PERTANIKA JOURNAL OF TROPICAL AGRICULTURAL SCIENCE

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• Amazirh, A., Merlin, O., Er-Raki, S., Gao, Q., Rivalland, V., Malbeteau, Y., Khabba, S., & Escorihuela, M. J. (2018). Retrieving surface soil moisture at high spatio-temporal resolution from a synergy between Sentinel-1 radar and Landsat thermal data: A study case over bare soil. Remote Sensing of Environment, 211, 321-337. https://doi.org/10.1016/j.rse.2018.04.013

• Babaeian, E., Paheding, S., Siddique, N., Devabhaktuni, V. K., & Tuller, M. (2021). Estimation of root zone soil moisture from ground and remotely sensed soil information with multisensor data fusion and automated machine learning. Remote Sensing of Environment, 260, Article 112434. https://doi.org/10.1016/j.rse.2021.112434

• Bandini, F., Sunding, T. P., Linde, J., Smith, O., Jensen, I. K., Köppl, C. J., Butts, M., & Bauer-Gottwein, P. (2020). Unmanned Aerial System (UAS) observations of water surface elevation in a small stream: Comparison of radar altimetry, LIDAR and photogrammetry techniques. Remote Sensing of Environment, 237, Article 111487. https://doi.org/10.1016/j.rse.2019.111487

• Barca, E., De Benedetto, D., & Stellacci, A. M. (2019). Contribution of EMI and GPR proximal sensing data in soil water content assessment by using linear mixed effects models and geostatistical approaches. Geoderma, 343, 280-293. https://doi.org/10.1016/j.geoderma.2019.01.030

• Bargiel, D., Herrmann, S., & Jadczyszyn, J. (2013). Using high-resolution radar images to determine vegetation cover for soil erosion assessments. Journal of Environmental Management, 124, 82-90. https://doi.org/10.1016/j.jenvman.2013.03.049

• Bazhenov, A., Sagdeev, K., Goncharov, D., & Grivennaya, N. (2021). Bistatic system for radar sensing of soil moisture. International Scientific Conference Engineering for Rural Development (ERDev), Jelgava, Latvia, 2021, 919-925. https://doi.org/10.22616/ERDev.2021.20.TF207

• Bhogapurapu, N., Dey, S., Homayouni, S., Bhattacharya, A., & Rao, Y.S. (2022). Field-scale soil moisture estimation using sentinel-1 GRD SAR data. Advances in Space Research, 70(12), 3845-3858. https://doi.org/10.1016/j.asr.2022.03.019

• Brook, A., De Micco, V., Battipaglia, G., Erbaggio, A., Ludeno, G., Catapano, I., & Bonfante, A. (2020). A smart multiple spatial and temporal resolution system to support precision agriculture from satellite images: Proof of concept on Aglianico vineyard. Remote Sensing of Environment, 240, Article 111679, https://doi.org/10.1016/j.rse.2020.111679

• Chandra, M., & Tanzi, T. J. (2018). Drone-borne GPR design: Propagation issues. Comptes Rendus Physique, 19(1-2), 72-84. https://doi.org/10.1016/j.crhy.2018.01.002

• Dari, J., Quintana-Seguí, P., Escorihuela, M. J., Stefan, V., Brocca, L., & Morbidelli, R., (2021). Detecting and mapping irrigated areas in a Mediterranean environment by using remote sensing soil moisture and a land surface model. Journal of Hydrology, 596, Article 126129. https://doi.org/10.1016/j.jhydrol.2021.126129

• Elkharrouba, E., Sekertekin, A., Fathi, J., Tounsi, Y., Bioud, H., & Nassim, A. (2022). Surface soil moisture estimation using dual-Polarimetric Stokes parameters and backscattering coefficient. Remote Sensing Applications: Society and Environment, 26, Article 100737. https://doi.org/10.1016/j.rsase.2022.100737

• Faye, G., Frison, P. L., Diouf, A. A., Wade, S., Kane, C. A., Fussi, F., Jarlan, L., Niang, M. F. K., Ndione, J. A., Rudant, J. P., & Mougin, E. (2018). Soil moisture estimation in Ferlo region (Senegal) using radar (ENVISAT/ASAR) and optical (SPOT/VEGETATION) data. The Egyptian Journal of Remote Sensing and Space Science, 21(Supplement 1), 13-22. https://doi.org/10.1016/j.ejrs.2017.11.005

