The Shadow Effect on Surface Biophysical Variables Derived from Remote Sensing: A Review
Abstract
:1. Introduction
2. Materials and Methods
2.1. Papers Search
2.2. Selection of Papers
2.3. Information Extraction and Integration
3. Results
3.1. Effect of Shadow on RS-Driven Variables and Outputs
3.1.1. Albedo
3.1.2. Evapotranspiration
3.1.3. Impervious Surface Cover (ISC)
3.1.4. Soil Moisture (SM)
3.1.5. LST and Urban Heat Islands (UHIs)
3.1.6. Vegetation Indices
3.1.7. Water Indices
3.1.8. Snow Indices
3.1.9. LULC Classification
3.2. Shadow Detection Methods
3.2.1. Model-Based Methods
3.2.2. Feature-Based Methods
3.2.3. Spectral Indices
3.2.4. Temporal Differences
3.2.5. Advantages and Disadvantages
3.3. De-Shadowing Methods
3.3.1. Urban Shadow Compensation
3.3.2. Topographic Shadow Compensation
3.3.3. Cloud Compensation
3.3.4. Compound Shadow Compensation
3.3.5. Advantages and Disadvantages
4. Discussion
Future Research Directions
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Method | Brief Description | Advantage | Disadvantage |
---|---|---|---|
Thresholding | Spectral values have the key role in thresholding | Simple and quick | Cannot discriminate between non-shadow dark areas and shadow areas |
Modeling | Location of the sensors, the light source direction and the observed objects geometry are the source of knowledge in shadow areas | Identifying the shadow regions with high accuracy | Source of light and scene Geometry are not vivid |
Invariant color model | It is based on ratio of HSV or RGB bands | Identifying shadow regions from other dark objects | Misclassification which is due to uncertainty in certain color values. |
Region growing segmentation | Cluster the image iteratively and find shadow and non-shadow regions. | Provides good segmentation results, performs well with respect to noise | Time-consuming |
Shade relief | Solar elevation, solar zenith, and DEM are the key elements of this technique | Simple nature | Not calculate shadow that is cast by topographic features onto surrounding surface |
Radar observations | (MDIS) images and modelled illumination with (MLA) topographic data are used | Detecting low reluctance surfaces at 1604 nm | Poor spatial resolution |
Shadow Detection Index (SDI) | Three bands are utilized to establish a new spectral index | Simple, distinguishing dark object from shadows | Cannotextract the small portion of shadow |
MSS clear-view mask (MSScvm) | A rule-based algorithm for identifying shadow in MSS data | Automatic, simple. Commission and omission errors are minimized | Customized only for MSS data |
Sub-Pixel Shadow Mapping | Sub-pixel method is used for shadow detection | Mapping shadow with finer resolution. Preserving memory and fill rate consumption | Aliasing problems can be happened due to close-up on the shadow |
Blackbody Radiator Model | Shadow is detected based on the chromaticity values | High accuracy | Medium complexity |
The automatic cloud/shadow detection method | Use MRF method for detection | It is a simple image processing algorithm | Preprocessing is needed for clouds |
Neural network and Pulse coupled neural networks (PCNN) | Based on neural network | Good shadow simulation | While hue and intensity of shadow and non-shadow region is similar, they can be miss classified |
Object-Based shadow extraction | Shadow is detected as an object | More accurate than pixel-based methods spatially for bright objects | Dependent on the radiometric resolution of the sensor |
Airborne Laser Scanner (ALS) | DSM is used for shadow simulation | Accurate | Data are rare, expensive, requires aircraft use, |
Visual interpretation | Shadow is detected by interpreter | Easy to use | Dependent on the interpreter experience |
Machine learning methods | Uses machine learning algorithms | Efficient | High computational cost and time |
Method | Brief Description | Advantage | Disadvantage |
---|---|---|---|
Visual interpretation | Visual analysis-based, | Simple, quick, and easy process | Expensive, time-consuming |
Band ratio indices | Uses band ratio | Very effective | Spectral resolution is lost |
Multisource classification | Combination of DEM and vegetation indices | Topographic component is omitted | Accuracy and resolution of DEM can affect the result |
Topographic correction models | Mathematical models-based | Normalizes area of sunlit canopy | Relationship between terrain and shadow is ambiguous |
Recovery techniques | Mathematical models-based. | Simple Cost efficient | Problems with mixed pixels in complex landscapes |
Data mining techniques | Recovering information from shadow regions using data mining | Information of shadow regions can be restored without removing them | Time-consuming and background issue |
Histogram matching | Uses image processing techniques | The values of pixels covered by shadows can be recovered | Sensitive to window size |
Multisource data fusion | Apply fusion techniques | Use information of two images | Limitation of image acquisition Time Image registration error |
K-means clustering | Clustering-based | The edges of shadow can be precisely detected | Depended on the point distance measurement |
inner outer outline profile line (Ioopl) | Statistic and thresholding-based | Use of Image statistical features | Dark objects may be misclassified as shadows |
Microwave data | Use passive microwave data | Cloud-free imagery | Very low emitted energy, low resolution, large area should be imaged |
Multisource fusion and Multi-date imager | Wavelet technique Fusion | No information in shadow regions is missed | Inefficient for small cloudy regions |
Unmixing | Used concept of unmixing | Using information of shadow regions as endmember | Skylight diffusion is neglected while collecting endmember |
Gamma correction techniques | Use gamma parameter for shadow detection | Useful for accurate land use mapping | A single gamma parameter is used Time-consuming |
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Alavipanah, S.K.; Karimi Firozjaei, M.; Sedighi, A.; Fathololoumi, S.; Zare Naghadehi, S.; Saleh, S.; Naghdizadegan, M.; Gomeh, Z.; Arsanjani, J.J.; Makki, M.; et al. The Shadow Effect on Surface Biophysical Variables Derived from Remote Sensing: A Review. Land 2022, 11, 2025. https://doi.org/10.3390/land11112025
Alavipanah SK, Karimi Firozjaei M, Sedighi A, Fathololoumi S, Zare Naghadehi S, Saleh S, Naghdizadegan M, Gomeh Z, Arsanjani JJ, Makki M, et al. The Shadow Effect on Surface Biophysical Variables Derived from Remote Sensing: A Review. Land. 2022; 11(11):2025. https://doi.org/10.3390/land11112025
Chicago/Turabian StyleAlavipanah, Seyed Kazem, Mohammad Karimi Firozjaei, Amir Sedighi, Solmaz Fathololoumi, Saeid Zare Naghadehi, Samiraalsadat Saleh, Maryam Naghdizadegan, Zinat Gomeh, Jamal Jokar Arsanjani, Mohsen Makki, and et al. 2022. "The Shadow Effect on Surface Biophysical Variables Derived from Remote Sensing: A Review" Land 11, no. 11: 2025. https://doi.org/10.3390/land11112025
APA StyleAlavipanah, S. K., Karimi Firozjaei, M., Sedighi, A., Fathololoumi, S., Zare Naghadehi, S., Saleh, S., Naghdizadegan, M., Gomeh, Z., Arsanjani, J. J., Makki, M., Qureshi, S., Weng, Q., Haase, D., Pradhan, B., Biswas, A., & M. Atkinson, P. (2022). The Shadow Effect on Surface Biophysical Variables Derived from Remote Sensing: A Review. Land, 11(11), 2025. https://doi.org/10.3390/land11112025