scholarly journals Satellite-Observed Soil Moisture as an Indicator of Wildfire Risk

2020 ◽  
Vol 12 (10) ◽  
pp. 1543 ◽  
Author(s):  
Jaison Thomas Ambadan ◽  
Matilda Oja ◽  
Ze’ev Gedalof ◽  
Aaron A. Berg
Keyword(s):  
Soil Moisture ◽  
Hot Spots ◽  
Local Economy ◽  
Wildfire Management ◽  
Wildfire Risk ◽  
Wildfire Occurrence ◽  
Coarse Resolution ◽  
Soil Moisture Data ◽  
Ocean Salinity ◽  
Risk Areas

Wildfires are a concerning issue in Canada due to their immediate impact on people’s lives, local economy, climate, and environment. Studies have shown that the number of wildfires and affected areas in Canada has increased during recent decades and is a result of a warming and drying climate. Therefore, identifying potential wildfire risk areas is increasingly an important aspect of wildfire management. The purpose of this study is to investigate if remotely sensed soil moisture products from the Soil Moisture and Ocean Salinity (SMOS) satellite can be used to identify potential wildfire risk areas for better wildfire management. We used the National Fire Database (NFDB) fire points and polygons to group the wildfires according to ecozone classifications, as well as to analyze the SMOS soil moisture data over the wildfire areas, between 2010–2017, across fourteen ecozones in Canada. Timeseries of 3-day, 5-day, and 7-day soil moisture anomalies prior to the onset of each wildfire occurrence were examined over the ecozones individually. Overall, the results suggest, despite the coarse-resolution, SMOS soil moisture products are potentially useful in identifying soil moisture anomalies where wildfire hot-spots may occur.

scholarly journals Comparison of Surface Soil Moisture from SMOS Satellite and Ground Measurements

2014 ◽  
Vol 28 (3) ◽  
pp. 359-369 ◽  
Author(s):  
Bogusław Usowicz ◽  
Wojciech Marczewski ◽  
Jerzy B. Usowicz ◽  
Mateusz I. Lukowski ◽  
Jerzy Lipiec
Keyword(s):  
Land Use ◽  
Soil Moisture ◽  
Soil Cover ◽  
Natural Vegetation ◽  
Sustainable Land Use ◽  
Concordance Correlation ◽  
Marsh Soil ◽  
Soil Moisture Data ◽  
Ocean Salinity

Abstract Soil moisture datasets at various scales are needed for sustainable land use and water management. The aim of this study was to compare soil moisture ocean salinity satellite and in situ soil moisture data for the Podlasie and Polesie regions in Eastern Poland. Both regions have similar climatic and topographic conditions but are different in land use, vegetation, and soil cover. The test sites were located on agricultural fields on sandy soils and natural vegetation on marshy soils that prevail in the Podlasie and Polesie regions, respectively. The soil moisture ocean salinity soil moisture data were obtained from radiometric measurements (1.4 GHz) and the ground soil moisture from sensors at a depth of 5 cm during the years 2010-2011. In general, temporal patterns of soil moisture from both satellite and ground measurements followed the rainfall trend. The regression coefficients, Bland-Altman analysis, concordance correlation coefficient, and total deviation index showed that the agreement between ground and soil moisture ocean salinity derived soil moisture data is better for the Podlasie than the Polesie region. The lower agreement in Polesie was attributed mostly to the presence of the widespread natural vegetation on the wetter marsh soil along with minor contribution of agriculturally used drier coarse-textured soils.

scholarly journals Use of SMOS L3 Soil Moisture Data: Validation and Drought Assessment for Pernambuco State, Northeast Brazil

2018 ◽  
Vol 10 (8) ◽  
pp. 1314 ◽  
Author(s):  
Alzira Souza ◽  
Alfredo Neto ◽  
Luciana Rossato ◽  
Regina Alvalá ◽  
Laio Souza
Keyword(s):  
Soil Moisture ◽  
Pearson Correlation ◽  
Northeast Brazil ◽  
Dry Period ◽  
Drought Assessment ◽  
Additional Information ◽  
Soil Moisture Data ◽  
Ocean Salinity

