Error correcting Sentinel-2 1C imagery using SIAC in Google Earth Engine

Question

I'm working toward estimating leaf area index using vegetation indices derived from Sentinel-2 data. I would like to complete atmospheric corrections for all available TOA imagery within specific areas (as defined by polygons); however, when I attempt to use the methodology described here: Image.constant: Parameter 'value' is required. Error in Google Earth Engine, I get this error

Error in map(ID=20150711T165456_20150711T165450_T15SWR):
Image.constant: Parameter 'value' is required.

I'm not familiar with javascript and am sure I'm missing something here.

Any ideas?

My code is below.

var insts = ee.FeatureCollection('users/wpeay26/laiMeasured');

var startdate = '2015-06-01';
var enddate   = '2021-08-31';

/**
 * Function to mask clouds using the Sentinel-2 QA band
 * @param {ee.Image} image Sentinel-2 image
 * @return {ee.Image} cloud masked Sentinel-2 image
 */

function maskS2clouds(image) {
  var date = image.date().millis()
  var qa = image.select('QA60');

  // Bits 10 and 11 are clouds and cirrus, respectively.
  var cloudBitMask = 1 << 10;
  var cirrusBitMask = 1 << 11;

  // Both flags should be set to zero, indicating clear conditions.
  var mask = qa.bitwiseAnd(cloudBitMask).eq(0)
      .and(qa.bitwiseAnd(cirrusBitMask).eq(0));

  return image.updateMask(mask).divide(10000).set('system:time_start', date);
}

// EVI
var evi = function(image) {
  var evi = image.expression(
    '2.5 * ((NIR - RED) / (NIR + 6 * RED - 7.5 * BLUE + 1))', 
    {'NIR': image.select('B8'),
    'RED': image.select('B4'),
    'BLUE': image.select('B2')});
  return image.addBands(evi.rename('evi').float());
};

// NDMI
var ndmi  = function(image) {
  var ndmi = image.normalizedDifference(['B8', 'B11']).rename('ndmi');
  return image.addBands(ndmi);
};

// NDVI
var ndvi = function(image) {
  var ndvi = image.normalizedDifference(['B8', 'B4']).rename('ndvi');
  return image.addBands(ndvi);
};

// SR
var sr = function(image) {
  var sr = image.expression(
    'NIR / RED',
    {'NIR': image.select('B8'),
    'RED': image.select('B4')});
  return image.addBands(sr.rename('sr').float());
};

// Load Sentinel-2 TOA reflectance data.
var s2Col = ee.ImageCollection('COPERNICUS/S2')
              .filterDate(startdate, enddate)
              .filterBounds(insts)
              .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 10))
              .map(maskS2clouds)
              .map(evi)
              .map(ndmi)
              .map(ndvi)
              .map(sr);

var colorComp = {bands: ['B4', 'B3', 'B2'], min: 0, max: 0.3};
Map.addLayer(s2Col.first(), colorComp, 'TOA');
Map.centerObject(insts);
Map.addLayer(insts, {color: 'red'}, 'insts');

// Import the SIAC atmospheric correction module
var siac = require('users/marcyinfeng/utils:SIAC');

// Apply SIAC and retrieve bottom of atmosphere (BOA) reflectance
var s2Boa = s2Col.map(function(image){
    return siac.get_sur(image);
}) 

print(s2Boa)

// var s2BoaFilt = s2Boa
//   .filterMetadata('B2', 'not_equals', null)
//   .filterMetadata('B4', 'not_equals', null)
//   .filterMetadata('B8', 'not_equals', null)
//   .filterMetadata('B8A', 'not_equals', null)
//   .filterMetadata('B11', 'not_equals', null)
//   .filterMetadata('evi', 'not_equals', null)
//   .filterMetadata('ndmi', 'not_equals', null)
//   .filterMetadata('ndvi', 'not_equals', null)
//   .filterMetadata('sr', 'not_equals', null);

var getMeans = function(image) {
  return ee.Image(image).reduceRegions({
    collection: insts, 
    reducer: ee.Reducer.mean(), 
    scale: 10
  }).map(function(f) {return f.set("date", image.date().format("YYYY-MM-dd")
                                    )});
};

var s2BoaMerged = s2Boa.map(getMeans).flatten();

Export.table.toDrive({
    collection: s2BoaMerged,
    selectors: (['date', 'INST', 'TRT', 'B2', 'B4', 'B8', 'B8A', 'B11', 'evi', 'ndmi', 'ndvi', 'sr']),
    description: 'mrtSentDat2A',
    folder:  'GEE',
    fileNamePrefix: 'sentDat2A',
    fileFormat: 'CSV'
})

Answer 1

The error comes from maskS2clouds function. Since you are not familiar with JS I recommend you to use Fito Principe's functions (check github repo). Instead of:

function maskS2clouds(image) {
  var date = image.date().millis()
  var qa = image.select('QA60');

  // Bits 10 and 11 are clouds and cirrus, respectively.
  var cloudBitMask = 1 << 10;
  var cirrusBitMask = 1 << 11;

  // Both flags should be set to zero, indicating clear conditions.
  var mask = qa.bitwiseAnd(cloudBitMask).eq(0)
      .and(qa.bitwiseAnd(cirrusBitMask).eq(0));

  return image.updateMask(mask).divide(10000).set('system:time_start', date);
}

Use:

var cloud_masks = require('users/fitoprincipe/geetools:cloud_masks');
var maskS2clouds = cloud_masks.sentinel2();

Although, you will need to add the factor scale for getting reflectance values in 0 - 1 range for computing vegetation indices:

var cloud_masks = require('users/fitoprincipe/geetools:cloud_masks');
var maskS2clouds = cloud_masks.sentinel2();

var scale_factor = function(image){
  image.divide(10000);
}

var s2Col = ee.ImageCollection('COPERNICUS/S2')
              .filterDate(startdate, enddate)
              .filterBounds(insts)
              .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 10))
              .map(maskS2clouds)
              .map(scale_factor)