New Zealand's freshwater systems face significant strain from agricultural runoff — nitrogen and phosphorus from a $12 billion dairy industry — alongside urbanisation and industrial discharge. Traditional water sampling is expensive, labour-intensive, and captures only isolated snapshots of water quality.
The National Policy Statement for Freshwater Management (NPS-FM 2020) now mandates strict nutrient limits, creating an urgent demand for high-density, catchment-wide monitoring that legacy methods simply cannot deliver.
$40–$150 per analyte, single-point measurements
Cyclone Gabrielle (2023) overwhelmed traditional sampling methods
NPS-FM 2020 imposes strict limits with fines up to $80,000
A Space-to-Water Intelligence platform that repurposes Sentinel-2 multispectral satellite data to monitor water quality across entire catchments — from rivers and lakes to coastlines and marine reserves.
European Space Agency's multispectral imagers with 13 spectral bands, providing 10m² resolution and global coverage every 5 days.
Matched Filtering techniques extract faint "optically inactive" signals from satellite reflectance — detecting what traditional remote sensing cannot.
Proprietary calibration against New Zealand's largest freshwater monitoring archive ensures hyper-local accuracy and reduces false positives.
Neural networks and deep learning achieve R² = 0.89 on independent test sets — a breakthrough for non-optically active parameters like nitrogen.
Pinpoint contamination sources with precision — catchment-wide insights that replace single-point lab snapshots.
Data analysed within 24 hours of satellite overflights, enabling rapid identification of water quality changes.
WATERSHOT doesn't replace the lab — it triages it. While a lab sample is a single point, satellite monitoring provides a comprehensive map that pinpoints plumes lab teams might otherwise miss entirely.
Our models prioritise confidence over volume. If atmospheric conditions are compromised beyond a 10% cloud threshold, the pixel is masked. We deliver high-confidence data or no data at all.
| State | Threshold | Model Precision | Implication |
|---|---|---|---|
| Low | ≤ 0.44 mg/L | ± 0.12 mg/L | Reliably confirms compliance |
| Medium | 0.44 – 0.8 mg/L | ± 0.12 mg/L | Detects approaching thresholds |
| High | > 0.8 mg/L | ± 0.12 mg/L | Non-compliance reliably detected |
Comprehensive catchment-wide analytics at a fraction of traditional lab costs — with labour savings of 30–80% on monitoring budgets.
| Analyte | Traditional Lab Cost | WATERSHOT Cost |
|---|---|---|
| E. coli / Coliform | $80 – $130 / sample | Included |
| Total Nitrogen | $40 – $70 / sample | Included |
| Harmful Algae | $150 / sample | Included |
| Total per sq km | $hundreds – $thousands | $33 – $63 / sq km |
Ground sampling labour accounts for 30–80% of monitoring budgets
Near-real-time verification defends against fines of $35,000–$80,000
Per dollar spent versus traditional monitoring methods
Gain actionable, defensible data to enforce NPS-FM 2020 limits and issue timely public warnings with full audit trails.
Reduce non-compliance fines and optimise fertiliser use under Freshwater Farm Plans with continuous, evidence-based monitoring.
Tools to honour Te Mana o te Wai, protect mahinga kai, and monitor rohe with culturally informed, real-time data.
A scalable, software-driven model ready for water-stressed regions worldwide — with Australia and the EU as initial targets.
Up to 2,000 data points per kilometre — comprehensive catchment-wide averages rather than localised snapshots from traditional sampling.
Calibrated against a 24,000-point historical archive from LAWA with hyper-local New Zealand freshwater profiles — ensuring nutrient-specific accuracy.
Youden's J-Index of 0.88 for nitrogen — 97.1% specificity and 90.1% sensitivity for compliance actions. A breakthrough for "optically inactive" parameters.
Purely software-based architecture enables deployment anywhere Sentinel-2 operates — no hardware, no on-site sensors, just intelligence from space.
Supports NPS-FM 2020 compliance and iwi-led environmental stewardship, with customisable Te Mana o te Wai indicators and iwi rohe boundary overlays.
Dual expertise in dairy farming and astrophysical signal processing — balancing scientific rigour with practical, on-the-ground usability.
Machine Learning and Neural Network models consistently achieve higher prediction accuracy than empirical models — confirmed by meta-analysis of 66 published studies from 2001–2025.
The growing recognition of Te Mana o te Wai — the intrinsic value of water — in resource management creates demand for solutions that support kaitiakitanga (stewardship) and enable iwi/hapū to monitor and protect their rohe.
WATERSHOT enables communities to monitor rohe in real-time, fulfilling co-governance obligations and supporting culturally informed resource management. The platform provides targeted mitigation insights — from riparian planting to nutrient reduction strategies — with auditable, high-resolution data.
Real-time tracking of nutrient dynamics across diverse aquatic ecosystems
Large-scale monitoring previously unfeasible with localised sampling methods
Data-driven assessments enable precise mitigation and adaptive management
WATERSHOT fusion of astrophysics, AI, and regulatory rigour transforms water management — delivering ecological integrity, economic savings, and risk mitigation. The platform's Sentinel-2 capabilities provide a robust foundation, while future SAR integration will redefine Total Catchment Awareness.