Publications and reports

Jha, S. K., Brinkhoff, J., Robson, A. J., & Dunn, B. W. (2026). Multi-Source Time Series Integration for Progressive In-Season Prediction of Rice Yield, Aboveground Biomass, and Harvest Index. Remote Sensing, 18(11), 1785 https://doi.org/10.3390/rs18111785.

Lai, Y., Pringle, M.J., Kopittke, P.M., Menzies, N.W., Orton, T.G., & Dang, Y.P. (2026). A comparative study of methods to quantify wheat yield penalty attributable to soil sodicity across north-eastern Australia. Soil & Tillage Research, vol 262. https://doi.org/10.1016/j.still.2026.107209

Rahman, M.M., Robson, A., & Bekker, T. (2025). Machine Learning Approaches for Assessing Avocado Alternate Bearing Using Sentinel-2 and Climate Variables—A Case Study in Limpopo, South Africa. Remote Sens. 2025, 17(24), 3935; https://doi.org/10.3390/rs17243935

Brinkhoff, J., Houborg, R., & Clark, A. (2025). Empirical Correction of Sentinel-2 Time Series Data to Enhance Real-Time Rice Crop Monitoring. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 18. https://doi.org/10.1109/JSTARS.2025.3615125

Jha, S. K., Brinkhoff, J., Robson, A. J., & Dunn, B. W. (2025). Integrating Remote Sensing and Weather Time Series for Australian Irrigated Rice Phenology Prediction. Remote Sensing, 17(17), 3050. https://doi.org/10.3390/rs17173050

Clark, A., Brinkhoff, J., Robson, A.J., Shephard, C. (2025). Deep Learning Improves Planting Year Estimation of Macadamia Orchards in Australia, KeAi. 10.3390/agriculture15222346

Brinkhoff, J., Dunn, B. W., Dunn, T., Schultz, A., & Hart, J. (2025). Forecasting field rice grain moisture content using Sentinel-2 and weather data. Precision Agriculture, 26(1), 28. https://doi.org/10.1007/s11119-025-10228-2

Torgbor, B. A., Sinha, P., Rahman, M. M., Robson, A., Brinkhoff, J., & Suarez, L. A. (2024). Exploring the Relationship Between Very-High-Resolution Satellite Imagery Data and Fruit Count for Predicting Mango Yield at Multiple Scales. Remote Sensing, 16(22), 4170. https://doi.org/10.3390/rs16224170

Alam, M.S., Lamb, D.W., Rahman, M.M., & Randall, M. (2024). Developing a reference method for indirect measurement of pasture evapotranspiration at sub-meter spatial resolution, Smart Agricultural Technology, 9. https://doi.org/10.1016/j.atech.2024.100567

Brinkhoff, J., Clarke, A., Dunn, B. W., & Groat, M., (2024). Analysis and forecasting of Australian rice yield using phenology-based aggregation of satellite and weather data. Agricultural and Forest Meteorology, 353, 110055. https://doi.org/10.1016/j.agrformet.2024.110055

Suarez, L. A., Robertson-Dean, M., Brinkhoff, J., & Robson, A. (2024). Forecasting carrot yield with optimal timing of Sentinel 2 image acquisition. Precision Agriculture. https://doi.org/10.1007/s11119-023-10083-z

Clark, A., Shephard, C., Robson, A., McKechnie, J., Morrison, R.B., & Rankin A. (2023). A Multifaceted Approach to Developing an Australian National Map of Protected Cropping Structures. Land. https://doi.org/10.3390/land12122168

Suarez, L. A., Robson, A., & Brinkhoff, J. (2023). Early-Season forecasting of citrus block-yield using time series remote sensing and machine learning: A case study in Australian orchards. International Journal of Applied Earth Observation and Geoinformation, 122, 103434. https://doi.org/10.1016/j.jag.2023.103434

Aeberli, A., Robson, A., Phinn, S., Lamb, D.W., & Johansen, K. (2023). A Comparison of Analytical Approaches for the Spectral Discrimination and Characterisation of Mite Infestations on Banana Plants. Remote Sensing. 2022, 14(21), 5467; https://doi.org/10.3390/rs14215467

