Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data
Abstract
:1. Introduction
2. Retrieval Approaches: Concepts and Challenges
2.1. Limitations and Challenges
- The complexity and non-linearity that often characterize the relationship between remote sensing measurements and target variables [10]: On the one hand, geo-/bio-physical variables may affect the electromagnetic properties of a target differently along their range of variability, potentially leading to signal saturation and other nonlinear effects [11]. On the other hand, electromagnetic radiation usually shows a non-uniform sensitivity to the different physical phenomena depending, for instance, on the wavelength of the signal or the acquisition geometry [12,13,14].
- The ill-posed nature of the retrieval problem: The total electromagnetic response of a target is typically the result of multiple contributions, each one determined by a different structural, chemical or physical characteristic [15]. This aspect determines the so-called variable equifinality issue, or parameter ambiguity, i.e., the phenomenon whereby similar electromagnetic responses can be associated with different geo-/bio-physical variable configurations [16,17].
- The image formation process at the sensor level: Remote sensing sensors provide a quantized representation of the investigated scene in the spatial domain. The electromagnetic energy measured within an elementary resolution cell is the result of the presence of multiple objects on the ground with slightly (or sometimes strongly) different characteristics. This behavior is the origin of a mixed contribution at the sensor level. Even by increasing the spatial resolution, this mixing phenomenon cannot be completely canceled, as it remains in pixels representing the boundaries between objects [18]. Moreover, the response corresponding to a pixel can also be affected by radiation components coming from the surrounding of the investigated area [19].
- The influence of external disturbing factors: The remote sensing acquisition system is not ideal, but affected by disturbing factors, such as the noise and non-linearity at the sensor level and the presence of the atmosphere. Even if these issues can be determined and corrected to some extent with the help of calibration and atmospheric correction procedures, they may still corrupt the signal measured at the sensor level and, thus, introduce further ambiguity and complexity in the retrieval process [20,21].
2.2. Retrieval Problem
2.3. Classical Parameter Retrieval Methodologies
2.4. Machine Learning Methodologies
3. Retrieval of Essential Variables: Biomass
Biome | Coverage (%) [42] | Carbon Stocks (Mg/ha) [43] | ||
---|---|---|---|---|
Above Ground | Soil | Total | ||
Grasslands/Herbaceous | 31.5 | 21 | 160 | 181 |
Forests | 27.7 | 97 | 113 | 210 |
Croplands | 12.6 | 2 | 80 | 82 |
- Utilization of satellite-driven parameters (i.e., vegetation indices, textural features, backscatter) for the development of regression-based retrieval models,
- Machine learning algorithms and
- Simulation or biophysical models (data assimilation)
3.1. Grassland Biomass Retrieval
3.2. Croplands Biomass Retrieval
Reference | Sensor | Crop/Parameter | Model/Method | Performance |
---|---|---|---|---|
[57] | UAV | Wheat and rapeseed crops; green area index | Radiative transfer inversion model | |
[55] | Test-bed, X-band spectrometer | Spinach; biomass, LAI, average plant height, soil moisture content | ANN | Performance analysis of different transfer functions |
