Merge pull request #1651 from xuzhenwu:main

PiperOrigin-RevId: 871523922

Author: copybara-github

catalog/pml_evapotranspiration/projects_pml_evapotranspiration_PML_OUTPUT_PML_V22a.jsonnet

@@ -27,8 +27,8 @@ local catalog_base_url = ee_const.catalog_base;
   version: '2.2a',
   'gee:type': ee_const.gee_type.image_collection,
   description: |||
-    The dataset is produced by the Large Scale Hydrology Lab, which specializes in advancing global and regional water cycle research 
-    through big data analytics and advanced hydrological modeling. 
+    The dataset is produced by the Large Scale Hydrology Lab, which specializes in advancing global and 
+    regional water cycle research by synthesizing multi-source Earth observations with process-based modeling. 
 
     The PML-V2.2a product provides 500m 8-day resolution global terrestrial evapotranspiration (ET) 
     and gross primary production (GPP) from 2000 to 2024. Driven by MSWEP and MSWX, this version 
@@ -39,8 +39,9 @@ local catalog_base_url = ee_const.catalog_base;
     and reliable basin-scale water-balance performance (NSE: 0.89–0.91). This MODIS-based 
     record is optimized for high-resolution near-present monitoring.
 
-    This dataset is part of the broader PML-V2.2 suite. For the long-term 43-year consolidated 
-    record (PML-V2.2b/c at 0.1° with different remote sensing forcing), please visit the [TPDC data repository](https://doi.org/10.11888/Terre.tpdc.303314).
+    This dataset is part of the broader PML-V2.2 suite. For the long-term and consolidated record 
+    (1982-near present, PML-V2.2a/b/c at 0.1° resolution with different remote sensing forcings), 
+    please visit the [TPDC data repository](https://doi.org/10.11888/Terre.tpdc.303314).
   |||,
   license: license.id,
   links: ee.standardLinks(subdir, id) + [
@@ -56,7 +57,7 @@ local catalog_base_url = ee_const.catalog_base;
     //'water-carbon',
   ],
   providers: [
-    ee.producer_provider('Large Scale Hydrology Lab', 'https://scholar.google.com/citations?user=t5iGFeAAAAAJ&hl=en'),
+    ee.producer_provider('Large Scale Hydrology Lab', 'https://zhang-hydrolab.github.io/'),
     ee.host_provider(self_ee_catalog_url),
   ],
   extent: ee.extent(-180.0, -60.0, 180.0, 90.0, '2000-03-05T00:00:00Z', null),
@@ -96,19 +97,17 @@ local catalog_base_url = ee_const.catalog_base;
       { 
         name: 'PET', 
         description: |||
-          Potential evapotranspiration (PET). Calculated using the Shuttleworth-simplified 
-          version of the Penman equation, which provides estimates of evaporation 
-          from water bodies, snow, and ice.
+          Potential evapotranspiration (PET) estimated using the Shuttleworth-simplified 
+          Penman equation.
         |||, 
         'gee:units': units.millimeter_per_day,
         'gee:scale': 0.01
       },
       { 
         name: 'Ew', 
         description: |||
-          Evaporation from water bodies, snow, and ice. Calculated using the
-          Penman equation, which is considered a good estimate of actual
-          evaporation for these surfaces.
+          Evaporation from water bodies, snow, and ice, estimated using the
+          Penman equation with unlimited water supply.
         |||, 
         'gee:units': units.millimeter_per_day,
         'gee:scale': 0.01

examples/pml_evapotranspiration/projects_pml_evapotranspiration_PML_OUTPUT_PML_V22a.js

@@ -1,19 +1,25 @@
 var dataset = ee.ImageCollection('projects/pml_evapotranspiration/PML/OUTPUT/PML_V22a');
 
