Surface Soil Moisture Monitoring by Remote Sensing: Applications to Ecosystem Processes and Scale Effects

2013 ◽  
pp. 323-348
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Surface Soil ◽  
Scale Effects ◽  
Ecosystem Processes ◽  
Surface Soil Moisture ◽  
Sensing Applications ◽  
Remote Sensing Applications ◽  
Soil Moisture Monitoring

Integrative soil moisture monitoring in Switzerland for a better preparedness for projected drying trends

2021 ◽  
Author(s):  
Dominik Michel ◽  
Martin Hirschi ◽  
Sonia I. Seneviratne
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Real Time ◽  
Surface Soil ◽  
Microwave Remote Sensing ◽  
Data Availability ◽  
Climate Projections ◽  
Surface Soil Moisture ◽  
Soil Moisture Monitoring

<p>Climate projections indicate an increasing risk of dry and hot episodes in Central Europe, including in Switzerland. However, models display a large spread in projections of changes in summer drying, highlighting the importance of related observations to evaluate climate models and constrain projections. Land hydrological variables play an essential role for these projections. This is particularly the case for soil moisture and land evaporation, which are directly affecting the development of droughts and heatwaves in both present and future.</p><p>The recent 2020 spring as well as 2015 and 2018 summer droughts in Switzerland have highlighted the importance of monitoring and assessing changes of soil moisture and land evaporation, which are strongly related to drought impacts on agriculture, forestry, and ecosystems. The country was affected by major drought and heatwave conditions in 2015 and 2018. While the meteorological conditions started to recover at the end of the summer, the soil moisture conditions (and runoff) continued to be anomalously low for most of the fall. This illustrates the decoupling between meteorological drought and soil moisture drought conditions related to the intrinsic memory of the soil.</p><p>The only Switzerland-wide soil moisture monitoring programme currently in place is the SwissSMEX (Swiss Soil Moisture Experiment) measurement network. It was initiated in 2008 and comprises 19 soil moisture measurement profiles at 17 different sites (grassland, forest and arable land). Since 2017, seven grassland SwissSMEX sites were complemented with land evaporation measurements from mini-lysimeters.</p><p>First, a quality assessment and inter-comparison of the in-situ soil moisture and land evaporation observations at 12 grassland sites revealed substantial discrepancies between different sensor types in terms of absolute values and data availability. A standard procedure for processing and interpreting the SwissSMEX data is thus being established. Second, analyses have been carried out comparing the SwissSMEX measurements with gridded remote-sensing and reanalysis products that provide near real time soil moisture data. In particular, the European Space Agency (ESA) Climate Change Initiative (CCI) surface soil moisture product (ESA-CCI soil moisture) as well as the new ECMWF reanalysis ERA5 are considered. The seasonal evolution of the soil moisture anomalies (with respect to the long-term mean) show for 2020 two pronounced phases of dryness. These are consistently represented in SwissSMEX in-situ observations and ERA5. Also the other recent drought events of 2015 and 2018 show a similar temporal evolution in both datasets. The response of ESA-CCI surface soil moisture is less pronounced, more variable and also dependent on the measurement methodology, i.e., active or passive microwave remote sensing.</p><p>These first analyses provide useful insights in order to provide near-real time monitoring, enhance process understanding at the national scale and a better preparedness for future droughts.</p>

scholarly journals Estimating annual water storage variations in medium-scale (2000–10 000 km<sup>2</sup>) basins using microwave-based soil moisture retrievals

2017 ◽  
Vol 21 (3) ◽  
pp. 1849-1862 ◽  
Author(s):  
Wade T. Crow ◽  
Eunjin Han ◽  
Dongryeol Ryu ◽  
Christopher R. Hain ◽  
Martha C. Anderson
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Water Budget ◽  
Surface Soil ◽  
Water Storage ◽  
Microwave Remote Sensing ◽  
The United States ◽  
Surface Soil Moisture ◽  
Medium Scale ◽  
Annual Water

