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But water shortages, severe groundwater over-exploitation and drought problems make it difficult to exercise the topographic resource advantages of the plain. Therefore, the precise monitoring of soil moisture is of great significance for the rational use of water resources. Soil characteristics vary in natural farmland ecosystems, crops are constrained by multiple compound stresses and the precise extraction of soil moisture stress is a difficult and critical problem. The long time series was decomposed via complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to obtain different intrinsic mode function (IMF) components, and the statistical descriptors of each component were calculated to realize the precise discrimination of soil moisture stress. A quantitative evaluation model of soil moisture was established, and the different noise addition ratios and modeling types were set respectively to investigate the optimal inversion model. The results showed that: (1) The reconstruction error of the CEEMDAN was small and almost 0; it had a high reconstruction accuracy and was more suitable for the decomposition of the long time series. The first two components, IMF1 and IMF2, were soil moisture stress subsequences, and it could effectively reflect the moisture stress situation. (2) The inversion model performed well when \u03b5 was 0.05 and the model type was quadratic, with a coefficient of determination R2 of 0.98, which gave a better fit and less error. (3) The overall soil moisture content in the study area was low, basically in the range of 6.9% to 15.7%, with the central part, especially the south-central part, being the most affected by soil moisture stress, and the overall impact of soil moisture stress showed a decreasing trend from February to May. The utilization of CEEMDAN further enhances the accuracy of soil moisture inversion in agricultural fields, realizing the effective application of remote sensing observation technology and time-frequency analysis technology in the field of soil moisture research.<\/jats:p>","DOI":"10.3390\/rs15205008","type":"journal-article","created":{"date-parts":[[2023,10,18]],"date-time":"2023-10-18T10:36:56Z","timestamp":1697625416000},"page":"5008","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Soil Moisture Monitoring and Evaluation in Agricultural Fields Based on NDVI Long Time Series and CEEMDAN"],"prefix":"10.3390","volume":"15","author":[{"given":"Xuqing","family":"Li","sequence":"first","affiliation":[{"name":"College of Remote Sensing and Information Engineering, North China Institute of Aerospace Engineering, Langfang 065000, China"},{"name":"Collaborative Innovation Center of Aerospace Remote Sensing Information Processing and Application of Hebei Province, Langfang 065000, China"}]},{"given":"Xiaodan","family":"Wang","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center of Aerospace Remote Sensing Information Processing and Application of Hebei Province, Langfang 065000, China"},{"name":"College of Information Engineering, Shandong Vocational University of Foreign Affairs, Weihai 264504, China"}]},{"given":"Jianjun","family":"Wu","sequence":"additional","affiliation":[{"name":"Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8876-4958","authenticated-orcid":false,"given":"Wei","family":"Luo","sequence":"additional","affiliation":[{"name":"College of Remote Sensing and Information Engineering, North China Institute of Aerospace Engineering, Langfang 065000, China"},{"name":"Collaborative Innovation Center of Aerospace Remote Sensing Information Processing and Application of Hebei Province, Langfang 065000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1968-2836","authenticated-orcid":false,"given":"Lingwen","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geoscience, Beijing 100083, China"}]},{"given":"Yancang","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Remote Sensing and Information Engineering, North China Institute of Aerospace Engineering, Langfang 065000, China"},{"name":"Collaborative Innovation Center of Aerospace Remote Sensing Information Processing and Application of Hebei Province, Langfang 065000, China"}]},{"given":"Yuyan","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Remote Sensing and Information Engineering, North China Institute of Aerospace Engineering, Langfang 065000, China"},{"name":"Collaborative Innovation Center of Aerospace Remote Sensing Information Processing and Application of Hebei Province, Langfang 065000, China"}]},{"given":"Liang","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Remote Sensing and Information Engineering, North China Institute of Aerospace Engineering, Langfang 065000, China"},{"name":"Collaborative Innovation Center of Aerospace Remote Sensing Information Processing and Application of Hebei Province, Langfang 065000, China"}]},{"given":"Chenyu","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Remote Sensing and Information Engineering, North China Institute of Aerospace