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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >Uncertainty in Soil Moisture Retrievals Using the SMAP Combined Active–Passive Algorithm for Growing Sweet Corn
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Uncertainty in Soil Moisture Retrievals Using the SMAP Combined Active–Passive Algorithm for Growing Sweet Corn

机译：使用SMAP组合式主动-被动算法生长甜玉米的土壤水分获取的不确定性

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摘要

The baseline active and passive (AP) algorithm of the NASA Soil Moisture Active Passive (SMAP) mission disaggregates the brightness temperature (${T}_text{B}$) from a spatial resolution of 36 km to 9 km for the soil moisture (SM) using the radar backscattering coefficient ( $sigma ^0$) at 3 km. This algorithm was derived based upon an assumption of a linear relationship between ${T}_text{B}$ and $sigma ^0$. In this study, we investigated the robustness of this assumption with plot-scale AP measurements obtained under different conditions of surface roughness and stages of growing sweet corn. The uncertainties in the estimated ${T}_text{B}$ at 9 km and, hence, the retrieved SM, due to uncertainties in the algorithm parameters, $beta$ and $Gamma$, were assessed under different landcover heterogeneities. Overall, the linear regression was robust, with $r^2$ $>$ 0.75 under bare soil conditions when surface scattering is dominant and $>$0.52 during the growing season. The uncertainties in $beta$ and $Gamma$ due to AP observations result in uncertainties in retrieved SM $<$ 0.04 $text{m}^3/text{m}^3$ for most conditions of heterogeneity. The differences in ${T}_text{B}$ at 9 km, obtained when using $beta$ derived from vegetation water content (VWC) and using those from radar vegetation index, were also assessed. The errors in retrieved SM could reach as high as 0.5 $text{m}^3/text{m}^3$ for the worst-case scenario, when an intermediate scale contains high VWC, but the coarse scale region has low averaged VWC. These results suggest that determination of growth stages using a biophysical parameter is essential for $beta$ estimations, particularly for highly heterogeneous landcovers.
机译：NASA土壤水分主动被动（SMAP）任务的基线主动和被动（AP）算法可将亮度温度（$ {T} _text {B} $）从36 km的空间分辨率分解为9 km的土壤湿度（ SM）使用3公里处的雷达后向散射系数（sigma ^ 0 $）。该算法基于$ {T} _text {B} $与$ sigma ^ 0 $之间的线性关系的假设而得出。在这项研究中，我们通过在不同表面粗糙度和甜玉米生长阶段的不同条件下获得的样地比例AP测量研究了该假设的鲁棒性。在9 km处估计的$ {T} _text {B} $中的不确定性，以及由于算法参数$ beta $和$ Gamma $的不确定性而导致的检索到的SM的不确定性，是根据不同的土地覆盖异质性进行评估的。总体而言，线性回归是稳健的，在裸露土壤条件下，当表面散射占主导时，r ^ 2 $ $> $ 0.75，而在生长季节中，r> 2 $ 0.52。由于AP观察而导致的$ beta $和$ Gamma $的不确定性导致大多数异质性条件下检索到的SM $ <$ 0.04 $ text {m} ^ 3 / text {m} ^ 3 $的不确定性。还评估了使用来自植被含水量（VWC）的$ beta $和使用雷达植被指数的$ beta $时在9 km处的$ {T} _text {B} $的差异。在最坏的情况下，当中间规模包含较高的VWC时，但在较粗规模的区域中，平均VWC较低，因此检索到的SM的错误可能高达0.5 $ text {m} ^ 3 / text {m} ^ 3 $ 。这些结果表明，使用生物物理参数确定生长阶段对于$β$估算至关重要，特别是对于高度异质的土地覆盖而言。

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