN CATEN A, GRUNWALD S. A systematic study on the application of scatter-corrective and spectral-derivative preprocessing for multivariate prediction of soil organic carbon by Vis-NIR spectra. Geoderma, 2018, 314: 262-274.
doi: 10.1016/j.geoderma.2017.11.006
[30] KUANG B, TEKIN Y, MOUAZEN A M. Comparison between artificial neural network and partial least squares for on-line visible and near infrared spectroscopy measurement of soil organic carbon, pH and clay content. Soil and Tillage Research, 2015, 146: 243-252.
doi: 10.1016/j.still.2014.11.002
[31] HONG Y, LIU Y, CHEN Y, LIU Y, YU L, LIU Y, CHENG H. Application of fractional-order derivative in the quantitative estimation of soil organic matter content through visible and near-infrared spectroscopy. Geoderma, 2019, 337: 758-769.
doi: 10.1016/j.geoderma.2018.10.025
[32] 纪文君, 李曦, 李成学, 周银, 史舟. 基于全谱数据挖掘技术的土壤有机质高光谱预测建模研究. 光谱学与光谱分析, 2012, 32(9): 2393-2398.
JI W J, LI X, LI C X, ZHOU Y, SHI Z. Using different data mining algorithms to predict soil organic matter based on visible-near infrared spectroscopy. Spectroscopy and Spectral Analysis, 2012, 32(9): 2393-2398. (in Chinese)
[33] 郭熙, 谢碧裕, 叶英聪, 谢文. 高光谱特征辨别潴育型麻沙泥田和潮沙泥田水稻土. 农业工程学报, 2014, 30(21): 184-191.
GUO X, XIE B Y, YE Y C, XIE W. Discrimination between hydromorphic alluvial sandy mud paddy and tide sandy mud paddy based on hyperspectral characteristics. Transactions of the Chinese Society of Agricultural Engineering, 2014, 30(21): 184-191. (in Chinese)
[34] KAWAMURA K, NISHIGAKI T, TSUJIMOTO Y, ANDRIAMANANJARA A, RABENARIBO M, ASAI H, RAZAFIMBELO T. Exploring relevant wavelength regions for estimating soil total carbon contents of rice fields in Madagascar from Vis-NIR spectra with sequential application of backward interval PLS. Plant Production Science, 2021, 24(1): 1-14.
doi: 10.1080/1343943X.2020.1785898
[35] JI W, SHI Z, HUANG J, LI S. Correction: In situ measurement of some soil properties in paddy soil using visible and near-infrared spectroscopy. PLoS ONE, 2016, 11(7): e0159785.
doi: 10.1371/journal.pone.0159785
[36] SHI T, CHEN Y, LIU H, WANG J, WU G. Soil organic carbon content estimation with laboratory-based visible-near-infrared reflectance spectroscopy: Feature selection. Applied Spectroscopy, 2014, 68(8): 831-837.
doi: 10.1366/13-07294 pmid: 25061784
[37] VOHLAND M, BESOLD J, HILL J, FRÜND H C. Comparing different multivariate calibration methods for the determination of soil organic carbon pools with visible to near infrared spectroscopy. Geoderma, 2011, 166(1): 198-205.
doi: 10.1016/j.geoderma.2011.08.001
[38] XU L, HONG Y, WEI Y, GUO L, SHI T, LIU Y, CHEN Y. Estimation of organic carbon in anthropogenic soil by VIS-NIR spectroscopy: Effect of variable selection. Remote Sensing, 2020, 12(20): 3394.
doi: 10.3390/rs12203394
[39] HONG Y, CHEN Y, YU L, LIU Y, LIU Y, ZHANG Y, CHENG H. Combining fractional order derivative and spectral variable selection for organic matter estimation of homogeneous soil samples by VIS-NIR spectroscopy. Remote Sensing, 2018, 10(3): 479.
doi: 10.3390/rs10030479
[40] HONG Y, CHEN S, CHEN Y, LINDERMAN M, MOUAZEN A M, LIU Y, LIU Y. Comparing laboratory and airborne hyperspectral data for the estimation and mapping of topsoil organic carbon: Feature selection coupled with random forest. Soil and Tillage Research, 2020, 199: 104589.
doi: 10.1016/j.still.2020.104589
[41] ENGLAND J R, VISCARRA ROSSEL R A. Proximal sensing for soil carbon accounting. Soil, 2018, 4(2): 101-122.
doi: 10.5194/soil-4-101-2018
[42] MOURA-BUENO J M, DALMOLIN R S D, TEN CATEN A, DOTTO A C, DEMATTÊ J A. Stratification of a local VIS-NIR-SWIR spectral library by homogeneity criteria yields more accurate soil organic carbon predictions. Geoderma, 2019, 337: 565-581.
doi: 10.1016/j.geoderma.2018.10.015
[43] ARAÚJO S R, WETTERLIND J, DEMATTÊ J A M, STENBERG B. Improving the prediction performance of a large tropical vis-NIR spectroscopic soil library from Brazil by clustering into smaller subsets or use of data mining calibration techniques. European Journal of Soil Science, 2014, 65(5): 718-729.
doi: 10.1111/ejss.12165
[44] BAO Y, MENG X, USTIN S, WANG X, ZHANG X, LIU H, TANG H. Vis-SWIR spectral prediction model for soil organic matter with different grouping strategies. Catena, 2020, 195: 104703.
doi: 10.1016/j.catena.2020.104703
[45] GHOLIZADEH A, ROSSEL R A V, SABERIOON M, BORŮVKA L, KRATINA J, PAVLŮ L. National-scale spectroscopic assessment of soil organic carbon in forests of the Czech Republic. Geoderma, 2021, 385: 114832.
doi: 10.1016/j.geoderma.2020.114832
[1] WANG ShuHui,TAO Wen,LIANG Shuo,ZHANG XuBo,SUN Nan,XU MingGang. The Spatial Characteristics of Soil Organic Carbon Sequestration and N2O Emission with Long-Term Manure Fertilization Scenarios from Dry Land in North China Plain [J]. Scientia Agricultura Sinica, 2022, 55(6): 1159-1171.