Engineering, Langfang 065000, China"},{"name":"Collaborative Innovation Center of Aerospace Remote Sensing Information Processing and Application of Hebei Province, Langfang 065000, China"}]},{"given":"Wenlong","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Remote Sensing and Information Engineering, North China Institute of Aerospace Engineering, Langfang 065000, China"},{"name":"Collaborative Innovation Center of Aerospace Remote Sensing Information Processing and Application of Hebei Province, Langfang 065000, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhao, J., Zhang, C., Min, L., Guo, Z., and Li, N. (2022). Retrieval of Farmland Surface Soil Moisture Based on Feature Optimization and Machine Learning. Remote Sens., 14.","DOI":"10.3390\/rs14205102"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"105850","DOI":"10.1016\/j.atmosres.2021.105850","article-title":"Drought monitoring based on a new combined remote sensing index across the transitional area between humid and arid regions in China","volume":"264","author":"Zhang","year":"2021","journal-title":"Atmos. Res."},{"key":"ref_3","first-page":"115","article-title":"The effects of drought stress on seed yield and some agronomic traits of canola cultivars at different growth stages","volume":"2","author":"Mirzaei","year":"2013","journal-title":"Bull. Environ. Pharmacol. Life Sci."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Kapoor, D., Bhardwaj, S., Landi, M., Sharma, A., Ramakrishnan, M., and Sharma, A. (2020). The Impact of Drought in plant metabolism: How to exploit tolerance mechanisms to increase crop production. Appl. Sci., 10.","DOI":"10.3390\/app10165692"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.agwat.2015.05.023","article-title":"The response of agricultural drought to meteorological drought and the influencing factors: A case study in the Wei River Basin, China","volume":"159","author":"Huang","year":"2015","journal-title":"Agric. Water Manag."},{"key":"ref_6","first-page":"110","article-title":"Effect of water and fertilizer coupling on root growth, soil water and nitrogen distribution of cabbage with drip irrigation under mulch","volume":"35","author":"Wu","year":"2019","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_7","first-page":"91","article-title":"Regional heavy metal pollution in crops by integrating physiological function variability with spatio-temporal stability using multi-temporal thermal remote sensing","volume":"51","author":"Liu","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.scitotenv.2018.04.415","article-title":"Heavy metal-induced stress in rice crops detected using multi-temporal Sentinel-2 satellite images","volume":"637\u2013638","author":"Liu","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_9","first-page":"97","article-title":"Study on Variation Curve and Fitting Model of Winter Wheat Canopy NDVI","volume":"12","author":"Cui","year":"2018","journal-title":"Water Sav. Irrig."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wang, Q., Li, M., Li, G., Zhang, J., Yan, S., Chen, Z., Zhang, X., and Chen, G. (2023). High-Resolution Remote Sensing Image Change Detection Method Based on Improved Siamese U-Net. Remote Sens., 15.","DOI":"10.3390\/rs15143517"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Wang, R., Zhao, J., Yang, H., and Li, N. (2023). Inversion of Soil Moisture on Farmland Areas Based on SSA-CNN Using Multi-Source Remote Sensing Data. Remote Sens., 15.","DOI":"10.3390\/rs15102515"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Milewski, R., Schmid, T., Chabrillat, S., Jim\u00e9nez, M., Escribano, P., Pelayo, M., and Ben-Dor, E. (2022). Analyses of the Impact of Soil Conditions and Soil Degradation on Vegetation Vitality and Crop Productivity Based on Airborne Hyperspectral VNIR\u2013SWIR\u2013TIR Data in a Semi-Arid Rainfed Agricultural Area (Camarena, Central Spain). Remote Sens., 14.","DOI":"10.3390\/rs14205131"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Albertini, C., Gioia, A., Iacobellis, V., and Manfreda, S. (2022). Detection of Surface Water and Floods with Multispectral Satellites. Remote Sens., 14.","DOI":"10.3390\/rs14236005"},{"key":"ref_14","first-page":"4699","article-title":"Temporal dynamics of vegetation NDVI and its response to drought conditions in Yunnan Province","volume":"36","author":"Liu","year":"2016","journal-title":"Acta Ecol. Sin."