The goal of this study was to validate soil moisture data from Soil Moisture Ocean Salinity (SMOS) using two in situ databases for Pernambuco State, located in Northeast Brazil. The validation process involved two approaches, pixel-station comparison and areal average, for three regions in Pernambuco with different climatic characteristics. After validation, the SMOS data were used for drought assessment by calculating soil moisture anomalies for the available period of data. Four statistical criteria were used to verify the quality of the satellite data: Pearson correlation coefficient, Willmott index of agreement, BIAS, and root mean squared difference (RMSD). The average RMSD calculated from the daily time series in the pixel and the areal assessment were 0.071 m3m−3 and 0.04 m3m−3, respectively. Those values are near to the expected 0.04 m3m−3 accuracy of the SMOS mission. The analysis of soil moisture anomalies enabled the assessment of the dry period between 2012 and 2017 and the identification of regions most impacted by the drought. The driest year for all regions was 2012, when the anomaly values achieved −50% in some regions. The use of SMOS data provided additional information that was used in conjunction with the precipitation data to assess drought periods. This may be particularly relevant for planning in agriculture and supporting decision makers and farmers.

UAS Based Soil Moisture Downscaling Using Random Forest Regression Model

2021 ◽  
Author(s):  
Ruodan Zhuang ◽  
Salvatore Manfreda ◽  
Yijian Zeng ◽  
Nunzio Romano ◽  
Eyal Ben Dor ◽  
...  
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Random Forest ◽  
Surface Temperature ◽  
Regression Model ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Surface Features ◽  
Coarse Resolution ◽  
Soil Moisture Data

<p>Soil moisture (SM) is an essential element in the hydrological cycle influencing land-atmosphere interactions and rainfall-runoff processes. High-resolution mapping of SM at field scale is vital for understanding spatial and temporal behavior of water availability in agriculture. Unmanned Arial Systems (UAS) offer an extraordinary opportunity to bridge the existing gap between point-scale field observations and satellite remote sensing providing high spatial details at relatively low costs. Moreover, this data can help the construction of downscaling models to generate high-resolution SM maps. For instance, random Forest (RF) regression model can link the land surface features and SM to identify the importance level of each predictor.</p><p>The RF regression model has been tested using a combination of satellite imageries, UAS data and point measurements collected on the experimental area Monteforte Cilento site (MFC2) in the Alento river basin (Campania, Italy) which is an 8 hectares cropland area (covered by walnuts, cherry, and olive trees). This area has been selected given the number of long-term studies on the vadose zone that have been conducted across a range of spatial scales.</p><p>The coarse resolution data cover from Jan 2015 to Dec 2019 and include SENTINEL-1 CSAR 1km SM product, 1km Land surface temperature and NDVI products from MODIS and 30m thermal band (brightness temperature), red and green band data (atmospherically corrected surface reflectance) from LANDSAT-8, and SRTM DEM from NASA. High-resolution land-surface features data from UAS-mounted optical, thermal, multispectral, and hyperspectral sensors were used to generate high-resolution SM and related soil attributes.</p><p>It is to note that the available satellite-based soil moisture data has a coarse resolution of 1km while the UAS-based land surface features of the extremely high resolution of 16cm. We deployed a two-step downscaling approach to address the smooth effect of spatial averaging of soil moisture, which depends on different elements at small and large scale. Specifically, different combinations of predictors were adopted for different scales of gridded soil moisture data. For example, in the downscaling procedure from 1km resolution to 30m resolution, precipitation, land-surface temperature (LST), vegetation indices (VIs), and elevation were used while LST, VIs, slope, and topographic index were selected for the downscaling from 30m to 16cm resolution. Indeed, features controlling the spatial distributions of soil moisture at different scale reflect the characteristics of the physical process: i) the surface elevation and rainfall patterns control the first downscaling model; ii) the topographic convergence and local slope become more relevant to reach a more detailed resolution. In conclusion, the study highlighted that RF regression model is able to interpret fairly well the spatial patterns of soil moisture at the scale of 30m starting from a resolution of 1km, while it is highlighted that the second downscaling step (up to few centimeters) is much more complex and requires further studies.</p><p>This research is a part of EU COST-Action “HARMONIOUS: Harmonization of UAS techniques for agricultural and natural ecosystems monitoring”.</p><p><strong>Keywords:</strong> soil moisture, downscaling, Unmanned Aerial Systems, random forest, HARMONIOUS</p>

scholarly journals Global soil moisture data derived through machine learning trained with in-situ measurements

