Soil Moisture Data [BETTER] Download

The NASA-USDA Global soil moisture data provides soil moisture information across the globe at 0.25x0.25spatial resolution. These data sets include: surface and subsurface soil moisture (mm), soil moisture profile (%), surface and subsurface soil moisture anomalies (-). The data set is generated by integrating satellite-derived Soil Moisture Active Passive (SMAP) and Soil Moisture Ocean Salinity (SMOS) soil moisture observations into the modified two-layer Palmer model using Ensemble Kalman Filter (EnKF) data assimilation approach. The assimilation of the satellite-derived soil moisture observations helped improve the model-based soil moisture predictions, particularly over poorly instrumented areas of the world that lack good quality precipitation data.

A new, long-term and global dataset of soil moisture measurements from space has been released to help us better understand the water cycle and climate, monitor agriculture and manage our water resources.

Soil Moisture Data Download

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This is because soil moisture is a key variable controlling the exchange of water and energy between the land and the atmosphere: dry soil emits little or no moisture to the atmosphere and heats up more strongly during a hot day, thus intensifying heatwaves. Wet soils, on the other hand, have a cooling effect on the atmosphere.

When a severe drought hit central Italy this summer, the dataset was consulted and scientists found that lower-than-average soil moisture as far back as December contributed to the severity of the drought.

Soil moisture is a state variable of the land that crosses the interfaces of several disciplines, including meteorology, hydrology, climatology and ecology. Soil moisture content controls the partitioning of net radiation at the land surface between sensible and latent heat fluxes. This occurs primarily in the subtopics and tropics where rainfall is not abundant, and in the mid-latitudes during the warm season. Thus, accurate representation of soil moisture and its controls on surface fluxes are essential for water and energy cycle understanding and simulation, weather and climate forecasting (particularly at subseasonal to seasonal time scales) and the representation of changing feedbacks in a changing climate (Seneviratne et al. 2010; Dirmeyer et al. 2012, 2015).

Ground networks offer spotty coverage and inter-network inconsistencies. Polar-orbiting satellites provide global coverage and consistency but have their own limitations. Satellite measurements of soil moisture based on microwave sensors are representative of only the top few centimeters of soil, and where vegetation cover is moderate to dense, they are not a measure of soil water at all. Over vegetation, water in and on the canopy is being measured. Until recently, most satellite measurements were collected from instruments designed for other purposes that happened to be sensitive to variations in soil moisture. These are chiefly from passive microwave-based sensors (AMSR-E, SMMR, TRMM, WindSat) and scatterometers (ERS-SCAT, METOP-ASCAT). Recently, two satellite missions have been launched with the specific purpose of measuring soil moisture, the Soil Moisture Ocean Salinity (SMOS; Kerr et al. 2010) and Soil Moisture Active/Passive (SMAP; Entekhabi et al. 2010) missions.

The International Soil Moisture Network (ISMN; ) is a global network of networks. It was conceived as a database to provide ground-truth for satellite products, particularly SMOS. ISMN provides raw data from many in situ networks arranged by continent, but minimal processing beyond standardization of data formats and metadata. The ISMN data set includes precipitation, temperature (soil, surface and near surface air), and snow measurements where available. All data from every site in each network is archived, including multiple sensors and sensor replacements stored in separate ASCII files so all information is visible to the user. No gap-filling is applied to missing data. Detailed QC flags are included, and methodologies are well documented on the website.

The National Soil Moisture Network (NSMN; ), formerly the North American Soil Moisture Database (NASMD; Quiring et al. 2016), is limited to the United States, but contains more networks than ISMN, particularly state-level networks and mesonets. NSMN is working towards inter-network homogenization to produce a consistent observational product using high-resolution soils and precipitation data. Station having multiple sensors are blended into a single time series, and gap-filling is applied when gaps are not to large. Only soil moisture data are presented without ancillary variables. The result is more complete than ISMN but with less metadata provided and more geographically limited.

There are hybrid / blended products that combine multiple sources of soil moisture information in an attempt to produce consistent data sets over long durations with minimal gaps in space and time. These are gridded products amenable to use for model comparison. The ESA CCI soil moisture project ( -soilmoisture-cci.org/) blends scatterometer and microwave remote sensing products with versions using only active or passive instruments available. Data coverage spans 1978-2010 (when the ERS2 scatterometer data ended) daily at resolution. NOAA has begun a Soil Moisture Products System (SMOPS; -bin/iso?id=gov.noaa.ncdc:C00994) effort to create daily blended analyses using AMSR2, SMOS, ASCAT and SMAP data at . SMOPS data begin in March 2017.