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Celik, M.F., Isik, M.S., Yuzugullu, O., Fajraoui, N., and Erten, E. (2022). Soil Moisture Prediction from Remote Sensing Images Coupled with Climate, Soil Texture and Topography via Deep Learning. Remote Sens., 14.","DOI":"10.3390\/rs14215584"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Liu, W., Zhu, S., Huang, Y., Wan, Y., Wu, B., and Liu, L. (2020). Spatiotemporal Variations of Drought and Their Teleconnections with Large-Scale Climate Indices over the Poyang Lake Basin, China. Sustainability, 12.","DOI":"10.3390\/su12093526"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Hirschberg, V., and Rodrigue, D. (2021). Fourier Transform (FT) Analysis of the Stress as a Tool to Follow the Fatigue Behavior of Metals. Appl. Sci., 11.","DOI":"10.3390\/app11083549"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Hien, L.T.T., Gobin, A., Lim, D.T., Quan, D.T., Hue, N.T., Thang, N.N., Binh, N.T., Dung, V.T.K., and Linh, P.H. (2022). Soil Moisture Influence on the FTIR Spectrum of Salt-Affected Soils. Remote Sens., 14.","DOI":"10.3390\/rs14102380"},{"key":"ref_19","first-page":"476","article-title":"Crop discrimination in Northern China with double cropping systems using Fourier analysis of time-series MODIS data","volume":"10","author":"Zhang","year":"2008","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_20","first-page":"2089","article-title":"Curvelet Transform based Denoising of Multispectral Remote Sensing Images","volume":"1","author":"Lokeshwara","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wongsaroj, W., Hamdani, A., Thong-Un, N., Takahashi, H., and Kikura, H. (2018). Extended Short-Time Fourier Transform for Ultrasonic Velocity Profiler on Two-Phase Bubbly Flow Using a Single Resonant Frequency. Appl. Sci., 9.","DOI":"10.3390\/app9010050"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.ndteint.2016.04.006","article-title":"Defect detection in magnetic tile images based on stationary wavelet transform","volume":"83","author":"Yang","year":"2016","journal-title":"NDT E Int."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Feng, X., Zhang, W., Su, X., and Xu, Z. (2021). Optical Remote Sensing Image Denoising and Super-Resolution Reconstructing Using Optimized Generative Network in Wavelet Transform Domain. Remote Sens., 13.","DOI":"10.3390\/rs13091858"},{"key":"ref_24","first-page":"5","article-title":"Predicting the ground-level pollutants concentrations and identifying the influencing factors using machine learning, wavelet transformation, and remote sensing techniques","volume":"12","author":"Ebrahimi","year":"2021","journal-title":"Atmos. Pollut. Res."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Hu, Z., Chai, L., Crow, W.T., Liu, S., Zhu, Z., Zhou, J., Qu, Y., Liu, J., Yang, S., and Lu, Z. (2022). Applying a Wavelet Transform Technique to Optimize General Fitting Models for SM Analysis: A Case Study in Downscaling over the Qinghai\u2013Tibet Plateau. Remote Sens., 14.","DOI":"10.3390\/rs14133063"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Liu, Z., Zhao, L., Peng, Y., Wang, G., and Hu, Y. (2020). Improving Estimation of Soil Moisture Content Using a Modified Soil Thermal Inertia Model. Remote Sens., 12.","DOI":"10.3390\/rs12111719"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4664","DOI":"10.1002\/mma.5668","article-title":"Digital watermarking scheme for colour remote sensing image based on quaternion wavelet transform and tensor decomposition","volume":"42","author":"Li","year":"2019","journal-title":"Math. Methods Appl. Sci."},{"key":"ref_28","first-page":"95","article-title":"Optimal scale of crop classification using unmanned aerial vehicle remote sensing imagery based on wavelet packet transform","volume":"32","author":"Zhang","year":"2016","journal-title":"Editor. Off. Trans. Chin. Soc. Agric. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2724","DOI":"10.1016\/j.asr.2022.01.023","article-title":"Wavelet transform approach for denoising and decomposition of satellite-derived ocean color time-series: Selection of optimal mother wavelet","volume":"69","author":"Masoud","year":"2022","journal-title":"Adv. Space Res."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wang, C., Sun, W., Fan, D., Liu, X., and Zhang, Z. (2021). Adaptive Feature Weighted Fusion Nested U-Net with Discrete Wavelet Transform for Change Detection of High-Resolution Remote Sensing Images. Remote Sens., 13.","DOI":"10.3390\/rs13244971"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Nassar, A., Torres-Rua, A., Hipps, L., Kustas, W., McKee, M., Stevens, D., Nieto, H., Keller, D., Gowing, I., and Coopmans, C. (2022). Using Remote Sensing to Estimate Scales of Spatial Heterogeneity to Analyze Evapotranspiration Modeling in a Natural Ecosystem. Remote Sens., 14.","DOI":"10.3390\/rs14020372"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.catena.2017.08.019","article-title":"Scale\u2013location specific soil spatial variability: A comparison of continuous wavelet transform and Hilbert\u2013Huang transform","volume":"160","author":"Asim","year":"2018","journal-title":"Catena"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wang, X., Liu, A., Zhang, Y., and Xue, F. (2019). Underwater Acoustic Target Recognition: A Combination of Multi-Dimensional Fusion Features and Modified Deep Neural Network. Remote Sens., 11.","DOI":"10.3390\/rs11161888"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"111211","DOI":"10.1016\/j.ecoenv.2020.111211","article-title":"A novel spectral analysis method for distinguishing heavy metal stress of maize due to copper and lead: RDA and EMD-PSD","volume":"206","author":"Fu","year":"2020","journal-title":"Ecotoxicol. Environ. Saf."