• Filion, R., Bernier, M., Paniconi, C., Chokmani, K., Melis, M., Soddu, A., Talazac, M., & Lafortune, F.-X. (2016). Remote sensing for mapping soil moisture and drainage potential in semi-arid regions: Applications to the Campidano plain of Sardinia, Italy. Science of the Total Environment, 543(Part B), 862-876. https://doi.org/10.1016/j.scitotenv.2015.07.068

• Fugazza, D. G., Aletti, Bertoni, D., & Cavicchioli, D. (2022). Farmland use data and remote sensing for ex-post assessment of CAP environmental performances: An application to soil quality dynamics in Lombardy, Remote Sensing Applications: Society and Environment, 26, Article 100723. https://doi.org/10.1016/j.rsase.2022.100723

• Gago, J., Douthe, C., Coopman, R.E., Gallego, P.P., Ribas-Carbo, M., Flexas, J., Escalona, J., & Medrano, H. (2015). UAVs challenge to assess water stress for sustainable agriculture. Agricultural Water Management, 153, 9-19. https://doi.org/10.1016/j.agwat.2015.01.020

• Gopaiah, M., Saha, R., Das, I. C., Sankar, G. J., & Kumar, K. V. (2021). Quantitative assessment of aquifer potential in near shore coastal region using geospatial techniques and ground penetrating radar, Estuarine, Coastal and Shelf Science, 262, Article 107590. https://doi.org/10.1016/j.ecss.2021.107590

• Ivushkin, K., Bartholomeus, H., Bregt, A. K., Pulatov, A., Franceschini, M. H. D., Kramer, H., van Loo, E. N., Jaramillo Roman, V. J., & Finkers, R. (2019). UAV based soil salinity assessment of cropland. Geoderma, 338, 502-512. https://doi.org/10.1016/j.geoderma.2018.09.046

• Kaasiku, T., Praks, J., Jakobson, K., & Rannap, R. (2021). Radar remote sensing as a novel tool to assess the performance of an agri-environment scheme in coastal grasslands. Basic and Applied Ecology, 56, 464-475. https://doi.org/10.1016/j.baae.2021.07.002

• Kim, J. Y. (2021). Software design for image mapping and analytics for high throughput phenotyping. Computers and Electronics in Agriculture, 191, Article 106550. https://doi.org/10.1016/j.compag.2021.106550

• 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, Article 103673. https://doi.org/10.1016/j.earscirev.2021.103673

• Ludeno, G., Catapano, I., Renga, A., Vetrella, A.R., Fasano, G., & Soldovieri, F. (2018). Assessment of a micro-UAV system for microwave tomography radar imaging. Remote Sensing of Environment, 212, 90-102. https://doi.org/10.1016/j.rse.2018.04.040

• Mallet, F., Marc, V., Douvinet, J., Rossello, P., Joly, D., & Ruy, S. (2020). Assessing soil water content variation in a small mountainous catchment over different time scales and land covers using geographical variables. Journal of Hydrology, 591, Article 125593. https://doi.org/10.1016/j.jhydrol.2020.125593

• Mandal, D., Kumar, V., Ratha, D., Dey, S., Bhattacharya, A., Lopez-Sanchez, J. M., McNairn, H., & Rao, Y. S. (2020). Dual polarimetric radar vegetation index for crop growth monitoring using sentinel-1 SAR data. Remote Sensing of Environment, 247, Article 111954. https://doi.org/10.1016/j.rse.2020.111954

• Martins, R. N., Portes, M. F., Fialho e Moraes, H. M., F. Junior, M. R., Fim Rosas, J. T., & Orlando Junior, W. A. (2021). Influence of tillage systems on soil physical properties, spectral response and yield of the bean crop. Remote Sensing Applications: Society and Environment, 22, Article 100517, https://doi.org/10.1016/j.rsase.2021.100517

• Nguyen, T. T., Ngo, H. H., Guo, W., Chang, S. W., Nguyen, D. D., Nguyen, C. T., Zhang, J., Liang, S., Bui, X. T., & Hoang, N. B. (2022). A low-cost approach for soil moisture prediction using multi-sensor data and machine learning algorithm. Science of the Total Environment, 833, Article 155066. https://doi.org/10.1016/j.scitotenv.2022.155066

