# DOSIF: Long-Term Daily SIF from OCO-3 with Global Contiguous Coverage

## TL;DR

This paper introduces DOSIF, a long-term daily SIF dataset with global coverage, created using a data-driven approach on the Google Earth Engine platform.

## Contribution

The novel contribution is a data-driven method using MSTWS to reconstruct long-term daily SIF with global contiguous coverage.

## Key findings

- DOSIF achieved an R2 of 0.92 on training and 0.81 on validation, showing strong predictive performance.
- The dataset accurately captures spatiotemporal SIF patterns and is validated with airborne measurements.
- DOSIF provides daily resolution from 2001 to the present with global coverage.

## Abstract

Satellite-retrieved solar-induced chlorophyll fluorescence (SIF) provides an advanced proxy for global vegetation productivity. Recently, new high-quality remote sensing SIF datasets and reanalysis products have significantly advanced the application of SIF. However, the lack of long-term, daily resolution datasets continues to limit the precise exploration of vegetation dynamics, primarily due to challenges in daily modeling accuracy, substantial data volume, and computational demands. In this study, supported by the Google Earth Engine (GEE) platform, we developed a data-driven approach based on the Moving Spatial–Temporal Window Sampling (MSTWS) strategy for reconstructing long-term daily SIF. By learning the relationship between high-spatial-resolution Orbiting Carbon Observatory (OCO)-3 SIF and MODIS surface reflectance, we established a spatially and temporally specific daily prediction model for each day of the year (DOY), reconstructing the long-term daily OCO-3 SIF (DOSIF) from 2001 to the present with a global contiguous distribution. The prediction framework demonstrated robust performance with an R2 of 0.92 on the training set and 0.81 on the validation set, indicating strong predictive ability and resistance to overfitting. Systematic evaluation of the dataset showed that DOSIF accurately captures the expected spatiotemporal distribution patterns. Cross-sensor validation with independent airborne SIF measurements further enhanced the reliability of the DOSIF dataset.