The world’s best beaches

Survey: Between February 20 and March 14, 2026, we surveyed travel experts, content creators and local experts, inviting them to share their submissions for the world’s best beaches. Respondents were instructed to list their top 5-10 beaches in order and rate each beach’s beauty, amenities, water quality and overall appeal. Respondents also shared why they think each beach deserved a global spotlight. 

The survey was distributed via social media channels and direct outreach to specific experts, inviting them to participate. In total, 58 experts and content creators submitted 322 locations for consideration. After removing duplicate submissions, identifying cross-expert submissions for the same beaches, and excluding submissions from non-experts, a total of 230 unique beaches remained for our consideration.

Expert Ranking: Survey respondents were assessed and assigned an expertise score on a three-tier scale (1–3), which was used to apply differential weighting to submissions in the final rankings, giving greater weight to those with stronger demonstrated expertise in travel content.

• Tier 3: Designates respondents with active, professional-level engagement in travel: those who work in travel content, regularly produce travel journalism or editorial, are employed by a travel-focused publication or platform, or maintain a dedicated travel-focused social media presence with a high follower count.
• Tier 2: Covers respondents with moderate or intermittent travel content involvement: occasional travel producers, those who receive travel coverage periodically, employees of travel-adjacent companies in non-editorial roles, or those with a dedicated travel social presence at a smaller scale.
• Tier 1: Reflects limited or incidental travel content activity: a handful of travel pieces published over a long period, prior travel industry experience that is no longer current, a social media presence that is not travel-focused, or a follower count in the low hundreds.

Respondents who did not meet the Tier 1 threshold were marked as non-qualifying and excluded from the dataset.

Criteria Filtering: If respondents submitted a town, city, or neighborhood rather than a specific beach, the beach with the most reviews within that location was selected. Or, if a specific beach was not named but a respondent’s beach description clearly pointed to a specific beach based on certain characteristics, then that beach was selected. Whole island submissions were accepted when only one or fewer specific named beaches existed on the island. Beaches on lakes were not accepted. 

Then we found every expert-submitted beach on Google Maps, identified and counted submissions for the same beaches, and created a de-duplicated master list of 226 unique beaches for consideration. We removed any beaches that lacked a Google Maps location or had no reviews, as a 5-category natural language processing (NLP) sentiment analysis of Google Maps reviews was part of our final scoring.

Preliminary Ranks: To create our preliminary rankings, we aggregated the data to the beach level. We used our assigned expertise score, the order in which a beach was submitted (i.e., #1 vs. #5), the number of times a beach was submitted and the expert-submitted category rankings for each beach on beauty, amenities, water quality and overall rating. For beaches with multiple submissions, respondents’ aspect rankings were averaged. The highest submission order was used for beaches with multiple submissions, as well as the highest expert-level score among respondents who submitted the same beach. Submission frequency was normalized against the highest submission count in the dataset. Rank position was converted to a 0–1 score on a fixed 10-point scale. 

The following weights were used in our final preliminary scoring, after all data were normalized: 

• Submission order (40%)
• Submission frequency (25%)
• Category rankings (35%) 

Within the category rankings, beauty and water quality were each weighted at 30%, amenities at 15%, and the overall rating at 25%. Finally, our expertise scores were entered into the formula as a multiplier, meaning the scores based on the weights above were multiplied by the expertise tier multiplier. Those multipliers are: 

• 1X for Tier 1
• 1.5X for Tier 2
• 2X for Tier 3 

Finally, preliminary scores were normalized to a 10-point scale.

Water Color and Clarity: We analyzed water clarity, turbidity, and color data to identify the beaches with the clearest blue water. We used satellite-derived ocean color measurements from NASA’s MODIS-Aqua satellite mission. Using Google Earth Engine (GEE), we accessed Level-3 Standard Mapped Image (L3SMI) data for the following three remote sensing bands: 

• Chlorophyll-a concentration (chlor_a)
• Remote sensing reflectance at 443 nm (Rrs_443)
• Remote sensing reflectance at the 555 nm (Rrs_555)

Data for each beach was extracted using point geometries corresponding to the latitude and longitude of each beach site. We collected data on water conditions over five years, from 2019 to 2023, for all months. To ensure all beaches returned GEE values, we used a buffer-based spatial averaging approach instead of point sampling. We expanded the sample area by up to 24 km using a buffer zone. Values were averaged across all MODIS Aqua observations within that radius for each of these beaches.  For each beach coordinate pair, the mean value for each band was calculated over the five-year composite, with a spatial resolution of 4 km. We used a published empirical model (Mueller 2000) to estimate the Derived light attenuation coefficient (Kd_490) by a ratio of the Rrs_443 and Rrs_555 bands (Kd₄₉₀ = 0.0166 + 0.15645 × (Rrs₄₄₃ / Rrs₅₅₅)^(–1.540)).

Sentiment Analysis: Then, using our list of 226 beaches, we searched each one individually on Google Maps and gathered all Google Maps reviews that contain one of the 340 keywords on our list. Our list of 340 keywords was used to measure public opinion of these beaches surrounding four categories: Accessibility, environment, facilities, and general public opinion. Only 202 beaches remained that had any reviews that matched any of our 340 keywords. 

Next, we conducted a large-scale natural language processing (NLP) analysis of the 93,626 Google Map reviews across the 202 remaining beaches. Using Python RE, we employed keyword-based extraction to identify the specific sentence in which each keyword appears within a review. Then, we ran the Roberta Base Sentiment model on each review sentence containing one of the keywords on our list. This allowed us to analyze the individual sentiment surrounding that keyword rather than the entire review. Many reviews contain more than one of our keywords, and reviewers can address multiple points in a single review. Keyword-based sentence extraction, performed before sentiment analysis, enabled us to identify the individual sentiments associated with each keyword for categorical analysis. 

We organized sentiment metrics for each category using a pre-defined mapping table of 340 keywords across four categories. Sentiment values were then averaged per category for each review, plus an overall sentiment score for the entire review. The per-review sentiment values were then aggregated to the beach level using the mean sentiment score for each category, plus the overall mean score. We then eliminated any beaches from further analysis that did not have at least 10 reviews in any given keyword category. This left 141 beaches for further analysis.

Airport Accessibility: To quantify airport accessibility for each beach, we computed the geodesic distance in miles from the nearest regional commercial airport. We sourced the full global airport dataset from OurAirports. We applied the following filtering criteria: Inclusion restricted to airports categorized as “medium” or “large” with scheduled commercial service worldwide. Then, using each beach’s coordinate points, we applied the Haversine formula (with a standard Earth radius of 3,958.8 miles) to identify the nearest airport and its computed Haversine distance.

Weather Analysis: To determine the best time of year to visit each beach, we analyzed a full decade of global climate data. Our goal was to identify each beach’s optimal three-month weather window. We began by sourcing atmospheric data from the European Centre for Medium-Range Weather Forecasts via its ERA5 Reanalysis dataset. Part of the Copernicus Climate Change Service, this is one of the most comprehensive climate archives on the planet, integrating billions of ground-based, satellite, and airborne observations into a global Earth model for mass data assimilation. 

By accessing this data via the Climate Data Store API, we extracted monthly averages of 6 key atmospheric variables spanning 10 years (2014-2023) for each month. In total, 120 months of observational data, representing hundreds of millions of individual data points, were collected worldwide. Then, by mapping each beach to the nearest grid cell, we gathered the following data for each beach across the 10 years:

• Air temperature (2m)
• Dew point temperature (humidity proxy)
• Skin temperature (surface conditions)
• Wind speed (derived from u/v components at 10m)
• Surface solar radiation (sunshine hours and brightness)

We produced a 12-month climatological profile for each beach by averaging monthly values over the 10 years, ensuring a typical representation of weather patterns that smooths out short-term anomalies. Then, we established baseline thresholds for what constitutes “good” beach weather for three of our atmospheric variables:

• -Temperature: ≥ 23°C (73.4°F)
• -Wind speed: ≤ 5.5 m/s (≈12.3 mph)

Using these thresholds, we identified the number of months in a year during which each beach has “optimal beach weather.” Then, to determine the “best” time to visit each beach, we calculated a composite score for every month based on 4 key variables with the following weights:

• Temperature (40%)
• Wind speed (20%)
• Humidity (via dew point, 20%)
• Solar radiation (20%)

Then, taking it all in, we identified the best consecutive three-month window for each beach by evaluating all 12 possible rolling three-month periods and calculating the average score for each beach. The window with the highest average score was then selected as the beach’s peak season. Therefore, by combining absolute thresholds of suitable weather conditions with relative scoring, we were able to capture both how good a beach’s weather is and when it is at its best.

Final Score: To construct a composite ranking of the world’s best beaches, we used a weighted scoring model that combined 18 quantitative and sentiment-based metrics. All data were normalized, and the formula was calculated using a weighted linear equation. The metrics and their weights are as follows:

Weights across the main categories: 

• Expert Rankings: 30%
• Water Quality: 26%
• Accessibility: 2%
• Sentiment: 14%
• Popularity: 27%
• Climate: 1%

Specific weights for each variable:

• Number of times a beach was submitted across all respondents — 0.04
• Submission order of a beach by an expert — 0.1
• Expert level of respondent — 0.09
• Beauty ranking assigned by respondents — 0.02
• Amenity ranking assigned by respondents — 0.01
• Water quality rank assigned by respondents — 0.02
• Overall rank assigned by respondents — 0.02
• Chlor_a (water color) — 0.13
• KD490 (water clarity) — 0.13
• Distance to closest airport — 0.02
• Accessibility sentiment — 0.04
• Environment sentiment — 0.01
• Facilities sentiment — 0.01
• Strong opinion sentiment — 0.05
• Overall sentiment — 0.03
• Number of Google Reviews — 0.14
• Average Google rating — 0.13
• Optimal beach months — 0.01