Adamu B, Rasul A, Whanda SJ, Headboy P, Muhammed I, & Maiha IA (2021) Evaluating the accuracy of spectral indices from Sentinel-2 data for estimating forest biomass in urban areas of the tropical savanna. Remote Sensing Applications: Society and Environment 22:100484
Attema E, & Ulaby FT (1978) Vegetation modeled as a water cloud. Radio Science 13:357-364
Baghdadi N, Boyer N, Todoroff P, El Hajj M, & Bégué A (2009) Potential of SAR sensors TerraSAR-X, ASAR/ENVISAT and PALSAR/ALOS for monitoring sugarcane crops on Reunion Island. Remote Sensing of Environment 113(8):1724-1738
Baghdadi N, El Hajj M, Zribi M, & Bousbih S (2017) Calibration of the water cloud model at C-band for winter crop fields and grasslands. Remote Sensing 9(9):969
Balenzano A, Satalino G, Pauwels V, & Mattia F (2011) Soil moisture retrieval from dense temporal series of C-band SAR data over agricultural sites. In, 2011 IEEE International Geoscience and Remote Sensing Symposium (pp. 3136-3139): Canada, Vancouver
Bao Y, Lin L, Wu S, Deng K A K, & Petropoulos G P (2018) Surface soil moisture retrievals over partially vegetated areas from the synergy of Sentinel-1 and Landsat 8 data using a modified water-cloud model. International Journal of Applied Earth Observation and Geoinformation 72:76-85
Bouchat J, Tronquo E, Orban A, Neyt, X, Verhoest N E, & Defourny P (2022) Green area index and soil moisture retrieval in maize fields using multi-polarized C-and L-Band SAR data and the water cloud model. Remote Sensing 14(10):2496
Brogioni M, Pettinato S, Macelloni G, Paloscia S, Pampaloni P, Pierdicca N, & Ticconi F (2010) Sensitivity of bistatic scattering to soil moisture and surface roughness of bare soils. International Journal of Remote Sensing 31(15):4227-4255
Champagne C, White J, Berg A, Belair S, & Carrera M (2019) Impact of soil moisture data characteristics on the sensitivity to crop yields under drought and excess moisture conditions. Remote Sensing 11(4):372
Das B, Rathore P, Roy D, Chakraborty D, Bhattacharya B K, Mandal D, Jatav R, Sethi D, Mukherjee J, & Sehgal V K (2023) Ensemble surface soil moisture estimates at farm-scale combining satellite-based optical-thermal-microwave remote sensing observations. Agricultural and Forest Meteorology 339:109567
Den Besten N, Dunne S S, Mahmud A, Jackson D, Aouizerats B, de Jeu R, Burger R, Houborg R, McGlinchey M, & van der Zaag P (2023) Understanding Sentinel-1 backscatter response to sugarcane yield variability and waterlogging. Remote Sensing of Environment 290:113555
Ebrahimi-Khusfi M, Alavipanah S.K, Hamzeh S, Amiraslani F, Samany N N, & Wigneron J P (2018) Comparison of soil moisture retrieval algorithms based on the synergy between SMAP and SMOS-IC. International Journal of Applied Earth Observation and Geoinformation 67:148-160
El Hajj M, Baghdadi N, Zribi M, & Bazzi H (2017) Synergic use of Sentinel-1 and Sentinel-2 images for operational soil moisture mapping at high spatial resolution over agricultural areas. Remote Sesing 9(12):1292
El Hajj M, Baghdadi N, Zribi M, Belaud G, Cheviron B, Courault D, & Charron F (2016) Soil moisture retrieval over irrigated grassland using X-band SAR data. Remote Sensing of Environment 176:202-218
Entekhabi D, Reichle R H, Koster R D, & Crow W T (2010) Performance metrics for soil moisture retrievals and application requirements. Journal of Hydrometeorology 11(3):832-840
Fathololoumi S, Vaezi A R, Alavipanah S K, Ghorbani A, & Biswas A (2020) Comparison of spectral and spatial-based approaches for mapping the local variation of soil moisture in a semi-arid mountainous area. Science of the Total Environment 724:138319
Ge L, Hang R, Liu Y, & Liu Q (2018) Comparing the performance of neural network and deep Convolutional neural network in estimating soil moisture from satellite observations. Remote Sensing 10(9):1327
Hajeb M, Hamzeh S, Alavipanah S K, Neissi L, & Verrelst J (2023) Simultaneous retrieval of sugarcane variables from Sentinel-2 data using Bayesian regularized neural network. International Journal of Applied Earth Observation and Geoinformation 116:103168
