Precision Irrigation Management and Water Stress Assessment in Cotton for Sustainable Agriculture

Precision Irrigation Management and Water Stress Assessment in Cotton for Sustainable Agriculture is a comprehensive study that explores the need for efficient water resource management and precision irrigation in semi-arid cotton production systems. Water scarcity and climate continue to challenge sustainable agriculture, making it urgent to integrate remote sensing, machine learning, and precision irrigation techniques to optimize water use efficiency (WUE) and maintain crop productivity. This dissertation investigates how satellite- and UAS-based remote sensing can improve precision irrigation strategies, assess water stress, and enhance cotton yield prediction. The research is structured around five objectives: (1) assessing precision water management using satellite remote sensing, (2) optimizing cotton water use and yield for precision irrigation, (3) evaluating spatial and temporal variabilities of evapotranspiration (ET) and WUE using satellite-derived ET, (4) predicting cotton yield using satellite remote sensing and machine learning, and (5) flight altitude and sensor angle affect unmanned aerial system cotton plant height assessments. Water management in cotton production requires a deep understanding of how irrigation treatments influence crop response at different spatial and temporal scales. The objective of this chapter is to assess the effects of irrigation variability on crop growth and yield, and to identify the key remote sensing indicators for precision irrigation management. Variable rate irrigation (VRI) was integrated with high-resolution PlanetScope imagery to monitor cotton phenotypic traits across different irrigation regimes. Results reveal that NDRE exhibited the highest predictive accuracy (R² = 0.90, RMSE = 5.77%) for biomass and leaf area index (LAI), making it a valuable tool for irrigation optimization. While mid-season biomass accumulation was significantly influenced by irrigation (p < 0.001), the final yield variations were primarily driven by site-specific factors such as apparent soil electrical conductivity (ECa), slope, and elevation (p < 0.001), rather than irrigation volume alone. These findings showed the need for site-specific irrigation strategies rather than uniform water applications to enhance irrigation efficiency. Maximizing cotton productivity under minimal water use is critical for sustainable agricultural systems in semi-arid regions. The objective of this chapter is to identify the optimal irrigation rates that improve WUE while maintaining or increasing cotton yields. A Random Forest machine learning model was used to evaluate the effects of irrigation levels, soil properties, and topography on WUE and yield. Key findings revealed that optimized, site-specific irrigation strategies improved WUE by up to 21.4 % compared to conventional methods, while increasing cotton yields by 31%. Spatial variability significantly influenced irrigation efficiency, with soil ECa, elevation, and slope playing crucial roles in yield response. These findings highlight the importance of tailoring irrigation practices to local field conditions to enhance productivity while reducing unnecessary water use, ensuring both water conservation and optimal yields in water-limited regions. Understanding the spatial and temporal dynamics of evapotranspiration (ET) and WUE within irrigated cotton fields is crucial for precision irrigation. The objective of this chapter is to quantify the influence of field topography and irrigation treatments on water use efficiency. OpenET-derived satellite data were used to assess spatial and temporal variability in ET and its relationship with WUE and yield. Results show substantial spatial heterogeneity in ET and WUE across fields, with some fields achieving the highest mean WUE (2.20 kg ha-1 mm-1) and the lowest coefficient of variation (CV = 15.79 %), indicating more uniform water use. In contrast, other fields exhibited greater variability (CV = 25.46 % and 31.20 %, respectively), suggesting inconsistent water utilization. A positive correlation between ET and yield was observed, where regions with higher ET had increased cotton lint yield. However, ANOVA results indicate that landscape position significantly influenced ET and WUE (p < 0.05), while irrigation treatments had no significant effect, emphasizing the dominant role of field topography in shaping water use dynamics. These findings highlight the effectiveness of OpenET in monitoring within-field water variability and guiding precision irrigation strategies to optimize WUE and cotton yield. Accurate and timely yield prediction models are essential for optimizing resource allocation and improving decision-making in precision agriculture. The objective of this chapter is to evaluate the accuracy of different remote sensing datasets in predicting cotton yield and determining the most effective spectral indicators. Sentinel-1 and PlanetScope satellite imagery were compared using machine learning models to assess their effectiveness at different growth stages. Results show that PlanetScope significantly outperformed Sentinel-1, achieving an R² of 0.84 with an RMSE of 7.01 kg ha-1, while Sentinel-1’s best model achieved an R² of 0.76. Among vegetation indices, EVI was the strongest predictor of cotton yield, contributing over 42% feature importance at mid-growth, outperforming NDVI and NDRE indices. Mid-season predictions provided the most actionable insights, allowing early intervention through optimized irrigation and nutrient management before final yield determination. These findings highlight the importance of multi-temporal remote sensing and machine learning in improving data-driven decision-making for sustainable cotton production. Accurate plant height measurements provide critical insights into crop growth and productivity, playing a key role in precision agriculture applications. The objective of this chapter is to determine the best UAS flight configurations for capturing reliable plant height data. Images were collected at two altitudes (40 m and 80 m) and three sensor angles (45°, 60°, and 90°) to assess their impact on measurement accuracy. Results show that lower-altitude flights (40 m) provided more accurate height measurements than higher-altitude flights (80 m), particularly in early-season growth stages. Additionally, oblique sensor angles (45°) improved plant height estimation accuracy compared to nadir (90°) views, particularly in late-season assessments. These findings establish clear guidelines for optimizing UAS-based crop monitoring, ensuring that remote sensing-derived plant height measurements are both precise and reliable for cotton growth assessments. The findings of this dissertation collectively demonstrate the potential of integrating remote sensing, machine learning, and site-specific irrigation strategies to enhance water use efficiency, optimize yield, and promote sustainable cotton production in semi-arid regions. By leveraging high-resolution satellite data, OpenET-derived evapotranspiration monitoring, and advanced predictive modeling, this research provides a scalable and data-driven framework for precision agriculture, offering practical solutions to the challenges of water scarcity, climate variability, and resource optimization. The insights gained from this study extend beyond cotton production and have broader implications for sustainable water management in other water-limited cropping systems. Future research should build upon these advancements by incorporating real-time sensor integration, decision support systems, and multi-sensor fusion techniques to refine precision irrigation strategies and enhance agricultural resilience in the face of global climate challenges.