Clark, A., Phinn, S., & Scarth, P. (2023). Pre-Processing Training Data Improves Accuracy and Generalisability of Convolutional Neural Network Based Landscape Semantic Segmentation. Land, 12(7), 1268. https://doi.org/10.3390/land12071268

Torgbor, B. A., Rahman, M. M., Brinkhoff, J., Sinha, P., & Robson, A. (2023). Integrating Remote Sensing and Weather Variables for Mango Yield Prediction Using a Machine Learning Approach. Remote Sensing, 15(12), 3075. https://doi.org/10.3390/rs15123075

Clark, A., Phinn, S., & Scarth, P. (2023). Optimised U-Net for Land Use–Land Cover Classification Using Aerial Photography. PFG–Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 1-23. https://doi.org/10.1007/s41064-023-00233-3

Brinkhoff, J., Dunn, B. W., & Dunn, T. (2023). The influence of nitrogen and variety on rice grain moisture content dry-down. Field Crops Research, 302, 109044. https://doi.org/10.1016/j.fcr.2023.109044

Brinkhoff, J., McGavin, S. L., Dunn, T., & Dunn, B. W. (2023). Predicting rice phenology and optimal sowing dates in temperate regions using machine learning. Agronomy Journal. https://doi.org/10.1002/agj2.21398

Brinkhoff, J., Orford, R., Suarez, L. A., & Robson, A. R. (July 2023). Data Requirements for Forecasting Tree Crop Yield-A Macadamia Case Study. In Precision Agriculture ’23 (14^th European Conference on Precision Agriculture). https://doi.org/10.3920/978-90-8686-947-3 https://dx.doi.org/10.2139/ssrn.4443667

Aeberli, A., Phinn, S., Johansen, K., Robson, A., & Lamb, D.W. (2023). Characterisation of Banana Plant Growth Using High-Spatiotemporal-Resolution Multispectral UAV Imagery. Remote Sens. 2023, 15(3), 679; https://doi.org/10.3390/rs15030679

Rahman, M. M., Robson, A., & Brinkhoff, J. (2022). Potential of Time-Series Sentinel 2 Data for Monitoring Avocado Crop Phenology. Remote Sensing, 14(23), 5942. https://doi.org/10.3390/rs14235942

Brinkhoff, J., Houborg, R., & Dunn, B. W. (2022). Rice ponding date detection in Australia using Sentinel-2 and Planet Fusion imagery. Agricultural Water Management, 273, 107907. https://doi.org/10.1016/j.agwat.2022.107907

Brinkhoff, J. (2022, July). Early-Season Industry-Wide Rice Maps Using Sentinel-2 Time Series. In IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium (pp. 5854-5857). IEEE. https://doi.org/10.1109/IGARSS46834.2022.9883755

Brinkhoff, J, Backhouse, G, Saunders, M E., Bower, D S., and Hunter, J T.. 2022. “ Remote Sensing to Characterize Inundation and Vegetation Dynamics of Upland Lagoons.” Ecosphere 13( 1): e3906. https://doi.org/10.1002/ecs2.3906

Torgbor, B A, et al. "Assessing the Potential of Sentinel-2 Derived Vegetation Indices to Retrieve Phenological Stages of Mango in Ghana." Horticulturae 8.1 (2022): 11. https://doi.org/10.3390/horticulturae8010011

Brinkhoff, J, B W. Dunn, and A J. Robson. "Rice nitrogen status detection using commercial-scale imagery." International Journal of Applied Earth Observation and Geoinformation 105 (2021): 102627. https://doi.org/10.1016/j.jag.2021.102627

Brinkhoff, J, et al. "Olive Tree Water Stress Detection Using Daily Multispectral Imagery." 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS. IEEE, 2021. https://doi.org/10.1109/IGARSS47720.2021.9553729

Aeberli A, Johansen K, Robson A, Lamb DW, Phinn S. Detection of Banana Plants Using Multi-Temporal Multispectral UAV Imagery. Remote Sensing. 2021; 13(11):2123. https://doi.org/10.3390/rs13112123

Brinkhoff, J., Robson, A.J., Block-level macadamia yield forecasting using spatio-temporal datasets, Agricultural and Forest Meteorology, Volume 303, 2021.

