Updated notebooks

examples/notebooks/02_demo_using_GEE_data.ipynb

@@ -87,7 +87,7 @@
     "    DT = img.select('dewpoint_temperature') # in Kelvin\n",
     "    P = img.select('surface_pressure') # in Pascals\n",
     "    from geeet.meteo import relative_humidity # eq 7.91 in ECMWF (2016)\n",
-    "    RH = relative_humidity(T, DT, P)  \n",
+    "    RH = relative_humidity(T, DT)  \n",
     "    img = img.addBands(Rn).addBands(RH)\n",
     "    return(img)\n",
     "\n",
@@ -321,7 +321,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.12.2"
   }
  },
  "nbformat": 4,

examples/notebooks/03_eepredefined_landsat_era5.ipynb

@@ -17,7 +17,7 @@
         "\n",
         "---\n",
         "\n",
-        "The `geeet.eepredefined` library contains a collection of modules used to make it easy to ingest GEE data into the geeet models. \n",
+        "The `geeet.eepredefined` library contains a collection of modules that make it easy to ingest GEE data into the geeet models. \n",
         "\n",
         "This notebook demonstrates how to:\n",
         "\n",
@@ -226,7 +226,7 @@
         "\n",
         "Let's generate a small visualization by clipping one of the images to a small region. \n",
         "\n",
-        "> You will need [geemap](geemap.org) for this visualization. Alternatively, open [this script in the code editor](https://code.earthengine.google.com/628832ffbd371d828a048e9de2f0903d). "
+        "> You will need [geemap](geemap.org) for this visualization. Alternatively, open [this script in the code editor](https://code.earthengine.google.com/f71913e4516d36de0da2c0bce4f03d39). "
       ]
     },
     {
@@ -262,7 +262,7 @@
         "Map.addLayer(image.select('NDVI'), {'min':0, 'max':1, 'palette':ndvi_pal, 'opacity': 0.8}, 'NDVI')\n",
         "Map.addLayer(image.select('radiometric_temperature'), {'min':20+273, 'max':50+273, 'palette':red_pal}, 'Landsat radiometric temperature (K)')\n",
         "Map.addLayer(image.select('Rn'), {'min':0, 'max':500, 'palette':red_pal}, 'Net radiation (W/m²)', False)\n",
-        "Map.addLayer(image.select('LEc'), {'min':0, 'max':500, 'palette':blue_pal}, 'Latent heat flux (W/m²) from the canopy source', True)\n",
+        "Map.addLayer(image.select('LEc'), {'min':0, 'max':300, 'palette':blue_pal}, 'Latent heat flux (W/m²) from the canopy source', True)\n",
         "\n",
         "Map"
       ]
@@ -273,7 +273,7 @@
       "source": [
         "Here's a preview of how the map should look like:\n",
         "\n",
-        "![image](https://github.com/kaust-halo/geeet/assets/14804652/30aad357-5ced-4420-b264-7b935c9fd171)"
+        "![image](https://github.com/kaust-halo/geeet/assets/14804652/8379c468-1264-47ff-bdfb-89a7f9d8f61b)"
       ]
     },
     {
@@ -308,7 +308,7 @@
         "                                     crs=\"EPSG:32637\",\n",
         "                                     crsTransform=[30,0,296685,0,-30,3470115],\n",
         "                                     )\n",
-        "task.start() # Uncomment to submit the task."
+        "#task.start() # Uncomment to submit the task."
       ]
     }
   ],
@@ -333,7 +333,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.13"
+      "version": "3.12.2"
     },
     "widgets": {
       "application/vnd.jupyter.widget-state+json": {

examples/notebooks/04_eepredefined_landsat_mapped_collection.ipynb

@@ -98,6 +98,7 @@
        " 'cloud_cover',\n",
        " 'surface_pressure',\n",
        " 'air_temperature',\n",
+       " 'dewpoint_temperature',\n",
        " 'u_component_of_wind_10m',\n",
        " 'v_component_of_wind_10m',\n",
        " 'surface_solar_radiation_downwards_hourly',\n",
@@ -160,7 +161,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.12.2"
   }
  },
  "nbformat": 4,

examples/notebooks/05_xarray_landsat_era5.ipynb

@@ -54,8 +54,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "inputs = data[[\"NDVI\", \"albedo\", \"radiometric_temperature\", \n",
-    "\"surface_pressure\", \"air_temperature\", \"wind_speed\", \"solar_radiation\", \"thermal_radiation\"]]\n",
+    "inputs = data[[\"NDVI\", \"albedo\", \"radiometric_temperature\",  \n",
+    "\"surface_pressure\", \"air_temperature\", \"dewpoint_temperature\", \"wind_speed\", \"solar_radiation\", \"thermal_radiation\"]]\n",
     "inputs"
    ]
   },
@@ -178,8 +178,8 @@
     "inputsb = (datab[[\n",
     "    \"longitude\", \"latitude\",\n",
     "    \"NDVI\", \"albedo\", \"radiometric_temperature\", \n",
-    "    \"surface_pressure\", \"air_temperature\", \"wind_speed\", \n",
-    "    \"solar_radiation\", \"thermal_radiation\"]]\n",
+    "    \"surface_pressure\", \"air_temperature\", \"dewpoint_temperature\",\n",
+    "    \"wind_speed\", \"solar_radiation\", \"thermal_radiation\"]]\n",
     ")\n",
     "\n",
     "xetb = geeet.tseb.tseb_series(inputsb, **dict(\n",
@@ -219,7 +219,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.12.2"
   }
  },
  "nbformat": 4,

geeet/ptjpl.py

@@ -123,6 +123,7 @@ def ptjpl_arid(img=None, # ee.Image with inputs as bands (takes precedence over
         band_names = img.bandNames()
         NDVI = img.select('NDVI')
         Ta = img.select('air_temperature')
+        Td = img.select('dewpoint_temperature')
         P = img.select('surface_pressure')
         F_aparmax = img.select('fapar_max')
         time = img.get('time')
@@ -154,7 +155,7 @@ def ptjpl_arid(img=None, # ee.Image with inputs as bands (takes precedence over
     ft = compute_ft_arid(Ta)
     f_apar = compute_fapar(NDVI)
     fm = compute_fm(f_apar, F_aparmax)
-    met_params = compute_met_params(Ta, P)  
+    met_params = compute_met_params(Ta, Td, P)  
     fsm = compute_fsm(RH, Ta, Beta)
 
     if is_img(img):

geeet/resistances.py

@@ -7,7 +7,7 @@
 def RN95(U, CH, rough_params, LAI, leaf_width, zU, zT, L=None, Ustar = None, rough_bands = ['ZM','ZH','D0'] , band_names = ['Ra', 'Rs', 'Rx']):
     """
     Calculate the original TSEB resistances from Norman et al., 1995 (N95) 
-#//description 
+    
     Inputs:
         - U: wind speed in m/s, numpy array or an ee.Image
         - CH: canopy height in m, numpy array or ee.Image 
@@ -98,7 +98,6 @@ def RN95(U, CH, rough_params, LAI, leaf_width, zU, zT, L=None, Ustar = None, rou
         where C1 ~ 90 s-1/2 m-1
         s is the average leaf width
         Udzm is given by equation A.9 and is obtained after computing Uc (Equations B2-B4)
-#//enddescription 
     """
     from geeet.MOST import PsiM as compute_psim
     from geeet.MOST import PsiH as compute_psih