English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Accurate crop mapping is critical for agricultural applications. Although studies have combined deep learning methods and time-series satellite images to crop classification with satisfactory results, most of them focused on supervised methods, which are usually applicable to a specific domain and lose their validity in new domains. Unsupervised domain adaptation (UDA) was proposed to solve this limitation by transferring knowledge from source domains with labeled samples to target domains with unlabeled samples. Particularly, multi-source UDA (MUDA) is a powerful extension that leverages knowledge from multiple source domains and can achieve better results in the target domain than single-source UDA (SUDA). However, few studies have explored the potential of MUDA for crop mapping. This study proposed a MUDA crop classification model (MUCCM) for unsupervised crop mapping. Specifically, 11 states in the U.S. were selected as the multi-source domains, and three provinces in Northeast China were selected as individual target domains. Ten spectral bands and five vegetation indexes were collected at a 10-day interval from time-series Sentinel-2 images to build the MUCCM. Subsequently, a SUDA model Domain Adversarial Neural Network (DANN) and two direct transfer methods, namely, the deep neural network and random forest, were constructed and compared with the MUCCM. The results indicated that the UDA models outperformed the direct transfer models significantly, and the MUCCM was superior to the DANN, achieving the highest classification accuracy (OA&gt;85%) in each target domain. In addition, the MUCCM also performed best in in-season forecasting and crop mapping. This study is the first to apply a MUDA to crop classification and demonstrate a novel, effective solution for high-performance crop mapping in regions without labeled samples.

Exploring the potential of multi-source unsupervised domain adaptation in crop mapping using Sentinel-2 images