イリノイ州の研究者が蒸発散予測の不確実性を低減するAIモデルを開発(Illinois researchers develop an AI model to reduce uncertainty in evapotranspiration prediction)

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2024-04-30 イリノイ大学アーバナ・シャンペーン校

イリノイ大学アーバナ・シャンペーン校の新しい研究では、地球上の利用可能な水量を評価する際に、降水だけでなく、蒸発散(ET)も考慮します。ETは、土壌や水面からの蒸発と植物の葉からの蒸散を含みます。研究チームは「Dynamic Land Cover Evapotranspiration Model Algorithm」(DyLEMa)というAIを用いたコンピュータモデルを開発し、遠隔センシングを基にETを予測します。このモデルは、異なる土地利用や作物に応じてETを詳細に予測し、地表のダイナミクスを正確に捉えることができます。また、長期的な土地管理の影響を評価し、政策立案に貢献することを目指しています。

<関連情報>

動的土地被覆蒸発散モデルアルゴリズム: DyLEMa Dynamic land cover evapotranspiration model algorithm: DyLEMa

Jeongho Han, Jorge A. Guzman, Maria L. Chu
Computers and Electronics in Agriculture  Available online:27 March 2024
DOI:https://doi.org/10.1016/j.compag.2024.108875

イリノイ州の研究者が蒸発散予測の不確実性を低減するAIモデルを開発(Illinois researchers develop an AI model to reduce uncertainty in evapotranspiration prediction)

Highlights

  • Among decision tree-based MLs, RF Performance compared better than CART and XGB.
  • DyLEMa can reconstruct ET based on seasonal segregated atmospheric and land data.
  • DyLEMa ET PBIAS was reduced in temporal validation compared to the USGS ET data.
  • DyLEMa ET estimates are robust across various cloud contamination rates.
  • DyLEMa ET estimates are robust across Landsat sensor failure.

Abstract

This study presents the “Dynamic Land Cover Evapotranspiration Model Algorithm: DyLEMa” for continuous spatiotemporal evapotranspiration (ET) estimates across diverse land uses. DyLEMa employs a coupled Random Forest model with a novel dynamic recalibrating strategy to improve pre-optimized seasonal hyperparameters following satellite acquisition alongside land cover classes. An analysis of feature importance indicated the significant variability in ET processes across different land cover classes and seasons. Hence, DyLEMa was applied to 20 years of daily 30×30 m pixel resolution Landsat-derived ET data in Illinois to address spatial and temporal discontinuities due to cloud contamination and sensor failures. DyLEMa performance was evaluated on Eddy Covariance measurements to find out that DyLEMa predictions reduced the average PBIAS error from + 31 % to −7% compared to existing US Geological Survey ET products. Spatially, DyLEMa underscores the value of a land cover-aware approach in ET estimation under varied cloud cover rates and their ability to preserve landscape features. However, the performance of DyLEMa was affected by the quality of land cover classification, suggesting the need for a refined region-specific land cover classification. DyLEMa’s flexibility and performance suggest its applicability to other regions and satellite datasets, offering a promising reduction in uncertainty of ET estimates with impacts on environmental and water resources assessments on regional scales.

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