Ballester, C., Hornbuckle, J., Brinkhoff, J., & Quayle, W. C. (2021). Effects of three frequencies of irrigation and nitrogen rates on lint yield, nitrogen use efficiency and fibre quality of cotton under furrow irrigation. Agricultural Water Management, 248, 106783.

Johansen, Kasper, Duan, Qibin, Tu, Yu-Hsuan, Searle, Chris, Wu, Dan, Phinn, Stuart, Robson, Andrew, and McCabe, Matthew F. (2020). Mapping the condition of macadamia tree crops using multi-spectral UAV and WorldView-3 imagery. ISPRS Journal of Photogrammetry and Remote Sensing 165 28-40. https://doi.org/10.1016/j.isprsjprs.2020.04.017

Brinkhoff, J.; Dunn B.W.; Hart, J.; Dunn, T., Impact of UAV Time-of-Flight on Rice Nitrogen Uptake Models, IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, 2020.

Sinha, P., Robson, A., Schneider, D., Kilic, T., Mugera, H., Ilukor, J., Tindamanyire, J. The potential of in-situ hyperspectral remote sensing for differentiating 12 banana genotypes grown in Uganda ISPRS Journal of Photogrammetry and Remote Sensing. 2020, 167, 85-103.

Brinkhoff, J.; Robson, A.J. Macadamia Orchard Planting Year and Area Estimation at a National Scale. Remote Sens. 2020, 12, 2245.

Dunn, B. W., Dunn, T. S., Mitchell, J. H., & Brinkhoff, J. (2020). Effects of plant population and row spacing on grain yield of aerial-sown and drill-sown rice. Crop and Pasture Science, 71(3), 219-228.

Dan Wu, Kasper Johansen, Stuart Phinn, Andrew Robson & Yu-HsuanTu (2020). Inter-comparison of remote sensing platforms for height estimation of mango and avocado tree crowns. International Journal of Applied Earth Observation and Geoinformation. Volume 89, July 2020, 102091.

Suarez, L.A., Robson, A., McPhee, J. et al. Accuracy of carrot yield forecasting using proximal hyperspectral and satellite multispectral data. Precision Agric (2020).

Rahman, M.M.; Robson, A. Integrating Landsat-8 and Sentinel-2 Time Series Data for Yield Prediction of Sugarcane Crops at the Block Level. Remote Sens. 2020, 12, 1313.

Brinkhoff, J.; Vardanega, J.; Robson, A.J. Land Cover Classification of Nine Perennial Crops Using Sentinel-1 and -2 Data. Remote Sens. 2020, 12, 96.

Brinkhoff, J., Hornbuckle, J., & Lurbe, C. B. (2019). Soil moisture forecasting for irrigation recommendation. IFAC-PapersOnLine, 52(30), 385-390.

Optimising drone flight planning for measuring horticultural tree crop structure (2019). Yu-HsuanTu, Stuart Phinn, Kasper Johansen, Andrew Robson and Dan Wu. ISPRS Journal of Photogrammetry and Remote Sensing. Volume 160, February 2020, Pages 83-96.

Assessment of Canopy Porosity in Avocado Trees as a Surrogate for Restricted Transpiration Emanating from Phytophthora Root Rot (2019). Arachchige Surantha Ashan Salgadoe, Andrew James Robson, David William Lamb and Elizabeth Kathryn Dann. Remote Sens. 2019, 11(24), 2972.

Willcox, B., Howlett, B., Robson, A., Cutting, B., Evans, L., Jesson, L., Kirkland, L., Jean-Meyzonnier, M., Potdevin, V., Saunders, M. and Rader, R. (2019). Evaluating the taxa that provide shared pollination services across multiple crops and regions. Scientific Reports, 9(1). DOI 10.1038/s41598-019-49535-w

Brinkhoff, J., Dunn, B. W., Robson, A. J., Dunn, T. S., & Dehaan, R. L. (2019). Modeling Mid-Season Rice Nitrogen Uptake Using Multispectral Satellite Data. Remote Sensing 2019, Vol. 11, Page 1837, 11(15), 1837. DOI: 10.3390/RS11151837

