Well, plenty of literature on this topic. First, it depends on the spatial and temporal scale in which you are interested to. Then, where you are? Finally, temporal coverage and timeliness.

Generally, SMAP, ASCAT, and SMOS currently provide the best satellite soil moisture products

I usually use free Landsat and Sentinel data for this aim

Hi Mohammad,

The satellite itself isn't as important as the bands available. For moisture content the best means of capture are through NIR (750 nm-950 nm) and SWIR (1400nm-3000nm) wavelengths. So any multispectral or hyperspectral satellite with these wavelengths will work, but ultimately the exact choice comes down to the specific goals of your project.

Well, plenty of literature on this topic. First, it depends on the spatial and temporal scale in which you are interested to. Then, where you are? Finally, temporal coverage and timeliness.

Generally, SMAP, ASCAT, and SMOS currently provide the best satellite soil moisture products

It depend upon the objective of your study and your study area. Following article maybe helpful to you:

Article Global-scale assessment and combination of SMAP with ASCAT (...

Hi,

This info could be useful.

The SMAP Level-2 and Level-3 standard and enhanced radiometer soil moisture products from the NASA National Snow and Ice Data Center Distributed Active Archive Center (NSIDC DAAC) are being updated in summer 2019. The Level-2 updates will introduce an improved version of the existing Dual Channel Algorithm, DCA (option 3). The new version, known as M-DCA, achieves better retrieval performance by modeling the mixing of vertically and horizontally polarized brightness temperature channels and using new estimates of single-scattering albedo and roughness coefficients. M-DCA (option 3) will supersede optional algorithms MPRA (option 4) and E-DCA (option 5), so the following data fields will be removed from the SMAP Level-2 standard and enhanced radiometer soil moisture products:

soil_moisture_option4

vegetation_opacity_option4

retrieval_qual_flag_option4

soil_moisture_option5

vegetation_opacity_option5

retrieval_qual_flag_option5

In addition, a clay fraction field and a bulk density field will be added to both the Level-2 and Level-3 products. The baseline retrieval algorithm remains SCA-V, which is unchanged from the previous release.

Access to each data set and related documentation can be found at:

SMAP L2 Radiometer Half-Orbit 36 km EASE-Grid Soil Moisture, Version 5

DOI: https://doi.org/10.5067/SODMLCE6LGLL

SMAP Enhanced L2 Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture, Version 2

DOI: https://doi.org/10.5067/K4A1SNL5DLON

SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil Moisture, Version 5

DOI: https://doi.org/10.5067/ZX7YX2Y2LHEB

SMAP Enhanced L3 Radiometer Global Daily 9 km EASE-Grid Soil Moisture, Version 2

DOI: https://doi.org/10.5067/RFKIZ5QY5ABN

Luca Brocca hi dr.brocca , My target area is Iran Lake Urmia

Thank you for your guidance

Well, see our very recent paper on Lake Urmia region: Jalilvand, E., Tajrishy, M., Hashemi, S.A.G., Brocca, L. (2019). Quantification of irrigation water using remote sensing of soil moisture in a semi-arid region. Remote Sensing of Environment, 231, 111226, doi:10.1016/j.rse.2019.111226. https://doi.org/10.1016/j.rse.2019.111226

SMAP Level-3 soil moisture data has the best performance in North of China. And SMAP data has high resolution. But soil moisture from remote sensing satellite could not monitor deep level soil moisture. Is there any data sets with good performance in 10-20cm level soil moisture ?

Haiqing Soong From what site can I download data?

https://nsidc.org/data/smap/smap-data.html

Mohammad Saeedi We found similar results with both SMAP and SMOS soil moisture products in our study area.

https://www.sciencedirect.com/science/article/pii/S0022169419302628

We've given a brief introduction to SMAP and SMOS datasets in this article.

SMAP data can be obtained through: https://nsidc.org/

SMOS data can be obtained through: https://www.catds.fr

SM products such as SMAP, SMOS, and AMSR-E, et al. are the most widely available products.