Hoch S.J (2001) Combining models with intuition to improve decisions. Wharton on Making Decisions, New York: Wiley, 81-101
Hu Q, Yang J, Xu B, Huang J, Memon M S, Yin G, Zeng Y, Zhao J, & Liu K (2020) Evaluation of global decametric-resolution LAI, FAPAR and FVC estimates derived from Sentinel-2 imagery. Remote Sensing 12(6):912
Khazaei M, Hamzeh S, Samani N N, Muhuri A, Goïta K, & Weng Q (2023) A web-based system for satellite-based high-resolution global soil moisture maps. Computers & Geosciences 170:105250
Leghayat R, Hamzeh S, Neysani Samani N, MOhammadi Moalehzadeh J, & Naseri A A (2023) Estimation of soil moisture using WCM model and Sentinel satellite imagery for irrigation scheduling of sugarcane fields. Engineering Journal of Geospatial Information Technology 10(4):109-123 (In Persian)
Leng P, Yang Z, Yan Q Y, Shang G F, Zhang X, Han X J, & Li Z L (2023) A framework for estimating all-weather fine resolution soil moisture from the integration of physics-based and machine learning-based algorithms. Computers and Electronics in Agriculture 206:107673
Li H z, Guo S, Li C j, & Sun, J q (2013) A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm. Knowledge-Based Systems 37:378-387
Li Z L, Leng P, Zhou C, Chen K S, Zhou F C, & Shang G F (2021) Soil moisture retrieval from remote sensing measurements: Current knowledge and directions for the future. Earth-Science Reviews 218:103673
Liu Y, Liu Y, & Wang W (2019) Inter-comparison of satellite-retrieved and Global Land Data Assimilation System-simulated soil moisture datasets for global drought analysis. Remote Sensing of Environment 220:1-18
Madelon R, Rodríguez-Fernández N J, Bazzi H, Baghdadi N, Albergel C, Dorigo W, & Zribi M (2023) Soil moisture estimates at 1 km resolution making a synergistic use of Sentinel data. Hydrology and Earth System Sciences 27(6):1221-1242
Malone T W, Laubacher R, & Dellarocas C (2009) Harnessing crowds: Mapping the genome of collective intelligence. MIT, Sloan School of Management
Martínez-Fernández J, González-Zamora A, Sánchez N, Gumuzzio A, & Herrero-Jiménez C (2016) Satellite soil moisture for agricultural drought monitoring: Assessment of the SMOS derived Soil Water Deficit Index. Remote Sensing of Environment 177:277-286
Moalezade J M, Hamze S, & Naseri A.A (2022) Estimating soil surface moisture content and investigating irrigation schedule of sugarcane fields using Thermal Trapezoidal Model. Iranian Journal of Soil and Water Research 53(10):2209-2223 (In Persian)
Mohammadi Moalezade J, Hamzeh S, & Naseri A (2023) Evaluation of optical remote sensing efficiency in estimating soil surface moisture and comparing it with thermal data for irrigation management of sugarcane. Journal of Water Research in Agriculture 37(1):85-101 (In Persian)
Mohammadpouri S, Sadeghnejad M, Rezaei H, Ghanbari R, Tayebi S, Mohammadzadeh N, Mijani N, Raeisi A, Fathololoumi S, & Biswas A (2023) A generalized regression neural network model for accuracy improvement of global precipitation products: A climate zone-based local optimization. Sustainability 15(11):8740
Molijn R A, Iannini L, Vieira Rocha J, & Hanssen R F (2019) Sugarcane productivity mapping through C-band and L-band SAR and optical satellite imagery. Remote Sensing 11(9):1109
Moran M S, Peters-Lidard C D, Watts J M, & McElroy S (2004) Estimating soil moisture at the watershed scale with satellite-based radar and land surface models. Canadian Journal of Remote Sensing 30(5):805-826
Murphy .P (2012) Machine learning: A probabilistic perspective. MIT Press
Ouaadi N, Jarlan L, Ezzahar J, Zribi M, Khabba S, Bouras E, Bousbih S, & Frison P-L (2020) Monitoring of wheat crops using the backscattering coefficient and the interferometric coherence derived from Sentinel-1 in semi-arid areas. Remote Sensing of Environment 251:112050
