Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data

Ali, Iftikhar; Greifeneder, Felix; Stamenković, Jelena; Neumann, Maxim; Notarnicola, Claudia

doi:10.3390/rs71215841

Cited by 379 publications

(263 citation statements)

References 126 publications

Supporting

Mentioning

262

Contrasting

Unclassified

Order By: Relevance

“…DL is a machine learning method similar to ANN but is capable of processing the complicated, huge input data by learning tasks by using feed-forward multi-layer network (Ali et al, 2015). Training process of DL usually consists of pre-training and fine-tuning.…”

Section: Deep Learningmentioning

confidence: 99%

Machine Learning Approaches to Corn Yield Estimation Using Satellite Images and Climate Data: A Case of Iowa State

Kim¹,

Lee²

2016

Journal of the Korean Society of Surveying, Geodesy, Photogramm

View full text Add to dashboard Cite

Remote sensing data has been widely used in the estimation of crop yields by employing statistical methods such as regression model. Machine learning, which is an efficient empirical method for classification and prediction, is another approach to crop yield estimation. This paper described the corn yield estimation in Iowa State using four machine learning approaches such as SVM (Support Vector Machine), RF (Random Forest), ERT (Extremely Randomized Trees) and DL (Deep Learning). Also, comparisons of the validation statistics among them were presented. To examine the seasonal sensitivities of the corn yields, three period groups were set up: (1) MJJAS (May to September), (2) JA (July and August) and (3) OC (optimal combination of month). In overall, the DL method showed the highest accuracies in terms of the correlation coefficient for the three period groups. The accuracies were relatively favorable in the OC group, which indicates the optimal combination of month can be significant in statistical modeling of crop yields. The differences between our predictions and USDA (United States Department of Agriculture) statistics were about 6-8 %, which shows the machine learning approaches can be a viable option for crop yield modeling. In particular, the DL showed more stable results by overcoming the overfitting problem of generic machine learning methods. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http:// creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

show abstract

Section: Deep Learningmentioning

confidence: 99%

Machine Learning Approaches to Corn Yield Estimation Using Satellite Images and Climate Data: A Case of Iowa State

Kim¹,

Lee²

2016

Journal of the Korean Society of Surveying, Geodesy, Photogramm

View full text Add to dashboard Cite

show abstract

“…The results of Atzberger et al [7] indicate that the noise immunity of PCR, PLSR, and SMLR is ranked as PCR > PLSR > MLR, which is exactly the same ranking as obtained in the present work ( Figure 7). The present noise immunity results ( Figure 7) are important because repeated observations by remote-sensing techniques occur at different times; so techniques with poor noise immunity may lead to low accuracy because of data errors [26,78,79] (e.g., due to atmosphere, clouds, observation times, sensor noise). Our noise immunity results may explain why different regression studies of vegetation parameters based on remote sensing obtain significantly different results.…”

Section: Analysis Of Noise Immunitymentioning

confidence: 99%

“…Numerous studies have used hyperspectral remote-sensing data and empirical regression techniques to estimate AGB [26], and some analyses of the performance of these techniques have also been carried out, although they focus mostly on comparing the estimation accuracy. No comprehensive study is available as yet that evaluates these regression techniques for estimating AGB, and no studies have evaluated the different statistical techniques to better understand their respective advantages and disadvantages.…”

Section: Introductionmentioning

confidence: 99%

“…These techniques essentially fall into two categories: (i) machine-learning techniques such as artificial neural network (ANN) [22], decision tree regression (DT) [23], boosted binary regression tree (BBRT) [24], random forest regression (RF) [25], support vector machine regression (SVM) [26], and (ii) conventional regression techniques such as multivariable linear regression (MLR) [26,27], partial least squares regression (PLSR) [7,8,22], and principal component regression (PCR) [7]. Many studies have obtained promising results by using these techniques [8][9][10][11]26]. However, hyperspectral data redundancy is a big problem because of the high spectral dimensions and large number of bands [28].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Comparison of Regression Techniques for Estimation of Above-Ground Winter Wheat Biomass Using Near-Surface Spectroscopy

