# ⚛  L1 Principle — Ocean Color Remote Sensing (chlorophyll / CDOM / TSM)

**ID:** `L1-110` · **Status:** ⊙ Testnet (genesis catalog)

> **🌐 Domain:** Remote Sensing — *Coastal/ocean bio-optics via satellite*
> **🎯 Problem class:** nonlinear inverse · **🧮 Solution space:** ocean iop map
> **📡 Carrier:** photon · **🌫 Noise:** gaussian
> **⚖ Difficulty (δ):** 3 · **⛓ Block:** 41554198

---

## 🧠 1. Introduction

**Ocean Color Remote Sensing (chlorophyll / CDOM / TSM)** is a **nonlinear inverse problem** whose unknown lives in **ocean iop map** space, within the **Coastal/ocean bio-optics via satellite** sub-domain of **Remote Sensing**.

Measurements consist of photons collected by an optical detector via a **ocean color multispectral** sensing mechanism.

The forward operator applies, in order: L · multispectral sensor operator; L · atmospheric correction operator; L · bio optical inversion operator; pixel-level spatial averaging on the detector.

Observations are corrupted by additive Gaussian noise. Existence of the recovered ocean iop map is guaranteed within the declared Omega bounds. Uniqueness is local rather than global (non-convex landscape); convergence depends on initialisation and priors. Stability is moderately conditioned (kappa_eff ~= 12); atmospheric_aerosol dominates the stability cliff; glint and the remaining mismatch parameters contribute higher-order bias terms. Additive gaussian thermal/electronic noise sets the irreducible data-fidelity floor, while mild Tikhonov or analytic inversion is sufficient at the nominal Omega point.

## ⚙ 2. Forward Model

Physical chain: **x** → L · multispectral sensor → L · atmospheric correction → L · bio optical inversion → Spatial integration → **y** (detector).

```
y = ∫_A dA `L.bio_optical_inversion` `L.atmospheric_correction` `L.multispectral_sensor` x + n,    n ~ 𝒩(0, σ²)
```

**Measurement DAG:**

| Primitive | What it does |
|---|---|
| `L.multispectral_sensor` | L · multispectral sensor operator |
| `L.atmospheric_correction` | L · atmospheric correction operator |
| `L.bio_optical_inversion` | L · bio optical inversion operator |
| `int.spatial` | Pixel-level spatial averaging on the detector |

## 🔬 3. Physics Fingerprint

| Property | Value |
|---|---|
| Domain | Remote Sensing |
| Sub domain | Coastal/ocean bio-optics via satellite |
| Carrier | photon |
| Problem class | nonlinear_inverse |
| Solution space | ocean_iop_map |
| Noise model | gaussian |
| Integration axis | spatial |
| Difficulty delta | 3 |
| L dag | 3.5 |

## 📡 4. Measurement Model

Existence of the recovered ocean iop map is guaranteed within the declared Omega bounds. Uniqueness is local rather than global (non-convex landscape); convergence depends on initialisation and priors. Stability is moderately conditioned (kappa_eff ~= 12); atmospheric_aerosol dominates the stability cliff; glint and the remaining mismatch parameters contribute higher-order bias terms. Additive gaussian thermal/electronic noise sets the irreducible data-fidelity floor, while mild Tikhonov or analytic inversion is sufficient at the nominal Omega point.

| Metric | Value |
|---|---|
| Metric | PSNR_dB |
| Secondary | SSIM |

## 📏 5. Operating Range (Ω)

**Center problem class:** `ocean_color` · **Forward operator:** `ocean_color_forward`

**Center point:**

| Parameter | Unit | Value |
|---|---|---|
| H | px | 1024 |
| W | px | 1024 |
| Gsd m | m | 1000 |
| Glint | — | 0 |
| Snr db | dB | 40 |
| N bands | bands | 9 |
| Whitecaps | — | 0 |
| Bottom reflectance | — | 0 |
| Atmospheric aerosol | — | 0.1 |

**Allowed bounds:**

| Parameter | Unit | Range |
|---|---|---|
| H | px | 256 – 8192 |
| W | px | 256 – 8192 |
| Gsd m | m | 10 – 5000 |
| Glint | — | 0.0 – 0.3 |
| Snr db | dB | 15.0 – 45.0 |
| N bands | bands | 4 – 20 |
| Whitecaps | — | 0.0 – 0.2 |
| Bottom reflectance | — | 0.0 – 0.5 |
| Atmospheric aerosol | — | 0.0 – 0.5 |

## 🎯 6. Tolerance (ε)

**Center tolerance:** 25.0

| Metric | Range |
|---|---|
| Psnr db | 5.0 – 40.0 |

## ⚖ 7. Hardness Function

Hardness scales as **`epsilon_fn`** on **PSNR_dB**, with κ = `240` and δ = `3`.

## 💾 8. Reference Dataset

- **primary** · weight 1.0 · IPFS _(not pinned yet)_

## 9. On-chain Registration

- **Chain hash:** `0x2872ae4cf04d30781ff708ce9b1274ff7628fc15626b54e5b57131ca9a037839`
- **Chain tx hash:** `0xfe76fc657d6abac267c037d3ec50e6bdfacd2cfec84c6fd5eeb948049c0dccf2`
- **Chain block:** `41554198`

---

## File Mapping

This bundle consists of: `L1-110.md`, `L1-110.json`.

| File | Role | How to regenerate |
|------|------|-------------------|
| `L1-110.md` | Source of truth — edit this | Human or LLM |
| `L1-110.json` | Structured metadata for the registry | LLM regenerates from the sections above |

**Prompt for your LLM after editing this Markdown:**

> Read the attached Markdown. Regenerate the sibling `.json` so every field matches.
> Preserve the schema documented in the rows above.
> Output each file in its own fenced code block tagged with the filename.
> Output only the JSON object.

_This Markdown was auto-synthesized from the catalog row for `L1-110`._
_Edit it, regenerate the JSON, and submit at [/submit](/submit) to claim the artifact._