ArticleScientific data2026
A large-scale, multitask, multisensory dataset for climate-aware crop monitoring in the US from 2018-2022.
Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- A multimodal geospatial foundation model anticipates crop stress and yield failure across climates and species.Frontiers in plant science · 2026Article
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
The escalating challenges of climate change, extreme weather events, and increasing food demand impose a significant strain on global food production. To develop and apply sustainable agriculture practices, farmers and organizations require detailed, timely information about weather, crops, and yields. While efficient agricultural monitoring relies heavily on remote sensing, the existing literature suffers from a notable lack of comprehensive, large-scale crop monitoring datasets. This paper introduces CropClimateX, a novel database built by optimizing location sampling to substantially cover cultivated areas throughout the contiguous United States. The database comprises 15,500 small 12 × 12 km data cubes spanning 1,527 counties. Crucially, each data cube integrates a rich array of multi-source information, including multi-sensor imagery (Sentinel-1/2, Landsat-8, MODIS), weather and extreme events (Daymet, heat/cold waves, and drought monitor maps), and environmental features (soil and terrain characteristics). This comprehensive, integrated dataset is designed to support a wide range of agricultural monitoring tasks, providing a vital resource for advancing research in sustainable farming and crop modeling.
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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.