Remote Sensing of Environment Submission Guide

Source: Clarivate JCR 2024, Elsevier editorial disclosures (accessed April 2026).

Source: Remote Sensing of Environment author guidelines.

Use this page when deciding:

• whether the environmental contribution is substantive
• whether validation is rigorous
• whether scope is adequate

• a clear environmental contribution beyond algorithm development
• rigorous validation with reference data
• appropriate remote-sensing methodology with uncertainty analysis
• adequate geographical or temporal scope
• a cover letter establishing the environmental contribution

• Pure algorithm development without environmental application.
• Weak environmental contribution.
• Missing validation with reference data.
• Engineering remote sensing without environmental focus.

Remote Sensing of Environment is the flagship environmental-remote-sensing journal.

Environmental-application standard: the journal differentiates from IEEE Transactions on Geoscience and Remote Sensing (algorithm-focused) and Remote Sensing (broader applied) by demanding substantive environmental contributions.

Validation expectation: editors expect rigorous validation with reference data.

The 40-50% desk rejection rate: decisive editorial screen.

The strongest Remote Sensing of Environment cover letters establish:

• the environmental contribution
• the validation approach
• the methodological rigor
• the central finding

• Is Remote Sensing of Environment a good journal?

Before upload, run your manuscript through a Remote Sensing of Environment validation readiness check.

In our pre-submission review work with remote-sensing manuscripts targeting Remote Sensing of Environment, three patterns generate the most consistent desk rejections.

In our experience, roughly 35% of Remote Sensing of Environment desk rejections trace to algorithm-only framing without environmental application. In our experience, roughly 25% involve weak environmental contribution. In our experience, roughly 20% arise from missing validation.

• Pure algorithm development without environmental application. Remote Sensing of Environment editors look for environmental contribution, not just algorithm performance. We observe submissions framed as algorithm-development papers without environmental application routinely desk-rejected.

• Weak environmental contribution. Editors expect substantive environmental advances. We see manuscripts using remote sensing peripherally to support a different primary contribution routinely declined.

• Missing validation with reference data. Remote Sensing of Environment specifically expects validation with ground truth or independent observations. We find papers reporting only model-output comparisons without independent validation routinely flagged. A Remote Sensing of Environment validation check can identify whether the package supports a submission.

Clarivate JCR 2024 bibliometric data places Remote Sensing of Environment among top remote-sensing journals.

In pre-submission diagnostic work for top environmental remote-sensing journals, we consistently see four signals that distinguish strong submissions from weak ones. First, the contribution must be environmental, not algorithmic; submissions framing the contribution as algorithm performance fail at desk screening. Second, validation should include comparison to reference data, ground truth, or independent observations. Third, methodology should be appropriate to the remote-sensing question with explicit uncertainty analysis. Fourth, geographical or temporal scope should be adequate for environmental conclusions.

The single most consistent feedback class we deliver in pre-submission diagnostics for Remote Sensing of Environment is the algorithm-versus-environmental distinction. Remote Sensing of Environment editors expect environmental application as the primary frame, not algorithm performance. Submissions framed as "we developed algorithm X with accuracy Y" routinely receive "where is the environmental application?" feedback during desk screening. We coach authors to lead with the environmental question and frame the algorithm in service of that question. Papers framed as "we addressed the environmental question X by developing remote-sensing approach Y, validated against reference data Z" receive better editorial traction. The same logic applies across applied remote-sensing journals: editors are operating with limited slot inventory, and the submissions that get traction lead with the environmental question.

Beyond the rubric checks, three pre-submission diagnostic patterns recur most often in the manuscripts we review for Remote Sensing of Environment. First, manuscripts where the abstract emphasizes algorithm accuracy without environmental application are flagged at desk for algorithm-only framing. We recommend the abstract's central sentences state the environmental question, the remote-sensing approach, and the environmental finding. Second, manuscripts where validation uses only synthetic data or model outputs are flagged for validation gaps. We recommend including independent reference data validation. Third, manuscripts that lack engagement with Remote Sensing of Environment's recent issues are at risk of being told the contribution doesn't fit the publication conversation.

The strongest manuscripts we coach distinguish themselves on three operational behaviors. First, they confine the cover letter to one page and use it to make the case for fit, contribution, and significance, not to summarize the abstract. Second, they include a one-sentence elevator pitch in the cover letter's opening that the editor can use when discussing the manuscript internally. Third, they identify the specific recent papers in the journal that this manuscript builds on and the specific competing or contradicting work; this signals the authors are operating inside the publication conversation rather than outside it.