Remote sensing: the use of polarized visible light (PVL) to estimate surface soil moisture

1981 ◽  
Vol 1 (1) ◽  
pp. 41-53 ◽  
Author(s):  
Paul J. Curran
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Visible Light ◽  
Surface Soil ◽  
Surface Soil Moisture

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.

scholarly journals An improved global remote-sensing-based surface soil moisture (RSSSM) dataset covering 2003–2018

2021 ◽  
Vol 13 (1) ◽  
pp. 1-31
Author(s):  
Yongzhe Chen ◽  
Xiaoming Feng ◽  
Bojie Fu
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Soil Moisture ◽  
Data Fusion ◽  
Surface Soil ◽  
Microwave Remote Sensing ◽  
Satellite Monitoring ◽  
Surface Soil Moisture ◽  
Spatial Coverage

Abstract. Soil moisture is an important variable linking the atmosphere and terrestrial ecosystems. However, long-term satellite monitoring of surface soil moisture at the global scale needs improvement. In this study, we conducted data calibration and data fusion of 11 well-acknowledged microwave remote-sensing soil moisture products since 2003 through a neural network approach, with Soil Moisture Active Passive (SMAP) soil moisture data applied as the primary training target. The training efficiency was high (R2=0.95) due to the selection of nine quality impact factors of microwave soil moisture products and the complicated organizational structure of multiple neural networks (five rounds of iterative simulations, eight substeps, 67 independent neural networks, and more than 1 million localized subnetworks). Then, we developed the global remote-sensing-based surface soil moisture dataset (RSSSM) covering 2003–2018 at 0.1∘ resolution. The temporal resolution is approximately 10 d, meaning that three data records are obtained within a month, for days 1–10, 11–20, and from the 21st to the last day of that month. RSSSM is proven comparable to the in situ surface soil moisture measurements of the International Soil Moisture Network sites (overall R2 and RMSE values of 0.42 and 0.087 m3 m−3), while the overall R2 and RMSE values for the existing popular similar products are usually within the ranges of 0.31–0.41 and 0.095–0.142 m3 m−3), respectively. RSSSM generally presents advantages over other products in arid and relatively cold areas, which is probably because of the difficulty in simulating the impacts of thawing and transient precipitation on soil moisture, and during the growing seasons. Moreover, the persistent high quality during 2003–2018 as well as the complete spatial coverage ensure the applicability of RSSSM to studies on both the spatial and temporal patterns (e.g. long-term trend). RSSSM data suggest an increase in the global mean surface soil moisture. Moreover, without considering the deserts and rainforests, the surface soil moisture loss on consecutive rainless days is highest in summer over the low latitudes (30∘ S–30∘ N) but mostly in winter over the mid-latitudes (30–60∘ N, 30–60∘ S). Notably, the error propagation is well controlled with the extension of the simulation period to the past, indicating that the data fusion algorithm proposed here will be more meaningful in the future when more advanced microwave sensors become operational. RSSSM data can be accessed at https://doi.org/10.1594/PANGAEA.912597 (Chen, 2020).

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