[59] | Field spectrometry | Winter wheat; LAI | Data assimilation; Kalman filter; Crop Environment REsource Synthesis (CERES) wheat crop model | |
[60] | Field spectrometry | Rice; LAI, green leaf chlorophyll density (GLCD) | Support Vector Machines (SVM) | units, mg m |
[58] | Field spectrometry (Hyperspectral) | Sugar beet (detection of plant diseases) | SVM (classification) | 84.05%–92.35% |
[56] | Aerial hyperspectral | Corn; biomass, yield, plant height, nitrogen, chlorophyll, leaf greenness | SVM | |
[54,55] | Aerial (color infrared) | Mapping Ridolfia segetum infestations in sunflower crop | Evolutionary Product-Unit Neural Networks (EPUNNs), SVM, Logistic Regression (LR), Logistic Regression using Initial covariates and Product Units (LRIPU), logistic model trees (LMT) | LRIPU: |
[61] | Aerial photographs | Sunflower yield mapping | EPUNN, Sparse Multinomial Logistic Regression (SMLR) | SMLR: ; EPUNN: |
[56] | Airborne (Hyperspectral) | Corn; weed, nitrogen stress | ANN, SVM | SVM: ; ANN: |
[62] | L-/X-band field radiometer | Wheat; plant water content (PWC), soil moisture content (SMC) | ANN | , |
Reference | Sensor | Crop/Parameter | ML Classifier | Performance |
---|---|---|---|---|
[63] | Landsat-5 TM and -7 ETM+ | Discriminating various crop types | SVM | |
[64] | TerraSAR-X, RADARSAT-2 | Corn, soybeans | Decision tree classification (DTC) | |
[65] | Hyperion satellite hyperspectral sensor | Soybeans | Optimally-pruned extreme learning machines (OP-ELM), SVM, 1-NN, C 4.5 | OP-ELM () produced the best results |
[66] | RapidEye | Different crop types | SVM, random forest (RF) | |
[67] | Hyperion (hyperspectral), QuickBird | Land cover types, including permanent crops | SVM, object-based classification (OBC) | SVM: ; OBC: |
[68] | Landsat TM | Land cover (14 classes) | RF, classification tree (CT) | Crops (, ) |
[69] | Hyperion (hyperspectral) | Land cover/use (10 classes) | SVM, ANN | SVM: ; ANN: |
[70] | SPOT-5 | Corn, cotton, grain sorghum, sugarcane | SVM | 84.3%–94.0% |
[71] | ALOS | Paddy rice mapping | SVM | |
[72] | MODIS, AVHRR | Land cover mapping (25 classes) | DTC, Gaussian adaptive resonance theory (ART), fuzzy ART neural network (ARTNN), maximum likelihood classification (MLC) | MLC: 495–53%; DT: ; Gaussian ART: ; fuzzy ARTNN: |
3.3. Forest Biomass Retrieval
Reference | Sensor | Parameter(s) | ML algorithm | Performance |
---|---|---|---|---|
[102] | ALOS PALSAR | Biomass | Bagging stochastic gradient boosting (BagSGB) | |
[103] | QuickBird | Height, biomass, volume | Support vector regression (SVR) | |
[104] | TerraSAR-X | Stem volume (v), basal area (a), height (h), diameter (d) | Random forest | RMSE (%): v = 34, a = 29, h = 14, d = 19.7 |
[105] | WorldView-2 | Biomass | Random forest (RF), regression | RF: RMSE = 12.9%, regression: RMSE = 15.9% |
[106] | Landsat | Above-ground woody biomass | RF | |
[107] | SPOT-5, LiDAR | Above-ground biomass | RF | |
[108] | ASTER | Volume (v), basal area (a), stems (s) | k-NN, SVR, RF | RF: , , ; SVR: , , ; k-NN: , |
[79] | Landsat-7 | Biomass | SVM | SVM = 84.62%; regressive analysis = 82.93% |
[109] | Landsat time series | Forest biomass dynamics | Reduced major axis, gradient nearest neighbor, RF | RF: , |
4. Retrieval of Essential Variables: Soil Moisture
- Empirical approaches
- Approaches based on theoretical electromagnetic models
- Machine learning approaches.
4.1. Machine Learning Methodologies for Soil Moisture Retrieval
- ANN:
- Fuzzy logic:
- Multivariate statistics:
- ANN: , and ;
- RVM: , and ;
- SVM: , and ;
- GLM: , and .
