scholarly journals Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution over Agricultural Areas

2017 ◽  
Vol 9 (12) ◽  
pp. 1292 ◽  
Author(s):  
Mohammad El Hajj ◽  
Nicolas Baghdadi ◽  
Mehrez Zribi ◽  
Hassan Bazzi
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Agricultural Areas ◽  
Sentinel 2

Combined use of Sentinel-2 images and Sentinel-1-derived moisture maps for soil organic carbon content mapping in croplands, South-western France

2021 ◽  
Author(s):  
Diego Urbina Salazar ◽  
Emmanuelle Vaudour ◽  
Nicolas Baghdadi ◽  
Eric Ceschia ◽  
Dominique Arrouays
Keyword(s):  
Soil Moisture ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Carbon Content ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Soil Sampling ◽  
Organic Carbon Content ◽  
Spectral Models ◽  
Sentinel 2

<p>In terms of agronomy, soil organic carbon (SOC) content is important for crop growth and development. From the environmental viewpoint, SOC sequestration is essential to mitigate the emission of greenhouse gases into the atmosphere. The use of sensors for carbon monitoring over croplands is a key issue in recent works. Sentinel-1/2 (S1, S2) satellites acquire data with regular frequency (weekly) and high spatial resolution (10 and 20 meters). Previous studies have demonstrated their potential for quantification of soil attributes including topsoil organic carbon content on single dates. Soil surface roughness and soil moisture influence the performance of spectral models according to acquisition date, particularly surface soil moisture (SM), as shown by multidate models of predicted SOC content (Vaudour et al., 2021). Still, the sensitivity of Sentinel-1/2 to SM must be better understood and exploited for a given single date. A possible solution to determine the influence of SM on single date model performance consists of including it as a covariate.</p><p>In order to predict the topsoil SOC content over croplands in the Pyrenees region, France (22177 km²), this study addresses: (i) the influence of the Sentinel image date and that of the soil sampling year; (ii) the contribution of SM products derived from the Sentinel-1/2 data (El Hajj et al., 2017) in the spectral models.</p><p>The influence of the image date and soil sampling date was analyzed for springs 2017 and 2018. Clouds, shadows and NDVI (> 0.35) values were excluded from the images. Best single performances (RPD ≥ 1.3) were scored for soil sampling sets collected in 2016-2018. The same dates were analyzed using either SM maps, or signal values of VV and VH polarizations from S1 images. SM or polarization values were extracted for each sample and integrated into the partial least squares regression (PLSR) models, respectively. The best performance (RPD = 1.57) was obtained with SM as a covariate in 2017, with lowest mean SM throughout the map.</p><p> </p><p>References</p><p>El Hajj, M.; Baghdadi, N.; Zribi, M.; Bazzi, H. Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution over Agricultural Areas. Remote Sensing <strong>2017</strong>, 9, 1292, doi:10.3390/rs9121292.</p><p>Vaudour, E.; Gomez, C.; Lagacherie, P.; Loiseau, T.; Baghdadi, N.; Urbina-Salazar, D.; Loubet, B.; Arrouays, D. Temporal Mosaicking Approaches of Sentinel-2 Images for Extending Topsoil Organic Carbon Content Mapping in Croplands. International Journal of Applied Earth Observation and Geoinformation <strong>2021</strong>, 96, 102277, doi:10.1016/j.jag.2020.102277.</p>

Generation of spatially complete and daily continuous surface soil moisture of high spatial resolution

2019 ◽  
Vol 233 ◽  
pp. 111364 ◽  
Author(s):  
Di Long ◽  
Liangliang Bai ◽  
La Yan ◽  
Caijin Zhang ◽  
Wenting Yang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Surface Soil ◽  
High Spatial Resolution ◽  
Surface Soil Moisture ◽  
Continuous Surface

Fusion of Sentinel and PlanetScope images for high resolution soil moisture mapping: Algorithm and preliminary validation

