Advances and applications of multivariate statistics and soil-crop sensing to improve nutrient use efficiency and monitor carbon cycling

Pullanagari, R. R.; Cavalli, Daniele

doi:10.1007/s10705-023-10310-z

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…Techniques like data augmentation or incorporating prior knowledge about SOC distribution could mitigate the bias towards low values and enhance the models' ability to represent the full spectrum of SOC variability. Exploring alternative feature selection methods, such as those based on expert opinion [77], as well as alternative machine learning approaches, such as ensemble methods or meta-learners that combine multiple algorithms with diverse structures, may improve prediction accuracy and overcome the problem of the limit of singular models in predicting SOC values outside the limit of dominant values. Furthermore, investigating computationally efficient methods for generating uncertainty maps remains crucial for enhancing the interpretability and reliability of SOC predictions.…”

Section: Contributions Limitations and Future Research Directionsmentioning

confidence: 99%

Synergistic Use of Multi-Temporal Radar and Optical Remote Sensing for Soil Organic Carbon Prediction

Dahhani,

Raji,

Bouslihim

2024

Remote Sensing

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Exploring soil organic carbon (SOC) mapping is crucial for addressing critical challenges in environmental sustainability and food security. This study evaluates the suitability of the synergistic use of multi-temporal and high-resolution radar and optical remote sensing data for SOC prediction in the Kaffrine region of Senegal, covering over 1.1 million hectares. For this purpose, various scenarios were developed: Scenario 1 (Sentinel-1 data), Scenario 2 (Sentinel-2 data), Scenario 3 (Sentinel-1 and Sentinel-2 combination), Scenario 4 (topographic features), and Scenario 5 (Sentinel-1 and -2 with topographic features). The findings from comparing three different algorithms (Random Forest (RF), XGBoost, and Support Vector Regression (SVR)) with 671 soil samples for training and 281 samples for model evaluation highlight that RF outperformed the other models across different scenarios. Moreover, using Sentinel-2 data alone yielded better results than using only Sentinel-1 data. However, combining Sentinel-1 and Sentinel-2 data (Scenario 3) further improved the performance by 6% to 11%. Including topographic features (Scenario 5) achieved the highest accuracy, reaching an R2 of 0.7, an RMSE of 0.012%, and an RPIQ of 5.754 for the RF model. Applying the RF and XGBoost models under Scenario 5 for SOC mapping showed that both models tended to predict low SOC values across the study area, which is consistent with the predominantly low SOC content observed in most of the training data. This limitation constrains the ability of ML models to capture the full range of SOC variability, particularly for less frequent, slightly higher SOC values.

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Section: Contributions Limitations and Future Research Directionsmentioning

confidence: 99%