ArticleEnvironmental science and pollution research international2022
Research on cropping intensity mapping of the Huai River Basin (China) based on multi-source remote sensing data fusion.
Article in Environmental science and pollution research international, 2022. 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
Abstract
As a key input variable to many global climates, land surfaces and crop models, cropping intensity (CI) accurately assesses and predicts crops' output, in view of the global decline in food production in recent years due to declining natural resources, urban expansion and declining quality of arable land. Hence, research on CI mapping can have a contribution to solve this problem. Unfortunately, existing remote sensing data for CI mapping research, including Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat images, are not adequate for obtaining CI information at higher spatial and temporal resolution. In this regard, we develop an algorithm to extract CI based on per-pixel physiognomy. To be specific, the algorithm is based on the Google Earth Engine (GEE) platform and constructs a high temporal (10 days) spatial (30 m) resolution dataset with the fusion of Landsat 7/8 and Sentinel-2 A/B image data and extracts CI information using a time series of peak discovery method, threshold method and phenological period feature extraction to obtain the 2018 Chinese Huai River Basin (HRB) CI map. Our results suggest that the overall accuracy (OA) of CI extraction in the HRB is 92.72%, with a kappa coefficient of 0.864. The single-season crop, double-season crop and three-season crop account for 41.6%, 57.7% and 0.7% of the total farmland area, respectively. Compared to existing CI identification and extraction methods, this approach achieves higher accuracy in the identification and extraction of CI information over a larger area.
Indexed as
Identifiers
34554403What 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.