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

## TL;DR

This study shows that high-resolution satellite images can predict Japanese squash yields early in the growing season, helping small farms improve productivity and resource use.

## Contribution

The study demonstrates the feasibility of using high-resolution satellite imagery for early yield prediction of Japanese squash, a crop with limited prior research.

## Key findings

- SkySat imagery provided the best yield prediction accuracy (R2 = 0.75–0.76) compared to Sentinel-2 and PlanetScope.
- Vegetation indices correlated strongly with yield as early as 29 days after planting in 2022 and 37 and 76 days in 2023.
- Early detection of yield variability allows for timely farm management decisions to optimize productivity.

## Abstract

Yield maps and in-season forecasts help optimize agricultural practices. The traditional approaches to predicting yield during the growing season often rely on ground-based observations, which are time-consuming and labor-intensive. Remote sensing offers a promising alternative by providing frequent and spatially extensive information on crop development. In this study, we evaluated the feasibility of high-resolution satellite imagery for the early yield prediction of an under-investigated crop, Japanese squash (Cucurbita maxima), in a small farm in Hollister, California, over the growing seasons of 2022 and 2023 using vegetation indices, including the Normalized Difference Vegetation Index (NDVI) and the Soil-Adjusted Vegetation Index (SAVI). We identified the optimal time for yield prediction and compared the performances across satellite platforms (Sentinel-2: 10 m; PlanetScope: 3 m; SkySat: 0.5 m). Pearson’s correlation coefficient (r) was employed to determine the dependencies between the yield and vegetation indices measured at various stages throughout the squash growing season. The results showed that SkySat-derived vegetation indices outperformed those of Sentinel-2 and PlanetScope in explaining the squash yields (R2 = 0.75–0.76; RMSE = 0.8–1.9 tons/ha). Remote sensing showed very strong correlations with yield as early as 29 days after planting in 2022 and 37 and 76 days in 2023 for the NDVI and the SAVI, respectively. These early dates corresponded with the vegetative stages when the crop canopy became denser before fruit development. These findings highlight the utility of high-resolution imagery for in-season yield estimation and within-field variability detection. Detecting yield variability early enables timely management interventions to optimize crop productivity and resource efficiency, a critical advantage for small-scale farms, where marginal yield changes impact economic outcomes.