Remote Sensing and AI-Based Monitoring of Soil Properties for Tier-3 MRV Framework of Complex Mediterranean Agroforestry Systems
複雑な地中海式アグロフォレストリーシステムのTier-3 MRVフレームワークのためのリモートセンシングとAIベースの土壌特性モニタリング (AI 翻訳)
Dimitra Palantza, Konstantinos Karyotis, Judit Torres Fernández del Campo, Laura Hernández Mateo, Georgios Zalidis
🤖 gxceed AI 要約
日本語
本研究は、地中海式アグロフォレストリーシステムにおいて、機械学習とリモートセンシングを統合した土壌有機炭素（SOC）の高解像度マッピング手法を開発。Sentinel-2データと環境共変量を用いたハイブリッドモデルにより、R2 0.76、RMSE 0.03の精度を達成し、Tier-3 GHGインベントリやMRVシステムへの応用可能性を示した。
English
This study develops a hybrid machine learning and remote sensing framework for high-resolution soil organic carbon (SOC) mapping in Mediterranean agroforestry systems. Using Sentinel-2 data and environmental covariates, the model achieves R2 up to 0.76 and RMSE as low as 0.03, demonstrating scalability for Tier-3 GHG inventories and MRV systems.
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
日本では農地の炭素貯留能評価が注目されており、本手法の裸地・植生下でのSOC推定アプローチは、日本のアグロフォレストリーや農地でのMRV高度化に応用可能。特にSSBJや有報でのGHG報告におけるScope 1排出量算定の不確実性低減に寄与する可能性がある。
In the global GX context
This framework addresses the challenge of SOC monitoring in heterogeneous landscapes, supporting Tier-3 MRV under global initiatives like the Paris Agreement. The hybrid approach combining bare soil and digital soil mapping is relevant for carbon farming projects and national GHG inventories, especially in regions with complex vegetation cover.
👥 読者別の含意
🔬研究者:Highlights the effectiveness of integrating multi-source remote sensing and ML for SOC mapping, with uncertainty quantification methods applicable to environmental modeling.
🏢実務担当者:Provides a scalable MRV methodology for carbon credit projects and agricultural sustainability reporting, using open-source data (Sentinel-2) and automated ML.
🏛政策担当者:Offers a data-driven approach to improve national GHG inventories and support Tier-3 reporting, aligned with IPCC guidelines and emerging MRV standards.
📄 Abstract（原文）
Soil organic carbon (SOC) plays a critical role in climate regulation, soil fertility, and ecosystem resilience, making its accurate spatial quantification essential for sustainable land management and greenhouse gas (GHG) reporting. However, mapping SOC in heterogeneous agroforestry systems remains challenging due to vegetation cover and landscape complexity. In this study, we develop and evaluate a hybrid bare soil modelling- Digital Soil Mapping supported by ML framework to generate high-resolution soil properties predictions in Mediterranean agroforestry systems (Extremadura, Spain). A dual modelling approach was implemented, combining (i) Bare Soil modelling using Sentinel-2 multi-temporal reflectance composites and (ii) Digital Soil Mapping (DSM) supported by environmental covariates (climate, terrain, vegetation) following the SCORPAN framework. Machine learning models, namely Quantile Regression Forests (QRF) and Extreme Gradient Boosting (XGBoost), were applied and optimised using automated hyperparameter tuning (FLAML). A total of 107 LUCAS topsoil samples and 36 complementary points from the Forest ICP Level I were used for calibration and validation, with a 70/30 train–test split. Results show that Sentinel-2-based modelling can effectively capture SOC spatial variability in bare soil conditions, while DSM improves predictions in vegetated areas. Model performance reached R2 values up to 0.76 (QRF, pH) and RMSE as low as 0.03 (XGBoost, N), with uncertainty quantified using the Prediction Interval Ratio (PIR) and performance further supported by RPIQ values up to 3.15. However, prediction accuracy remains sensitive to vegetation structure and sample density. The proposed framework provides a scalable and uncertainty-aware approach for SOC mapping, supporting Tier-3 GHG inventories and emerging Monitoring, Reporting, and Verification (MRV) systems. The results highlight the importance of integrating multi-source datasets and hybrid modelling strategies for reliable SOC estimation in complex landscapes.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/rs18132077first seen 2026-06-29 06:07:00
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