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Sungmin O. ◽  
Rene Orth
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Large Scale ◽  
Short Term Memory ◽  
Temporal Dynamics ◽  
Soil Moisture Data ◽  
Wide Range ◽  
Global Soil

AbstractWhile soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture derived through machine learning trained with in-situ measurements, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0–10 cm, 10–30 cm, and 30–50 cm) at 0.25° spatial and daily temporal resolution over the period 2000–2019. The performance of the resulting dataset is evaluated through cross validation and inter-comparison with existing soil moisture datasets. SoMo.ml performs especially well in terms of temporal dynamics, making it particularly useful for applications requiring time-varying soil moisture, such as anomaly detection and memory analyses. SoMo.ml complements the existing suite of modelled and satellite-based datasets given its distinct derivation, to support large-scale hydrological, meteorological, and ecological analyses.

scholarly journals Navigating the Wildfire–Pandemic Interface: Public Perceptions of COVID-19 and the 2020 Wildfire Season in Arizona

Fire ◽  
2020 ◽  
Vol 3 (3) ◽  
pp. 41
Author(s):  
Catrin M. Edgeley ◽  
Jack T. Burnett
Keyword(s):  
Risk Mitigation ◽  
Public Perceptions ◽  
Fire Season ◽  
Future Research ◽  
Wildland Urban Interface ◽  
Wildfire Management ◽  
Wildfire Risk ◽  
Close Proximity ◽  
Literature Highlight ◽  
Wildfire Season

COVID-19 has complicated wildfire management and public safety for the 2020 fire season. It is unclear whether COVID-19 has impacted the ability of residents in the wildland–urban interface to prepare for and evacuate from wildfire, or the extent to which residents feel their household’s safety has been affected. Several areas with high wildfire risk are also experiencing record numbers of COVID-19 cases, including the state of Arizona in the southwestern United States. We conducted a mixed-mode survey of households in close proximity to two recent wildfires in rural Arizona to better understand whether residents living in the wildland–urban interface perceive COVID-19 as a factor in wildfire safety. Preliminary data suggest that the current challenges around collective action to address wildfire risk may be further exacerbated due to COVID-19, and that the current pandemic has potentially widened existing disparities in household capacity to conduct wildfire risk mitigation activities in the wildland–urban interface. Proactive planning for wildfire has also increased perceived ability to practice safe distancing from others during evacuation, highlighting the benefits that household planning for wildfire can have on other concurrent hazards. Parallels in both the wildfire and pandemic literature highlight vast opportunities for future research that can expand upon and advance our findings.

scholarly journals The Role of Averaging for Improving Sea Surface Salinity Retrieval from the Soil Moisture and Ocean Salinity (SMOS) Satellite and Impact of Auxiliary Data

2007 ◽  
Vol 24 (2) ◽  
pp. 255-269 ◽  
Author(s):  
Sabine Philipps ◽  
Christine Boone ◽  
Estelle Obligis
Keyword(s):  
Soil Moisture ◽  
Sensitivity Study ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Global Ocean ◽  
Surface Salinity ◽  
Auxiliary Data ◽  
Additional Information ◽  
Ocean Salinity

Abstract Soil Moisture and Ocean Salinity (SMOS) was chosen as the European Space Agency’s second Earth Explorer Opportunity mission. One of the objectives is to retrieve sea surface salinity (SSS) from measured brightness temperatures (TBs) at L band with a precision of 0.2 practical salinity units (psu) with averages taken over 200 km by 200 km areas and 10 days [as suggested in the requirements of the Global Ocean Data Assimilation Experiment (GODAE)]. The retrieval is performed here by an inverse model and additional information of auxiliary SSS, sea surface temperature (SST), and wind speed (W). A sensitivity study is done to observe the influence of the TBs and auxiliary data on the SSS retrieval. The key role of TB and W accuracy on SSS retrieval is verified. Retrieval is then done over the Atlantic for two cases. In case A, auxiliary data are simulated from two model outputs by adding white noise. The more realistic case B uses independent databases for reference and auxiliary ocean parameters. For these cases, the RMS error of retrieved SSS on pixel scale is around 1 psu (1.2 for case B). Averaging over GODAE scales reduces the SSS error by a factor of 12 (4 for case B). The weaker error reduction in case B is most likely due to the correlation of errors in auxiliary data. This study shows that SSS retrieval will be very sensitive to errors on auxiliary data. Specific efforts should be devoted to improving the quality of auxiliary data.

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