Google Earth engine -- null value problem in image statistics

Here's a question , Is there only one image , But here we need to look at the code , The function used in the code is raw data filtering , then This function is equal to , Equal to the value of a single image id

filterMetadata(name, operator, value)
deleted . Use filter() And ee.Filter.eq(), ee.Filter.gte() etc. .

A shortcut to filter a collection through metadata . This is equivalent to this.filter(ee.Filter.metadata(..)).

Return filtered collection .

Parameters .
this:collection（ aggregate ）.
Collection instance .

name（ character string ）.
Name of the attribute to be filtered .

operator (String):
The name of the comparison operator . The possible value is ."equals", "less_than", "greater_than",

"not_equals", "not_less_than", "not_greater_than", "start_with",

"end_with", "not_starts_with", "not_ends_with", " contains",

" It doesn't contain ".

value（ object ）.
  - The value to compare .

return . aggregate

So here is the first question to explain why there are only single scene images  

  The second problem here is that when we count the function of single scene image here, we return the statistical value to an empty vector , Therefore, the statistical value is not given here , And here we choose first, That is, the first choice shp The vector itself here is a null value

SCL

Scene Classification Map (The "No Data" value of 0 is masked out)

1

11

20 meters

0

Class Table: SCL Here is the specific classification information

Value	Color	Color Value	Description
1		#ff0004	Saturated or defective
2		#868686	Dark Area Pixels
3		#774b0a	Cloud Shadows
4		#10d22c	Vegetation
5		#ffff52	Bare Soils
6		#0000ff	Water
7		#818181	Clouds Low Probability / Unclassified
8		#c0c0c0	Clouds Medium Probability
9		#f1f1f1	Clouds High Probability
10		#bac5eb	Cirrus
11		#52fff9	Snow / Ice

SLC Band will be removed 0 The data of  

Code ：

// Image filtering here is the filtering of single scene images 
var S2 = ee.ImageCollection('COPERNICUS/S2_SR')
  .filterMetadata("system:index", 'equals', '20210705T165901_20210705T165855_T20XNN')
 
//  Define a cloud masking function .
var maskcloud1 = function(image) {
  var QA60 = image.select(['QA60']);
  return image.updateMask(QA60.lt(1))
};

//  Define a vegetation masking function . You need to see the image information of this band 
var masklandcover = function(image){
  var SCL = image.select(['SCL']);
  return image.updateMask(SCL.eq(4).or(SCL.eq(5)).or(SCL.eq(6)).or(SCL.eq(11)))
}

//  The choice is greater than 0 Of 
var maskpstv = function(image){
  var RE = image.select(['AbsError']);
  return image.updateMask(RE.gte(0))
}

// Choose less than 0 Of 
var maskngtv = function(image){
  var RE = image.select(['AbsError']);
  return image.updateMask(RE.lt(0))
}

var AbsoluteError_oldc_f = function(image) {
  return image.expression(
    '((2.5 * (NIR - RED) / (NIR + (6-7.5/2.08)*RED + 1))-(2.5 * (NIR - RED) / (NIR + 6*RED-7.5*BLUE + 1)))',
    {
      'NIR': image.select('B8').multiply(0.0001), 
      'RED': image.select('B4').multiply(0.0001), 
      'BLUE': image.select('B2').multiply(0.0001),
    }).rename('AbsError').copyProperties(image, image.propertyNames());
};

// Use the image to do the above map Work 
var ImgsMasked = S2.map(maskcloud1).map(masklandcover).map(AbsoluteError_oldc_f);

// Then filter for greater than and less than 
var ImgsMasked_pstv = ImgsMasked.map(maskpstv)
var ImgsMasked_ngtv = ImgsMasked.map(maskngtv)

// Find the average value of the image 
var BecomeTs = function(image){
  var date = image.get("DATE_ACQUIRED")//.format("Y-M-D");
  var mean = image.reduceRegion({
    reducer: ee.Reducer.mean(),
    geometry: image.geometry(),
    scale: 10,
    bestEffort: true
  });
//
//  And return a with  "null " Characteristics of geometry , With attributes （ Dictionaries ）. 
  return ee.Feature(null, {'mean': mean.get('AbsError'), 'date': date,'geometry':image.geometry().coordinates()})
}

print('ImgsMasked_ngtv',ImgsMasked_ngtv) 
// There is only one image with name 20210705T165901_20210705T165855_T20XNN
print('Result using map() function', ImgsMasked_ngtv.map(BecomeTs))  
// get the mean value -119345390125318.16
print('Result using first() function',BecomeTs(ImgsMasked_ngtv.first()))  
// get the mean value null