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Spatiotemporal cotton yield variations in relation to time-series satellite imagery: Implications for within-season crop management.

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Title: Spatiotemporal cotton yield variations in relation to time-series satellite imagery: Implications for within-season crop management.
Authors: Sun, Yazhou1 (AUTHOR), Lin, Zhe1 (AUTHOR), Guo, Wenxuan1,2 (AUTHOR) wenxuan.guo@ttu.edu
Source: Precision Agriculture. Aug2026, Vol. 27 Issue 4, p1-30. 30p.
Abstract: Purpose: Understanding the spatial and temporal variability of crop growth and yield is fundamental to optimizing site-specific, time-sensitive crop management to improve productivity and resource use efficiency. This study assessed the spatiotemporal cotton yield patterns and their relationships with satellite-derived vegetation indices, topography, and apparent soil electrical conductivity (ECa) across multiple sites and seasons. Methods: Landsat-based NDVI and its temporal derivatives were calculated to estimate cotton spatiotemporal growth patterns. Management zones were delineated using K-means clustering based on topography and ECa. Correlation analysis was conducted to assess relationships among yield, NDVI, NDVI derivatives, topography, and ECa, and to identify variables strongly associated with cotton yield variability. Random Forest was employed to predict yield and evaluate the contributions and interactions of environmental factors. Results: NDVI and its derivatives are strong predictors of cotton yield, with topography dynamically influencing these patterns in response to seasonal moisture. Lower elevations and slopes exhibited higher NDVI and yields due to improved water availability and lower erosion, whereas summits displayed moderate but unstable crop growth and yield. Random Forest models predicted yield robustly in dry years (R² = 0.75–0.83) but moderately in wet years (R² = 0.53–0.65). Conclusion: NDVI and NDVI derivatives are good predictors of spatiotemporal cotton growth and yield. Integrating NDVI and NDVI derivatives with zones delineated using ECa and topography provides robust yield prediction for precision crop management. Furthermore, applying machine learning techniques enhances the interpretability of complex environmental interactions, providing deeper insights to facilitate site- and time-specific management.Keypoints/Highlights: ● The study evaluated cotton yield variability in relation to spatial and temporal patterns of cotton growth, using time-series satellite Normalized Difference Vegetation Index (NDVI) data as a proxy to support site- and time-specific management. ● Cotton yield showed a strong positive correlation with NDVI and a moderate but significant correlation with NDVI derivatives. ● Topographic attributes and soil properties were critical factors influencing the spatiotemporal variation in crop growth and yield across zones, with higher slopes associated with lower yields and NDVI values. ● Explainable machine learning models revealed that NDVI and its temporal derivatives were among the most important predictors of yield, with topography dynamically influencing these patterns in response to seasonal moisture conditions.Impacts: This study developed a method to evaluate crop yield in response to spatiotemporal dynamics of environmental factors using time-series satellite imagery. It provides a practical basis for site- and time-specific management to optimize resource use and maximize crop yield potential and sustainability. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
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Abstract:Purpose: Understanding the spatial and temporal variability of crop growth and yield is fundamental to optimizing site-specific, time-sensitive crop management to improve productivity and resource use efficiency. This study assessed the spatiotemporal cotton yield patterns and their relationships with satellite-derived vegetation indices, topography, and apparent soil electrical conductivity (ECa) across multiple sites and seasons. Methods: Landsat-based NDVI and its temporal derivatives were calculated to estimate cotton spatiotemporal growth patterns. Management zones were delineated using K-means clustering based on topography and ECa. Correlation analysis was conducted to assess relationships among yield, NDVI, NDVI derivatives, topography, and ECa, and to identify variables strongly associated with cotton yield variability. Random Forest was employed to predict yield and evaluate the contributions and interactions of environmental factors. Results: NDVI and its derivatives are strong predictors of cotton yield, with topography dynamically influencing these patterns in response to seasonal moisture. Lower elevations and slopes exhibited higher NDVI and yields due to improved water availability and lower erosion, whereas summits displayed moderate but unstable crop growth and yield. Random Forest models predicted yield robustly in dry years (R² = 0.75–0.83) but moderately in wet years (R² = 0.53–0.65). Conclusion: NDVI and NDVI derivatives are good predictors of spatiotemporal cotton growth and yield. Integrating NDVI and NDVI derivatives with zones delineated using ECa and topography provides robust yield prediction for precision crop management. Furthermore, applying machine learning techniques enhances the interpretability of complex environmental interactions, providing deeper insights to facilitate site- and time-specific management.Keypoints/Highlights: ● The study evaluated cotton yield variability in relation to spatial and temporal patterns of cotton growth, using time-series satellite Normalized Difference Vegetation Index (NDVI) data as a proxy to support site- and time-specific management. ● Cotton yield showed a strong positive correlation with NDVI and a moderate but significant correlation with NDVI derivatives. ● Topographic attributes and soil properties were critical factors influencing the spatiotemporal variation in crop growth and yield across zones, with higher slopes associated with lower yields and NDVI values. ● Explainable machine learning models revealed that NDVI and its temporal derivatives were among the most important predictors of yield, with topography dynamically influencing these patterns in response to seasonal moisture conditions.Impacts: This study developed a method to evaluate crop yield in response to spatiotemporal dynamics of environmental factors using time-series satellite imagery. It provides a practical basis for site- and time-specific management to optimize resource use and maximize crop yield potential and sustainability. [ABSTRACT FROM AUTHOR]
ISSN:13852256
DOI:10.1007/s11119-026-10394-x