Abstract. Due to their shallow vertical support, remotely sensed surface soil moisture retrievals are commonly regarded as being of limited value for water budget applications requiring the characterization of temporal variations in total terrestrial water storage (dS ∕ dt). However, advances in our ability to estimate evapotranspiration remotely now allow for the direct evaluation of approaches for quantifying dS ∕ dt via water budget closure considerations. By applying an annual water budget analysis within a series of medium-scale (2000–10 000 km2) basins within the United States, we demonstrate that, despite their clear theoretical limitations, surface soil moisture retrievals derived from passive microwave remote sensing contain statistically significant information concerning dS ∕ dt. This suggests the possibility of using (relatively) higher-resolution microwave remote sensing products to enhance the spatial resolution of dS ∕ dt estimates acquired from gravity remote sensing.

scholarly journals Establishing an Empirical Model for Surface Soil Moisture Retrieval at the U.S. Climate Reference Network Using Sentinel-1 Backscatter and Ancillary Data

2020 ◽  
Vol 12 (8) ◽  
pp. 1242 ◽  
Author(s):  
Sumanta Chatterjee ◽  
Jingyi Huang ◽  
Alfred E. Hartemink
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Soil Properties ◽  
Soil Water ◽  
Surface Soil ◽  
Reference Network ◽  
Surface Soil Moisture ◽  
Ancillary Data ◽  
Terrain Parameters ◽  
The U.S

Progress in sensor technologies has allowed real-time monitoring of soil water. It is a challenge to model soil water content based on remote sensing data. Here, we retrieved and modeled surface soil moisture (SSM) at the U.S. Climate Reference Network (USCRN) stations using Sentinel-1 backscatter data from 2016 to 2018 and ancillary data. Empirical machine learning models were established between soil water content measured at the USCRN stations with Sentinel-1 data from 2016 to 2017, the National Land Cover Dataset, terrain parameters, and Polaris soil data, and were evaluated in 2018 at the same USCRN stations. The Cubist model performed better than the multiple linear regression (MLR) and Random Forest (RF) model (R2 = 0.68 and RMSE = 0.06 m3 m-3 for validation). The Cubist model performed best in Shrub/Scrub, followed by Herbaceous and Cultivated Crops but poorly in Hay/Pasture. The success of SSM retrieval was mostly attributed to soil properties, followed by Sentinel-1 backscatter data, terrain parameters, and land cover. The approach shows the potential for retrieving SSM using Sentinel-1 data in a combination of high-resolution ancillary data across the conterminous United States (CONUS). Future work is required to improve the model performance by including more SSM network measurements, assimilating Sentinel-1 data with other microwave, optical and thermal remote sensing products. There is also a need to improve the spatial resolution and accuracy of land surface parameter products (e.g., soil properties and terrain parameters) at the regional and global scales.

scholarly journals Machine Learning to Estimate Surface Soil Moisture from Remote Sensing Data

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3223
Author(s):  
Hamed Adab ◽  
Renato Morbidelli ◽  
Carla Saltalippi ◽  
Mahmoud Moradian ◽  
Gholam Abbas Fallah Ghalhari
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Soil Moisture ◽  
Surface Soil ◽  
Remote Sensing Data ◽  
Surface Soil Moisture ◽  
Machine Learning Methods ◽  
Land Use Types ◽  
Semi Arid Region ◽  
Semi Arid

Soil moisture is an integral quantity parameter in hydrology and agriculture practices. Satellite remote sensing has been widely applied to estimate surface soil moisture. However, it is still a challenge to retrieve surface soil moisture content (SMC) data in the heterogeneous catchment at high spatial resolution. Therefore, it is necessary to improve the retrieval of SMC from remote sensing data, which is important in the planning and efficient use of land resources. Many methods based on satellite-derived vegetation indices have already been developed to estimate SMC in various climatic and geographic conditions. Soil moisture retrievals were performed using statistical and machine learning methods as well as physical modeling techniques. In this study, an important experiment of soil moisture retrieval for investigating the capability of the machine learning methods was conducted in the early spring season in a semi-arid region of Iran. We applied random forest (RF), support vector machine (SVM), artificial neural network (ANN), and elastic net regression (EN) algorithms to soil moisture retrieval by optical and thermal sensors of Landsat 8 and knowledge of land-use types on previously untested conditions in a semi-arid region of Iran. The statistical comparisons show that RF method provided the highest Nash–Sutcliffe efficiency value (0.73) for soil moisture retrieval covered by the different land-use types. Combinations of surface reflectance and auxiliary geospatial data can provide more valuable information for SMC estimation, which shows promise for precision agriculture applications.