2020 ◽  
Author(s):  
Mateo Gašparović ◽  
Sudhir Kumar Singh
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Spatial Resolution ◽  
Land Degradation ◽  
New Technologies ◽  
Critical Role ◽  
Soil Parameters ◽  
Area Index ◽  
Ground Truth Data ◽  
Sentinel 2

<p>High resolution remote sensing images plays a critical role in detection and monitoring of land degradation and development. Monitoring the soil parameters represents great importance for sustainable development and agriculture, as well as smart food production. The space segment component of the Copernicus programme (e.g., Sentinel-1, Sentinel-2 etc.) enables continuously monitoring of Earth’s surface at 10-m spatial resolution. New technologies, development, and minimization of sensors led to the development of micro-satellites (e.g., PlanetScope). These satellites allow us to monitor the Earth's surface daily in 3-m spatial resolution. The developed algorithm for soil moisture mapping is based on the fusion of Sentinel-2 and PlanetScope images. This allows a soil moisture mapping in high spatial resolution. Soil moisture was estimated based on the Leaf Area Index (LAI) and Enhanced Vegetation Index (EVI) using modified water cloud model. Ground-truth data were collected from 15 stations of the International Soil Moisture Network across the globe and used for mapping and validation of soil moisture. The developed algorithm provides a new knowledge that can be widely applied in various research for the detection and monitoring of land degradation and development.</p>

Application of DNDC biogeochemistry model to estimate greenhouse gas emissions from Italian agricultural areas at high spatial resolution

2010 ◽  
Vol 139 (4) ◽  
pp. 546-556 ◽  
Author(s):  
Emanuele Lugato ◽  
Michel Zuliani ◽  
Giorgio Alberti ◽  
Gemini Delle Vedove ◽  
Beniamino Gioli ◽  
...  
Keyword(s):  
Greenhouse Gas Emissions ◽  
Greenhouse Gas ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Gas Emissions ◽  
Agricultural Areas

scholarly journals An evaluation of the potential of Sentinel 1 for improving flash flood predictions via soil moisture–data assimilation

2017 ◽  
Vol 44 ◽  
pp. 89-100 ◽  
Author(s):  
Luca Cenci ◽  
Luca Pulvirenti ◽  
Giorgio Boni ◽  
Marco Chini ◽  
Patrick Matgen ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Hydrological Model ◽  
High Spatial Resolution ◽  
Flash Flood ◽  
Soil Moisture Data ◽  
Time Period ◽  
The Impact

Abstract. The assimilation of satellite-derived soil moisture estimates (soil moisture–data assimilation, SM–DA) into hydrological models has the potential to reduce the uncertainty of streamflow simulations. The improved capacity to monitor the closeness to saturation of small catchments, such as those characterizing the Mediterranean region, can be exploited to enhance flash flood predictions. When compared to other microwave sensors that have been exploited for SM–DA in recent years (e.g. the Advanced SCATterometer – ASCAT), characterized by low spatial/high temporal resolution, the Sentinel 1 (S1) mission provides an excellent opportunity to monitor systematically soil moisture (SM) at high spatial resolution and moderate temporal resolution. The aim of this research was thus to evaluate the impact of S1-based SM–DA for enhancing flash flood predictions of a hydrological model (Continuum) that is currently exploited for civil protection applications in Italy. The analysis was carried out in a representative Mediterranean catchment prone to flash floods, located in north-western Italy, during the time period October 2014–February 2015. It provided some important findings: (i) revealing the potential provided by S1-based SM–DA for improving discharge predictions, especially for higher flows; (ii) suggesting a more appropriate pre-processing technique to be applied to S1 data before the assimilation; and (iii) highlighting that even though high spatial resolution does provide an important contribution in a SM–DA system, the temporal resolution has the most crucial role. S1-derived SM maps are still a relatively new product and, to our knowledge, this is the first work published in an international journal dealing with their assimilation within a hydrological model to improve continuous streamflow simulations and flash flood predictions. Even though the reported results were obtained by analysing a relatively short time period, and thus should be supported by further research activities, we believe this research is timely in order to enhance our understanding of the potential contribution of the S1 data within the SM–DA framework for flash flood risk mitigation.

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