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In-Season Estimation of Japanese Squash Using High-Spatial-Resolution Time-Series Satellite Imagery.

Nan Li¹, Todd H Skaggs², Elia Scudiero^1,2

¹Environmental Sciences, University of California, Riverside, CA 92521, USA.

Sensors (Basel, Switzerland)

|April 12, 2025

Summary

High-resolution satellite imagery, specifically SkySat, accurately predicts Japanese squash yield early in the season. This technology aids small farms in optimizing crop management and resource use.

Keywords:

NDVI remote sensing satellite imagery yield estimation

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Area of Science:

Agricultural Science
Remote Sensing
Crop Monitoring

Background:

Traditional yield prediction methods are labor-intensive.
Remote sensing offers efficient, spatially extensive crop data.
Japanese squash yield prediction lacks extensive research.

Purpose of the Study:

Evaluate high-resolution satellite imagery for early Japanese squash yield prediction.
Compare satellite platforms (Sentinel-2, PlanetScope, SkySat).
Identify optimal timing for yield prediction using vegetation indices.

Main Methods:

Utilized Normalized Difference Vegetation Index (NDVI) and Soil-Adjusted Vegetation Index (SAVI).
Collected data over 2022-2023 growing seasons in Hollister, California.
Analyzed correlations between vegetation indices and yield using Pearson's correlation coefficient.

Main Results:

SkySat imagery showed superior performance (R² = 0.75-0.76) compared to Sentinel-2 and PlanetScope.
Strong correlations with yield were observed as early as 29-76 days post-planting.
Early predictions correlated with dense crop canopy stages before fruit development.

Conclusions:

High-resolution satellite imagery is effective for in-season yield estimation.
Early yield variability detection enables timely management interventions.
This approach is particularly beneficial for small-scale farms to improve efficiency.