Spatiotemporal monitoring of surface soil moisture using optical remote sensing data: a case study in a semi-arid area

2018 ◽  
Vol 65 (3) ◽  
pp. 481-499 ◽  
Author(s):  
Rida Khellouk ◽  
Ahmed Barakat ◽  
Abdelghani Boudhar ◽  
Rachid Hadria ◽  
Hayat Lionboui ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Surface Soil ◽  
Remote Sensing Data ◽  
Arid Area ◽  
Optical Remote Sensing ◽  
Surface Soil Moisture ◽  
Sensing Data ◽  
Semi Arid

scholarly journals Remote Sensing Monitoring of Soil Moisture in the Daliuta Coal Mine Based on SPOT 5/6 and Worldview-2

2019 ◽  
Vol 11 (1) ◽  
pp. 866-876
Author(s):  
Ying Liu ◽  
Hui Yue
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Scale Effect ◽  
Coal Mine ◽  
Surface Soil ◽  
Underground Mining ◽  
Mining Area ◽  
Surface Soil Moisture ◽  
Temporal And Spatial ◽  
Spot 5

Abstract To understand the influence of underground mining disturbances on the shallow soil moisture in the Daliuta coal mine, remote sensing monitoring of the temporal and spatial evolution of surface soil moisture and the influence of mining on multi-source, multi-temporal and high spatial resolution remote sensing data were carried out. The scale effect of monitoring the soil moisture at different scales was analyzed using the Scaled Soil Moisture Monitor Index (S-SMMI). In this paper, SPOT 5/6 and Worldview-2 were used as the data source and mainly made up two aspects of the research: 1) based on the three SPOT data sets with the use of S-SMMI from different angles from the Daliuta mine from nearly three years of soil moisture temporal and spatial changes, the results show that the perturbation has a negative effect on the shallow soil moisture in the Daliuta coal mine, and average soil moisture of the mining area is smaller than the non-mining area, but the surface ecological construction has effectively improved the impact of the underground mining disturbance on the surface soil moisture. 2) the scale conversion of Worldview-2 data was carried out based on the resampling method. S-SMMI was used to analyze the scale effect of soil moisture monitoring at different scales. The results show that the difference between the soil moisture is only 0.0016 during the conversion process of 2 m-30 m.

scholarly journals The spatiotemporal patterns of surface soil moisture in Northeast China based on remote sensing products

2016 ◽  
Vol 7 (4) ◽  
pp. 708-720 ◽  
Author(s):  
Xingming Zheng ◽  
Kai Zhao ◽  
Yanling Ding ◽  
Tao Jiang ◽  
Shiyi Zhang ◽  
...  
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Soil Moisture ◽  
Northeast China ◽  
Surface Soil ◽  
Temporal Dynamics ◽  
Remote Sensing Data ◽  
Surface Soil Moisture ◽  
The Past ◽  
Warming Rate

Northeast China (NEC) has become one of China's most obvious examples of climate change because of its rising warming rate of 0.35 °C/10 years. As the indicator of climate change, the dynamic of surface soil moisture (SSM) has not been assessed yet. We investigated the spatiotemporal dynamics of SSM in NEC using a 32-year SSM product and found the following. (1) SSM displayed the characteristics of being dry in the west and wet in the east and decreased with time. (2) The seasonal difference was found for the temporal dynamics of SSM: it increased in summer and decreased in spring and autumn. (3) For all four regions studied, the temporal dynamics of SSM were similar to those of the whole of NEC, but with different rates of SSM change. Moreover, SSM in regions B and D had a lower spatial variance than the other two regions because of the stable spatial pattern of cropland. (4) The change rates for SSM were consistent with that observed for the warming rates, which indicated that SSM levels derived from remote sensing data will correlate with climate change. In summary, a wetter summer and a drier spring and autumn were observed in NEC over the past 30 years.

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