Dr Davoud Ashourloo

Research Fellow

Queensland Alliance for Agriculture and Food Innovation

d.ashourloo@uq.edu.au

Researcher biography

My research focuses on the application of remote sensing technologies for vegetation monitoring, crop yield estimation, and agricultural decision support. I employ a combination of multispectral, hyperspectral, and radar satellite data, together with UAV imagery and field-based measurements, to better understand crop biophysical and biochemical dynamics. My work integrates advanced spectral analysis and machine learning techniques to enhance the accuracy and transferability of predictive models across diverse agro-ecological environments. In addition to developing and validating innovative approaches for yield estimation, I have designed several automated methods for crop monitoring, including the development of new vegetation indices that effectively capture canopy quality, structure, and stress conditions. These methods contribute to improving the efficiency of large-scale agricultural assessments and promoting sustainable management practices. Overall, my research aims to advance precision agriculture through data-driven insights derived from multi-source remote sensing and artificial intelligence integration.

Publications

Journal Articles (14)

Conference Paper (1)

Journal Articles

Sh, Mohammad Amin, Aghighi, Hossein, Aza, Mohsen, Ashourloo, Davoud, Matkan, Ali Akbar, Brakhasi, Foad and Walker, Jeffrey P. (2025). Soil moisture estimation using combined SAR and optical imagery: Application of seasonal machine learning algorithms. Advances in Space Research, 75 (8), 6207-6221. doi: 10.1016/j.asr.2025.01.064

Ezimand, Keyvan, Aghighi, Hossein, Ashourloo, Davod and Shakiba, Alireza (2024). The analysis of the spatio-temporal changes and prediction of built-up lands and urban heat islands using multi-temporal satellite imagery. Sustainable Cities and Society, 103 105231, 1-14. doi: 10.1016/j.scs.2024.105231

Ashourloo, D., Manafifard, M., Behifar, M. and Kohandel, M. (2022). Wheat yield prediction based on Sentinel-2, regression, and machine learning models in Hamedan, Iran. Scientia Iranica, 29 (6), 3230-3243. doi: 10.24200/sci.2022.57809.5429

Ashourloo, Davoud, Nematollahi, Hamed, Huete, Alfredo, Aghighi, Hossein, Azadbakht, Mohsen, Shahrabi, Hamid Salehi and Goodarzdashti, Salman (2022). A new phenology-based method for mapping wheat and barley using time-series of Sentinel-2 images. Remote Sensing of Environment, 280 113206, 1-14. doi: 10.1016/j.rse.2022.113206

Azadbakht, Mohsen, Ashourloo, Davoud, Aghighi, Hossein, Homayouni, Saeid, Shahrabi, Hamid Salehi, Matkan, AliAkbar and Radiom, Soheil (2022). Alfalfa yield estimation based on time series of Landsat 8 and PROBA-V images: an investigation of machine learning techniques and spectral-temporal features. Remote Sensing Applications, 25 100657, 1-16. doi: 10.1016/j.rsase.2021.100657

Shahrabi, Hamid Salehi, Ashourloo, Davoud, Rad, Amir Moeini, Aghighi, Hossein, Azadbakht, Mohsen and Nematollahi, Hamed (2020). Automatic silage maize detection based on phenological rules using Sentinel-2 time-series dataset. International Journal of Remote Sensing, 41 (21), 8406-8427. doi: 10.1080/01431161.2020.1779377

Ashourloo, Davoud, Shahrabi, Hamid Salehi, Azadbakht, Mohsen, Aghighi, Hossein, Nematollahi, Hamed, Alimohammadi, Abbas and Matkan, Ali Akbar (2019). Automatic canola mapping using time series of sentinel 2 images. ISPRS Journal of Photogrammetry and Remote Sensing, 156, 63-76. doi: 10.1016/j.isprsjprs.2019.08.007

Rad, Amir Moeini, Ashourloo, Davoud, Shahrabi, Hamid Salehi and Nematollahi, Hamed (2019). Developing an automatic phenology-based algorithm for rice detection using Sentinel-2 time-series data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12 (5), 1471-1481. doi: 10.1109/jstars.2019.2906684

Azadbakht, Mohsen, Ashourloo, Davoud, Aghighi, Hossein, Radiom, Soheil and Alimohammadi, Abbas (2019). Wheat leaf rust detection at canopy scale under different LAI levels using machine learning techniques. Computers and Electronics in Agriculture, 156, 119-128. doi: 10.1016/j.compag.2018.11.016

Ashourloo, Davoud, Shahrabi, Hamid Salehi, Azadbakht, Mohsen, Aghighi, Hossein, Matkan, Ali Akbar and Radiom, Soheil (2018). A novel automatic method for alfalfa mapping using time series of Landsat-8 OLI Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11 (11), 4478-4487. doi: 10.1109/jstars.2018.2874726

Ashourloo, Davoud, Aghighi, Hossein, Matkan, Ali Akbar, Mobasheri, Mohammad Reza and Rad, Amir Moeini (2016). An investigation into machine learning regression techniques for the leaf rust disease detection using hyperspectral measurement. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9 (9), 4344-4351. doi: 10.1109/jstars.2016.2575360

Ashourloo, Davoud, Matkan, Ali Akbar, Huete, Alfredo, Aghighi, Hossein and Mobasheri, Mohammad Reza (2016). Developing an index for detection and identification of disease stages. IEEE Geoscience and Remote Sensing Letters, 13 (6), 851-855. doi: 10.1109/lgrs.2016.2550529

Ashourloo, Davoud, Mobasheri, Mohammad Reza and Huete, Alfredo (2014). Evaluating the effect of different wheat rust disease symptoms on vegetation indices using hyperspectral measurements. Remote Sensing, 6 (6), 5107-5123. doi: 10.3390/rs6065107

Ashourloo, Davoud, Mobasheri, Mohammad Reza and Huete, Alfredo (2014). Developing two spectral disease indices for detection of wheat leaf rust (Pucciniatriticina). Remote Sensing, 6 (6), 4723-4740. doi: 10.3390/rs6064723

Conference Paper

Aghighi, Hossein, Azadbakht, Mohsen, Ashourloo, Davoud, Shahrabi, Hamid Salehi and Radiom, Soheil (2018). Machine learning regression techniques for the silage maize yield prediction using time-series images of Landsat 8 OLI. 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp), Bruges, Belgium, 27-29 June 2017. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/jstars.2018.2823361