Crop Removal Monitoring - Sentinel 1

Microwave Toolbox
#4

You could try to reduce the noise in the VH time-series by multitemporal speckle filtering.

Regarding coherence, is your test area in the 6-day repeat zone? If not I think it’s less likely to work. Also, since coherence is computed over a sliding window it is of inherently lower resolution than intensity -> unlikely to work on small fields.

#5

Thanks for the additional info. I will first try the multi-temporal speckle filtering and see how I go. As for the 6 day repeat zone, I don’t even know what that is unfortunately :disappointed_relieved:.

#6

Sentinel-1 takes images at 6 day intervals (in the best case). So if you want to get best coherence the shorter the time between both images, the less temporal decorrelation can be expected (coherence goes down).

Please have a look at this post: Subsidence map in 3d view
And on multi-temporal filtering: Single or Multi-temporal speckle filter?

#7

The 6-day repeat zone covers basically Europe and the Southern Mediterranean coast.

https://sentinel.esa.int/web/sentinel/missions/sentinel-1/observation-scenario

Sentinel-1-Revisit-Coverage-Frequency-Geometry-2018

#8

I am looking at an area in Southern Mozambique which is unfortunately not in the 6-day repeat zone. I tried the Multi-temporal speckle filter without much luck. It works on the larger fields (± 8 Hectares), but there is still a lot of speckle. Where fields are less than 1 hectare, I cannot see detect if they have been harvested.

Larger%20Fields
Larger Fields.PNG1336×956 757 KB

#9

how much images did you use for the multi-temporal speckle filter? I would at least suggest 12 images (1 per month) so you have robust statistics on surface characteristics: Single or Multi-temporal speckle filter?

#10

Hi Andreas

I used 5, I will download more and try again. Thank you for the advice. From a processing point of view, am I correct in using the following steps?

Start Batch
1: Read
2: Remove GRD Border Noise
3:Apply Orbit File
4:Calibrate
5:Multilook
6:Terrain Flattening
7:Terrain Correction
8:Write
End batch

9: Coregistraiton
10:Multi-temporal Speckle Filter

Thanks again for your help.

Regards
Gavin

#11

If you use GRD data, I see no reason to apply Multi-looking. As well - if your area is comparably flat, you can skip the Terrain Flattening, which surely takes most of the time, and directly calibrate to Sigma0.

1 Like
#12

Hi Andreas

Thanks, yes the area is very flat, so I will remove those steps. Once I have the datasets downloaded I will process and give some feedback.

Regards
Gavin

#13

@Gavin, seems you have a nice agricultural area. Could you share the location? Is it all sugarcane?

M

#14

Hi MCG

Yes it is all sugarcane. If you do any demo work on the area and get nice results, please let me know. I am fumbling in the dark here.

Lat: 25°27’18.03"S
Long: 32°50’3.75"E

Have a nice weekend.

Gavin

1 Like
#15

@Gavin Definitely, I’ll keep in touch

Thank you

#16

Hi Guys

I have another question. I am wanting to classify an agricultural area where they grow rice or sugarcane. The rice is dry-land and harvested about 3 times a year and the sugarcane is harvested once. What would be the best way of classifying these 2 crops?

Regards
Gavin

#17

@Gavin still want to use SAR or Optical?

#18

Hi MCG

SAR, the area has too much cloud cover to use optical.

Regards
Gavin

#19

@Gavin in that case,

First I would use GRD data. Coherence derived from SLC can help you but since you have different crops with different growing cycles I would say amplitude can give you already good results (plus take into account that you are new to SAR, so start first by understating GRD products and then move to complex data)

The methodology you have proposed before (plus the corrections done by Abraun) seems good enough.

I would suggest you to select S1 data taking into account the growing cycle of both crops. Try to find a time where the structural difference between both is as large as possible (S1 is sensitive to that).

Then you have to think about a classifier. Any experience on that? You can start with some basic approach and then move to SVM, RF etc. (I guess you are doing a supervised classification so you have training/validation data)

Another thing, you may want to derive texture information from your data to increase the number of layers for the classifier.

M

1 Like
#20

Hi @Gavin,

I also used Sentinel 1 GRD data in crop removal monitoring in my recent study. The processing steps are followed.

  1. Read
  2. Apply Orbit File
  3. Radiometric Calibration
  4. Terrain Correction
  5. Write
  6. Subset
  7. Multi-temporal Speckle Filter

However, one of the reviewer did not agree with the process. Following is his question.

Why did you perform terrain corrections before multi-temporal filtering? Performing the multi-temporal speckle filtering after the terrain geocoding means that the speckle statistic -first and second order - are changed, hence affecting filtering itself.

Any thoughts on that? Thank you.

Yuting

#21

The reviewer makes a fair point - speckle filtering should be done in radar geometry, in other words before Terrain Correction.

#22

Does this mean that I need to do single speckle filter (e.g. Lee Sigma ) on each product before Terrain Correction?

If my understanding is right, the multi-temporal filtering is essentially the same with single speckle filter (Single or Multi-temporal speckle filter?). Does that mean I don’t need to do the multi-temporal filtering after the single filtering has already been done for each product?

The new processing steps would be:

  1. Read
  2. Apply Orbit File
  3. Radiometric Calibration
  4. Single Product Speckle Filter
  5. Terrain Correction
  6. Write
  7. Subset
#23

Multitemporal filtering usually works better that single image filter - choose one or the other.