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Use of reflected GNSS SNR data to retrieve either soil moisture or vegetation height from a wheat crop

机译:使用反射的GNSS SNR数据从小麦作物中检索土壤水分或植被高度

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This work aims to estimate soil moisture and vegetation height from Global Navigation Satellite System?(GNSS) Signal to Noise Ratio?(SNR) data using direct and reflected signals by the land surface surrounding a ground-based antenna. Observations are collected from a rainfed wheat field in southwestern France. Surface soil moisture is retrieved based on SNR phases estimated by the Least Square Estimation method, assuming the relative antenna height is constant. It is found that vegetation growth breaks up the constant relative antenna height assumption. A vegetation-height retrieval algorithm is proposed using the SNR-dominant period (the peak period in the average power spectrum derived from a wavelet analysis of SNR). Soil moisture and vegetation height are retrieved at different time periods (before and after vegetation's significant growth in March). The retrievals are compared with two independent reference data sets: in?situ observations of soil moisture and vegetation height, and numerical simulations of soil moisture, vegetation height and above-ground dry biomass from the ISBA (interactions between soil, biosphere and atmosphere) land surface model. Results show that changes in soil moisture mainly affect the multipath phase of the SNR data (assuming the relative antenna height is constant) with little change in the dominant period of the SNR data, whereas changes in vegetation height are more likely to modulate the SNR-dominant period. Surface volumetric soil moisture can be estimated (R2??=??0.74, RMSE??=??0.009?m3?m?3) when the wheat is smaller than one wavelength (~19?cm). The quality of the estimates markedly decreases when the vegetation height increases. This is because the reflected GNSS signal is less affected by the soil. When vegetation replaces soil as the dominant reflecting surface, a wavelet analysis provides an accurate estimation of the wheat crop height (R2??=??0.98, RMSE??=??6.2?cm). The latter correlates with modeled above-ground dry biomass of the wheat from stem elongation to ripening. It is found that the vegetation height retrievals are sensitive to changes in plant height of at least one wavelength. A simple smoothing of the retrieved plant height allows an excellent matching to in?situ observations, and to modeled above-ground dry biomass.
机译:这项工作旨在从全球导航卫星系统估算土壤湿度和植被高度?(GNSS)信噪比?(SNR)数据使用围绕地面的地面的直接和反射信号围绕地面的天线。从法国西南部的雨水田地收集观察。基于由最小二乘估计方法估计的SNR相检测表面土壤水分,假设相对天线高度是恒定的。发现植被生长破坏了恒定的相对天线高度假设。使用SNR主导时段(源自SNR小波分析的平均功率谱中的峰值周期)提出了植被高度检索算法。在不同的时间段(植被在3月份的显着增长之前和之后)检索土壤水分和植被高度。将检索与两个独立的参考数据集进行比较:IN?原位观察土壤水分和植被高度,以及来自ISBA(土壤,生物圈和大气之间的相互作用的土壤水分,植被高度和地上干生物量的数值模拟表面模型。结果表明,土壤湿度的变化主要影响SNR数据的多径相位(假设相对天线高度是恒定的),SNR数据的主导时期几乎没有变化,而植被高度的变化更可能调制SNR-主导期。可以估计表面积体积水分(R2 ?? = ?? 0.74,RMSE ?? = 0.009?M3?m≤3)当小麦小于一个波长(〜19℃)时。当植被高度增加时,估计的质量显着降低。这是因为反射的GNSS信号受土壤的影响较小。当植被取代土壤作为主导反射表面时,小波分析提供了小麦作物高度的准确估计(R2 ?? = ?? 0.98,RMSE ?? = ?? 6.2?cm)。后者与来自茎伸长率的小麦的上面的地上干生物质模型相关。结果发现,植被高度检索对至少一个波长的植物高度的变化敏感。检索到的植物高度的简单平滑允许在原位观察中具有出色的匹配,并建模地上干生物质。

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