diff --git a/utils/sr2rgb.py b/utils/sr2rgb.py
index 6934fa2..23a5fb1 100644
--- a/utils/sr2rgb.py
+++ b/utils/sr2rgb.py
@@ -1,5 +1,5 @@
 """
-将 COG 格式的 Red, Green, Blue 单波段地表反射率图像合成 RGB 图像
+将 COG 格式的 Red, Green, Blue 单波段地表反射率图像合成为 RGB 图像
 
 1. 对比度线性拉伸至 0-255;
 2. 合并波段;
@@ -7,16 +7,18 @@
 """
 
 import os
-import rasterio as rio
+import xarray as xr
+from rioxarray import open_rasterio
 import numpy as np
 
-def sr2rgb(red_path, green_path, blue_path, output_path):
+
+def sr2rgb(red_path: str, green_path: str, blue_path: str, output_path: str) -> None:
     """
     将红、绿、蓝三个单波段地表反射率图像合成为RGB图像
-    
+
     参数:
         red_path (str): 红色波段文件路径
-        green_path (str): 绿色波段文件路径  
+        green_path (str): 绿色波段文件路径
         blue_path (str): 蓝色波段文件路径
         output_path (str): 输出RGB图像路径
     """
@@ -24,74 +26,79 @@ def sr2rgb(red_path, green_path, blue_path, output_path):
     for path in [red_path, green_path, blue_path]:
         if not os.path.exists(path):
             raise FileNotFoundError(f"文件不存在: {path}")
-    
-    print(f"正在处理文件:")
-    print(f"  红波段: {red_path}")
-    print(f"  绿波段: {green_path}")
-    print(f"  蓝波段: {blue_path}")
-    
+
     # 读取三个波段数据
-    with rio.open(red_path) as red_src:
-        red_band = red_src.read(1)
-        profile = red_src.profile
-        print(f"红波段形状: {red_band.shape}, 数据类型: {red_band.dtype}")
-        
-    with rio.open(green_path) as green_src:
-        green_band = green_src.read(1)
-        print(f"绿波段形状: {green_band.shape}, 数据类型: {green_band.dtype}")
-        
-    with rio.open(blue_path) as blue_src:
-        blue_band = blue_src.read(1)
-        print(f"蓝波段形状: {blue_band.shape}, 数据类型: {blue_band.dtype}")
-    
-    # 线性拉伸到0-255范围
+    red_band = open_rasterio(red_path, masked=True).squeeze(dim="band", drop=True)
+    green_band = open_rasterio(green_path, masked=True).squeeze(dim="band", drop=True)
+    blue_band = open_rasterio(blue_path, masked=True).squeeze(dim="band", drop=True)
+    # 暂存元数据
+    y_coords = red_band.y
+    x_coords = red_band.x
+    crs = red_band.rio.crs
+    transform = red_band.rio.transform()
+
     def stretch_band(band):
-        # 处理NaN值
+        """
+        线性拉伸到0-255范围
+        """
+        # 处理NaN值与负值
         band_no_nan = np.where(np.isnan(band), 0, band)
+        band_no_nan = np.where(band_no_nan < 0, 0, band_no_nan)
         band_min = np.min(band_no_nan)
         band_max = np.max(band_no_nan)
-        
         # 避免除零错误
         if band_max == band_min:
             stretched = np.zeros_like(band_no_nan, dtype=np.uint8)
         else:
-            stretched = ((band_no_nan - band_min) / (band_max - band_min) * 255).astype(np.uint8)
+            stretched = ((band_no_nan - band_min) / (band_max - band_min) * 255).astype(
+                np.uint8
+            )
         return stretched
-    
-    red_stretched = stretch_band(red_band)
-    green_stretched = stretch_band(green_band)
-    blue_stretched = stretch_band(blue_band)
-    
+
+    red_stretched = stretch_band(red_band.values)
+    green_stretched = stretch_band(green_band.values)
+    blue_stretched = stretch_band(blue_band.values)
     # 合并三个波段为RGB图像
-    rgb_array = np.dstack((red_stretched, green_stretched, blue_stretched))
-    
-    # 更新元数据
-    profile.update(
-        dtype=rio.uint8,
-        count=3,
-        photometric='RGB',
-        nodata=0  # 设置nodata为0，因为uint8的范围是0-255
+    rgb_array = xr.DataArray(
+        np.dstack((red_stretched, green_stretched, blue_stretched)),
+        dims=("y", "x", "band"),
+        coords={"band": [1, 2, 3], "y": y_coords, "x": x_coords},
     )
-    
-    # 保存RGB图像
-    with rio.open(output_path, 'w', **profile) as dst:
-        for i in range(3):
-            dst.write(rgb_array[:, :, i], i + 1)
-    
+    # 转置维度顺序以符合rioxarray要求
+    rgb_array = rgb_array.transpose("band", "y", "x")
+    # 写入元数据
+    rgb_array.rio.write_crs(crs, inplace=True)
+    rgb_array.rio.write_transform(transform, inplace=True)
+    rgb_array.rio.write_nodata(0, inplace=True)
+    # 保存为TIFF文件
+    rgb_array.rio.to_raster(output_path, dtype="uint8")
     print(f"RGB图像已保存到: {output_path}")
+    return
 