Salgadoe, A. S. A., Robson, A. J., Lamb, D. W., Schneider, D. (2019). A Non-Reference Temperature Histogram Method for Determining Tc from Ground-Based Thermal Imagery of Orchard Tree Canopies. Remote Sensing, 11(6), 714. DOI: 10.3390/rs11060714

Tu, Y.-H., Johansen, K., Phinn, S., Robson, A. J., (2019). Measuring Canopy Structure and Condition Using Multi-Spectral UAS Imagery in a Horticultural Environment. Remote Sensing, 11(3), 269. DOI: 10.3390/rs11030269
Suarez, L. A., McPhee, J., O'Halloran. J., Van Sprang. C., Robson, A., (2018). Literature review of adoption of precision system technologies in vegetable production Hort Innovation, Adoption of precision systems technology in vegetable production (VG16009).

Anderson, N. T., Underwood, J. P., Rahman, M. M., Robson, A. J., & Walsh, K. B. (2018). Estimation of fruit load in mango orchards: tree sampling considerations and use of machine vision and satellite imagery. Precision Agriculture, 1–17. DOI: 10.1007/s11119-018-9614-1

Rahman, M., Robson, A., Bristow, M. (2018). Exploring the Potential of High Resolution WorldView-3 Imagery for Estimating Yield of Mango. Remote Sensing, 10(12), 1866. DOI: 10.3390/rs10121866
Salgadoe, A., Robson, A., Lamb, D., Dann, E., Searle, C. (2018). Quantifying the Severity of Phytophthora Root Rot Disease in Avocado Trees Using Image Analysis. Remote Sensing, 10(2), 226. DOI: 10.3390/rs10020226
Tu, Y.-H., Phinn, S., Johansen, K., Robson, A. (2018). Assessing Radiometric Correction Approaches for Multi-Spectral UAS Imagery for Horticultural Applications. Remote Sensing, 10(11), 1684. DOI: 10.3390/rs10111684
Brinkhoff, J., Hornbuckle, J., Barton, J. (2018). Assessment of Aquatic Weed in Irrigation Channels Using UAV and Satellite Imagery. Water, 10(11), 1497. DOI: 10.3390/w10111497
Willcox, B. K., Robson, A. J., Howlett, B. G., & Rader, R. (2018). Toward an integrated approach to crop production and pollination ecology through the application of remote sensing. PeerJ, 6, e5806. DOI: 10.7717/peerj.5806
Wu, D., Phinn, S., Johansen, K., Robson, A., Muir, J., Searle, C. (2018). Estimating Changes in Leaf Area, Leaf Area Density, and Vertical Leaf Area Profile for Mango, Avocado, and Macadamia Tree Crowns Using Terrestrial Laser Scanning. Remote Sensing, 10(11), 1750. DOI: 10.3390/rs10111750
Brinkhoff, J., Hornbuckle, J., Dowling, T. (2018). Multisensor Capacitance Probes for Simultaneously Monitoring Rice Field Soil-Water-Crop-Ambient Conditions. Sensors, 18(2), 53. DOI: 10.3390/s18010053
Potgieter, A. B., George-Jaeggli, B., Chapman, S. C., Laws, K., Suárez Cadavid, L. A., Wixted, J., Hammer, G. L. (2017). Multi-spectral imaging from an unmanned aerial vehicle enables the assessment of seasonal leaf area dynamics of sorghum breeding lines. Frontiers in Plant Science, 8(1532). doi: 10.3389/fpls.2017.01532.
Robson, A., Rahman, M. M., Muir, J., Saint, A., Simpson, C., & Searle, C. (2017). Evaluating satellite remote sensing as a method for measuring yield variability in Avocado and Macadamia tree crops. Advances in Animal Biosciences, 8(2), 498–504. DOI: 10.1017/S2040470017000954
Robson, A., Rahman, M., Muir, J., Robson, A. (2017). Using Worldview Satellite Imagery to Map Yield in Avocado (Persea americana): A Case Study in Bundaberg, Australia. Remote Sensing, 9(12), 1223. DOI: 10.3390/rs9121223
Rahman, M. M., Muir, J., & Robson, A. J. (2017). Multi-temporal landsat algorithms for the yield prediction of sugarcane crops in Australia. 7th Asian-Australasian Conference on Precision Agriculture, 1–6. DOI: 10.5281/zenodo.891091
