Remote sensing and machine learning applications for aboveground biomass estimation in agroforestry systems: a review

Thapa, Bhuwan; Lovell, Sarah Taylor; Wilson, Jeffrey J.

doi:10.1007/s10457-023-00850-2

Cited by 9 publications

(2 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We explored four common ML regression models to create DSR ALIVE : RF, GBR, LSTM, and MLP [26], [27], [28].…”

Section: A Machine Learningmentioning

confidence: 99%

High-Frequency Mapping of Downward Shortwave Radiation from GOES-R Using Gradient Boosting

Ranjbar,

Losos,

Hoffman

et al. 2024

Preprint

View full text Add to dashboard Cite

This study investigates high-frequency mapping of downward shortwave radiation (DSR) at Earth's surface using the Advanced Baseline Imager (ABI) instrument mounted on Geostationary Operational Environmental Satellite -R Series (GOES-R). The existing GOES-R DSR product (DSR ABI ) offers hourly temporal resolution and spatial resolution of 0.25 o . To enhance these resolutions, we explore machine learning (ML) for DSR estimation at the native temporal resolution of GOES-R Level-2 Cloud and Moisture Imagery (CMI) product (five minutes) and its native spatial resolution of two-kilometer at nadir. We compared four common ML regression models through the Leave-One-Out Cross-Validation (LOOCV) algorithm for robust model assessment against ground measurements from AmeriFlux and SURFRAD networks. Results show that Gradient Boosting Regression (GBR) achieves the best performance (R² = 0.916, RMSE = 88.05 W m -2 ) with efficient computation compared to Long Short-Term Memory (LSTM), which exhibited similar performance. DSR estimates from the GBR model (DSR ALIVE ) outperform DSR ABI across various temporal resolutions and sky conditions. DSR ALIVE agreement with ground measurements at SURFRAD networks exhibits high accuracy at high temporal resolutions (five-minute intervals) with R² exceeding 0.85 and RMSE=122 W m -2 . We conclude that GBR offers a promising approach for high-frequency DSR mapping from GOES-R, enabling improved applications for near-real-time monitoring of terrestrial carbon and water fluxes.

show abstract

“…We explored four common ML regression models to create DSR ALIVE : RF, GBR, LSTM, and MLP [26], [27], [28].…”

Section: A Machine Learningmentioning

confidence: 99%

High-Frequency Mapping of Downward Shortwave Radiation from GOES-R Using Gradient Boosting

Ranjbar,

Losos,

Hoffman

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…It has more relaxed data acquisition conditions and simplicity of data collection. In addition, the modeling methods for estimating forest AGB based on PolSAR data can be divided into scattering mechanism methods [25], machine learning methods [26][27][28], and deep learning methods [29][30][31]. Scattering mechanism methods (such as the water cloud model (WCM)) [32,33] can be useful because with a simplified physical model it is difficult to describe the real scattering characteristics of a forest with a complex structure.…”

Section: Introductionmentioning

confidence: 99%

Inversion of Forest Aboveground Biomass in Regions with Complex Terrain Based on PolSAR Data and a Machine Learning Model: Radiometric Terrain Correction Assessment

Nie,

Sa,

Chumachenko

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

The accurate estimation of forest aboveground biomass (AGB) in areas with complex terrain is very important for quantifying the carbon sequestration capacity of forest ecosystems and studying the regional or global carbon cycle. In our previous research, we proposed the radiometric terrain correction (RTC) process for introducing normalized correction factors, which has strong effectiveness and robustness in terms of the backscattering coefficient of polarimetric synthetic aperture radar (PolSAR) data and the monadic model. However, the impact of RTC on the correctness of feature extraction and the performance of regression models requires further exploration in the retrieval of forest AGB based on a machine learning multiple regression model. In this study, based on PolSAR data provided by ALOS-2, 117 feature variables were accurately extracted using the RTC process, and then Boruta and recursive feature elimination with cross-validation (RFECV) algorithms were used to perform multi-step feature selection. Finally, 10 machine learning regression models and the Optuna algorithm were used to evaluate the effectiveness and robustness of RTC in improving the quality of the PolSAR feature set and the performance of the regression models. The results revealed that, compared with the situation without RTC treatment, RTC can effectively and robustly improve the accuracy of PolSAR features (the Pearson correlation R between the PolSAR features and measured forest AGB increased by 0.26 on average) and the performance of regression models (the coefficient of determination R2 increased by 0.14 on average, and the rRMSE decreased by 4.20% on average), but there is a certain degree of overcorrection in the RTC process. In addition, in situations where the data exhibit linear relationships, linear models remain a powerful and practical choice due to their efficient and stable characteristics. For example, the optimal regression model in this study is the Bayesian Ridge linear regression model (R2 = 0.82, rRMSE = 18.06%).

show abstract

Synergizing sustainability: a critical review on harnessing agroforestry for biomass, carbon sequestration, and water-food-energy nexus

Barman,

Dutta,

Bera

et al. 2024

Energ. Ecol. Environ.

View full text Add to dashboard Cite

Remote sensing and machine learning applications for aboveground biomass estimation in agroforestry systems: a review

Cited by 9 publications

References 67 publications

High-Frequency Mapping of Downward Shortwave Radiation from GOES-R Using Gradient Boosting

High-Frequency Mapping of Downward Shortwave Radiation from GOES-R Using Gradient Boosting

Inversion of Forest Aboveground Biomass in Regions with Complex Terrain Based on PolSAR Data and a Machine Learning Model: Radiometric Terrain Correction Assessment

Synergizing sustainability: a critical review on harnessing agroforestry for biomass, carbon sequestration, and water-food-energy nexus

Contact Info

Product

Resources

About