Sentinel-1 – Product Type Selection on Sentinel-1 Image, Single Look Complex (SLC), or Ground Range Detected (GRD)

google-earth-enginesentinel-1

I wanted to shift from sentinel-2 image to sentinel-1 for crop type mapping due to cloud coverage in the context of Nepal. So I started learning about the sentinel-1 image with references and tutorials on sentinel-1 image processing from multiple sources. Rather than getting a perfect concept, I got confused with various ideas and techniques. Among them, the selection of product type on sentinel-1 is the one.

Please feel free to discuss with following questions that I am having if you could enlighten me.

With crop type mapping, forest change detection, and Land Cover/ Land use classifications, what would be the effect of selecting either SLC or GRD?
Google Earth Engine(GEE) has GRD product type only, so the analysis with GRD is much simpler, but what will I be missing in using the SLC product?

Best Answer

For many applications, GRD images provide enough information. SLC require more skills to work with (they cannot be used directly for mapping). You will have to make your own GRD from the SLC anyway, but from SLC you can also make coherence images if needed. If you need coherence or other interferometry products, then SLC are required.

The GRD from GEE are OK, but you must know that there are different algorithms to make the GRD so they are not always optimal (beware in slopy areas). Are you only in the Plateaus of Nepal or also on the mountain sides ?

Related Solutions

Google Earth Engine – How to Use FeatureCollection to Mask an Image Efficiently

You could do something like:

var ic = ee.ImageCollection('...')
var fc = ee.FeatureCollection('....')
var mask = ee.Image.constant(1).clip(fc.geometry()).mask().not()

ic = ic.map(function(image){return image.updateMask(mask)})

Google Earth Engine – Local Incidence Angle from Sentinel-1 GRD Image Collection

The terrain correction implemented by Andreas Vollrath calculates the local incidence angle. You can modify the implementation to return that angle.

// Implementation by Andreas Vollrath (ESA), inspired by Johannes Reiche (Wageningen)
function terrainCorrection(image) {
  var imgGeom = image.geometry()
  var srtm = ee.Image('USGS/SRTMGL1_003').clip(imgGeom) // 30m srtm 
  var sigma0Pow = ee.Image.constant(10).pow(image.divide(10.0))

  // Article ( numbers relate to chapters)
  // 2.1.1 Radar geometry 
  var theta_i = image.select('angle')
  var phi_i = ee.Terrain.aspect(theta_i)
    .reduceRegion(ee.Reducer.mean(), theta_i.get('system:footprint'), 1000)
    .get('aspect')

  // 2.1.2 Terrain geometry
  var alpha_s = ee.Terrain.slope(srtm).select('slope')
  var phi_s = ee.Terrain.aspect(srtm).select('aspect')

  // 2.1.3 Model geometry
  // reduce to 3 angle
  var phi_r = ee.Image.constant(phi_i).subtract(phi_s)

  // convert all to radians
  var phi_rRad = phi_r.multiply(Math.PI / 180)
  var alpha_sRad = alpha_s.multiply(Math.PI / 180)
  var theta_iRad = theta_i.multiply(Math.PI / 180)
  var ninetyRad = ee.Image.constant(90).multiply(Math.PI / 180)

  // slope steepness in range (eq. 2)
  var alpha_r = (alpha_sRad.tan().multiply(phi_rRad.cos())).atan()

  // slope steepness in azimuth (eq 3)
  var alpha_az = (alpha_sRad.tan().multiply(phi_rRad.sin())).atan()

  // local incidence angle (eq. 4)
  var theta_lia = (alpha_az.cos().multiply((theta_iRad.subtract(alpha_r)).cos())).acos()
  var theta_liaDeg = theta_lia.multiply(180 / Math.PI)
  // 2.2 
  // Gamma_nought_flat
  var gamma0 = sigma0Pow.divide(theta_iRad.cos())
  var gamma0dB = ee.Image.constant(10).multiply(gamma0.log10())
  var ratio_1 = gamma0dB.select('VV').subtract(gamma0dB.select('VH'))

  // Volumetric Model
  var nominator = (ninetyRad.subtract(theta_iRad).add(alpha_r)).tan()
  var denominator = (ninetyRad.subtract(theta_iRad)).tan()
  var volModel = (nominator.divide(denominator)).abs()

  // apply model
  var gamma0_Volume = gamma0.divide(volModel)
  var gamma0_VolumeDB = ee.Image.constant(10).multiply(gamma0_Volume.log10())

  // we add a layover/shadow maskto the original implmentation
  // layover, where slope > radar viewing angle 
  var alpha_rDeg = alpha_r.multiply(180 / Math.PI)
  var layover = alpha_rDeg.lt(theta_i);

  // shadow where LIA > 90
  var shadow = theta_liaDeg.lt(85)

  // calculate the ratio for RGB vis
  var ratio = gamma0_VolumeDB.select('VV').subtract(gamma0_VolumeDB.select('VH'))

  var output = gamma0_VolumeDB.addBands(ratio).addBands(alpha_r).addBands(phi_s).addBands(theta_iRad)
    .addBands(layover).addBands(shadow).addBands(gamma0dB).addBands(ratio_1)

  return image.addBands(
    output.select(['VV', 'VH', 'slope_1', 'slope_2'], ['VV', 'VH', 'layover', 'shadow']),
    null,
    true
  )
}

Best Answer

Related Solutions

Google Earth Engine – How to Use FeatureCollection to Mask an Image Efficiently

Google Earth Engine – Local Incidence Angle from Sentinel-1 GRD Image Collection

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