et al. 2018

View full text Add to dashboard Cite

Above-ground biomass (AGB) provides a vital link between solar energy consumption and yield, so its correct estimation is crucial to accurately monitor crop growth and predict yield. In this work, we estimate AGB by using 54 vegetation indexes (e.g., Normalized Difference Vegetation Index, Soil-Adjusted Vegetation Index) and eight statistical regression techniques: artificial neural network (ANN), multivariable linear regression (MLR), decision-tree regression (DT), boosted binary regression tree (BBRT), partial least squares regression (PLSR), random forest regression (RF), support vector machine regression (SVM), and principal component regression (PCR), which are used to analyze hyperspectral data acquired by using a field spectrophotometer. The vegetation indexes (VIs) determined from the spectra were first used to train regression techniques for modeling and validation to select the best VI input, and then summed with white Gaussian noise to study how remote sensing errors affect the regression techniques. Next, the VIs were divided into groups of different sizes by using various sampling methods for modeling and validation to test the stability of the techniques. Finally, the AGB was estimated by using a leave-one-out cross validation with these powerful techniques. The results of the study demonstrate that, of the eight techniques investigated, PLSR and MLR perform best in terms of stability and are most suitable when high-accuracy and stable estimates are required from relatively few samples. In addition, RF is extremely robust against noise and is best suited to deal with repeated observations involving remote-sensing data (i.e., data affected by atmosphere, clouds, observation times, and/or sensor noise). Finally, the leave-one-out cross-validation method indicates that PLSR provides the highest accuracy (R 2 = 0.89, RMSE = 1.20 t/ha, MAE = 0.90 t/ha, NRMSE = 0.07, CV (RMSE) = 0.18); thus, PLSR is best suited for works requiring high-accuracy estimation models. The results indicate that all these techniques provide impressive accuracy. The comparison and analysis provided herein thus reveals the advantages and disadvantages of the ANN, MLR, DT, BBRT, PLSR, RF, SVM, and PCR techniques and can help researchers to build efficient AGB-estimation models.

show abstract

“…Vector Machines have become increasingly popular to address remote sensing classification problems in recent years [35]. These have advantages over more traditional classifiers such as maximum likelihood largely because they are non-parametric and therefore perform well regardless of spectral class distributions.…”

Section: Radarsat Machine Learning Such As Random Forest and See5 Decmentioning

confidence: 99%

Comparing Landsat and RADARSAT for Current and Historical Dynamic Flood Mapping

Olthof

Tolszczuk-Leclerc

2018

Remote Sensing

View full text Add to dashboard Cite

Abstract:Mapping the historical occurrence of flood water in time and space provides information that can be used to help mitigate damage from future flood events. In Canada, flood mapping has been performed mainly from RADARSAT imagery in near real-time to enhance situational awareness during an emergency, and more recently from Landsat to examine historical surface water dynamics from the mid-1980s to present. Here, we seek to integrate the two data sources for both operational and historical flood mapping. A main challenge of a multi-sensor approach is ensuring consistency between surface water mapped from sensors that fundamentally interact with the target differently, particularly in areas of flooded vegetation. In addition, automation of workflows that previously relied on manual interpretation is increasingly needed due to large data volumes contained within satellite image archives. Despite differences between data received from both sensors, common approaches to surface water and flooded vegetation mapping including multi-channel classification and region growing can be applied with sensor-specific adaptations for Derived historical products depicting inundation frequency and trends were also generated from each sensor's time-series of surface water maps and compared.

show abstract

Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data

Cited by 379 publications

References 126 publications

Machine Learning Approaches to Corn Yield Estimation Using Satellite Images and Climate Data: A Case of Iowa State

Machine Learning Approaches to Corn Yield Estimation Using Satellite Images and Climate Data: A Case of Iowa State

A Comparison of Regression Techniques for Estimation of Above-Ground Winter Wheat Biomass Using Near-Surface Spectroscopy

Comparing Landsat and RADARSAT for Current and Historical Dynamic Flood Mapping

Contact Info

Product

Resources

About