ArticleFrontiers in plant science2026
NSII: a novel soybean identification index based on Sentinel-2 imagery for accurate and efficient soybean mapping.
Article in Frontiers in plant science, 2026. 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate mapping of soybean cultivation areas is crucial for agricultural monitoring, resource management, and food security. However, the spectral overlap between soybean and other crops, such as corn, poses significant challenges for remote sensing-based identification. This study proposes a novel soybean identification index (NSII), which is calculated using the second red-edge band (RE2), the first short-wave infrared band (SWIR1), and the Enhanced Vegetation Index (EVI) derived from Sentinel-2 imagery within the optimal time window identified through spectral feature analysis. NSII was implemented in 12 major soybean producing regions in the United States and China over a three-year period (2020-2022). Experimental results from 2020 to 2022 show that the average accuracy of NSII is 0.85, and the average F1 score is 0.80. Compared with the existing Soybean Mapping Composite Index (SMCI), the accuracy increased by 8 percentage points and the F1 score increased by 6 percentage points. NSII also exhibits strong stability and transferability, with consistent performance across diverse climatic and cropping conditions. This study provides a robust and efficient tool for soybean mapping, offering significant potential for precision agriculture and sustainable resource management.
Indexed as
Identifiers
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.