Synergized multi-source crop mapping: A systematic review of satellite, UAV, and crowdsourcing approaches for precision agriculture and irrigation water management

Accurate crop area mapping underpins agricultural planning, food security and irrigation management, yet government records, satellite imagery, unmanned aerial vehicle (UAV) surveys and crowdsourced farmer reports each carry distinct limitations in accuracy, scalability, cost and timeliness. This paper reports a systematic review conducted in accordance with PRISMA 2020. From 3432 records retrieved through four structured queries, 350 peer-reviewed articles published between 2010 and 2025 were retained after de-duplication, multi-stage relevance scoring and eligibility appraisal. Each study was assigned one primary technology: satellite remote sensing (n = 249, 71.1%), multi-source fusion with satellite data (n = 41, 11.7%), UAV with satellite data (n = 33, 9.4%), standalone UAV (n = 16, 4.6%), UAV with multi-source fusion (n = 7, 2.0%) and crowdsourcing (n = 4, 1.1%). Counted non-exclusively, satellite data appear in 323 of the 350 studies, UAV data in 56 and crowdsourced data in 4. Optical and synthetic aperture radar platforms, UAV sensor suites, participatory platforms and machine-learning classifiers are appraised in turn. The review then sets out the Synergized Mapping framework, which combines administrative records, crowdsourced reports, satellite imagery and UAV surveys in a cost-ordered iterative validation sequence, and illustrates it at Loni Devkar, Maharashtra, India. There, government records understate cultivated area by 57–103% relative to UAV, crowdsourced and satellite estimates, leaving 306,975 m³ of crop water requirement unaccounted. Persistent gaps include the scarcity of crowdsourcing research, the absence of standardised accuracy benchmarks across agro-climatic zones, and limited cost–accuracy optimisation for smallholder systems.