Potentiel des donnees AMSR-E et RADARSAT-2 pour le suivi des cycles de gel/dégel du sol dans des zones agricoles au Canada
Notice bibliographique
Résumé
Soil freezing and thawing processes are of particular importance for agricultural areas. For example, frozen soils can increase the runoff during snowmelt in the spring. Freezing and thawing also have a direct influence on the sowing and harvesting dates, as well as on the crop yield. A better understanding of those phenomena is therefore important, and several researchers focused on this topic in the past. Due to its sensitivity to changes in the state of water, microwave remote sensing is an appropriate tool for that purpose. The main objective of this study is to monitor soil freezing and thawing processes using AMSR-E and RADARSAT-2 polarimetric data acquired over an agricultural area located near Saskatoon (Saskatchewan). With AMSR-E data, the goals are to compare different combinations of frequencies for the spectral gradient's algorithm regarding their capacity for detecting frozen soils, and to analyze the temporal dynamics of the brightness temperature in order to find a new indicator of soil freezing. As for RADARSAT-2 data, several polarimetric parameters and techniques are tested in order to identify soil freezing. For the first part concerning AMSR-E data, a global precision for the discrimination of frozen and thawed soils higher than 90% was obtained with the spectral gradient's algorithm, for the combinations including high (18.7 and 36.5 GHz) and low (6.9 and 10.7 GHz) frequencies as well as for the one using only high frequencies. It is shown that, for the combination based on the 18.7 and 36.5 GHz frequencies, results are improved when a negative threshold is used for the spectral gradient. When high and low AMSR-E frequencies are combined, a null threshold is on the contrary appropriate, which constitutes an operational advantage. A new algorithm for detecting frozen soils, based on a thresholding approach applied to the spectral gradient of polarization difference and the brightness temperature at 36.5 GHz, was also proposed. The performances of the new algorithm to discriminate frozen and thawed soils are very similar to those obtained using the spectral gradient of brightness temperature (global precision around 90% and probability of detecting frozen soils between 70% and 85%). The performances are also slightly higher for the combinations including the lower AMSR-E frequencies. However, annual statistics for the spectral gradient of polarization difference are required to calculate the thresholds. The results obtained with AMSR-E data highlight the relevance of including SMOS L-band brightness temperatures for the calculation of brightness temperature and polarization difference spectral gradients. The qualitative analysis of the results obtained using RADARSAT-2 data shows that surface scattering dominates volume scattering for frozen soils, which can be explained by the rough fields in the study area, as compared to the signal's wavelength (C-band). Nevertheless, several polarimetric parameters indicate a slight increase of the volume scattering in frozen soils, which is theoretically expected. This was observed for the linear and circular depolarization ratios, the amplitude of the HHVV, RLLL and RLRR correlation coefficients, as well as for the pedestal height. Also, the entropy and [alpha overline]-angle of the Cloude-Pottier target decomposition increase slightly in frozen soils ; the same is true for the volume scattering component of the Freeman-Durden and Yamaguchi target decompositions, with an equivalent decrease of the surface scattering component. Despite these interesting observations, a quantitative analysis of the results is necessary in order to evaluate the usefulness of polarimetry regarding the detection of frozen soils. This would allow the validation of the behavior, possibly caused by soil freezing, of the mean value and the standard deviation of the HHVV phase difference and the standard deviation of the RLLL and RLRR phase differences.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,004 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».