Peng J, Albergel C, Balenzano A, Brocca L, Cartus O, Cosh M H, Crow W T, Dabrowska-Zielinska K, Dadson S, & Davidson M W (2021) A roadmap for high-resolution satellite soil moisture applications-confronting product characteristics with user requirements. Remote Sensing of Environment 252:112162
Petropoulos G P, Ireland G, & Barrett B (2015) Surface soil moisture retrievals from remote sensing: Current status, products & future trends. Physics and Chemistry of the Earth (Parts A/B/C) 83:36-56
Pierdicca N, Pulvirenti L, & Bignami C (2010) Soil moisture estimation over vegetated terrains using multitemporal remote sensing data. Remote Sensing of Environment 114:440-448
Rawat K S, Singh S K, & Pal R K (2019) Synergetic methodology for estimation of soil moisture over agricultural area using Landsat-8 and Sentinel-1 satellite data. Remote Sensing Applications: Society and Environment 15:100250
Santi E, Paloscia S, Pettinato S, & Fontanelli G (2016) Application of artificial neural networks for the soil moisture retrieval from active and passive microwave spaceborne sensors. International Journal of Applied Earth Observation and Geoinformation 48:61-73
Singh G, & Das N N (2022) A data-driven approach using the remotely sensed soil moisture product to identify water-demand in agricultural regions. Science of the Total Environment 837:155893
Soper D S (2021) Greed is good: Rapid hyperparameter optimization and model selection using greedy k-fold cross validation. Electronics 10(16):1973
Specht D F (1991) A general regression neural network. IEEE Transactions on Neural Networks 2(6):568-576
Stumpf M P (2020) Multi-model and network inference based on ensemble estimates: Avoiding the madness of crowds. Journal of the Royal Society Interface 17(171):20200419
Surowiecki J (2005) The wisdom of crowds. Anchor
Ulaby F T, Aslam A, & Dobson M C (1982) Effects of vegetation cover on the radar sensitivity to soil moisture. IEEE Transactions on Geoscience and Remote Sensing 4:476-481
Wang Q, Li J, Jin T, Chang X, Zhu Y, Li Y, Sun J, & Li D (2020) Comparative analysis of Landsat-8, Sentinel-2, and GF-1 data for retrieving soil moisture over wheat farmlands. Remote Sensing 12(17):2708
Wang S, Li R, Wu Y, & Wang W (2023) Estimation of surface soil moisture by combining a structural equation model and an artificial neural network (SEM-ANN). Science of the Total Environment 876:162558
Wang Z, Zhao T, Qiu J, Zhao X, Li R, & Wang S (2021) Microwave-based vegetation descriptors in the parameterization of water cloud model at L-band for soil moisture retrieval over croplands. GIScience & Remote Sensing 58(1):48-67
Weiss M, Baret F, & Jay S (2020) S2ToolBox Level 2 products LAI, FAPAR, FCOVER. In: EMMAH-CAPTE, INRAe Avignon
Weiß T, Ramsauer T, Löw A, & Marzahn P (2020) Evaluation of different radiative transfer models for microwave backscatter estimation of wheat fields. Remote Sensing 12(18):3037
Wu S, Ren J, Chen Z, Yang P, & Li H (2020) Soil moisture estimation based on the microwave scattering mechanism during different crop phenological periods in a winter wheat-producing region. Journal of Hydrology 590:125521
Yamashita R, Nishio, M, Do R K G, & Togashi K (2018) Convolutional neural networks: An overview and application in radiology. Insights into Imaging 9:611-629
Yuan X, Li H, Han Y, Chen J, & Chen X (2019) Monitoring of sugarcane crop based on time series of Sentinel-1 data: A case study of Fusui, Guangxi. In, 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics) (pp. 1-5): Turkey, Istanbul
Zeyliger A, Muzalevskiy K, Zinchenko E, & Ermolaeva O (2022) Field test of the surface soil moisture mapping using Sentinel-1 radar data. Science of the Total Environment 807:151121
Zhang D, & Zhou G (2016) Estimation of soil moisture from optical and thermal remote sensing: A review. Sensors 16(8):1308
Zhang M, Lang F, & Zheng N (2021) Soil moisture retrieval during the wheat growth cycle using SAR and optical satellite data. Water 13(2):135
Zhang Y, Liang S, Zhu Z, Ma H, & He T (2022) Soil moisture content retrieval from Landsat 8 data using ensemble learning. ISPRS Journal of Photogrammetry and Remote Sensing 185:32-47
