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ARS Home » Pacific West Area » Riverside, California » Agricultural Water Efficiency and Salinity Research Unit » Research » Publications at this Location » Publication #418737
Research Project: Water Management for Crop Production in Arid and Semi-Arid Regions and the Safe Use of Alternative Water Resources

Location: Agricultural Water Efficiency and Salinity Research Unit

Title: In-season estimation of Japanese squash using high-spatial-resolution time-series satellite imagery

Author
item LI, NAN - University Of California, Riverside
item Skaggs, Todd
item SCUDIERO, ELIA - University Of California, Riverside

Submitted to: Sensors
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/14/2025
Publication Date: 3/22/2025
Citation: Li, N., Skaggs, T.H., Scudiero, E. 2025. In-season estimation of Japanese squash using high-spatial-resolution time-series satellite imagery. Sensors. 25(7). https://doi.org/10.3390/s25071999.
DOI: https://doi.org/10.3390/s25071999

Interpretive Summary: Remote sensing is a promising technology for obtaining real-time information on crop growth in agricultural fields. Questions exist as to the best remote sensing techniques and products for monitoring different types of agricultural production systems. In this study, we assessed the feasibility of using high-resolution satellite imagery to predict the yield of Japanese squash on a small farm in Hollister, California. Imagery obtained from satellites of varying resolution and overpass frequency were compared. The study found that the highest resolution satellite data were most effective in explaining squash yields on this small farm. High-resolution satellite imagery has considerable utility for timely and accurate identification of within-field yield variations, facilitating informed precision agricultural decision-making for growers.

Technical Abstract: Yield maps and in-season forecasts help optimize agricultural practices. The traditional approaches to predicting yield during the growing season often rely on groundbased 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, espectively. 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.