Remote Sensing Applications: Society and Environment Submission Guide

RSASE belongs to the Remote Sensing of Environment journal family but has its own application-centered job. Start with the environmental or societal decision and work backward to the observation requirement.

For example, a land-cover classifier becomes an RSASE story when the manuscript demonstrates how the product improves hazard exposure assessment, habitat monitoring, agricultural management, urban planning, pollution detection, or another defined decision, then shows the uncertainty relevant to that use. The same classifier presented only as an architecture improvement may fit a methods, computing, or broader remote-sensing journal better.

Write a four-part fit statement:

1. Decision: who needs to decide what?
2. Observation: what cannot currently be measured well enough?
3. Remote-sensing advance: what product, workflow, or inference changes that?
4. Transfer boundary: where should the result not yet be used?

If the fourth part is missing, the practical claim is probably too broad.

Remote-sensing performance can look strong while hiding spatial leakage, temporal leakage, class imbalance, scale mismatch, or weak reference data. Align validation with the claimed use.

Report preprocessing, cloud or quality masking, sampling frame, reference-label provenance, spatial resolution, temporal window, train/test partition, baseline choice, and uncertainty. If a decision depends on a threshold, show the consequences of plausible threshold choices.

The first map should do more than look polished. It should orient the reader to the study area, observation period, product, and relevant environmental or societal feature. Use accessible color choices, readable legends, units, scale, north indicator where appropriate, and captions that explain what was measured rather than restating the title.

Pair attractive maps with diagnostic artifacts: error maps, calibration plots, class-specific performance, uncertainty surfaces, or comparisons with accepted products. A single global accuracy number is rarely enough to support deployment language.

Where deployment conditions vary, include a sensitivity check that shows which acquisition, season, geography, or class-balance change first makes the intended use unreliable.

Use the live Guide for Authors for current article types, editable file formats, section structure, highlights, graphical-abstract prompts, declarations, research-data fields, and submission checklist. Elsevier requirements can change and are partly exposed inside the submission system.

Before upload:

• reconcile the title and author list across manuscript and portal;
• provide editable text, tables, and figures in accepted formats;
• name the corresponding author and complete affiliation details;
• check figure captions, accessibility, units, and callout order;
• complete funding, conflicts, authorship, data, ethics, and AI disclosures;
• switch off tracked changes and remove private annotations; and
• open the system-generated review PDF and inspect every page.

These failure patterns connect a visible weakness to a repair an author can make before upload; they do not predict the editorial outcome for a particular manuscript. In our pre-submission review work, the most useful test is whether the claimed application still holds when the validation geography, observation window, and decision context are stated explicitly.

The method is the story and society or environment is decoration. Rewrite the title and abstract around the decision. If that makes the technical contribution disappear, reassess the journal owner.

A local map is described as globally transferable. Separate what is demonstrated at the site from what is hypothesized elsewhere. Add geographic or temporal validation where the claim requires it.

Validation shares spatial or temporal structure with training data. Use partitions that reflect actual deployment and explain any residual leakage risk.

The conclusion promises implementation without decision-scale uncertainty. Translate model error into the management or policy consequence. State when the output should trigger field verification rather than action.

Worked example: from classification score to management evidence

A study may improve wetland classification by two percentage points. The RSASE value is not the percentage alone. The stronger manuscript shows which wetland classes improve, where errors remain, whether the change alters conservation prioritization, and how the workflow transfers across seasons or sensors. The decision consequence and boundary convert a benchmark into an application contribution.

Compare the Remote Sensing of Environment submission guide, ISPRS Journal submission guide, and Remote Sensing submission guide, browse the journal directory, or review how to choose a journal for a paper.

Once RSASE is the chosen route, a free manuscript readiness scan can help test whether the first map, validation, and claimed decision use support the same story.