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

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 (R 2 = 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.

Plant phenotyping relevance

高解像度衛星画像と植生指数を用いて作物収量を生育中に推定し、複数衛星プラットフォームの性能比較と精度評価を行っており、収量フェノタイピング手法が中心である。

Code and data availability

The supplied blocks contain no paper-specific public asset. The squash yield measurements, vegetation index time series, and analysis are described but no data or code availability statement, repository, or authors' public URL appears. Sentinel-2/Planet imagery sources cited are generic platform/catalog links, not a de

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