Optimizing in-season nitrogen management through satellite-guided fertigation in field-scale maize production

Abstract

Improving nitrogen use efficiency (NUE) in commercial maize production remains a persistent challenge. A major barrier is the lack of simple, remote sensing–based decision-support frameworks that enable broad adoption of in-season, site-specific nitrogen (N) management. This study developed and evaluated a practical framework that contextualizes the Holland–Schepers sensor algorithm using PlanetScope (PS) satellite imagery to guide multiple in-season, variable-rate fertigation delivered through a flow-proportional injection system integrated with a center pivot system equipped with variable-rate irrigation. Field implementation was carried out during the 2023 and 2024 seasons across four N rates (0-N, Low-N, Fertigation, Full-N) and three irrigation treatments: full (BMP), deficit (50 %BMP), and rainfed. Normalized Difference Red Edge (NDRE)-derived sufficiency index (SI) values informed the amount, timing, and spatial distribution of N applications. In 2024, PS-guided fertigation achieved yields statistically comparable to Full-N while reducing total N input by 23 %. Significant improvements in NUE were observed, with Fertigation outperforming Full-N by 12 % in agronomic efficiency (AE) and 26 % in partial factor productivity of N (PFPN). Satellite-derived SI values were strongly correlated with UAV benchmarks from the MicaSense Altum and RedEdge-3 sensors (ρ = 0.82–0.95). However, PS consistently overestimated NDRE relative to UAV data, particularly under N-deficient conditions, underscoring the need for local calibration and bias correction. To improve diagnostic specificity, a biologically informed, rule-based stress-classification framework was developed to differentiate nitrogen stress from water stress using NDRE and soil water depletion (SWD) as diagnostic variables. Retrospective yield and management data were used to establish physiologically meaningful NDRE–SWD thresholds for stress diagnosis during the critical in-season fertigation window (V10–R2). Full-Yield plots achieved approximately 12,000 kg/ha at NDRE = 0.78 and SWD = 64 mm. The resulting NDRE–SWD–yield patterns highlight the feasibility of disentangling stress types under commercial field conditions. However, further validation across seasons and environments, along with integration of canopy water or temperature indices, is needed to improve water-stress detection and enable real-time decision-making. Overall, these results offer actionable guidance for implementing satellite-guided fertigation at commercial scale. The developed framework delivers scalable, data-driven N recommendations that enhance profitability and support environmentally responsible maize production. It also provides a reference for future research and extension programs aiming to turn satellite remote sensing into practical tools for site-specific N management.

Plant phenotyping relevance

衛星・UAVリモートセンシングによるNDRE指標を用いて植物の窒素・水ストレスを診断し、センサー間の検証とルールベース分類法の開発を行っているため、植物状態の取得・推定手法が中心的である。

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