Suarez, L. A., Apan, A., & Werth, J. (2017). Detection of phenoxy herbicide dosage in cotton crops through the analysis of hyperspectral data. International Journal of Remote Sensing, 38(23), 6528-6553. doi: 10.1080/01431161.2017.1362128.
Brinkhoff, J., Hornbuckle, J., Quayle, W., Lurbe, C. B., & Dowling, T. (2017). WiField, an IEEE 802.11-based agricultural sensor data gathering and logging platform. Eleventh International Conference on Sensing Technology (ICST), 1–6. DOI: 10.1109/ICSensT.2017.8304434
Rahman, M. M., J. Robson, A. (2016). A Novel Approach for Sugarcane Yield Prediction Using Landsat Time Series Imagery: A Case Study on Bundaberg Region. Advances in Remote Sensing, 05(02), 93–102. DOI: 10.4236/ars.2016.52008
Rahman, M. M., & Robson, A. (2016). Multi-temporal remote sensing for yield prediction in sugarcane crops. 19th Precision Agriculture Symposium Proceedings, 24–29. Toowoomba, Australia.
Robson, A. J., Petty, J., Joyce, D. C., Marques, J. R., & Hofman, P. J. (2016). High resolution remote sensing, GIS and Google Earth for avocado fruit quality mapping and tree number auditing. Acta Horticulturae, (1130), 589–596. DOI: 10.17660/ActaHortic.2016.1130.88
Suarez, L. A., Apan, A., & Werth, J. (2016). Hyperspectral sensing to detect the impact of herbicide drift on cotton growth and yield. ISPRS Journal of Photogrammetry and Remote Sensing, 120, 65-76. doi: 10.1016/j.isprsjprs.2016.08.004.
Bell, M. J., Biggs, J., McKellar, L. B., Connellan, J., Di Bella, L., Dwyer, R., … Wood, A. (2015). A review of nitrogen use efficiency in sugarcane. Retrieved from https://elibrary.sugarresearch.com.au/handle/11079/14733
Dehaan, R., Dunn, B., Dunn, T., Hume, I., Orchard, B., & Robson, A. (2015). Using remote sensing to predict PI nitrogen uptake in rice. Southern NSW Research Results: Technical Report. DOI: 10.13140/RG.2.1.2482.6483
Systems, F., & Management, P. (2013). Final report SRDC project DPI021 Remote sensing-based precision agriculture tools for the sugar industry. Retrieved from https://elibrary.sugarresearch.com.au/handle/11079/12504
Sadras, V., Calderini, D., Rodriguez, D., Robson, A. J., & Belford, R. (2009). Dynamic and Functional Monitoring Technologies for Applications in Crop Management. Crop Physiology, 489–584. DOI: 10.1016/B978-0-12-374431-9.00019-0
A.J. Robson, Remote sensing applications for the determination of yield, maturity and aflatoxin contamination in Peanut. Ph.D. Thesis, the University of Queensland (2007).

Robson, A. J., Wright, G., & Phinn, S. (2006). Using field spectroscopy and quickbird imagery for the assessment of peanut crop maturity and aflatoxin. Journal of Spatial Science, 51(2), 151–162. DOI: 10.1080/14498596.2006.9635089
Thomasson, J. A., R. Sui., G. C. Wright, & A. J. Robson. (2006). Optical Peanut Yield Monitor: Development and testing. Applied Engineering in Agriculture. Vol. 22(6):809-818. 10.13031/2013.22249
Apan, A., Kelly, R., Phinn, S., Strong, W., Lester, D., Butler, D., and Robson, A. (2006). Predicting grain protein content in wheat using hyperspectral sensing of in- season crop canopies and partial least squares regression, International journal of Geoinformatics, 2(1):93-108.

Robson, A.J., Williams, R.L. and Farrell, T.C. (2001). Unique root mass in Australia’s rice. Proceedings of the Australian Agronomy Conference. Hobart Australia.

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