Reference | Sensor | Parameter | Model/Method | Performance |
---|---|---|---|---|
[119] | POLARimetric SCATterometer (POLARSCAT) (airborne) | Soil moisture | Integral equation model (IEM) + multilayer perceptron basis function | m/m |
[121] | ENVISAT ASAR/RADARSAT-2/ Optical data for vegetation correction | Soil moisture | IEM + multilayer perceptron (MLP) | m/m < < 0.06 m/m based on different input configurations |
[123] | Ground-based scatterometer data | Soil moisture/leaf area index/biomass | Back-propagation learning algorithm (BPNN) | m/m |
[122] | RADARSAT-2 | Soil moisture/surface roughness | IEM + MLP | m/m without prior information, m/m with prior information |
[124] | Advanced Microwave Scanning Radiometer-EOS (AMSR-E) | Soil moisture | BPNN | m/m |
[127] | RADARSAT-2 | Soil moisture | Support vector regression (SVR) | m/m |
[128] | TRMM + AVHRR | Soil moisture | SVM | m/m |
[129] | SMOS | Soil moisture downscaling | SVR, relevance vector machine (RVM), ANN | ANN: m/m, SVR and RVM: m/m |
[130] | Synthetic data | Soil moisture downscaling | Self-regularized regressive model (SRRM) | m/m |
[132] | Ground data (soil moisture and meteorological time series) | Soil moisture forecast | SVM | m/m ( m/m with only meteorological data, 0.076–0.086 m/m with only soil moisture data) |
[133] | AirSAR data (SMEX02) | Soil moisture | SVM, RVM | SVM: m/m RVM: m/m |
[122] | Multispectral sensor on UAV | Soil moisture | ANN | m/m |
5. Conclusions
Algorithms | Examples | Advantages | Disadvantages |
---|---|---|---|
Regression | Linear, power, logistic regression | The principal advantage of empirical modeling is its simplicity, availability, interpretability and acceptance among the scientific community. | In a nonlinear dynamic environment, the data from chaotic systems do not correspond to the strong assumptions of a linear model. These models do not have a physical basis and are mostly used for site-specific analysis or model development. |
Machine learning | Often much more accurate than human-crafted rules, as they are data driven. Automatic method to search for hypotheses explaining data. Flexible and can be applied to any learning task. Rich interplay between theory and practice, with improved results as datasets increase. | Data-driven methods need many labeled data, requiring extensive ground truth datasets. Typically require some programming knowledge. | |
Decision tree | Conditional decision trees, C5.0, decision stump | Simple to understand and to interpret. Trees can be visualized. Requires little data preparation. Fast and able to handle both numerical and categorical data. | Decision-tree learners can create over-complex trees that do not generalize the data well, and trees can be biased if some classes dominate. |
Bayesian | Bayesian network, naive, Gaussian naive and multinomial naive Bayes | Provide good results with small samples size. Past information about the parameter can be used for future analysis. It provides a natural and theoretically solid mechanism to combine prior information and data. | It is difficult to select prior, and posterior distributions are heavily influenced by the priors. The models with a large number of parameters are computationally high in cost. |
Artificial neural network | Perceptron, back-propagation, radial basis function network | Artificial neural networks have the power to retrieve the complex, dynamic and non-linear patterns from the data. Being one of the oldest machine learning methods, they are well studied and are easy to implement as many libraries and software tools are available. | Artificial neural networks are “black boxes”, and the user has no role/control, except providing the input data. With large datasets, the process gets slow. Back-propagation networks tend to be slower to train than other types of networks and sometimes require thousands of epochs. |
Deep learning | Deep belief networks, convolutional neural networks | Capable of processing the complex input data and learning tasks. It is capable of “learning features” from the data at each level. | Deep learning is not an easy to use method, but packages (Torch7 and Theano + Pylearn2) are available for users for different applications. |
Ensemble | Random forest, bagging, gradient boosting | The basic idea is to train a set of experts and to allow them to vote. | This provides an improved estimation accuracy. It is difficult to understand an ensemble of classifiers. |
Support vectors | Support vector machines, support vector regression | It has a regularization parameter and uses the kernel trick. SVM is defined by a convex optimization problem, and it is an approximation to a bound on the test error rate. | Kernel models are sensitive to over-fitting. From a practical perspective, it gives poor results if the number of features is much greater than the number of samples. |
- The development of retrieval methodologies that can fully exploit the high temporal frequency of new generation and upcoming satellite remote sensing systems to improve the temporal consistency and accuracy of the estimation process. Moreover, the combined use of multiple frequency (C-, X- and L-band) can further improve the retrieval process, but being in its infancy, this needs further development.
- The study of automatic methods for the adaptation of the retrieval system to different domains (e.g., several study areas with slightly different topographic and phenological conditions) [136].
- Generalization of the proposed methods and systems to the retrieval of different geo-/bio-physical variables from a new generation of satellite remote sensing imagery.
Author Contributions
Conflicts of Interest
References
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Ali, I.; Greifeneder, F.; Stamenkovic, J.; Neumann, M.; Notarnicola, C. Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data. Remote Sens. 2015, 7, 16398-16421. https://doi.org/10.3390/rs71215841
Ali I, Greifeneder F, Stamenkovic J, Neumann M, Notarnicola C. Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data. Remote Sensing. 2015; 7(12):16398-16421. https://doi.org/10.3390/rs71215841
Chicago/Turabian StyleAli, Iftikhar, Felix Greifeneder, Jelena Stamenkovic, Maxim Neumann, and Claudia Notarnicola. 2015. "Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data" Remote Sensing 7, no. 12: 16398-16421. https://doi.org/10.3390/rs71215841