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+/**
+ * Sentinel-1 & Sentinel-2 哨兵一号与二号长时序数据下载 —— 适用于小范围区域年度数据获取
+ *
+ * @author CVEO Team
+ * @date 2026-01-05
+ *
+ * 1. 加载 Sentinel-1, Sentinel-2 数据与 Cloud Score+ 云掩膜, 以及 HLS 数据
+ * 2. 合成年度Sentinel-1, Sentinel-2, HLS无云影像
+ * 3. 使用 HLS 影像作为 Sentinel-2 影像的补充
+ * 4. 合并Sentinel-1和Sentinel-2影像
+ * 5. 导出COG云优化并填补缺失值的GeoTIFF影像 (小区域不分块)
+ *
+ * 注: 截止至 2026-01-05, GEE 仍未集成 2015-2017 年的 L2A 数据.
+ */
+
+// 加载研究区域和影像
+var name_list = [
+  "台北中山区大直要塞区",
+  "花莲县新城乡佳山基地",
+  "高雄左营区左营军港",
+  "高雄旗山区陆军第八军团指挥部",
+];
+var target_index = 2; // 从 0 开始计数, 0-3
+var region_name = name_list[target_index];
+var region = ee.FeatureCollection(
+  demoPoints
+    .filter(ee.Filter.eq("Name", region_name))
+    .geometry()
+    .buffer(2500)
+    .bounds()
+);
+var start_year = 2025;
+var end_year = 2025;
+var start_date = start_year + "-" + "01-01";
+var end_date = start_year + "-" + "12-31";
+var cloud_threshold = 100; // 最大云量阈值
+// Use 'cs' or 'cs_cdf', depending on your use case; see docs for guidance.
+var QA_BAND = "cs_cdf";
+// The threshold for masking; values between 0.50 and 0.65 generally work well.
+// Higher values will remove thin clouds, haze & cirrus shadows.
+var CLEAR_THRESHOLD = 0.65;
+var L30Bands = ["B2", "B3", "B4", "B5", "B6", "B7"];
+// Sentinel-2的B8A波段(20m)可能更适合代表近红外波段
+// var s2Bands = ["B2", "B3", "B4", "B8", "B11", "B12"];
+var s2Bands = ["B2", "B3", "B4", "B8A", "B11", "B12"];
+var commonBands = ["Blue", "Green", "Red", "NIR", "SWIR1", "SWIR2"];
+
+var region_geo = region.geometry();
+var bounds = region_geo.bounds();
+var common_filter = ee.Filter.and(
+  ee.Filter.bounds(bounds),
+  ee.Filter.date(start_date, end_date)
+);
+
+/**
+ * 基于 Cloud Score+ 云掩膜
+ * @param {ee.Image} image Sentinel-2 image
+ * @return {ee.Image} cloud masked Sentinel-2 image
+ */
+function maskS2cloudsByCS(image) {
+  var cloudMask = image.select(QA_BAND).gte(CLEAR_THRESHOLD);
+  image = image.updateMask(cloudMask);
+  return image
+    .divide(10000)
+    .copyProperties(image)
+    .copyProperties(image, ["system:time_start", "system:index", "system:id"]);
+}
+
+/**
+ * Function to mask clouds using the HLS Fmask band
+ * @param {ee.Image} image HLS image
+ * @return {ee.Image} cloud masked HLS image
+ */
+function maskHLSclouds(image) {
+  var qa = image.select("Fmask");
+  // Bits 1 and 2 are cloud and shadow, respectively.
+  var cloudBitMask = 1 << 1;
+  var shadowBitMask = 1 << 2;
+  // Both flags should be set to zero, indicating clear conditions.
+  var mask = qa
+    .bitwiseAnd(cloudBitMask)
+    .eq(0)
+    .and(qa.bitwiseAnd(shadowBitMask).eq(0));
+  image = image.updateMask(mask);
+  return image
+    .copyProperties(image)
+    .copyProperties(image, ["system:time_start", "system:index", "system:id"]);
+}
+
+/**
+ * 按照属性值对影像集合进行排序
+ * @param {ee.ImageCollection} collection 影像集合
+ * @param {ee.Geometry} region 目标区域
+ * @param {string} cloud_field 云量属性字段名, 默认为 "CLOUDY_PIXEL_PERCENTAGE"
+ * @return {ee.ImageCollection} 排序后的影像集合
+ */
+function sortedByPriority(collection, region, cloud_field) {
+  cloud_field = cloud_field || "CLOUDY_PIXEL_PERCENTAGE";
+  // 1. 计算每张影像的有效像素 (非空像素) 覆盖面积
+  var withArea = collection.map(function (image) {
+    var area = image
+      .mask()
+      .reduceRegion({
+        reducer: ee.Reducer.sum(),
+        geometry: region,
+        scale: 10,
+        maxPixels: 1e9,
+      })
+      .values()
+      .get(0);
+    return image.set("coverage_area", area);
+  });
+
+  // 2. 剔除覆盖面积为 0 的影像
+  withArea = withArea.filter(ee.Filter.gt("coverage_area", 0));
+
+  // 3. 根据云量 (降序) 和覆盖率 (升序) 对影像进行综合排序
+  // 考虑影像集合是以栈的形式 (先入后出) 组织的, 在进行 mosaic 合成时, 从最后一张影像开始合并, 逆转排序
+  // sort 默认升序 True
+  var sorted = withArea.sort(cloud_field, false).sort("coverage_area");
+  return ee.ImageCollection(sorted);
+}
+
+// 加载Sentinel-1 GRD数据
+var S1dataset = ee
+  .ImageCollection("COPERNICUS/S1_GRD")
+  .filter(common_filter)
+  .filter(ee.Filter.eq("instrumentMode", "IW"));
+var S1VVdataset = S1dataset.filter(
+  ee.Filter.listContains("transmitterReceiverPolarisation", "VV")
+).select("VV");
+var S1VHdataset = S1dataset.filter(
+  ee.Filter.listContains("transmitterReceiverPolarisation", "VH")
+).select("VH");
+print(start_date + " - " + end_date + " S1dataset", S1dataset);
+var s1_vv_img = ee.Image(S1VVdataset.median());
+var s1_vh_img = ee.Image(S1VHdataset.median());
+
+// 加载Sentinel-2 L2A数据与Cloud Score+云掩膜
+// Cloud Score+ image collection.
