<p>You could fill the gaps with the analysis products of numerical weather prediction. NCAR for example (<a href="https://rda.ucar.edu" rel="nofollow noreferrer">RDA UCAR</a>) hosts the NCEP North American Regional Reanalysis (NARR) with data from 1979 to 2018 including snow cover (<a href="https://rda.ucar.edu/datasets/ds608.0/" rel="nofollow noreferrer">NARR</a>) in 3-hourly intervals.</p>

<p>But I would guess it depends on your goal: 
You can make an educated guess (bayesian approach, combining measured and predicted variables) about the true state of the snow cover, but what was not measured can not be pulled out of nowhere.</p>

CC BY-SA 4.0
2018-05-07T05:08:35.477Z
"what was not measured can not be pulled out of nowhere." I love it! Perhaps some day I'll find a place to use this again, and will cite you ;-)
uhoh
CC BY-SA 4.0
2018-05-07T09:43:19.503Z
WE do not have the measure data of the study area, so have to deal within the satellite product. Convolution could be a potential method the squeeze the gap.
irfan
CC BY-SA 4.0
2018-05-05T21:22:46.950Z
2018-05-05T21:22:46.950Z
Lukas
<p>Daily and monthly MODIS snow cover L3 data has downloaded, next to deal with missing and negative cells. 
Different types of interpolation are being used to fill the missing cells. I want to fill the missing cells with mean of 3 surrounding values but it is very hard because in some cases large portion of data is missing?
I know to mask the NaN cell by using (nanmean) command and long term mean of same area can also be use.</p>

<p>Permafrost Presence/Absence Mapping of the Qinghai-Tibet Plateau Based on Multi-Source Remote Sensing Data but I am looking any possible way beside these.</p>

<p>What is the best way beside above mentioned two to deal the missing and negative cells of MODIS snow cover data? </p>

<p>Note; I am using matlab environment for this purpose.</p>

CC BY-SA 4.0
2018-05-05T11:58:51.660Z
Have you considered using some form of inverse distance weighting?
Fred
CC BY-SA 4.0
2018-05-05T22:00:27.570Z
What do you want to do with the data? Perhaps you can mask over them as NaNs and just deal with the data that aren't NaNs.
BarocliniCplusplus
CC BY-SA 4.0
2018-05-07T00:02:13.770Z
mask is not much meaningful in case of large area missing.
irfan
<climate><snow><data-analysis><modis>
What is the better way to deal the missing and negative cells of satellite snow cover data
irfan
# What is the better way to deal the missing and negative cells of satellite snow cover data

(Asked by: username_0 on 2018-05-05)

Daily and monthly MODIS snow cover L3 data has downloaded, next to deal with missing and negative cells.
Different types of interpolation are being used to fill the missing cells. I want to fill the missing cells with mean of 3 surrounding values but it is very hard because in some cases large portion of data is missing?
I know to mask the NaN cell by using (nanmean) command and long term mean of same area can also be use.

Permafrost Presence/Absence Mapping of the Qinghai-Tibet Plateau Based on Multi-Source Remote Sensing Data but I am looking any possible way beside these.

What is the best way beside above mentioned two to deal the missing and negative cells of MODIS snow cover data?

Note; I am using matlab environment for this purpose.

**Tags:** climate, snow, data-analysis, modis

> **Comments:**
> **username_1**: Have you considered using some form of inverse distance weighting?  
> **username_2**: What do you want to do with the data? Perhaps you can mask over them as NaNs and just deal with the data that aren't NaNs.  
> **username_0**: mask is not much meaningful in case of large area missing.  

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## 1 answers

### Answer by username_3 on 2018-05-05. Score: 3 | Accepted answer

You could fill the gaps with the analysis products of numerical weather prediction. NCAR for example ([RDA UCAR](https://rda.ucar.edu)) hosts the NCEP North American Regional Reanalysis (NARR) with data from 1979 to 2018 including snow cover ([NARR](https://rda.ucar.edu/datasets/ds608.0/)) in 3-hourly intervals.

But I would guess it depends on your goal:
You can make an educated guess (bayesian approach, combining measured and predicted variables) about the true state of the snow cover, but what was not measured can not be pulled out of nowhere.

> **Comments:**
> **username_4**: "what was not measured can not be pulled out of nowhere." I love it! Perhaps some day I'll find a place to use this again, and will cite you ;-)  
> **username_0**: WE do not have the measure data of the study area, so have to deal within the satellite product. Convolution could be a potential method the squeeze the gap.  

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