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Monitoring of Soil Moisture Content of Winter Oilseed Rape (Brassica napus L.) Based on Hyperspectral and Machine Learning Models

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Abstract

Timely acquisition of soil moisture content (SMC) in the root zone of field crops is crucial for achieving precise irrigation. In this study, high-spectral technology was employed to collect both SMC data at different soil depths during the bolting stage of winter oilseed rape (Brassica napus L.) and corresponding hyperspectral data through two consecutive years of field experiments (2021–2022). Two categories of spectral parameters were established, including “three-edge” spectral parameters such as blue, yellow, and red edge areas, as well as empirical vegetation indices that showed strong correlations with previous research and crop parameters. The spectral parameters with the highest correlation coefficients with SMC at various soil depths were selected. Subsequently, the selected vegetation indices were utilized as inputs for modeling. Support vector machine (SVM), random forest (RF), back propagation neural network (BPNN), and extreme learning machine (ELM) were employed to construct SMC estimation models for different soil depths during the jointing stage of winter oilseed rape. The results indicated that the majority of the three-edge parameters and empirical vegetation indices displayed higher correlation coefficients with SMC in the 0–20 cm soil depth compared to the 20–40 cm and 40–60 cm depths. The RF model proved to be the optimal modeling method for SMC estimation, particularly for the 0–20 cm soil depth. The validation set of the estimation model for this depth achieved a high coefficient of determination (R2) of 0.944, a root-mean-square error (RMSE) of 0.005, and a mean relative error (MRE) of 3.074%. The findings of this study provide a basis for high-spectral monitoring of SMC in the root zone of winter oilseed rape, offering valuable insights for the rapid assessment of crop growth under water stress conditions.

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References

  • Alan GB (2007) Hyperspectral remote sensing of plant pigments. J Exp Bot 58:855–867. https://doi.org/10.1093/jxb/erl123

    Article  CAS  Google Scholar 

  • Chen JY, Wang XT, Zhang ZT, Han J, Yao ZH, Wei GF (2019a) Soil salinization monitoring method based on uav-satellite remote sensing scale-up. Transactions of the CSAM 50:161–169 (in Chinese)

    Google Scholar 

  • Chen JY, Xin Z, Zhang ZT, Lao CC, Li XW, Wang HF (2019b) Comprehensive evaluation of waste water quality based on quantitative inversion model hyperspectral technology. Transactions of the CSAM 50:200–209 (in Chinese)

    Google Scholar 

  • Cheng MH, Jiao XY, Guo WH, Wang SF, Pan YC, Zhang H, Sang HH (2020) Temporal and spatial distribution characteristics of irrigation water requirement for main crops in the plain area of Hebei Province. Irrig Drain 69:1051–1062. https://doi.org/10.1002/ird.2489

    Article  Google Scholar 

  • Dong JZ, Wade TC, Kenneth JT, Cosh MH, Bosch DD, Starks PJ, Seyfried M, Collins CH (2020) Comparison of microwave remote sensing and land surface modeling for surface soil moisture climatology estimation. Remote Sens Environ 242:111756. https://doi.org/10.1016/j.rse.2020.111756

    Article  Google Scholar 

  • Dong Z (2019) Study on the spectral characteristics of maize leaves and the estimation model of SPAD value. Shanxi Agr Univ. https://doi.org/10.27285/d.cnki.gsxnu.2019.000421

  • Evans JG, Ward HC, Blake JR, Hewitt EJ, Morrison M, Fry M, Ball LA, Doughty LC, Libre JW, Hitt OE, Rylett D, Ellis RJ, Warwick AC, Brooks M, Parkes MA, Wright GME, Singer AC, Boorman DB, Jenkins A (2016) Soil water content in southern England derived from a cosmic-ray soil moisture observing system–COSMOS-UK. Hydrol Process 30:4987–4999. https://doi.org/10.1002/hyp.10929

    Article  Google Scholar 

  • Fan MF, Cai HJ, Li ZJ (2014) Monitoring leaf and soil moisture status based on hyperspectral-derived vegetation moisture indices. Irrig Drain 33:213–217 (in Chinese)

    Google Scholar 

  • Fu ZP, Jiang J, Yang G, Krienke B, Wang M, Zhong KT, Cao Q, Tian YC, Zhu Y, Cao WX, Liu XJ (2020) Wheat growth monitoring and yield estimation based on multi-rotor unmanned aerial vehicle. Remote Sens-Basel 12:508. https://doi.org/10.3390/rs12030508

