Thanks for the info, I was going for the daily mean values of soil moisture and so having distinctly different ascending and descending soil moisture data seemed like a problem but if i get a mosaiced L3 data set of soil moisture then its well and good... thanks for the "ESA CCI" link

Hi Aniket,

In our approach to estimate Soil Moisture Content (SMC) , two variables are extracted from MODIS imagery: apparent thermal inertia (ATI) , since we aim to quantify ATI, and α0, broadband albedo. Moderate resolution (1 × 1 km) optical RS data, for example, Aqua MODIS L1B data are pre-processed for this purpose and SMC imagery produced for a complete year. To calculate ATI, brightness temperatures from the thermal IR (TIR) MODIS channels 31 and 32 are used. Broadband albedo is retrieved from calibrated radiances using the visible, NIR and middle infrared (MIR) channels. The imagery is geo-referenced in Plate Carrée projection using the World Geodetic System (WGS) ’84 geode. The Aqua overpass time for the Region of interest (ROI), Xinjiang, is around 13 h 30 min in its ascending mode, and 1 h 30 min in its descending mode (Chinese local time) . These overpass times correspond with daily and nightly overpasses. Day and night land surface temperature measurements are required to estimate ATI over a day, using maximal and minimal LST's obtained from a LST daily dynamics model. The two variables LSTmin and max allow to estimate ATI.

To cover a province like Xinjiang (China), it takes 2–3 days of overpasses with MODIS 1 × 1 km image segments. With regard to time-averaged SMC values, 3 days is too short a period to obtain full coverage of the Xinjiang province, mainly due to cloud cover over the large area of the province. It is taken as a criterion that when 80% of the province region of interest (ROI) is cloud-free, the ROI is considered to be clear sky. To comply with this condition, the retrieval period for subsequent SMC observations has been set to 10 days. The ATI derived SMC, hence, is a product defined as a 10-day averaged SMC, hence not a daily one. A daily product is possible for smaller ROI's, but not for large ones. The ROI (Xinjiang) has about a 1000 (West-East) on 1500 km (North-South) dimension. Hence it is a good proxy for global applications.

See also publications in RSE and IJRS from Verstraete et al. and Veroustraete et al. which can be downloaded from this site.

Hope this helps,

Frank

Hello Frank,

Thanks a lot for the description. I am going for the daily soil moisture map. And for a region like India the 2-Day average would give us the complete coverage, if i use either the ascending swath or the descending swath for preparing the composite.

The problem with variable like soil moisture and soil temperature which have a very high diurnal variation is that, there is a significant difference in the value of retrieved soil moisture from the descending and ascending swaths. So I think i can use either of the two for the generating composite.

In soil moisture applications, the merging of ascending and descending orbits is made sometimes rescaling both orbits to a common dataset by using a rescaling function (e.g. linear or CDF matching). This is the procedure used for the CCI soil moisture product. You can also select one orbit (e.g. descending) and then rescale the other (ascending) to its climatology. Then, the computed daily average could be more robust.

Hello Lucas, Thanks for the info. Do u happen to have the link for some papers from where i can learn how to do this rescaling of data, esp. rescaling by CDF matching

There are many examples...

On RG you can see my paper on RSE:

https://www.researchgate.net/publication/229237881_Soil_moisture_estimation_through_ASCAT_and_AMSR-E_sensors_An_intercomparison_and_validation_study_across_Europe?ev=prf_pub

where I explain the CDF matching with one example. On my website (http://hydrology.irpi.cnr.it/people/l.brocca), you can find a Matlb code for CDF matching.

I also suggest to look the papers explaining the development of the CCI soil moisture product (http://www.hydrol-earth-syst-sci.net/15/425/2011/hess-15-425-2011.html and http://www.sciencedirect.com/science/article/pii/S0034425712001332)

Article Soil moisture estimation through ASCAT and AMSR-E sensors: A...