Published
Hashemi, M. G., Jalilvand, E., Alemohammad, H., Tan, P. N., & Das, N. N. 2024. “Review of synthetic aperture radar with deep learning in agricultural applications”. ISPRS Journal of Photogrammetry and Remote Sensing, 218, 20-49. https://doi.org/10.1016/j.isprsjprs.2024.08.018
Hashemi, M. G., Tan, P. N., Jalilvand, E., Wilke, B., Alemohammad, H., & Das, N. N. 2024. “Yield estimation from SAR data using patch-based deep learning and machine learning techniques”. Computers and Electronics in Agriculture, 226, 109340. https://doi.org/10.1016/j.compag.2024.109340
Nanda, A., Das, N. N., Jalilvand, E., Singh, G., Bindlish, R., Andreadis, K. M., & Jayasinghe, S. 2024. “Harnessing SMAP satellite soil moisture product to optimize soil properties to improve water resource management for agriculture” [Agric. Water Manag., Volume 300, 1 July 2024, 108918]. Agricultural Water Management, 109065. https://doi.org/10.1016/j.agwat.2024.109065
Jalilvand, E., Abolafia‐Rosenzweig, R., Tajrishy, M., Kumar, S. V., Mohammadi, M. R., & Das, N. N. 2023. Is It Possible to Quantify Irrigation Water‐Use by Assimilating a High‐Resolution Satellite Soil Moisture Product?. Water Resources Research, 59(4), e2022WR033342. https://doi.org/10.1029/2022WR033342
Sotoudeheian S., Jalilvand E., Kermanshah A., 2022, Using High-Resolution Climate Models to Identify Climate Change Hotspots in the Middle East: A Case Study of Iran. Climate. 2022; 10(11):161. https://doi.org/10.3390/cli10110161
Hashemi, M. G., Abhishek, A., Jalilvand, E., Jayasinghe, S., Andreadis, K. M., Siqueira, P., & Das, N. N. 2022. Assessing the impact of Sentinel-1 derived planting dates on rice crop yield modeling. International Journal of Applied Earth Observation and Geoinformation, 114, 103047. https://doi.org/10.1016/j.jag.2022.103047
Jalilvand, E., Abolafia-Rosenzweig, R., Tajrishy, M. and Das, N. N., 2021 Evaluation of SMAP/Sentinel 1 High-Resolution Soil Moisture Data to Detect Irrigation Over Agricultural Domain. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 10733-10747, 2021, doi: 10.1109/JSTARS.2021.3119228.
Rabiei, S., Jalilvand, E., Tajrishy, M., 2022, A Method to Estimate Surface Soil Moisture and Map the Irrigated Cropland Area Using Sentinel-1 and Sentinel-2 Data. Sustainability. 2021; 13(20):11355. https://doi.org/10.3390/su132011355
Ghazi Zadeh Hashemi, M.S., Tajrishy, M. & Jalilvand, E., 2020. The Impact of Pavement Permeability on Time of Concentration in a Small Urban Watershed with a Semi-Arid Climate. Water Resour Manage. https://doi.org/10.1007/s11269-020-02596-3
Jalilvand, E., Tajrishy, M., Ghazi Zadeh Hashemi, S.A., Brocca, L., 2019. Quantification of irrigation water using remote sensing of soil moisture in a semi-arid region. Remote Sensing of Environment. 231, 111226. doi:10.1016/j.rse.2019.111226
Jalilvand, E., Tajrishy, M., Brocca, L., Massari, C., Hashemi, S.G.Z., Ciabatta, L., 2018. "Estimating the drainage rate from surface soil moisture drydowns: application of DfD model to in situ soil moisture data". Journal of Hydrology. doi:https://doi.org/10.1016/j.jhydrol.2018.08.035
Under Review
Jalilvand, E., Kumar, S.V., Haacker, E., Truong, C., Mahanama, S., 2024, Characterizing spatiotemporal variability in irrigation extent and timing through thermal remote sensing, submitted to Remote Sensing of Environment
Hashemi, M. G. Z., Alemohammad, H., Jalilvand, E., Tan, P.-N., Judge, J., Cosh, M., & Das, N. N. 2024. Estimating crop biophysical parameters from satellite-based SAR and optical observations using self-supervised learning with geospatial foundation models. Submitted to Remote Sensing of Environment.
Kumar, S.V., Kwon, Y., Liu, P., Navari, M., Bindlish, R., Kemp, E., Wegiel, J., Jalilvand, E., 2024 (under review). Development of low latency, high resolution SMAP soil moisture retrievals in support of near real-time applications, submitted to Remote Sensing of Environment
In Preparation
Jalilvand, E., Das, N.N., Colliander, A., Chan, S., 2024. "Developing a high-resolution satellite soil moisture product for the subarctic region accounting for the organic-rich Soil and transient water bodies". To be submitted to Remote Sensing of Environment
Conferences
Jalilvand, E., Abolafia-Rosenweig, R., Das, N., and Tajrishy, M.: Quantifying the irrigation water use by assimilating SMAP-Sentinel1 1km soil moisture data using a particle batch smoother approach, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9849, https://doi.org/10.5194/egusphere-egu22-9849, 2022.
Jalilvand, E., Abolafia-Rosenzweig, R., Tajrishy, M. and Das, N.N, Assimilating SMAP-Sentinel1 High-Resolution Soil Moisture Data in Numerical Modeling to Quantify the Irrigation Water Use, in: American Geophysical Union Abstracts. pp. AGU2021, H31F-05 (oral presntation)
Jalilvand, E., Tajrishy, M., Brocca, L., Massari, C., Hashmi, S.G.Z., 2018. Estimating drainage rate using satellite soil moisture drydowns, in: Geophysical Research Abstracts. pp. EGU2018-692. doi:10.1002/hyp.9766 (Oral presentation)
Ghazi Zadeh Hashemi, S., Brocca, L., Jalilvand, E., and Tajrishy, M., 2018 "Estimation of Irrigation Water Using Satellite Soil Moisture Data in a Semi-Arid Area", in: Geophysical Research Abstracts. pp. EGU2018-706.
Jalilvand, E., Brocca, L., Massari, C., Ghazi Zadeh Hashemi, S.S., Ciabatta, L., and Tajrishy, M., 2017 "Toward creating a global map of drainage rate using satellite soil moisture data as the only input", in: Geophysical Research Abstracts. pp. EGU2017- 14156. (Poster presentation)