    Article  Google Scholar 

  • Gamon JA, Peñuelas J, Field CB (1992) A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sens Environ 41:35–44. https://doi.org/10.1016/0034-4257(92)90059-S

    Article  Google Scholar 

  • Gitelson AA, Merzlyak NM (1997) Remote estimation of chlorophyll content in higher plant leaves. Int J Remote Sens 18:2691–2697. https://doi.org/10.1080/014311697217558

    Article  Google Scholar 

  • He H, Kang SZ, Cao HX (2003) Effect of fertigation depth on dry matter partition and water use efficiency of corn. Acta Bot Boreali-Occident Sin 8:1458–1461 (in Chinese)

    Google Scholar 

  • Horler HND, Dockray M, Barber J (1983) The red edge of plant leaf reflectance. Int J Remote Sens 4:273–288. https://doi.org/10.1080/01431168308948546

    Article  Google Scholar 

  • Huang GB, Zhou HM, Ding XJ, Zhang R (2011) Extreme learning machine for regression and multiclass classification. IEEE T Syst Man Cy-s 42:513–529. https://doi.org/10.1109/TSMCB.2011.2168604

    Article  Google Scholar 

  • João S, Shakib S, José MS (2019) Evaluation of normalized difference water index as a tool for monitoring pasture seasonal and inter-annual variability in a mediterranean agro-silvo-pastoral system. Water-sui 11:62. https://doi.org/10.3390/w11010062

    Article  Google Scholar 

  • Khdery GA, Helal NM, Saleh HA (2023) Using hyperspectral indices for investigation polluted plants and soil in Abu Zabaal area. Acta Ecol Sin 43:776–784. https://doi.org/10.1016/j.chnaes.2022.10.006

    Article  Google Scholar 

  • Kristine ML (2016) GPS interferometric reflectometry: applications to surface soil moisture, snow depth, and vegetation water content in the western United States. Wires Water 3:775–787. https://doi.org/10.1002/wat2.1167

    Article  Google Scholar 

  • Lao CC, Chen JY, Zhang ZT, Chen YW, Ma Y, Chen HR, Gu XB, Ning JF, Jing JM, Li XW (2021) Predicting the contents of soil salt and major water-soluble ions with fractional-order derivative spectral indices and variable selection. Comput Electron Agr 182:106031. https://doi.org/10.1016/j.compag.2021.106031

    Article  Google Scholar 

  • Lin RC, Chen H, Zhang DN, Wei Z, Cai JB, Zeng R, Zhang LL, Jia YL (2023) Estimating topsoil water content using crop water stress index and remote sensing technologies. J Irrig Drain Eng 42:1–7 (in Chinese)

    Google Scholar 

  • Liu S, Yu HY, Zhang JH, Zhou HG, Kong LJ, Zhang L, Dang JM, Sui YY (2021) Study on inversion model of chlorophyll content in soybean leaf based on optimal spectral indices. Spectros Spectral Anal 41:1912–1919. https://doi.org/10.3964/J.ISSN.1000-0593(2021)06-1912-08

    Article  CAS  Google Scholar 

  • Liu Y (2022) Research on wheat stripe rust monitoring method based on non-imaging and imaging hyperspectral remote sensing. Anhui Univ. https://doi.org/10.26917/d.cnki.ganhu.2022.000391 (In Chinese)

  • Lu JS, Chen SM, Huang WM, Hu TT (2023) Estimation of aboveground biomass and leaf area index of summer maize using SEPLS-ELM model. Transactions of the CSAE 37:128–135 (in Chinese)

    Google Scholar 

  • Lv XD, Wang JG, Sun QZ, Yao GP, Gao FQ (2014) Research on the hyperspectral remote sensing estimation models for the fresh yield of alfalfa grassland. Acta Prataculturae Sinica 23:84–91 (in Chinese)

    Google Scholar 

  • Maire LG, Francois C, Dufrene E (2004) Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements. Remote Sens Environ 89:1–28. https://doi.org/10.1016/j.rse.2003.09.004

    Article  Google Scholar 

  • Mammatt S, Wolfgang R, Peyman ZR (2014) Soil moisture derived using two apparent thermal inertia functions over Canterbury, New Zealand. J Appl Remote Sens 8:083624–083624. https://doi.org/10.1117/1.JRS.8.083624

