diff --git a/src/core/algorithms/waterindex_inversion/csv_processor.py b/src/core/algorithms/waterindex_inversion/csv_processor.py
index 53d3824..3573f8b 100644
--- a/src/core/algorithms/waterindex_inversion/csv_processor.py
+++ b/src/core/algorithms/waterindex_inversion/csv_processor.py
@@ -233,6 +233,8 @@ class WaterIndexCsvProcessor:
                     print(f"[WaterIndexCsvProcessor] {name}: 替换 {n_inf} 个 inf/-inf → NaN")
                 # ===== P0 防御结束 =====
                 out_df = pd.DataFrame({
+                    "proj_x": df["longitude"].values,
+                    "proj_y": df["latitude"].values,
                     "longitude": df["longitude"].values,
                     "latitude": df["latitude"].values,
                     name: per_idx.values,
diff --git a/src/postprocessing/map.py b/src/postprocessing/map.py
index 1f8fa65..7651c65 100644
--- a/src/postprocessing/map.py
+++ b/src/postprocessing/map.py
@@ -876,16 +876,41 @@ class ContentMapper:
         print("正在读取CSV文件...")
         df = pd.read_csv(csv_file, encoding='utf-8')
 
-        # 假设前三列分别是经度、纬度、含量
         if df.shape[1] < 3:
             raise ValueError("CSV文件必须至少包含3列：经度、纬度、含量")
 
-        # 获取列名
-        lon_col = df.columns[0]
-        lat_col = df.columns[1]
-        content_col = df.columns[2]
+        # ── 智能坐标列检测：按优先级匹配，兼容新旧格式 ──
+        # 优先级: proj_x/proj_y > longitude/latitude > x_coord/y_coord
+        _COORD_CANDIDATES = [
+            ('proj_x', 'proj_y'),
+            ('longitude', 'longitude'),   # 单数形式
+            ('longitude', 'latitude'),
+            ('lon', 'lat'),
+            ('x_coord', 'y_coord'),
+        ]
+        lon_col, lat_col = None, None
+        for x_cand, y_cand in _COORD_CANDIDATES:
+            if x_cand in df.columns and y_cand in df.columns:
+                lon_col, lat_col = x_cand, y_cand
+                break
 
-        print(f"检测到列名：经度({lon_col})，纬度({lat_col})，含量({content_col})")
+        if lon_col is None:
+            # 终极回退：按位置取前两列
+            lon_col, lat_col = df.columns[0], df.columns[1]
+            print(f"  ⚠ 未识别到标准坐标列名，按位置回退: "
+                  f"X={lon_col}, Y={lat_col}")
+
+        # 含量列：跳过坐标列后的第一列
+        coord_cols = {lon_col, lat_col}
+        content_col = None
+        for c in df.columns:
+            if c not in coord_cols and not c.startswith('pixel_'):
+                content_col = c
+                break
+        if content_col is None:
+            content_col = df.columns[2]
+
+        print(f"检测到列名：X({lon_col})，Y({lat_col})，含量({content_col})")
 
         # 自动检测不确定性列
         if uncertainty_col is None: