Map of the 20 lakes
Each circle is a lake. The buttons inside the map switch the layer: mean NDCI 2018–2026, the trend per year (large circles are statistically significant trends) and the anomaly of the latest month with data against the same month in 2019–2025. Tap a lake to see its full series.
Ranking of the 20 lakes
Sorted by mean NDCI 2018–2026; columns can be re-sorted. An asterisk marks significant trends (p < 0.05).
| # | Lake | Mean NDCI | Trend/yr | Months with data |
|---|---|---|---|---|
| 1 | Hinojo Grande Trenque Lauquen | 0.453 | +0.003 | 101 |
| 2 | Hinojo Chico (La Redonda) Trenque Lauquen | 0.431 | -0.013* | 101 |
| 3 | Lobos Lobos–San Miguel del Monte | 0.386 | 0.000 | 100 |
| 4 | Adela–Chis Chis Encadenadas de Chascomús | 0.348 | +0.031* | 104 |
| 5 | Las Barrancas Encadenadas de Chascomús | 0.314 | +0.021* | 105 |
| 6 | La Tablilla Encadenadas de Chascomús | 0.311 | +0.023* | 105 |
| 7 | Las Tunas Grande Trenque Lauquen | 0.300 | -0.007* | 101 |
| 8 | Rocha Junín | 0.298 | +0.013* | 104 |
| 9 | Chascomús Encadenadas de Chascomús | 0.279 | +0.023* | 105 |
| 10 | San Miguel del Monte Lobos–San Miguel del Monte | 0.276 | +0.032* | 102 |
| 11 | El Burro Encadenadas de Chascomús | 0.274 | +0.036* | 104 |
| 12 | Carpincho Junín | 0.269 | +0.001 | 104 |
| 13 | Gómez Junín | 0.268 | 0.000 | 105 |
| 14 | Vitel Encadenadas de Chascomús | 0.243 | +0.010* | 102 |
| 15 | Mar Chiquita Junín | 0.240 | +0.004 | 105 |
| 16 | Alsina Guaminí–Adolfo Alsina | 0.135 | +0.004* | 101 |
| 17 | Cochicó Guaminí–Adolfo Alsina | 0.101 | +0.001 | 101 |
| 18 | Del Venado Guaminí–Adolfo Alsina | 0.074 | -0.031* | 103 |
| 19 | Del Monte Guaminí–Adolfo Alsina | 0.018 | -0.016* | 102 |
| 20 | Epecuén Guaminí–Adolfo Alsina | -0.035 | -0.001 | 101 |
What the series shows
An open baseline. The Water Authority of Buenos Aires Province published a satellite report on these 20 lakes for 2018–2022. This series takes the same lakes and follows them through October 2026, with the same open satellite sources, in tables anyone can download, review and reuse.
Consistent with the official report. The three lakes that report describes as hypertrophic —Lobos, Hinojo Grande and Hinojo Chico (La Redonda)— rank top three in NDCI over the same 2018–2022 period, and the direction of the trend agrees in 16 of the 19 comparable lakes. Over the full 2018–2026 series, the highest mean NDCI is Hinojo Grande (0.45), followed by Hinojo Chico (0.43) and Lobos (0.39); the lowest is Epecuén (−0.035), with the five Guaminí–Adolfo Alsina lakes at the bottom of the ranking.
The Encadenadas, increasing. All six lakes of the Encadenadas de Chascomús —Vitel, Chascomús, Adela–Chis Chis, La Tablilla, Las Barrancas and El Burro— show a significant increase in NDCI between 2018 and 2026, from +0.010 to +0.036 per year. In the other direction, Del Venado, Del Monte, Hinojo Chico (La Redonda) and Las Tunas Grande show a significant decrease.
The drought, visible in the water. The 2022–2023 drought is recorded in the surface of the lakes. Vitel, between February and May 2023, and La Tablilla, between late 2023 and mid-2024, shrank until much of the lake bed was exposed: on those dates there is not enough water left to measure, and the series shows them as gaps. Mar Chiquita, with a reference surface of 139 km², had dates in 2023 with only 4 to 24 km² of water. The 2018–2026 trends include that period; the data describe what happened in the water, without attributing causes.
What it measures and what it does not
NDCI is a spectral index, not a laboratory measurement. It compares two satellite bands sensitive to chlorophyll in the water. High NDCI is a signal consistent with higher trophic state or algal bloom risk, useful to prioritise where and when to sample. A cyanobacteria or toxin diagnosis requires water samples analysed in a laboratory.
No field calibration. The index has not been calibrated with samples taken in these lakes: it serves to compare lakes with each other, and each lake with itself over time. In very turbid water, suspended sediment also influences NDCI.
Significance. Trends are computed on monthly medians with the seasonal cycle removed; the test does not correct for autocorrelation in the series, so significance may be overstated for the weakest trends.
Who publishes it. This is independent work by Copernicus Argentina, not an official publication of the Water Authority of Buenos Aires Province. Copernicus Argentina uses the open data of the European Union's Copernicus Programme, with no affiliation with the programme or ESA.
Technical sheet
| Parameter | Value |
|---|---|
| Sensor | Sentinel-2 L2A (Copernicus) · 10–20 m |
| Chlorophyll index | NDCI = (B05 − B04) / (B05 + B04) |
| Turbidity index | NDTI = (B04 − B03) / (B04 + B03) |
| Period | January 2018 → October 2026 · 106 months |
| Lakes | 20 · 5 zones · Buenos Aires Province |
| Dates | 8,750 valid out of 16,948 candidates · 8,198 discards declared |
| Monthly series | monthly median · anomaly vs. the same month 2019–2025 |
| Trend | Theil–Sen on deseasonalised monthly medians · Mann–Kendall p |
| Climate | ERA5 (Copernicus Climate Change Service) via Open-Meteo · monthly rainfall and temperature |
| License | CC BY 4.0 |
Open data
The full series is free to download, with attribution. DOI 10.5281/zenodo.23417060 · Zenodo · Hugging Face.
| File | Content |
|---|---|
| serie_mensual.csv | Per lake and month: NDCI, NDTI, water surface, anomalies, rainfall and temperature. |
| serie_por_fecha.csv | One row per lake and valid date, with the observable fraction. |
| lagunas.csv | The 20 lakes: zone, district, reference point and surface area. |
| resumen.json | Per lake: coverage, mean NDCI, trend with 95% CI and p, mean NDCI by calendar month. |
| fechas_rechazadas.csv | Every discarded date and its reason. |
How to cite the data: Copernicus Argentina, & Kovacevic, G. R. (2026). Lagunas pampeanas 2018–2026 — estado trófico satelital (Sentinel-2) [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.23417060
Contains modified Copernicus Sentinel data 2018–2026. Climate: ERA5 (Copernicus Climate Change Service) via Open-Meteo. Lake locations based on © OpenStreetMap contributors.
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