<p>The emission of unused energy absorbed from the sunlight by plants and other photosynthetic organisms is known as solar-induced fluorescence (SIF). SIF is a direct proxy for the photosynthetic activity of the plants used to monitor drought, crop yield estimation, ecological processes, and carbon cycles. Comprehending the SIF dynamics beforehand helps gain an understanding of vegetation dynamics, carbon cycle, and crop phenology. This study explores the potential of using Orbiting Carbon Observatory-2 (OCO-2) SIF data for forecasting SIF at regional scales. The research utilizes machine learning models and data fusion to forecast the SIF data, by establishing relationships between observed SIF from past timestamps and the Enhanced Vegetation Index. The lasso regression achieves minimal error of RMSE 0.0355 <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12524_2024_2097_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="116" /> </InlineMediaObject> <EquationSource Format="TEX">$\hbox {W}\,\hbox {m}^{-2}\hbox {nm}^{-1}\hbox {sr}^{-1}$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mtext>W</mtext> <mspace width="0.166667em" /> <msup> <mtext>m</mtext> <mrow> <mo>-</mo> <mn>2</mn> </mrow> </msup> <msup> <mtext>nm</mtext> <mrow> <mo>-</mo> <mn>1</mn> </mrow> </msup> <msup> <mtext>sr</mtext> <mrow> <mo>-</mo> <mn>1</mn> </mrow> </msup> </mrow> </math></EquationSource> </InlineEquation> and MAPE of 16.9093% for forecasting monthly SIF data. In contrast, the light gradient boosting machine model (LG) performs well for a larger non-linear dataset, i.e., seasonal models achieving a RMSE of 0.0389 <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12524_2024_2097_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="116" /> </InlineMediaObject> <EquationSource Format="TEX">$\hbox {W}\,\hbox {m}^{-2}\hbox {nm}^{-1}\hbox {sr}^{-1}$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mtext>W</mtext> <mspace width="0.166667em" /> <msup> <mtext>m</mtext> <mrow> <mo>-</mo> <mn>2</mn> </mrow> </msup> <msup> <mtext>nm</mtext> <mrow> <mo>-</mo> <mn>1</mn> </mrow> </msup> <msup> <mtext>sr</mtext> <mrow> <mo>-</mo> <mn>1</mn> </mrow> </msup> </mrow> </math></EquationSource> </InlineEquation> and MAPE of 17.4895%, respectively. Karnataka and Maharastra, the two Indian states, are considered as the study areas for this work for a temporal window of 2017–2019. Fine-grained, uniformly distributed SIF forecasting provides valuable insights for understanding vegetation responses to environmental changes, optimizing agricultural practices, and developing climate change mitigation strategies.</p>

Forecasting of Fine-Grained SIF of OCO-2 Using Multi-source Data and AI-Based Techniques

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发表日期：

2025年1月15日

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Spurthy Maria Pais,
Shrutilipi Bhattacharjee,
Anand Kumar Madasamy

摘要

The emission of unused energy absorbed from the sunlight by plants and other photosynthetic organisms is known as solar-induced fluorescence (SIF). SIF is a direct proxy for the photosynthetic activity of the plants used to monitor drought, crop yield estimation, ecological processes, and carbon cycles. Comprehending the SIF dynamics beforehand helps gain an understanding of vegetation dynamics, carbon cycle, and crop phenology. This study explores the potential of using Orbiting Carbon Observatory-2 (OCO-2) SIF data for forecasting SIF at regional scales. The research utilizes machine learning models and data fusion to forecast the SIF data, by establishing relationships between observed SIF from past timestamps and the Enhanced Vegetation Index. The lasso regression achieves minimal error of RMSE 0.0355 $\hbox {W}\,\hbox {m}^{-2}\hbox {nm}^{-1}\hbox {sr}^{-1}$ $W m^{- 2} {nm}^{- 1} {sr}^{- 1}$ and MAPE of 16.9093% for forecasting monthly SIF data. In contrast, the light gradient boosting machine model (LG) performs well for a larger non-linear dataset, i.e., seasonal models achieving a RMSE of 0.0389 $\hbox {W}\,\hbox {m}^{-2}\hbox {nm}^{-1}\hbox {sr}^{-1}$ $W m^{- 2} {nm}^{- 1} {sr}^{- 1}$ and MAPE of 17.4895%, respectively. Karnataka and Maharastra, the two Indian states, are considered as the study areas for this work for a temporal window of 2017–2019. Fine-grained, uniformly distributed SIF forecasting provides valuable insights for understanding vegetation responses to environmental changes, optimizing agricultural practices, and developing climate change mitigation strategies.