    Article  Google Scholar 

  • Peng ZG, Lin SZ, Zhang BZ, Wei Z, Liu L, Han NN, Cai JB, Chen H (2020) Winter wheat canopy water content monitoring based on spectral transforms and “three-edge” parameters. Agr Water Manage 240:106306. https://doi.org/10.1016/j.agwat.2020.106306

    Article  Google Scholar 

  • Qi HJ, Jin X, Zhao L, Dedo IM, Li SW (2017) Predicting sandy soil moisture content with hyperspectral imaging. Int J Agr Biol Eng 10:175–183. https://doi.org/10.25165/j.ijabe.20171006.2614

    Article  Google Scholar 

  • Rosa A, Thom AB, Roberto G, Aldo M, Luigi M, Susan CSD (2019) Steele-Dunne. Non-invasive water content estimation in a tuff wall by DTS. Constr Build Mater 197:821–829 https://doi.org/10.1016/j.conbuildmat.2018.11.250

    Article  Google Scholar 

  • Shahbaz A, Chandra AP, Amit K, V LN (2022) Airborne hyperspectral AVIRIS-NG data for vegetation carbon stock mapping based on red edge position parameter and narrowband vegetation indices in Sholayar reserve forest, Kerala. Geocarto Int 37: 8172-8189. https://doi.org/10.1080/10106049.2021.1993353

  • Sharma M, Bangotra P, Gautam AS, Gautam S (2021) Sensitivity of normalized difference vegetation index (NDVI) to land surface temperature, soil moisture and precipitation over district Gautam Buddh Nagar, UP, India. Stoch Env Res Risk A 36:1779–1789. https://doi.org/10.1007/s00477-021-02066-1

    Article  Google Scholar 

  • Shi HZ, Guo JJ, An JQ, Tang ZJ, Wang X, Li WY, Zhao X, Jin L, Xiang YZ, Li ZJ, Zhang FC (2023) Estimation of chlorophyll content in soybean crop at different growth stages based on optimal spectral index. Agronomy-Basel 13:663. https://doi.org/10.3390/agronomy13030663

    Article  CAS  Google Scholar 

  • Sims AD, Gamon AJ (2002) Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sens Environ 81:337–354. https://doi.org/10.1016/S0034-4257(02)00010-X

    Article  Google Scholar 

  • Sims AD, Luo H, Hastings S, Oechel WC, Rahman AF, Gamon JA (2005) Parallel adjustments in vegetation greenness and ecosystem CO2 exchange in response to drought in a Southern California chaparral ecosystem. Remote Sens Environ 103:289–303. https://doi.org/10.1016/j.rse.2005.01.020

    Article  Google Scholar 

  • Tang ZJ, Guo JJ, Xiang YZ, Lu XH, Wang Q, Wang HD, Cheng MH, Wang H, Wang X, An JQ, Abdelghany A, Li ZJ, Zhang FC (2022) Estimation of leaf area index and above-ground biomass of winter wheat based on optimal spectral index. Agronomy-Basel 12:1729–1729. https://doi.org/10.3390/agronomy12071729

    Article  CAS  Google Scholar 

  • Tang ZJ, Xiang YZ, Wang X, An JQ, Guo JJ, Wang H, Li ZJ, Zhang FC (2023) Comparison of SPAD value and LAI spectral estimation of soybean leaves based on different analysis models. Soybean Science 42:55–63 (in Chinese)

    Google Scholar 

  • Virnodkar SS, Pachghare VK, Patil VC, Jha SK (2020) Remote sensing and machine learning for crop water stress determination in various crops: a critical review. Precis Agric 21:1121–1155. https://doi.org/10.1007/s11119-020-09711-9

    Article  Google Scholar 

  • Wang JZ, Ding JL, Ma XK, Ge XY, Liu BH, Liang J (2018) Detection of soil moisture content based on UAV-derived hyperspectral imagery and spectral index in oasis cropland. Transactions of the CSAM 49:164–172 (in Chinese)

    CAS  Google Scholar 

  • Wu GS, Fang YL, Jiang QY, Cui M, Li N, Ou YM, Diao ZH, Zhang BH (2023) Early identification of strawberry leaves disease utilizing hyperspectral imaging combing with spectral features, multiple vegetation indices and textural features. Comput Electron Agr 204:107553. https://doi.org/10.1016/j.compag.2022.107553

    Article  Google Scholar 

  • Yang XT, Kang P, Wang AY, Zang ZL, Liu L (2023) Prediction of ozone pollution in Sichuan Basin based on random forest model. Environmental. Science 1-16. https://doi.org/10.13227/j.hjkx.202304226 (In Chinese)

