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首页> 外文期刊>Remote Sensing of Environment: An Interdisciplinary Journal >Potential of a two-component polarimetric decomposition at C-band for soil moisture retrieval over agricultural fields
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Potential of a two-component polarimetric decomposition at C-band for soil moisture retrieval over agricultural fields

机译:在农业领域对土壤水分检索的C频段双组分偏振分解的潜力

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This study proposes a two-component (surface and volume) C-band polarimetric decomposition to retrieve soil moisture over agricultural fields covered by different crop types. RADARSAT-2 data analysis shows that vegetation attenuation on the surface scattering component is stronger for the narrow-leaf (wheat) than broad-leaf crops (canola, corn and soybean). Thus, the vegetation attenuation factor is integrated into the proposed C-band polarimetric decomposition to better account for soil contribution over the whole crop phenological season. After removing the volume scattering component from the full coherency matrix, the surface scattering component was simulated by the Oh soil scattering model, instead of conventional Bragg or X-Bragg model, which is not physically valid at C-band due to limited roughness constraint condition. The proposed retrieval algorithm was applied to the RADARSAT-2 time series acquired during the Soil Moisture Active Passive Validation Experiment in 2012 (SMAPVEX12). Results indicate that during the crop growth, the volume scattering power of wheat shows weak temporal variation, while it increases for corn, soybean and canola. The sensitivity of the ground scattering component to soil moisture is enhanced due to the removal of the volume scattering from the full signature. The retrieved soil moisture was validated using the soil moisture ground measurements during the SMAPVEX12. The validations indicate correlation coefficients from 0.63 to 0.76, and RMSEs from 0.058 to 0.074 m(3)/m(3) for the entire phenological period of SMAPVEX12 campaign. The negligible dihedral scattering component (1.6-9.2%) at C-band greatly reduces the complexity of the soil moisture retrieval from the ground component.
机译:本研究提出了双组分(表面和体积)C波纹偏振分解,以检索不同作物类型覆盖的农业领域的土壤水分。 Radarsat-2数据分析表明,表面散射组分的植被衰减对于窄叶(小麦)比宽叶作物(油菜,玉米和大豆)更强。因此,植被衰减因子被整合到所提出的C波形偏振分解中,以更好地解释整个作物鉴季的土壤贡献。在从完全一致性矩阵中移除体积散射分量之后,通过OH土壤散射模型模拟表面散射分量,而不是传统的布拉格或X-BRAGG模型,由于有限的粗糙度约束条件,在C波段处没有物理上有效。将所提出的检索算法应用于2012年土壤湿度主动无源被动验证实验中获得的雷达拉特-2时间序列(Smapvex12)。结果表明,在作物生长期间,小麦的体积散射力显示出弱的时间变异,而玉米,大豆和油菜糖增加。由于从完全签名中移除体积散射,因此增强了地面散射成分对土壤水分的敏感性。使用水分湿度地测量在Smapvex12期间验证了检索到的土壤水分。验证表明来自0.63至0.76的相关系数为0.058至0.074米(3)/ m(3)的RMSES,用于Smapvex12活动的整个候选。在C波段的可忽略量的二面散射组分(1.6-9.2%)大大降低了从地面组分的土壤水分检索的复杂性。

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