ReviewScience progress
Research progress on agricultural water information sensing and applications in high-altitude cold regions.
Review in Science progress. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Agricultural water management is essential for food security in high-altitude cold regions, such as the Qinghai-Tibet Plateau, the Rocky Mountains, and the Alps. However, extreme environmental conditions-including low temperatures, intense radiation, and complex terrain-impede traditional hydrological monitoring. This narrative review systematically evaluates "space-air-ground" multi-source sensing technologies, summarizing recent advances in satellite remote sensing, low-altitude Unmanned Aerial Vehicles (UAVs), and cold-resistant ground sensors to address these constraints. Beyond individual platforms, this study synthesizes how multi-source data fusion and edge computing enhance hydrological modeling and decision-making for regional drought monitoring, crop assessment, and precision irrigation. Finally, current challenges-such as insufficient cross-platform integration, limited data consistency, and poor model generalization-are critically analyzed, and future research directions are proposed to provide a theoretical framework for advancing smart irrigation.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.