 
 if __name__ == "__main__":
     # tif_dir = "D:\\NASA_EarthData_Script\\data\\HLS\\2024\\2024012"
-    # red_path = os.path.join(tif_dir, "HLS.S30.T49RGP.2024012T031101.v2.0.RED.subset.tif")
-    # green_path = os.path.join(tif_dir, "HLS.S30.T49RGP.2024012T031101.v2.0.GREEN.subset.tif")
-    # blue_path = os.path.join(tif_dir, "HLS.S30.T49RGP.2024012T031101.v2.0.BLUE.subset.tif")
+    # red_path = os.path.join(
+    #     tif_dir, "HLS.S30.T49RGP.2024012T031101.v2.0.RED.subset.tif"
+    # )
+    # green_path = os.path.join(
+    #     tif_dir, "HLS.S30.T49RGP.2024012T031101.v2.0.GREEN.subset.tif"
+    # )
+    # blue_path = os.path.join(
+    #     tif_dir, "HLS.S30.T49RGP.2024012T031101.v2.0.BLUE.subset.tif"
+    # )
     # output_path = os.path.join(tif_dir, "HLS.S30.T49RGP.2024012T031101.v2.0.RGB.tif")
 
     tif_dir = "D:\\NASA_EarthData_Script\\data\\HLS\\2025\\2025011"
-    red_path = os.path.join(tif_dir, "HLS.S30.T49RGP.2025011T031009.v2.0.RED.subset.tif")
-    green_path = os.path.join(tif_dir, "HLS.S30.T49RGP.2025011T031009.v2.0.GREEN.subset.tif")
-    blue_path = os.path.join(tif_dir, "HLS.S30.T49RGP.2025011T031009.v2.0.BLUE.subset.tif")
+    red_path = os.path.join(
+        tif_dir, "HLS.S30.T49RGP.2025011T031009.v2.0.RED.subset.tif"
+    )
+    green_path = os.path.join(
+        tif_dir, "HLS.S30.T49RGP.2025011T031009.v2.0.GREEN.subset.tif"
+    )
+    blue_path = os.path.join(
+        tif_dir, "HLS.S30.T49RGP.2025011T031009.v2.0.BLUE.subset.tif"
+    )
     output_path = os.path.join(tif_dir, "HLS.S30.T49RGP.2025011T031009.v2.0.RGB.tif")
-    
-    sr2rgb(red_path, green_path, blue_path, output_path)
\ No newline at end of file
+
+    sr2rgb(red_path, green_path, blue_path, output_path)