Towards the Improvement of Soil Salinity Mapping in a Data-Scarce Context Using Sentinel-2 Images in Machine-Learning Models
Abstract
:1. Introduction
1.1. Soil Salinity: A Global Issue
1.2. Soil Salinity Monitoring: Remote Sensing and Machine-Learning Opportunities
1.3. Which Machine-Learning Model Performs Best?
1.4. Machine-Learning Training Set Weakness
1.5. Study Objectives
2. Materials
2.1. Study Area
2.2. Soil Sampling and Analysis
2.3. Sentinel-2 Images and Pre-Processing
2.4. Machine-Learning Models
3. Methods
3.1. Elaboration of the Original Learning Database
3.2. Elaboration of the Enhanced Learning Database
3.3. Elaboration of the Artificial Database
3.4. Comment on the Learning Databases
3.5. Machine-Learning Set-Up for Soil Salinity Estimation
- scenario-1: using all variables
- scenario-2: applying VIF to all variables and selecting variables with VIF < 10
- scenario-3: applying the GA to all variables
- scenario-4: applying the GA to the variables obtained in scenario-2.
3.6. Reliability Assessment of the Proposed Method
4. Results
4.1. Feature Selection
4.2. Benefits of the Proposed ED and Models Comparison
4.3. Reliability Assessment of the Proposed Method
4.4. Soil Salinity Map
5. Discussion
6. Summary and Conclusions
- The proposed method allowed to expand from a learning database of 97 field observations to 681 observations (a 700% increase);
- The use of the enhanced database (ED) significantly improved the model accuracy to estimate soil salinity, resulting in significantly better metrics for both models (i.e., RF and SVM) than when using the original database (OD);
- The improvements in performance obtained with the proposed method were better when considering the Electrical Conductivity (EC) classes. Indeed, for some EC classes, the number of observations in the OD were too low to correctly train and validate the models. The use of the ED allowed the overcoming of this problem by significantly increasing the number of observations in all classes;
- In the case of the limited training dataset (i.e., OD), the Genetic Algorithm (GA) led to more reliable model prediction than the use of Variance Inflation Factor (VIF) for the SVM model whereas the opposite was true for the RF model.
- In the case of the larger training dataset (i.e., ED). The GA led to more reliable model predictions than the VIF for both models.
- Overall, the RF model is more suitable than SVM for soil salinity mapping in the arid Lake Poopó region.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Acronym | Definition | Reference |
---|---|---|
B2-Blue, B3-Green, B4-Red, B5-Rededge1, B6-Rededge2, B7-Rededge3, B8-NIR, B8A-Rededge4, B11-SWIR1, B12-SWIR2 | Sentinel-2 bands | |
Salinity Index 1 (SI) | [14,43] | |
Salinity Index 2 (SI1) | [14,43] | |
Salinity Index 3 (SI2) | [14,43] | |
Salinity Index 4 (SI3) | [14,43] | |
Salinity Index 5 (S) | [44] | |
Salinity Index (S1) | [45] | |
Salinity Index (S2) | [45] | |
Salinity Index (S3) | [45] | |
Salinity Index (S5) | [45] | |
Salinity Index (S6) | [45] | |
Normalized Difference Salinity Index (NDSI) | [45] | |
Normalized Difference Vegetation Index (NDVI) | [46] | |
Normalized Difference Vegetation Index red-edge 1 (NDVIre1) | [47] | |
Normalized Difference Vegetation Index red-edge 2 (NDVIre2) | [47] | |
Normalized Difference Vegetation Index red-edge 2 (NDVIre3) | [48] | |
Renormalized Difference Vegetation Index (RDVI) | [49] | |
Weighted difference vegetation index (WDVI) | [50,51] | |
Tasseled cap wetness (TCW) | [52] |
Model | Scenario | Variables |
---|---|---|
SVM, RF | scenario-1 | B2, B3, B4, B5, B6, B7, B8, B11, B12, B8A, NDSI, NDVI, NDVIre1, NDVIre2, NDVIre3, RDVI, S, S1, S2, S3, S5, S6, SI, SI1, SI2, SI3, TCW, WDVI. |
scenario-2 | B2, B4, B8, B11, NDVIre3, RDVI, S, S2, S6, TCW, WDVI. | |
SVM | scenario-3 | B2, B4, B5, B7, B11, NDVI, NDVIre2, NDVIre3, RDVI, S, S1, S3, S5, S6, SI1, SI2. |
scenario-4 | B2, B4, B11, S, S6. | |
RF | scenario-3 | B2, B6, B7, B8, B11, B12, NDSI, NDVIre2, NDVIre3, S, S1, S2, S5, TCW, WDVI. |
scenario-4 | B8, B11, NDVIre3, S, S2, S6, TCW. |
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Sirpa-Poma, J.W.; Satgé, F.; Resongles, E.; Pillco-Zolá, R.; Molina-Carpio, J.; Flores Colque, M.G.; Ormachea, M.; Pacheco Mollinedo, P.; Bonnet, M.-P. Towards the Improvement of Soil Salinity Mapping in a Data-Scarce Context Using Sentinel-2 Images in Machine-Learning Models. Sensors 2023, 23, 9328. https://doi.org/10.3390/s23239328
Sirpa-Poma JW, Satgé F, Resongles E, Pillco-Zolá R, Molina-Carpio J, Flores Colque MG, Ormachea M, Pacheco Mollinedo P, Bonnet M-P. Towards the Improvement of Soil Salinity Mapping in a Data-Scarce Context Using Sentinel-2 Images in Machine-Learning Models. Sensors. 2023; 23(23):9328. https://doi.org/10.3390/s23239328
Chicago/Turabian StyleSirpa-Poma, J. W., F. Satgé, E. Resongles, R. Pillco-Zolá, J. Molina-Carpio, M. G. Flores Colque, M. Ormachea, P. Pacheco Mollinedo, and M.-P. Bonnet. 2023. "Towards the Improvement of Soil Salinity Mapping in a Data-Scarce Context Using Sentinel-2 Images in Machine-Learning Models" Sensors 23, no. 23: 9328. https://doi.org/10.3390/s23239328