• Pandey, A., & Jain, K. (2022) An intelligent system for crop identification and classification from UAV images using conjugated dense convolutional neural network. Computers and Electronics in Agriculture, 192, Article 106543.https://doi.org/10.1016/j.compag.2021.106543

• Rohil, M. K., & Mathur, S. (2022). CYGNSS-derived soil moisture: Status, challenges and future. Ecological Informatics, 69, Article 101621. https://doi.org/10.1016/j.ecoinf.2022.101621

• Rouf, T., Girotto, M., Houser, P., & Maggioni, V. (2021) Assimilating satellite-based soil moisture observations in a land surface model: The effect of spatial resolution. Journal of Hydrology, 13, Article 100105. https://doi.org/10.1016/j.hydroa.2021.100105

• Saddik, A., Latif, R., Elhoseny, M., & El Ouardi, A. (2021). Real-time evaluation of different indexes in precision agriculture using a heterogeneous embedded system. Sustainable Computing: Informatics and Systems, 30, Article 100506. https://doi.org/10.1016/j.suscom.2020.100506

• Sahaar, S. A., Niemann, J. D., & Elhaddad, A. (2022). Using regional characteristics to improve uncalibrated estimation of rootzone soil moisture from optical/thermal remote-sensing. Remote Sensing of Environment, 273, Article 112982. https://doi.org/10.1016/j.rse.2022.112982

• Salam, A., Vuran, M.C., & Irmak, S. (2019). Di-Sense: In situ real-time permittivity estimation and soil moisture sensing using wireless underground communications. Computer Networks, 151, 31-41. https://doi.org/10.1016/j.comnet.2019.01.001

• Su, C. H., Ryu, D., Crow, W. T., & Western, A. W. (2014). Stand-alone error characterisation of microwave satellite soil moisture using a Fourier method. Remote Sensing of Environment, 154, 115-126. https://doi.org/10.1016/j.rse.2014.08.014

• Tavakol, A., McDonough, K. R., Rahmani, V., Hutchinson, S. L., & Hutchinson, J. M. S. (2021). The soil moisture data bank: The ground-based, model-based, and satellite-based soil moisture data. Remote Sensing Applications: Society and Environment, 24, Article 100649. https://doi.org/10.1016/j.rsase.2021.100649

• Tran, A. P., Bogaert, P., Wiaux, F., Vanclooster, M., & Lambot, S. (2015). High-resolution space–time quantification of soil moisture along a hillslope using joint analysis of ground penetrating radar and frequency domain reflectometry data. Journal of Hydrology, 523, 252-261. https://doi.org/10.1016/j.jhydrol.2015.01.065

• Wang, H., Magagi, R., Goïta, K., Colliander, A., Jackson, T., McNairn, H., & Powers, J. (2021). Soil moisture retrieval over a site of intensive agricultural production using airborne radiometer data. International Journal of Applied Earth Observation and Geoinformation, 97, Article 102287. https://doi.org/10.1016/j.jag.2020.102287

• Wang, S., Zhang, K., Chao, L., Li, D., Tian, X., Bao, H., Chen, G., & Xia, Y. (2021). Exploring the utility of radar and satellite-sensed precipitation and their dynamic bias correction for integrated prediction of flood and landslide hazards. Journal of Hydrology, 603, Article 126964, https://doi.org/10.1016/j.jhydrol.2021.126964

• Xie, F., Lai, W.W. L., & Dérobert, X. (2022). Building simplified uncertainty models of object depth measurement by ground penetrating radar. Tunnelling and Underground Space Technology, 123, Article 104402. https://doi.org/10.1016/j.tust.2022.104402

• Yang, H., Xiong, L., Liu, D., Cheng, L., & Chen, J. (2021). High spatial resolution simulation of profile soil moisture by assimilating multi-source remote-sensed information into a distributed hydrological model. Journal of Hydrology, 597, Article 126311. https://doi.org/10.1016/j.jhydrol.2021.126311

• Zhu, L., Walker, J. P., Tsang, L., Huang, H., Ye, N., & Rüdiger, C. (2019). Soil moisture retrieval from time series multi-angular radar data using a dry down constraint. Remote Sensing of Environment, 231, Article 111237. https://doi.org/10.1016/j.rse.2019.111237