Research Interests:
Research Interests:
Research Interests:
This study developed a rapid rice yield estimation workflow and customized yield prediction model by integrating remote sensing and meteorological data with machine learning (ML). Several issues need to be addressed while developing a... more
This study developed a rapid rice yield estimation workflow and customized yield prediction model by integrating remote sensing and meteorological data with machine learning (ML). Several issues need to be addressed while developing a crop yield estimation model, including data quality issues, data processing issues, selecting a suitable machine learning model that can learn from few available time-series data, and understanding the non-linear relationship between historical crop yield and remote sensing and meteorological factors. This study applied a series of data processing techniques and a customized ML model to improve the accuracy of crop yield estimation at the district level in Nepal. It was found that remote sensing-derived NDVI product alone was not sufficient for accurate estimation of crop yield. After incorporating other meteorological variables into the ML models, estimation accuracy improved dramatically. Along with NDVI, the meteorological variables of rainfall, soi...
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In August 2022, one of the most severe floods in the history of Pakistan was triggered due to the exceptionally high monsoon rainfall. It has affected ~ 33 million people across the country. The agricultural losses in the most productive... more
In August 2022, one of the most severe floods in the history of Pakistan was triggered due to the exceptionally high monsoon rainfall. It has affected ~ 33 million people across the country. The agricultural losses in the most productive Indus plains aggravated the risk of food insecurity in the country. As part of the loss and damage (L&D) assessment methodologies, we developed an approach for evaluating crop-specific post-disaster production losses based on multi-sensor satellite data. An integrated assessment was performed using various indicators derived from pre- and post-flood images of Sentinel-1 (flood extent mapping), Sentinel-2 (crop cover), and GPM (rainfall intensity measurements) to evaluate crop-specific losses. The results showed that 2.5 million ha (18% of Sindh’s total area) was inundated out of which 1.1 million ha was cropland. The remainder of crop damage came from the extreme rainfall downpour, flash floods and management deficiencies. Thus approximately 57% (2....
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In an effort to adopt emerging technologies in food security assessment through a codevelopment approach, the Government of Nepal’s Ministry of Agriculture and Livestock Development (MoALD) and the International Centre for Integrated... more
In an effort to adopt emerging technologies in food security assessment through a codevelopment approach, the Government of Nepal’s Ministry of Agriculture and Livestock Development (MoALD) and the International Centre for Integrated Mountain Development’s (ICIMOD) SERVIR-HKH Initiative undertook a pilot study in Chitwan District in 2019 to jointly develop methods for satellite remote sensing and machine learning-based in-season crop assessment. MoALD experts and relevant stakeholders thoroughly reviewed the approach before the honourable minister approved it for formal use in the national-level assessment for 2020 and onwards. For wider adoption of the advanced data science methods established in the pilot study, we customised the technology by developing a digital suite of software, including GeoFairy (a mobile app to facilitate field data collection by field extension professionals at the district level) and RiceMapEngine (a simplified platform for machine learning-based crop cla...