  • Yuan WS, Meng Y, Li Y, Ji ZG, Kong QM, Gao R, Su ZB (2023) Research on rice leaf area index estimation based on fusion of texture and spectral information. Comput Electron Agr 211:108016. https://doi.org/10.1016/j.compag.2023.108016

    Article  Google Scholar 

  • Zhang C, Cai HJ, Li ZJ (2015a) Estimation of fraction of absorbed photosynthetically active radiation for winter wheat based on hyperspectral characteristic parameters. Spectrosc Spect Anal 35:2644–2649

    CAS  Google Scholar 

  • Zhang JH, Shang TH, Chen RH, Wang YJ, Ding QD, Li XL (2022) Inversion of soil organic matter content in Yinchuan plain using field spectral fractional-order derivatives combined with spectral optimization index. Transactions of the CSAM 53:379–387 (in Chinese)

    Google Scholar 

  • Zhang W, Li ZJ, Pu Y, Zhang YT, Tang ZJ, Fu JY, Xu WJ, Xiang YZ, Zhang FC (2023) Estimation of the leaf area index of winter rapeseed based on hyperspectral and machine learning. Sustainability-basel 15:12930. https://doi.org/10.3390/su151712930

    Article  Google Scholar 

  • Zhang Y, Zheng L, Li M, Deng X, Ji R (2015b) Predicting apple sugar content based on spectral characteristics of apple tree leaf in different phenological phases. Comput Electron Agr 112:20–27. https://doi.org/10.1016/j.compag.2015.01.006

    Article  Google Scholar 

  • Zhang ZP, Ding JL, Wang JZ, Ge XY (2020) Prediction of soil organic matter in northwestern China using fractional- order derivative spectroscopy and modified normalized difference indices. Catena 185:104257. https://doi.org/10.1016/j.catena.2019.104257

    Article  CAS  Google Scholar 

  • Zhang ZT, Bian J, Han WT, Fu QP, Chen SB, Cui T (2018) Cotton moisture stress diagnosis based on canopy temperature characteristics calculated from UAV thermal infrared image. Transactions of the CSAE 34:77–84 (in Chinese)

    Google Scholar 

  • Zheng JJ, Li F, Du X (2018) Using red edge position shift to monitor grassland grazing intensity in inner Mongolia. J Indian Soc Remote 46:81–88. https://doi.org/10.1007/s12524-017-0667-9

    Article  Google Scholar 

  • Zhou J, Zhou JF, Ye H, Ali ML, Nguyen HT, Chen PY (2020) Classification of soybean leaf wilting due to drought stress using UAV-based imagery. Comput Electron Agr 175:105576. https://doi.org/10.1016/j.compag.2020.105576

    Article  Google Scholar 

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Funding

This study was supported by the National Natural Science Foundation of China (No. 52179045).

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Authors and Affiliations

  1. Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of Ministry of Education, Northwest A&F University, Yangling, Xianyang, 712100, China

    Zijun Tang, Wei Zhang, Youzhen Xiang, Xiaochi Liu, Xin Wang, Hongzhao Shi, Zhijun Li & Fucang Zhang

  2. Institute of Water-Saving Agriculture in Arid Areas of China, Northwest A&F University, Yangling, Xianyang, 712100, China

    Zijun Tang, Wei Zhang, Youzhen Xiang, Xiaochi Liu, Xin Wang, Hongzhao Shi, Zhijun Li & Fucang Zhang

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  1. Zijun Tang
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  2. Wei Zhang
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  3. Youzhen Xiang
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  4. Xiaochi Liu
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  5. Xin Wang
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  6. Hongzhao Shi
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Contributions

Data curation: XW and ZT. Investigation: YX and FZ. Methodology: XW and ZL. Project administration: ZT and WZ. Resources: YX and FZ. Software: XW and ZT. Supervision: ZT and XL. Visualization: ZT and HS. Writing—original draft: ZT. Writing—review and editing: ZT.

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Correspondence to Youzhen Xiang.

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Tang, Z., Zhang, W., Xiang, Y. et al. Monitoring of Soil Moisture Content of Winter Oilseed Rape (Brassica napus L.) Based on Hyperspectral and Machine Learning Models. J Soil Sci Plant Nutr 24, 1250–1260 (2024). https://doi.org/10.1007/s42729-024-01626-y

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