An analogous process was used to produce the NCEP Climate Forecast System Reanalysis (CFSR; Saha et al. 2010), which used a semi-coupled data assimilation where the Noah LSM was run offline driven by observationally-based meteorological analysis. States from that simulation were substituted into the reanalysis cycle once a day to prevent land states from drifting. Thus, CFSR, like the products described above, has a soil moisture analysis that is somewhat more constrained by observations, especially precipitation, than a typical reanalysis. Otherwise, reanalyses have not ingested soil moisture data directly as part of their data assimilation schemes until ERA5, which does assimilate ASCAT scatterometer data. Work is underway at ECMWF to assimilate SMOS in the operational analyses, which is expected to become operational in 2019.

Each type of soil moisture data set has its strengths and weaknesses, which are summarized in the table above. The figure, from (Kumar et al. 2018), shows the information content of some of the soil moisture products discussed here, shown as the density of grid cells mapped as a function of two statistics. Metric entropy (x-axis) is 0.0 for a constant field, 1.0 for a purely random time series, and ~0.5 for maximum information content. Fluctuation complexity (y-axis) indicates whether the sequence of values represents a simple (low) or complex (high) ordering of values. The satellite products (AMSR-E, ASCAT, SMOS, AMSR2) show a great deal of noise (high entropy, low complexity) except SMAP is somewhat less noisy and more informative. SCAN represents in situ measurements, closest to truth but still containing instrument errors. The two model products (GLDAS-Noah and GLDAS-Mosaic) tend to be less variable than SCAN observations, which probably reflects a combination of the LSMs being a bit simplistic, the observations containing random error, and the spatial scale difference between point measurements and coarse models.

. The figure, from (Kumar et al. 2018), shows the information content of some of the soil moisture products discussed here, shown as the density of grid cells mapped as a function of two statistics. Metric entropy (x-axis) is 0.0 for a constant field, 1.0 for a purely random time series, and ~0.5 for maximum information content. Fluctuation complexity (y-axis) indicates whether the sequence of values represents a simple (low) or complex (high) ordering of values. The satellite products (AMSR-E, ASCAT, SMOS, AMSR2) show a great deal of noise (high entropy, low complexity) except SMAP is somewhat less noisy and more informative. SCAN represents in situ measurements, closest to truth but still containing instrument errors. The two model products (GLDAS-Noah and GLDAS-Mosaic) tend to be less variable than SCAN observations, which probably reflects a combination of the LSMs being a bit simplistic, the observations containing random error, and the spatial scale difference between point measurements and coarse model

This training series is intended for local, state, regional, federal, and international governmental and non-governmental organizations interested in using NASA data for drought monitoring and forecasting. Appropriate for operational end-users, decision-makers, and researchers with interests/needs in assessing drought, water resources, agriculture, and wildland fire management.

Abstract. In situ measurements of soil moisture are invaluable for calibrating and validating land surface models and satellite-based soil moisture retrievals. In addition, long-term time series of in situ soil moisture measurements themselves can reveal trends in the water cycle related to climate or land cover change. Nevertheless, on a worldwide basis the number of meteorological networks and stations measuring soil moisture, in particular on a continuous basis, is still limited and the data they provide lack standardization of technique and protocol. To overcome many of these limitations, the International Soil Moisture Network (ISMN; ) was initiated to serve as a centralized data hosting facility where globally available in situ soil moisture measurements from operational networks and validation campaigns are collected, harmonized, and made available to users. Data collecting networks share their soil moisture datasets with the ISMN on a voluntary and no-cost basis. Incoming soil moisture data are automatically transformed into common volumetric soil moisture units and checked for outliers and implausible values. Apart from soil water measurements from different depths, important metadata and meteorological variables (e.g., precipitation and soil temperature) are stored in the database. These will assist the user in correctly interpreting the soil moisture data. The database is queried through a graphical user interface while output of data selected for download is provided according to common standards for data and metadata. Currently (status May 2011), the ISMN contains data of 19 networks and more than 500 stations located in North America, Europe, Asia, and Australia. The time period spanned by the entire database runs from 1952 until the present, although most datasets have originated during the last decade. The database is rapidly expanding, which means that both the number of stations and the time period covered by the existing stations are still growing. Hence, it will become an increasingly important resource for validating and improving satellite-derived soil moisture products and studying climate related trends. As the ISMN is animated by the scientific community itself, we invite potential networks to enrich the collection by sharing their in situ soil moisture data. 006ab0faaa