Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at 100 m Resolution

Gao, Qi; Zribi, Mehrez; Escorihuela, María José; Baghdadi, Nicolas

doi:10.3390/s17091966

Cited by 226 publications

(159 citation statements)

References 81 publications

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“…) and Sentinel‐1 (Gao et al . ). In parallel to these developments of observational capabilities, Reichle and Koster () pioneered the assimilation of microwave measurements into land surface models, demonstrating a positive impact on the soil moisture analysis.…”

Section: Introductionmentioning

confidence: 97%

Assimilation of SMOS brightness temperatures in the ECMWF Integrated Forecasting System

Muñoz-Sabater

Lawrence

Albergel

et al. 2019

Quart J Royal Meteoro Soc

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The assimilation of Soil Moisture and Ocean Salinity (SMOS) brightness temperature ( B ) data in numerical weather prediction systems influences the state of the soil, which in turn affects the exchange of energy and water fluxes between the soil and the near-surface atmosphere, with potential implications for the prediction of atmospheric variables. In this paper, the impact of assimilating SMOS B alone or in combination with screen-level observations and Advanced Scatterometer (ASCAT) soil moisture retrievals is assessed. Independent quality controlled soil moisture observations belonging to several networks included in the International Soil Moisture Network, were used to validate the quality of both the new soil moisture analyses and the skill to predict soil moisture up to 5 days ahead. The impact on atmospheric variables is indirect and was evaluated through computation of the forecast skill at different lead times. The analysis period was selected to be around the boreal summer, a period of the year when evaporatranspiration fluxes are stronger, and when it is therefore expected that the assimilation of remote-sensing data provides the largest impact on the state of the soil. The results show that the soil moisture state benefits from the direct assimilation of SMOS B , especially in better representing the temporal variations of soil moisture. The skill of atmospheric variables is mainly driven by the screen-level observations. Despite the clear benefits to the soil state, remote-sensing data need to be used with screen-level variables to add value to the state of the atmosphere, pointing to inconsistencies in the physical coupling between the land and near-surface components of the ECMWF Earth system.

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Section: Introductionmentioning

confidence: 97%

Assimilation of SMOS brightness temperatures in the ECMWF Integrated Forecasting System

Muñoz-Sabater

Lawrence

Albergel

et al. 2019

Quart J Royal Meteoro Soc

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“…Synthetic aperture radar (SAR) and optic data fusion is one of the most widely used approaches [28][29][30][31]. The integration of data acquired by optical sensors and SAR data may provide useful information for reducing ambiguity due to the presence of vegetation.…”

Section: Introductionmentioning

confidence: 99%

Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at Plot Scale

et al. 2018

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This paper presents an approach for retrieval of soil moisture content (SMC) by coupling single polarization C-band synthetic aperture radar (SAR) and optical data at the plot scale in vegetated areas. The study was carried out at five different sites with dominant vegetation cover located in Kenya. In the initial stage of the process, different features are extracted from single polarization mode (VV polarization) SAR and optical data. Subsequently, proper selection of the relevant features is conducted on the extracted features. An advanced state-of-the-art machine learning regression approach, the support vector regression (SVR) technique, is used to retrieve soil moisture. This paper takes a new look at soil moisture retrieval in vegetated areas considering the needs of practical applications. In this context, we tried to work at the object level instead of the pixel level. Accordingly, a group of pixels (an image object) represents the reality of the land cover at the plot scale. Three approaches, a pixel-based approach, an object-based approach, and a combination of pixel-and object-based approaches, were used to estimate soil moisture. The results show that the combined approach outperforms the other approaches in terms of estimation accuracy (4.94% and 0.89 compared to 6.41% and 0.62 in terms of root mean square error (RMSE) and R 2 ), flexibility on retrieving the level of soil moisture, and better quality of visual representation of the SMC map.

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“…The Sentinel-2 twin satellites can provide various information on vegetation, soil, inland river and coastal environment [44][45][46]. The twin satellites are in the same orbit, phased at 180 • to each other.…”

Section: Pre-processingmentioning

confidence: 99%

Continuous Monitoring of Cotton Stem Water Potential using Sentinel-2 Imagery

et al. 2020

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Monitoring cotton status during the growing season is critical in increasing production efficiency. The water status in cotton is a key factor for yield and cotton quality. Stem water potential (SWP) is a precise indicator for assessing cotton water status. Satellite remote sensing is an effective approach for monitoring cotton growth at a large scale. The aim of this study is to estimate cotton water stress at a high temporal frequency and at a large scale. In this study, we measured midday SWP samples according to the acquisition dates of Sentinel-2 images and used them to build linear-regression-based and machine-learning-based models to estimate cotton water stress during the growing season (June to August, 2018). For the linear-regression-based method, we estimated SWP based on different Sentinel-2 spectral bands and vegetation indices, where the normalized difference index 45 (NDI45) achieved the best performance (R 2 = 0.6269; RMSE = 3.6802 (-1*swp (bars))). For the machine-learning-based method, we used random forest regression to estimate SWP and received even better results (R 2 = 0.6709; RMSE = 3.3742 (-1*swp (bars))). To find the best selection of input variables for the machine-learning-based approach, we tried three different data input datasets, including (1) 9 original spectral bands (e.g., blue, green, red, red edge, near infrared (NIR), and shortwave infrared (SWIR)), (2) 21 vegetation indices, and (3) a combination of original Sentinel-2 spectral bands and vegetation indices. The highest accuracy was achieved when only the original spectral bands were used. We also found the SWIR and red edge band were the most important spectral bands, and the vegetation indices based on red edge and NIR bands were particularly helpful. Finally, we applied the best approach for the linear-regression-based and the machine-learning-based methods to generate cotton water potential maps at a large scale and high temporal frequency. Results suggests that the methods developed here has the potential for continuous monitoring of SWP at large scales and the machine-learning-based method is preferred.have devastating impacts on the environment [3]. For agriculture, drought can endanger food security which can have critical effects on the economy, geopolitics, and society [5]. Drought can also negatively affect agriculture production especially in arid and semiarid regions [6]. Therefore, detecting crop water stress is of vital importance to improve agricultural quality and productivity.Cotton is cultivated in arid and semiarid regions and is the most important fiber crop in the world [7]. Cotton is susceptible to water stress in all growth stages, particularly during the flowering and boll development stages [8]. Moreover, water deficit is an important factor limiting growth and development in cotton, including plant height, leaf and root size, as well as economic yield and fiber quality [8]. To better understand the cotton's response and adaption to water stress, continuous monitoring of cotton water status in large a...

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Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at 100 m Resolution

Cited by 226 publications

References 81 publications

Assimilation of SMOS brightness temperatures in the ECMWF Integrated Forecasting System

Assimilation of SMOS brightness temperatures in the ECMWF Integrated Forecasting System

Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at Plot Scale

Continuous Monitoring of Cotton Stem Water Potential using Sentinel-2 Imagery

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