Corn Biomass Estimation by Integrating Remote Sensing and Long-Term Observation Data Based on Machine Learning Techniques

Geng, Liying; Che, Tao; Ma, Mingguo; Tan, Junlei; Wang, Haibo

doi:10.3390/rs13122352

Cited by 40 publications

(17 citation statements)

References 89 publications

(131 reference statements)

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“…A model provided by XGB appeared promising as an alternative, especially when balanced class-based accuracy is considered. Nonetheless, XGB has limitedly been evaluated in forest cover, 52,53 agricultural fields, 54,55 and plantation. 56 These publications, in line with the result of this study, suggest that XGB could serve as a potential approach in thematic data extraction.…”

Section: Discussionmentioning

confidence: 99%

Rural land cover mapping using short-term Sentinel-2 temporal composites and machine learning

Trisasongko

Panuju

Karyati

et al. 2023

J. Appl. Rem. Sens.

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Multispectral sensors have been invaluable for land cover mapping. Nonetheless, cloud cover is frequent in many parts in the tropics, meaning that relying on single-date multispectral data can be problematic. The limited number of variables, i.e., only two, in conventional dual-polarized synthetic aperture radar data usually drives low classification accuracies. Researchers have explored an augmentation strategy that uses textural features, such as Haralick's Gray Level Co-occurrence Matrix and/or decomposition features. However, this also does not produce an acceptable outcome. Another approach involves short-term temporal stacking. This masks out atmospheric disturbance and means every pixel has a single value representing the object. Our research explored this last approach to identify woody vegetation in a rural area of Indonesia from Sentinel-2 data. Ten machine learning algorithms were explored to investigate limitations and opportunities. We investigated algorithms capable of reporting variable importance to indicate crucial spectral bands. This study indicated that highly important spectra include red edge and shortwave infrared bands and to a lesser extent, the red band. Part of these bands were confirmed by the variable importance. The spectral similarity between bands 8 and 8A could be seen as a drawback when both bands are employed. Variable importance indicated a negative impact due to this similarity, suggesting that only one should be used in the classification. Overall, classification accuracy was generally high (over 96%), except in the case of the benchmark, i.e., classification and regression trees. Highest accuracy (97.6%) was provided by C5.0 model, although the difference from random forests (RFs) was negligible. The RF was favored as it produced a better estimate of timber plantations. A few algorithms were unable to separate woody vegetation types, including the popular support vector machine. Successful separation was mainly achieved by ensemble tree learners, such as RFs and the variants of extreme gradient boosting.

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Section: Discussionmentioning

confidence: 99%

Rural land cover mapping using short-term Sentinel-2 temporal composites and machine learning

Trisasongko

Panuju

Karyati

et al. 2023

J. Appl. Rem. Sens.

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“…Slightly better were logarithmic or polynomial regressions [ 77 , 94 ]. In contrast, some of the best algorithms for biomass prediction were XGBoost and RF [ 83 , 91 , 95 , 96 ]. However, both algorithms are suitable for biomass prediction, with XGBoost and RF achieving 86.9% and 84.4% accuracy, respectively, in one study [ 83 ].…”

Section: Remote Sensing Of Ecosystem Functions In Researchmentioning

confidence: 99%

Optical Remote Sensing in Provisioning of Ecosystem-Functions Analysis—Review

Vyvlečka

Pechanec

2023

Sensors

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Keeping natural ecosystems and their functions in the proper condition is necessary. One of the best contactless monitoring methods is remote sensing, especially optical remote sensing, which is used for vegetation applications. In addition to satellite data, data from ground sensors are necessary for validation or training in ecosystem-function quantification. This article focuses on the ecosystem functions associated with aboveground-biomass production and storage. The study contains an overview of the remote-sensing methods used for ecosystem-function monitoring, especially methods for detecting primary variables linked to ecosystem functions. The related studies are summarized in multiple tables. Most studies use freely available Sentinel-2 or Landsat imagery, with Sentinel-2 mostly producing better results at larger scales and in areas with vegetation. The spatial resolution is a key factor that plays a significant role in the accuracy with which ecosystem functions are quantified. However, factors such as spectral bands, algorithm selection, and validation data are also important. In general, optical data are usable even without supplementary data.

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“…As the active remote sensing data, Radar and Light Detection and Ranging (LiDAR), which were commonly used to access forest AGB estimation, have intense penetration into vegetation (Foody et al, 2003;Lu, 2005;Lu et al, 2012). LiDAR was still hard to apply in large areas due to data collection being costly and non-spatially continuous (Listopad et al, 2011;Geng et al, 2021;Ehlers et al, 2022). The signal of Radar was easily limited by fluctuating landforms, leading to Radar being unsuitable in complex landform areas (Minh et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Improving the accuracy of forest aboveground biomass using Landsat 8 OLI images by quantile regression neural network for Pinus densata forests in southwestern China

Zhang

Liu

et al. 2023

Front. For. Glob. Change

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It is a challenge to reduce the uncertainties of the underestimation and overestimation of forest aboveground biomass (AGB) which is common in optical remote sensing imagery. In this study, four models, namely, the linear stepwise regression (LSR), artificial neural network (ANN), quantile regression (QR), and quantile regression neural network (QRNN) were used to estimate Pinus densata forest AGB data by collecting 146 sample plots combined with Landsat 8-Operational Land Imager (OLI) images in Shangri-La City, Yunnan Province, southwestern China. The results showed that compared with the LSR, the R2 and mean square error (RMSE) of the ANN, QR, and QRNN had improved significantly. In particular, the QRNN was able to significantly improve the situation of overestimation and underestimation when we estimated forest biomass, which had the highest R2 (0.971) and lowest RMSE (9.791 Mg/ha) for the whole biomass segment. Meanwhile, through model validation, we found that the QRNN had the highest R2 (0.761) and lowest RMSE (6.486 Mg/ha) on the biomass segment of <40 Mg/ha. Furthermore, it had the highest R2 (0.904) and lowest RMSE (9.059 Mg/ha) on the biomass segment of >160 Mg/ha, which offered great potential for improving the estimation accuracy of the Pinus densata forest AGB. In conclusion, the QRNN, combining the advantages of QR and ANN, provides great potential for reducing the precision influence caused by the overestimation and underestimation in forest AGB estimation using optical remote sensing data.

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Corn Biomass Estimation by Integrating Remote Sensing and Long-Term Observation Data Based on Machine Learning Techniques

Cited by 40 publications

References 89 publications

Rural land cover mapping using short-term Sentinel-2 temporal composites and machine learning

Rural land cover mapping using short-term Sentinel-2 temporal composites and machine learning

Optical Remote Sensing in Provisioning of Ecosystem-Functions Analysis—Review

Improving the accuracy of forest aboveground biomass using Landsat 8 OLI images by quantile regression neural network for Pinus densata forests in southwestern China

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