In-Season Estimation of Japanese Squash Using High-Spatial-Resolution Time-Series Satellite Imagery
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...
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MDPI AG
2025-03-01
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| author | Nan Li Todd H. Skaggs Elia Scudiero |
| author_facet | Nan Li Todd H. Skaggs Elia Scudiero |
| author_sort | Nan Li |
| collection | DOAJ |
| description | 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 (<i>Cucurbita maxima</i>), 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 (<i>r</i>) 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<sup>2</sup> = 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. |
| format | Article |
| id | doaj-art-869ff967af0c44609aefb3652b83891d |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-869ff967af0c44609aefb3652b83891d2025-08-20T03:08:59ZengMDPI AGSensors1424-82202025-03-01257199910.3390/s25071999In-Season Estimation of Japanese Squash Using High-Spatial-Resolution Time-Series Satellite ImageryNan Li0Todd H. Skaggs1Elia Scudiero2Environmental Sciences, University of California, Riverside, CA 92521, USAUSDA-ARS U.S. Salinity Laboratory, Riverside, CA 92507, USAEnvironmental Sciences, University of California, Riverside, CA 92521, USAYield 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 (<i>Cucurbita maxima</i>), 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 (<i>r</i>) 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<sup>2</sup> = 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.https://www.mdpi.com/1424-8220/25/7/1999remote sensingyield estimationsatellite imageryNDVI |
| spellingShingle | Nan Li Todd H. Skaggs Elia Scudiero In-Season Estimation of Japanese Squash Using High-Spatial-Resolution Time-Series Satellite Imagery Sensors remote sensing yield estimation satellite imagery NDVI |
| title | In-Season Estimation of Japanese Squash Using High-Spatial-Resolution Time-Series Satellite Imagery |
| title_full | In-Season Estimation of Japanese Squash Using High-Spatial-Resolution Time-Series Satellite Imagery |
| title_fullStr | In-Season Estimation of Japanese Squash Using High-Spatial-Resolution Time-Series Satellite Imagery |
| title_full_unstemmed | In-Season Estimation of Japanese Squash Using High-Spatial-Resolution Time-Series Satellite Imagery |
| title_short | In-Season Estimation of Japanese Squash Using High-Spatial-Resolution Time-Series Satellite Imagery |
| title_sort | in season estimation of japanese squash using high spatial resolution time series satellite imagery |
| topic | remote sensing yield estimation satellite imagery NDVI |
| url | https://www.mdpi.com/1424-8220/25/7/1999 |
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