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Phenological Windows for UAV and PlanetScope Monitoring of Greenhouse Gas Fluxes in AWD Rice on the Peruvian North Coast

ペルー北海岸のAWD水田におけるGHGフラックスモニタリングのためのUAVおよびPlanetScopeのフェノロジーウィンドウ (AI 翻訳)

Javier Quille-Mamani, José Huanuqueño-Murillo, Grover Jesús Yapuchura-Morales, David Quispe-Tito, Roxana Peña-Amaro, Lena Cruz-Villacorta, Lia Ramos-Fernández

Remote Sensing📚 査読済 / ジャーナル2026-06-17#agricultureOrigin: Global対象セクター: agriculture
DOI: 10.3390/rs18122011
原典: https://doi.org/10.3390/rs18122011

🤖 gxceed AI 要約

日本語

ペルー北海岸の水田で、AWD灌漑によるGHG排出削減効果をリモートセンシングで評価する探索的研究。UAVとPlanetScopeの画像から、CH4・N2O・CO2フラックスを推定するためのフェノロジーウィンドウを特定した。最大分げつ期がUAVにとって有望であり、PlanetScopeは穂孕期に最適だった。単一サイト・単一シーズンの結果であり、実用化にはさらなる検証が必要。

English

This exploratory study in Peruvian rice paddies evaluates AWD irrigation's GHG reduction potential using remote sensing. UAV and PlanetScope imagery were used to identify phenological windows for estimating CH4, N2O, and CO2 fluxes. Maximum tillering was promising for UAV, while PlanetScope peaked at booting stage. Single-site, single-season results require further validation.

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

📝 gxceed 編集解説 — Why this matters

日本のGX文脈において

日本では水田からのメタン排出削減が重要な課題であり、AWDは既に普及している。本研究成果は、リモートセンシングによる効率的なモニタリング手法の可能性を示し、日本の水田管理やJ-クレジット制度における排出削減効果の検証に応用できる可能性がある。

In the global GX context

Globally, AWD is recognized as a key mitigation strategy for rice methane emissions. This study provides a methodological framework for remote sensing-based GHG monitoring, which could support MRV in carbon credit projects and national GHG inventories, aligning with international climate reporting standards.

👥 読者別の含意

🔬研究者:Provides a methodological approach for identifying phenological windows in remote sensing of GHG fluxes, useful for designing multi-site validation studies.

🏢実務担当者:Offers insights for developing cost-effective monitoring of AWD rice fields, potentially supporting carbon credit verification.

🏛政策担当者:Highlights the potential of remote sensing for MRV in agricultural climate policies, relevant for national GHG reporting.

📄 Abstract（原文）

Alternate wetting and drying (AWD) irrigation reduces CH4 emissions from flooded rice but amplifies N2O pulses; identifying candidate phenological windows for the remote screening of greenhouse gas (GHG) fluxes remains challenging with small datasets. In a single-site, single-season exploratory study at INIA Vista Florida (Lambayeque, Peru), eight UAV flights were paired with eight PlanetScope SuperDove scenes (|Δ|≤1 d) and closed-chamber CH4, N2O and CO2 fluxes under four water regimes (CF, AWD5, AWD10, AWD20; 96 sub-plot × date observations). Multivariate explanatory power was assessed by bootstrap Ridge regression on each sensor’s native predictors (VI + GLCM + Tmean for the UAV, VI for PlanetScope). Maximum tillering (79 DAS) emerged as a candidate UAV window, ranking in the top three for all gases through GLCM textures, whereas PlanetScope peaked at Mid-boot and Late-boot (103–107 DAS), with median R2˜UAV at 0.34–0.71 and R2˜Planet at 0.20–0.60. Nested Leave-One-Plot-Out (LOPO) validation gave RCV2 between +0.57 and +0.69 for four of six platform × gas combinations (UAV-CH4 and Planet-N2O stayed weak), and Tmean was decisive for N2O on the UAV (ΔR2=+0.48). Repeating the stage selection inside every LOPO fold preserved the leading combinations and their ranking. These exploratory windows and sensor-native descriptors need multi-site, multi-season validation before operational use.

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