+// Note Cloud Score+ is produced from Sentinel-2 level 1C data and can be applied to either L1C or L2A collections.
+// 加载Sentinel-2 L2A数据
+// Cloud Score+ image collection.
+// Note Cloud Score+ is produced from Sentinel-2 level 1C data and can be applied to either L1C or L2A collections.
+var S2csPlus = ee.ImageCollection("GOOGLE/CLOUD_SCORE_PLUS/V1/S2_HARMONIZED");
+var S2dataset = ee
+  .ImageCollection("COPERNICUS/S2_SR_HARMONIZED")
+  .filter(common_filter)
+  .filter(ee.Filter.lt("CLOUDY_PIXEL_PERCENTAGE", cloud_threshold))
+  .linkCollection(S2csPlus, [QA_BAND])
+  .map(maskS2cloudsByCS)
+  .select(s2Bands, commonBands);
+var targetS2 = sortedByPriority(S2dataset, region);
+var s2_img = ee.Image(targetS2.mosaic());
+print(start_date + " - " + end_date + " S2dataset", S2dataset);
+var s2_img_rgb = s2_img.select(["Red", "Green", "Blue"]);
+var s2_img_frgb = s2_img.select(["NIR", "Red", "Green"]);
+
+var HLSL30 = ee
+  .ImageCollection("NASA/HLS/HLSL30/v002")
+  .filter(common_filter)
+  .filter(ee.Filter.lt("CLOUD_COVERAGE", cloud_threshold))
+  .map(maskHLSclouds)
+  .select(L30Bands, commonBands);
+var HLSS30 = ee
+  .ImageCollection("NASA/HLS/HLSS30/v002")
+  .filter(common_filter)
+  .filter(ee.Filter.lt("CLOUD_COVERAGE", cloud_threshold))
+  .map(maskHLSclouds)
+  .select(s2Bands, commonBands);
+var HLS = ee.ImageCollection(HLSL30.merge(HLSS30));
+var targetHLS = sortedByPriority(HLS, region, "CLOUD_COVERAGE");
+var hls_img = ee.Image(targetHLS.mosaic());
+
+var s1_vis = {
+  min: -25,
+  max: 5,
+};
+
+var true_rgb_vis = {
+  bands: ["Red", "Green", "Blue"],
+  min: 0.0,
+  max: 0.3,
+};
+
+var false_rgb_vis = {
+  min: 0.0,
+  max: 0.4,
+  gamma: 1.4,
+  bands: ["NIR", "Red", "Green"],
+};
+
+var styling = {
+  color: "blue",
+  fillColor: "00000000",
+};
+
+Map.centerObject(region, 14);
+Map.addLayer(s1_vv_img, s1_vis, start_year + " Sentinel-1 GRD VV");
+Map.addLayer(s1_vh_img, s1_vis, start_year + " Sentinel-1 GRD VH");
+Map.addLayer(hls_img, false_rgb_vis, start_year + " HLS False RGB", false);
+Map.addLayer(hls_img, true_rgb_vis, start_year + " HLS True RGB");
+Map.addLayer(
+  s2_img_frgb,
+  false_rgb_vis,
+  start_year + " Sentinel-2 False RGB",
+  false
+);
+Map.addLayer(s2_img_rgb, true_rgb_vis, start_year + " Sentinel-2 True RGB");
+Map.addLayer(region.style(styling), {}, region_name);
+
+// 导出合并后的影像
+// 明确设置数据类型为Float32, 否则默认类型为Float64, 会占用更多内存
+// 并对缺失值进行填充, 否则默认为nan不便于后续本地处理
+var processed_img = s2_img
+  .addBands(s1_vv_img)
+  .addBands(s1_vh_img)
+  .toFloat()
+  .unmask(-9999.0);
+print(
+  "Start exporting " + start_year + " Sentinel-2 and S1 GRD VV/VH merged image",
+  processed_img
+);
+target_index = target_index + 1;
+Export.image.toDrive({
+  image: processed_img,
+  description: "S1_S2_" + start_year + "_Target" + target_index,
+  folder: "Sentinel",
+  region: region, // 添加后会自动裁剪
+  scale: 10,
+  maxPixels: 1e13, // GEE 最多支持 1e8 像素, 当超过时会自动分块
+  fileFormat: "GeoTIFF",
+  // 导出COG云优化的GeoTIFF影像, 并明确设置缺失值为-9999.0
+  formatOptions: {
+    cloudOptimized: true,
+    noData: -9999.0,
+  },
+});