Crop monitoring and yield mapping with sentinel 1

s1tbx
#1

Hello

Please, am new on radar image processing and crop monitoring and yield mapping. I recently downloaded a SNAP and sentinel 1 image over my study area for crop monitoring and yield mapping purpose, but i don’t really know how to go about using the SNAP software in deducing and processing the image for crop monitoring and yield mapping. Please. anyone who can help me on the steps and processes it takes to manauver the software and image to detect changes in the growth of crops and predict the yield of such crop.

Thanks in advance
Best regards
Adeoye .O.T

4 Likes
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#2

Hi Adeoyeoluwatobi261

Check out this link https://sentinel.esa.int/web/sentinel/toolboxes/sentinel-1/tutorials…

Regards

#3

Hi Gabriela Quintana Sánchez

Thank you for response to my question, according to my post on the forum. I am new on SAR image processing, but i have gotten all the tutorial videos on ESA site as it relates to using SNAP in processing SENTINEL 1 image. But my problems now lies on how to use the SNAP software and the S1A_IW_GRDH_1SDV_20160909T173722_20160909T173747_012979_014897_9998 data i got from Scihub to monitor crop and predict its yield. So i am confused with the steps to take in other to achieve my aim. Please, i will like you to help me on the steps i need to take in the SNAP sofware in monitoring and predicting the yield of crop.

Best regards with great anticipation

Adeoye .O

#4

maybe you could ask more specific at which point you need help.

#5

Ok. I mean step by step guide, that i can go through with SNAP and sentinel
1 data to monitor crop growth and predict the yield of the crop through out
its season.

Thanks.

1 Like
#6

sorry, you can’t just ask for a whole analysis.
You saw the tutorials for pre-processing which show you how to deal with S1 GRDH data.

Then have a look at some studies to get an idea of what different approaches in agriculture exist.

Search in this forum, there are lots of great examples:

Radar remote sensing isn’t something you can master over night and just apply by following a recipe. Read some stuff, develop ideas, discuss problems in here - you are free to make your own mind. Especially yield estimation is ambituous and you would need to have reference data from the field (or statistics on your crops).

Here are some great documents / tutorials if you are new to SAR remote sensing:
Introduction to SAR remote sensing
A tutorial on synthetic aperture radar
Forest Monitoring with multi-temporal SAR data
Agricultural Applications with SAR Data
Guidelines for SAR interferometry

If you have problems at specific steps of processing - feel free to ask! But you can’t expect others to do your science.

12 Likes
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#7

Hello

Thanks for your reply and the shared tutorials. I recently went through some resource materials on the above topic, and i got to know that polarimetric SAR from radar images alongside with optical images are been used for monitoring the phenological stages of crop and estimating its yield based on the intensities of there backscatter between the germination and maturity stages. My question now is how to achieve a polarimetric SAR with sentinel 1 image using SNAP or any resourceful software.

Best regards
Adeoye

#8

You could get some of what you need from the polarimetric tutorial in http://step.esa.int/main/doc/tutorials/sentinel-1-toolbox-tutorials/

#9

Hello everyone

Thank you for your time.

I have gone through this discussion. It’s very helpful. I recently started working with sentinel 1 data.
I have to do crop growth monitoring and classification of different types of crops. I have downloaded sentinel 1 IW GRDH Dual polarisation (VV+VH) data. I am using SNAP for processing.

I want to know does these data have thermal noise . (if yes)
What should be the order for processing data i.e. which operation should be done first
speckle filtering , thermal noise removal , calibrating (calculating sigma_o).

Am i missing something in sequence of processing ?

Thank you for help

#10

I think your question is already answered here, please check out it

#11

Thank you for response Falahfakhri

I want to know does "IW GRDH " data have thermal noise and is it ok to work with “IW GRDH” or should i proceed with “IW SLC”.

Thanks

#12

I found this for you, would you please to take a look at it,

http://publications.jrc.ec.europa.eu/repository/bitstream/JRC92666/lbna27031enn.pdf

#14

Hi guys,
I am working with S1 GRD data(VV and VH band) for mapping monsoon crops. I have extracted the backscatter values of the crop fields for different months. I not getting the expected change in the backscatter values for different months.
Kindly HELP!!

#15

Did you look at all available data? Images from the drier season should have clearly different backscatter from the wet season.

#16

I am working on kharif crops(monsoon),and had worked on data of Early july, late august and mid october. The backscatter values were expected to increase, but has not increased.The backscatter values are however different for different months.

#17

I think this can be related to the different stages of your crops. Also, C-band of Sentinel-1 interacts with different parts of the plants.
Maybe this is helpful to you, especially the slides on temporal signatures and scattering mechanisms.
https://eo-college.org/resources/agriculture/

1 Like
#18

@ABraun In case of sentinel-2 images if i select 5 images that cover complete cropping season how can i make multi temporal dataset by simpling stacking all 5 images bands or select some specific band from each image which would give better results ???

#19

yes, stacking the whole is a good idea. But Yiu can also start with stacking the infrared band because it is often a good indicator for vegetation.

#20

I think total 50 bands stacking make my dataset a bit heavy for processing if i select 3 to 4 bands according to crop situation from each image ???

#21

you can start with the IR bands at first and if you see that they do not provide enough information for your analysis, sucessively entering more bands to the stack.

1 Like