[2] LI JiaYan,SUN LiangJie,MA Nan,WANG Feng,WANG JingKuan. Carbon and Nitrogen Fixation Characteristics of Maize Root and Straw Residues in Brown Soil Under High and Low Fertility [J]. Scientia Agricultura Sinica, 2022, 55(23): 4664-4677.
[3] WANG ChuHan,LIU Fei,GAO JianYong,ZHANG HuiFang,XIE YingHe,CAO HanBing,XIE JunYu. The Variation Characteristics of Soil Organic Carbon Component Content Under Nitrogen Reduction and Film Mulching [J]. Scientia Agricultura Sinica, 2022, 55(19): 3779-3790.
[4] GUO Can,YUE XiaoFeng,BAI YiZhen,ZHANG LiangXiao,ZHANG Qi,LI PeiWu. Research on the Application of a Balanced Sampling-Random Forest Early Warning Model for Aflatoxin Risk in Peanut [J]. Scientia Agricultura Sinica, 2022, 55(17): 3426-3436.
[5] SHEN Zhe,ZHANG RenLian,LONG HuaiYu,XU AiGuo. Research on Spatial Distribution of Soil Texture in Southern Ningxia Based on Machine Learning [J]. Scientia Agricultura Sinica, 2022, 55(15): 2961-2972.
[6] BiSheng WANG,WeiShui YU,XuePing WU,LiLi GAO,Jing LI,XiaoJun SONG,ShengPing LI,JinJing LU,FengJun ZHENG,DianXiong CAI. Effects of Straw Addition on Soil Organic Carbon and Related Factors Under Different Tillage Practices [J]. Scientia Agricultura Sinica, 2021, 54(6): 1176-1187.
[7] CAO HanBing,XIE JunYu,LIU Fei,GAO JianYong,WANG ChuHan,WANG RenJie,XIE YingHe,LI TingLiang. Mineralization Characteristics of Soil Organic Carbon and Its Temperature Sensitivity in Wheat Field Under Film Mulching [J]. Scientia Agricultura Sinica, 2021, 54(21): 4611-4622.
[8] LI Na,SUN ZhanXiang,ZHANG YanQing,LIU EnKe,LI FengMing,LI ChunQian,LI Fei. Contribution of Carbon Sources in Sedimentary Soils Combining Carbon and Nitrogen Isotope with Stable Isotope Model [J]. Scientia Agricultura Sinica, 2021, 54(14): 3057-3064.
[9] ZHOU Ke,LIU Le,ZHANG YanNa,MIAO Ru,YANG Yang. Area Extraction and Growth Monitoring of Winter Wheat in Henan Province Supported by Google Earth Engine [J]. Scientia Agricultura Sinica, 2021, 54(11): 2302-2318.
[10] ZHANG ZhenHua,DING JianLi,WANG JingZhe,GE XiangYu,WANG JinJie,TIAN MeiLing,ZHAO QiDong. Digital Soil Properties Mapping by Ensembling Soil-Environment Relationship and Machine Learning in Arid Regions [J]. Scientia Agricultura Sinica, 2020, 53(3): 563-573.
[11] XIA ShuFeng,WANG Fan,WANG LongJun,ZHOU Qin,CAI Jian,WANG Xiao,HUANG Mei,DAI TingBo,JIANG Dong. Study on the Adaptability of Wheat Reaching the Protein Content Standard of Soft Wheat in Jiangsu Province [J]. Scientia Agricultura Sinica, 2020, 53(24): 4992-5004.
[12] GAO HongJun,PENG Chang,ZHANG XiuZhi,LI Qiang,ZHU Ping,WANG LiChun. Effects of Corn Straw Returning Amounts on Carbon Sequestration Efficiency and Organic Carbon Change of Soil and Aggregate in the Black Soil Area [J]. Scientia Agricultura Sinica, 2020, 53(22): 4613-4622.
[13] SHEN Zhe,ZHANG RenLian,LONG HuaiYu,WANG Zhuan,ZHU GuoLong,SHI QianXiong,YU KeFan,XU AiGuo. Research on Spatial Distribution of Soil Particle Size Distribution in Loess Region Based on Three Spatial Prediction Methods—Taking Haiyuan County in Ningxia as an Example [J]. Scientia Agricultura Sinica, 2020, 53(18): 3716-3728.
[14] LI JingYu,LI Qian,WU XuePing,WU HuiJun,SONG XiaoJun,ZHANG YongQing,LIU XiaoTong,DING WeiTing,ZHANG MengNi,ZHENG FengJun. Regional Variation in the Effects of No-Till on Soil Water Retention and Organic Carbon Pool [J]. Scientia Agricultura Sinica, 2020, 53(18): 3729-3740.
[15] XU Meng,XU LiJun,CHENG ShuLan,FANG HuaJun,LU MingZhu,YU GuangXia,YANG Yan,GENG Jing,CAO ZiCheng,LI YuNa. Responses of Soil Organic Carbon Fractionation and Microbial Community to Nitrogen and Water Addition in Artificial Grassland [J]. Scientia Agricultura Sinica, 2020, 53(13): 2678-2690.
Full text



No Suggested Reading articles found!