Department of Geography and Environmental Studies, Arsi University, Arsi, Ethiopia.
Department of Geography and Environmental Studies, Debre Markos University, Debre Markos, Ethiopia.
PLoS One. 2023 Jan 13;18(1):e0279895. doi: 10.1371/journal.pone.0279895. eCollection 2023.
Microwave remote sensing instrument like Soil Moisture Active Passive ranging from 1 cm to 1 m has provided spatial soil moisture information over the entire globe. However, Soil Moisture Active Passive satellite soil moisture products have a coarse spatial resolution (36km x 36km), limiting its application at the basin scale. This research, subsequently plans to; (1) Evaluate the capability of SAR for the retrieval of surface roughness variables in the Awash River basin; (2) Measure the performance of Random Forest (RF) regression model to downscale SMAP satellite soil moisture over the Awash River basin; (3) validate downscaled soil moisture data with In-situ measurements in the river basin. Random Forest (RF) based downscaling approach was applied to downscale satellite-based soil moisture product (36km x 36km) to fine resolution (1km x 1km). Fine spatial resolution (1km) soil moisture data for the Awash River basin was generated. The downscaled soil moisture product also has a strong spatial correlation with the original one, allowing it to deliver more soil moisture information than the original one. In-situ soil moisture and downscaled soil moisture had a 0.69 Pearson correlation value, compared to a 0.53 correlation between the original and In-situ soil moisture. In-situ soil moisture measurements were obtained from the Middle and Upper Awash sub-basins for validation purposes. In the case of Upper Awash, downscaled soil moisture shows a variation of 0.07 cm3 /cm3, -0.036 cm3 /cm3, and 0.112 cm3 /cm3 with Root Mean Square Error, Bias error, and Unbiased Root Mean Square Error respectively. Following that, the accuracy of downscaled soil moisture against the Middle Awash Sub-basin reveals a variance of 0.1320 cm3 /cm3, -0.033 cm3 /cm3, and 0.148 cm3 /cm3 with Root Mean Square Error, Bias error, and Unbiased Root Mean Square Error respectively. Future studies should take into account the temporal domain of Soil Moisture Active Passive satellite soil moisture product downscaling over the study region.
从 1 厘米到 1 米的微波遥感仪器（如土壤水分主动被动）已经在全球范围内提供了空间土壤水分信息。然而，土壤水分主动被动卫星土壤水分产品的空间分辨率较粗（36km x 36km），限制了其在流域尺度上的应用。本研究随后计划：（1）评估 SAR 对 Awash 河流域地表粗糙度变量反演的能力；（2）衡量随机森林（RF）回归模型对 Awash 河流域 SMAP 卫星土壤水分降尺度的性能；（3）在流域内用原位测量验证降尺度土壤水分数据。随机森林（RF）降尺度方法被应用于降尺度卫星土壤水分产品（36km x 36km）到细分辨率（1km x 1km）。生成了 Awash 河流域的细空间分辨率（1km）土壤水分数据。降尺度土壤水分产品与原始产品具有很强的空间相关性，因此比原始产品提供了更多的土壤水分信息。与原始土壤水分相比，原位土壤水分和降尺度土壤水分的 Pearson 相关系数为 0.69，而原始和原位土壤水分的相关性为 0.53。为了验证目的，从中上游 Awash 子流域获得了原位土壤水分测量值。在上游 Awash 地区，降尺度土壤水分的均方根误差、偏差误差和无偏均方根误差分别为 0.07 cm3 /cm3、-0.036 cm3 /cm3 和 0.112 cm3 /cm3。随后，对 Middle Awash 子流域的降尺度土壤水分精度的准确性揭示了方差分别为 0.1320 cm3 /cm3、-0.033 cm3 /cm3 和 0.148 cm3 /cm3，与均方根误差、偏差误差和无偏均方根误差有关。未来的研究应考虑研究区域土壤水分主动被动卫星土壤水分产品降尺度的时间域。
