PIE-Engine水稻影像面积提取3

/**
 * @Name    :   基于 PIE Engine Studio 的水稻自动提取
 * @Author  :   武汉大学VHR队
 * @Desc    :   淮安市水稻提取 - 合成sentinel2月度影像
 * @Source  :   航天宏图第四届 “航天宏图杯”PIE软件二次开发大赛云开发组三等奖获奖作品
 */

//3.合成sentinel2月度影像
//选择淮安市矢量
var roi = pie.FeatureCollection("NGCC/CHINA_CITY_BOUNDARY").filter(pie.Filter.eq('name', '淮安市')).first().geometry()
Map.centerObject(roi, 8)
Map.addLayer(roi)

//LSWI
function LSWI(image) {
    var swir = image.select("B11")
    var nir = image.select("B8")
    var lswi = (nir.subtract(swir)).divide(nir.add(swir))
    return image.addBands(lswi.rename("LSWI"));
}
//EVI
function EVI(image) {
    var red = image.select("B4").divide(10000)
    var nir = image.select("B5").divide(10000)
    var blue = image.select("B2").divide(10000)
    var evi = ((nir.subtract(red)).multiply(2.5))
        .divide(nir.add(red.multiply(6)).subtract(blue.multiply(7.5)).add(1))
        .multiply(10000)
    return image.addBands(evi.rename("EVI"));
}
//去云处理
function maskS2clouds(image) {
    //选择有关云掩膜的波段
    var qa = image.select('QA60')
    //位10和11分别代表云和卷云。
    var cloudBitMask = 1 << 10;
    var cirrusBitMask = 1 << 11;
    // 将有关云的像元置为0
    var mask = qa.bitwiseAnd(cloudBitMask).eq(0)
        .and(qa.bitwiseAnd(cirrusBitMask).eq(0))
    return image.updateMask(mask)
}
var area = pie.Geometry.Polygon([
    [
        [
            118.33558377212347,
            32.62288800615681
        ],
        [
            119.24768569883133,
            32.62288800615681
        ],
        [
            119.24768569883133,
            31.21438784070145
        ],
        [
            118.33558377212347,
            31.21438784070145
        ],
        [
            118.33558377212347,
            32.62288800615681
        ]
    ]
], null);
//合成lswi波段的月度影像（做这个实验时由于map函数存在问题不能同时进行两个波段计算，因此分开计算）
var lswi1 = pie.ImageCollection('S2/L1C')
    .filterBounds(roi)
    .filterDate("2019-12-1", "2019-12-31")
    .select(['B2', 'B4', 'B8', 'B11', 'QA60'])
    .map(maskS2clouds)
    .map(LSWI)
    .select(['LSWI'])
    .mosaic()
    .clip(roi)
var lswi2 = pie.ImageCollection('S2/L1C')
    .filterBounds(roi)
    .filterDate("2020-12-1", "2020-12-31")
    .select(['B2', 'B4', 'B8', 'B11', 'QA60'])
    .map(maskS2clouds)
    .map(LSWI)
    .select(['LSWI'])
    .mosaic()
    .clip(roi);
//合成evi波段的月度影像              
var evi1 = pie.ImageCollection('S2/L1C')
    .filterBounds(roi)
    .filterDate("2019-12-1", "2019-12-31")
    .select(['B2', 'B4', 'B8', 'B11', 'QA60'])
    .map(maskS2clouds)
    .map(EVI)
    .select(['EVI'])
    .mosaic()
    .clip(roi)
var evi2 = pie.ImageCollection('S2/L1C')
    .filterBounds(roi)
    .filterDate("2020-12-1", "2020-12-31")
    .select(['B2', 'B4', 'B8', 'B11', 'QA60'])
    .map(maskS2clouds)
    .map(EVI)
    .select(['EVI'])
    .mosaic()
    .clip(roi);
//将2019年和2020年对应月份影像取均值合成              
var lswi = pie.ImageCollection.fromImages([lswi1, lswi2]).mean()
var evi = pie.ImageCollection.fromImages([evi1, evi2]).mean()
var jan = lswi.addBands(evi);
//导出月度影像
Export.image({
    image: jan,
    description: 'jan',
    assetId: "jan",
    region: area,
    scale: 30,
})