diff --git a/vegetationPhenology/VegetationPhenologyMain.py b/vegetationPhenology/VegetationPhenologyMain.py
index 39983da1..6d92ce3a 100644
--- a/vegetationPhenology/VegetationPhenologyMain.py
+++ b/vegetationPhenology/VegetationPhenologyMain.py
@@ -28,6 +28,7 @@ from VegetationPhenologyAuxData import PhenoloyMeasCsv_geo
 
 from tool.algorithm.xml.CreateMetaDict import CreateMetaDict, CreateProductXml
 from tool.file.fileHandle import fileHandle
+from tool.algorithm.algtools.ROIAlg import ROIAlg as roi
 import sys
 from tool.algorithm.transforml1a.transHandle import TransImgL1A
 from tool.csv.csvHandle import csvHandle
@@ -47,7 +48,8 @@ logger = logging.getLogger("mylog")
 FILTER_SIZE = int(cf.get('filter_size'))
 MAX_TRAN_NUM = int(cf.get('max_tran__num_per_class'))
 file =fileHandle(DEBUG)
-env_str = os.path.split(os.path.realpath(__file__))[0]
+# env_str = os.path.split(os.path.realpath(__file__))[0]
+env_str =os.path.dirname(os.path.abspath(sys.argv[0]))
 os.environ['PROJ_LIB'] = env_str
 class PhenologyMain:
     """
@@ -150,12 +152,13 @@ class PhenologyMain:
         for name in self.__processing_paras['name_list']:
             self.preprocess_single_tar(name, scopes_roi)
 
-            para_names_geo = [name + '_sim_ori']
+            para_names_geo = [name + '_sim_ori', 'Covering']
             self.__feature_name_list = para_names_geo
             p = pp()
             cutted_img_paths, scopes_roi = p.cut_geoimg(self.__workspace_preprocessing_path, para_names_geo,
                                                         self.__processing_paras)
-            self.__preprocessed_paras.update({name + 'sim_ori': cutted_img_paths.get(name + '_sim_ori')})
+            self.__preprocessed_paras.update({name + '_sim_ori': cutted_img_paths.get(name + '_sim_ori')})
+            self.__preprocessed_paras.update({name + '_Covering': cutted_img_paths.get('Covering')})
         logger.info('preprocess_handle success!')
         logger.info('progress bar: 10%')
 
@@ -258,7 +261,7 @@ class PhenologyMain:
         sim_ori_path = self.__preprocessed_paras[sim_ori_key]
 
         hh_path = self.__preprocessed_paras[name + "_HH"]
-        hh_geo_path = os.path.join(self.__workspace_processing_path, 'hh_geo.tif')
+        hh_geo_path = os.path.join(self.__workspace_processing_path, name.split('-')[0] + '_geo.tif')
         paramter = self.__processing_paras[name + "paraMeter"]
         self.calInterpolation_bil_Wgs84_rc_sar_sigma(paramter, sim_ori_path, hh_path, hh_geo_path)
 
@@ -343,7 +346,7 @@ class PhenologyMain:
             # 生成训练集
             X_train_part, Y_train_part, optimal_feature = ml.gene_optimal_train_set(train_data_dic, feature_dir, 0.07, 0.85)
             name_list = ml.get_name_list(feature_dir)
-            if optimal_feature == []:
+            if len(optimal_feature) <= 0:
                 logger.error('特征筛选结果为空，无可用特征作为训练集')
                 continue
             # 生成测试集合
@@ -382,11 +385,19 @@ class PhenologyMain:
             roi_img = self.imageHandler.get_band_array(self.create_roi(in_tif_paths[0]))
             # 获取影像roi区域
             cover_data_pro = cover_data * roi_img
-            cover_geo_path = os.path.join(self.__product_dic, os.path.basename(product_path).split('.tif')[0] + '-VP.tif')
+            cover_geo_path = os.path.join(self.__workspace_processing_path, os.path.basename(product_path).split('.tif')[0] + '-VPtemp.tif')
             self.imageHandler.write_img(cover_geo_path, proj_geo, geo_geo, cover_data_pro)
-            self.imageHandler.write_quick_view(cover_geo_path, color_img=True)
 
-            meta_xml_path = self.create_meta_file(cover_geo_path)
+            self.resampleImgs(name, cover_geo_path)
+            para_names = ['Covering']
+            mask_dir = os.path.join(self.__workspace_processing_path, name + '\\')
+            bare_land_mask_path = roi().roi_process_VP(para_names, mask_dir,
+                                                    self.__processing_paras, self.__preprocessed_paras, name)
+            product_path_pro = os.path.join(self.__product_dic, os.path.basename(product_path).split('.tif')[0] + '-VP.tif')
+            # 获取影像roi区域
+            roi.cal_roi(product_path_pro, cover_geo_path, bare_land_mask_path, background_value=0)
+            self.imageHandler.write_quick_view(product_path_pro, color_img=True)
+            meta_xml_path = self.create_meta_file(product_path_pro)
 
         temp_folder = os.path.join(self.__workspace_path, EXE_NAME, 'Output')
         out_xml = os.path.join(temp_folder, os.path.basename(meta_xml_path))
@@ -511,6 +522,12 @@ class PhenologyMain:
 
         return product_geo_path
 
+    def resampleImgs(self, name, refer_img_path):
+
+        cover_rampling_path = os.path.join(self.__workspace_processing_path, name + "_cover.tif")
+        pp.resampling_by_scale(self.__preprocessed_paras[name + "_Covering"], cover_rampling_path, refer_img_path)
+        self.__preprocessed_paras[name + "_Covering"] = cover_rampling_path
+
     def create_roi(self, img_path):
         """
         计算ROI掩膜