On the Relationship Between Radar Backscatter and Radiometer Brightness Temperature From SMAP
On the Relationship Between Radar Backscatter and Radiometer Brightness Temperature From SMAP
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DOI：
10.1109/tgrs.2021.3115140
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发表时间：
2021
影响因子：
8.2
通讯作者：
J. Zeng;Pengfei Shi;Kunshan Chen;Hongliang Ma;H. Bi;C. Cui
中科院分区：
文献类型：
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作者：
J. Zeng;Pengfei Shi;Kunshan Chen;Hongliang Ma;H. Bi;C. Cui
近年来，主动和被动微波测量的协同作用引起了相当大的关注，因为它们提供了有关观测目标特征（例如土壤湿度）的补充信息，这促使美国宇航局启动土壤湿度主动被动（SMAP）任务。 SMAP主动-被动基线算法中假设了主动和被动测量之间的近线性关系，这对于使用高分辨率雷达后向散射（σ⁰）缩小粗分辨率辐射计亮度温度（TB）至关重要，但尚未在各种地面条件下进行充分测试。受此启发，我们首先通过在不同环境因素（例如土地覆盖、气候类型、地形及其复杂性、土壤质地、植被覆盖、土壤湿度及其动态）下使用并发和同步的SMAP主动和被动观测来检验线性假设的有效性。我们还采用 SMAP 增强 TB 来评估分解 TB 在相同网格分辨率 9 km 下的性能。结果表明，在全球范围内，σ⁰（无论是dB还是线性单位）与TB之间普遍存在良好的线性关系。四种极化组合（$σ ⁰_{hh}$ 与 TB $_{h}$、$σ⁰_{hh}$ 与 TB $_{v}$、$σ⁰_{vv}$ 与 TB $_{h}$ 以及 $σ⁰_{vv}$ 与 TB 之间的相关性没有显着差异 $_{v}$ )，$σ⁰_{vv}$ 和 TB $_{h}$ 组合显示出整体略高的相关性。 σ⁰和TB之间的线性关系受环境因素影响显着。特别是在裸土和植被茂密的地区（例如，森林比例和植被覆盖度大）以及干旱和极地气候区，主动和被动测量之间的线性相关性变差，而在中等植被和土壤湿度以及大土壤湿度动态条件下则有利。有趣的是，线性相关性通常随着砂含量的增加而减小，而随着粘土含量的增加而增加。土壤水分动态越大，绝对线性相关系数越高。与 SMAP 增强 TB 相比，它表明线性假设对缩小 TB 的相关性（即时间演化）的影响可能比其绝对精度更大。这些发现可以增强对雷达和辐射计特征之间的地球物理关系的理解，从而有利于未来卫星任务的主被动联合算法。
The synergy of active and passive microwave measurements has attracted considerable attention in recent years since they offer complementary information on the characteristics of the observed target (e.g., soil moisture), which motivates the launch of NASA's Soil Moisture Active Passive (SMAP) mission. An assumption of a near-linear relationship between active and passive measurements has been made in the SMAP active-passive baseline algorithm, which is essential to downscale coarse-resolution radiometer brightness temperature (TB) using high-resolution radar backscatter (σ⁰) but has not yet been fully tested under a wide range of ground conditions. Motivated by this, we first examined the validity of the linear assumption by using concurrent and coincident SMAP active and passive observations under diverse environmental factors (e.g., land cover, climate types, terrain and its complexity, soil texture, vegetation coverage, soil moisture, and its dynamics). We also adopted SMAP enhanced TB to evaluate the performance of the disaggregated TB at the same grid resolution of 9 km. The results reveal there is a generally good linear relationship between σ⁰ (no matter in dB or in linear unit) and TB at a global scale. There is no significant difference in the correlation among the four polarization combinations ( $σ ⁰_{hh}$ versus TB $_{h}$ , $σ⁰_{hh}$ versus TB $_{v}$ , $σ⁰_{vv}$ versus TB $_{h}$ , and $σ⁰_{vv}$ versus TB $_{v}$ ) with the $σ⁰_{vv}$ and TB $_{h}$ combination displaying an overall slightly higher correlation. The linear relationship between σ⁰ and TB is significantly affected by environmental factors. Particularly in bare soils and densely vegetated areas (e.g., large forest fraction and vegetation coverage), and arid and polar climate zones, the linear correlation between active and passive measurements worsens, whereas it is favorable in moderate vegetation and soil moisture as well as large soil moisture dynamic conditions. Interestingly, the linear correlation generally decreases as sand content increases while increases with the increase of clay content. The absolute linear correlation coefficient is higher with larger soil moisture dynamics. When compared to SMAP enhanced TB, it shows the linear assumption may have more influence on the correlation (i.e., temporal evolution) of downscaled TB than its absolute accuracy. These findings can enhance the understanding of the geophysical relationship between radar and radiometer signatures, and thus benefit active-passive joint algorithms for future satellite missions.