It depends on many factors such as the extension of your study area, the study period, and the purpose of your research. Beside SMOS and SMAP, you may also consider LDAS products (e.g., GLDAS, FLDAS) available at: https://ldas.gsfc.nasa.gov/

The LDAS data are produced by integrating satellite- and ground-based observational data products. The data are available at different spatial resolution ranging from 0.125º × 0.125º to 1.0º × 1.0º .

I would add another perspective to the interesting discussion here. The performance can also depend on the density of in-situ sensors used for validation.

In our study, we validated eight passive, two active, and one merged soil moisture products over CONUS. These include operational SMAP, SMOS, AMSR2, ASCAT products. We considered 1058 ISMN stations, which include sparse networks.

Our results indicate that SMAP performs well, followed by SMOS and ASCAT with comparable accuracy over CONUS. AMSR2 (JAXA algorithm) product accuracy got affected due to the fact that the algorithm doesn't filter the retrievals during winter periods (snow, frozen ground).

The point I would like to convey is that the results may vary if we consider only dense networks for validation.

You can refer to our recent work in RSE:

Article Satellite surface soil moisture from SMAP, SMOS, AMSR2 and E...

Go through the enclosed research article, this will provide you, information about which reanalysis datasets are best for hydrological studies over the Indian region.

Article Does ERA‐5 Outperform Other Reanalysis Products for Hydrolog...

Dear Mohammad Saeedi, I agree with the opinion expressed by Hossein Sahour. It is good to define the criteria that are significant and show traceable results. Simple methods are useful, but in your case it is also advisable to consider the possibilities for further analysis of the results. With a wish for controversial scientific work!

Mohammad Saeedi

please check the link

https://www.hydrol-earth-syst-sci.net › hess-22-5889-2018

I agree with Prof. Luca. Additionally, the TCW of the Landsat imagery can provide good information. Best.

You can use Sentinel data

Generally you can used SMAP, ASCAT, SMOS and Sentinel-1 data it was currently provide the best satellite soil moisture products

If your study area is small then use Sentinel 1A otherwise use GLDAS product or ESA CCI product.

SMAP

Hi Dear.

I think Sentinel-1 is the best satellite for soil moisture.

Sentinel-1 is the first of the Copernicus Programme satellite constellation conducted by the European Space Agency. This mission is composed of a constellation of two satellites, Sentinel-1A and Sentinel-1B, which share the same orbital plane. They carry a C-band synthetic-aperture radar instrument which provides a collection of data in all-weather, day or night. This instrument has a spatial resolution of down to 5m and a swath of up to 400 km. The constellation is on a sun synchronous, near-polar (98.18°) orbit. The orbit has a 12-day repeat cycle and completes 175 orbits per cycle.

Sentinel-1 has worked in conjunction with SMAP (Soil Moisture Active and Passive) to help achieve a more accurate measure of soil moisture estimates. Observations from both instruments show to be complementary of each other as they combine data of soil moisture contents.

also, you can see this paper:

--Lievens, H.; Reichle, R. H.; Liu, Q.; De Lannoy, G. J. M.; Dunbar, R. S.; Kim, S. B.; Das, N. N.; Cosh, M.; Walker, J. P. (2017-06-27). "Joint Sentinel-1 and SMAP data assimilation to improve soil moisture estimates". Geophysical Research Letters. 44 (12): 6145–6153. Bibcode:2017GeoRL..44.6145L. doi:10.1002/2017gl073904. ISSN 0094-8276. PMC 5896568. PMID 29657343.

Sentinal 1

The TCW Landsat image and Sentinel 1 may be used.

S1a isavailable at GEE with script editable.

😉

As folow:

https://pysmm.readthedocs.io/en/latest/

SMAP soil moisture

Accuracy of all data sets may change region wise. You can try SMAP soil moisture data for better results.

The base algorithm is same and the accuracy of these data depends on many factors like atmospheric conditions. The main drawback of these data is their spacial resolution.

If you want to estimate soil mositure with high spacial resolution, I strongly suggest you our new article to read it.

“Machine learning inversion approach for soil parameters estimation over vegetated agricultural areas using a combination of water cloud model and calibrated integral equation model”

follow its flowchart to estimate reliable soil moisture.