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1017\/S0004972719000893","article-title":"Streamflow and soil moisture forecasting with hybrid data intelligent machine learning approaches: Case studies in the Australian Murray-Daring basin","volume":"100","author":"Prasad","year":"2019","journal-title":"Bull. Aust. Math. Soc."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"663403","DOI":"10.3389\/fnins.2021.663403","article-title":"Energy-Period Profiles of Brain Networks in Group fMRI Resting-State Data: A Comparison of Empirical Mode Decomposition With the Short-Time Fourier Transform and the Discrete Wavelet Transform","volume":"15","author":"Cordes","year":"2021","journal-title":"Front. Neurosci."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zhang, X., Feng, X., Zhang, Z., Kang, Z., Chai, Y., You, Q., and Ding, L. (2019). Dip Filter and Random Noise Suppression for GPR B-Scan Data Based on a Hybrid Method in f\u2013x Domain. Remote Sens., 11.","DOI":"10.3390\/rs11182180"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Fan, G.F., Peng, L.L., Zhao, X., and Hong, W.C. (2017). Applications of Hybrid EMD with PSO and GA for an SVR-Based Load Forecasting Model. Energies, 10.","DOI":"10.3390\/en10111713"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Lu, P., Ye, L., Sun, B., Zhang, C., Zhao, Y., and Zhu, T. (2018). A new hybrid prediction method of ultra-short-term wind power forecasting based on EEMD-PE and LSSVM optimized by the GSA. Energies, 11.","DOI":"10.3390\/en11040697"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1142\/S1793536909000047","article-title":"Ensemmble empirical mode decomposition: A noise-assisted data analysis method","volume":"1","author":"Wu","year":"2005","journal-title":"Adv. Adapt. Data Anal."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Li, T.Y., Zhou, M., Guo, C.Q., Luo, M., Wu, J., Pan, F., Tao, Q.Y., and He, T. (2016). Forecasting crude oil price using EEMD and RVM with adaptive pso-based kernels. Energies, 9.","DOI":"10.3390\/en9121014"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Torres, M.E., Colominas, M.A., Schlotthauer, G., and Flandrin, P. (2011, January 22\u201327). A complete ensemble empirical mode decomposition with adaptive noise. Proceedings of the 2011 IEEE International Conference on Acoustics, Speech and Signal Processing, Prague, Czech Republic.","DOI":"10.1109\/ICASSP.2011.5947265"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.bspc.2014.06.009","article-title":"Improved complete ensemble EMD: A suitable tool for biomedical signal processing","volume":"14","author":"Colominas","year":"2014","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Yang, Z., Zou, L., Xia, J., Qiao, Y., and Cai, D. (2022). Inner Dynamic Detection and Prediction of Water Quality Based on CEEMDAN and GA-SVM Models. Remote Sens., 14.","DOI":"10.3390\/rs14071714"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1142\/S1793536910000422","article-title":"Complementary Ensemble Empirical Mode Decomposition: A Novel Noise Enhanced Data Analysis Method","volume":"2","author":"Yeh","year":"2011","journal-title":"Adv. Adapt. Data Anal."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Ahmed, A.A.M., Deo, R.C., Raj, N., Ghahramani, A., Feng, Q., Yin, Z., and Yang, L. (2021). Deep Learning Forecasts of Soil Moisture: Convolutional Neural Network and Gated Recurrent Unit Models Coupled with Satellite-Derived MODIS, Observations and Synoptic-Scale Climate Index Data. Remote Sens., 13.","DOI":"10.3390\/rs13040554"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Liu, Y., Feng, H., Yue, J., Li, Z., Jin, X., Fan, Y., Feng, Z., and Yang, G. (2022). Estimation of Aboveground Biomass of Potatoes Based on Characteristic Variables Extracted from UAV Hyperspectral Imagery. Remote Sens., 14.","DOI":"10.3390\/rs14205121"},{"key":"ref_48","first-page":"102560","article-title":"Long-term and seasonal variation in groundwater storage in the North China Plain based on GRACE","volume":"104","author":"Xu","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_49","first-page":"28","article-title":"Survey Research on Coordinated Disposal of Contaminated Soil by Cement Kiln in Hebei Province","volume":"33","author":"Xu","year":"2021","journal-title":"Chem. Enterp. Manag."},{"key":"ref_50","first-page":"75","article-title":"Phosphorus and Potassium Nutrient Abundance and Deficiency Index and Fertilization Target of Summer Maize Farmland in Hebei Plain","volume":"49","author":"Jia","year":"2021","journal-title":"Jiangsu Agric. Sci."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/20\/5008\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:09:04Z","timestamp":1760130544000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/20\/5008"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,18]]},"references-count":50,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2023,10]]}},"alternative-id":["rs15205008"],"URL":"https:\/\/doi.org\/10.3390\/rs15205008","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,18]]}}}