-// Scale the first image to physical units (0.01 scale factor)
-var firstImage = dataset.first().multiply(0.01);
-
+// Convert 8-day averages to annual estimate with 0.01 scale factor
+var annualImage = dataset.filterDate('2024-01-01', '2024-12-31')
+    .select('ET')
+    .mean()
+    .multiply(365)
+    .multiply(0.01);
+    
+// Final unit: mm yr-1
 var visualization = {
-  bands: ['GPP'],
+  bands: ['ET'],
   min: 0.0,
-  max: 9.0,
+  max: 1600, 
   palette: [
-    'a50026', 'd73027', 'f46d43', 'fdae61', 'fee08b', 'ffffbf',
-    'd9ef8b', 'a6d96a', '66bd63', '1a9850', '006837',
+    "#A02323", "#C80000", "#FF0000", "#FF6E00", "#FFAA00", "#FFE132", 
+    "#FFFFC8", "#D2F5FF", "#A0D2FF", "#00BFFF", "#1E90FF", "#4169E1", 
+    "#0000CD", "#000096"
   ]
 };
 
 Map.setCenter(0.0, 15.0, 2);
 
 Map.addLayer(
-    firstImage, visualization, 'PML_V2.2a Gross Primary Product (GPP)');
+    annualImage, visualization, 'PML_V2.2a Evapotranspiration (ET)');

examples/pml_evapotranspiration/projects_pml_evapotranspiration_PML_OUTPUT_PML_V22a_preview.js

@@ -1,25 +1,33 @@
 var dataset = ee.ImageCollection('projects/pml_evapotranspiration/PML/OUTPUT/PML_V22a');
 
+// Convert 8-day averages to annual estimate with 0.01 scale factor
+var image = dataset.filterDate('2024-01-01', '2024-12-31')
+    .select('ET')
+    .mean()
+    .multiply(365)
+    .multiply(0.01);
+    
+// Final unit: mm yr-1
 var visualization = {
+  bands: ['ET'],
   min: 0.0,
-  max: 9.0,
+  max: 1600, 
   palette: [
-    'a50026', 'd73027', 'f46d43', 'fdae61', 'fee08b', 'ffffbf',
-    'd9ef8b', 'a6d96a', '66bd63', '1a9850', '006837',
+    "#A02323", "#C80000", "#FF0000", "#FF6E00", "#FFAA00", "#FFE132", 
+    "#FFFFC8", "#D2F5FF", "#A0D2FF", "#00BFFF", "#1E90FF", "#4169E1", 
+    "#0000CD", "#000096"
   ]
 };
 
-var gray = 150;
-var background = ee.Image.rgb(gray, gray, gray).visualize({min: 0, max: 255});
-
-// Calculate mean GPP and apply the 0.01 scale factor
-var image = dataset.select('GPP')
-    .filterDate('2001-01-01', '2023-12-31')
-    .mean()
-    .multiply(0.01);
+Map.setCenter(0.0, 15.0, 2);
 
 Map.addLayer(
-    image, visualization, 'PML_V2.2a Gross Primary Product (GPP)');
+    image, visualization, 'PML_V2.2a Evapotranspiration (ET)');
+
+    
+var white = 255; 
+var background = ee.Image.rgb(white, white, white).visualize({min: 0, max: 255});
+
 
 // Visualize the image and layer it over the gray background
 var visualizedImage = image.visualize(visualization);

owners/pml_evapotranspiration.jsonnet

@@ -2,9 +2,9 @@
     "id": "pml_evapotranspiration",
     "homeBucket": "projects/pml_evapotranspiration",
     "name": "Large Scale Hydrology Lab",
-    "description": "The Large Scale Hydrology Lab specializes in advancing global and regional water cycle research through big data analytics and advanced hydrological modeling. We are widely recognized for the Penman-Monteith-Leuning (PML) model and its high-accuracy, long-term evapotranspiration (ET) and gross primary productivity (GPP) products. By integrating remote sensing, machine learning, and surface process modeling, our goal is to provide essential data infrastructure for monitoring water-carbon coupling and understanding climate change impacts at a global scale.",
+    "description": "The Large Scale Hydrology Lab specializes in advancing global and regional water cycle research by synthesizing multi-source Earth observations with process-based modeling. We are widely recognized for the Penman-Monteith-Leuning (PML) model and its high-accuracy, long-term evapotranspiration (ET) and gross primary productivity (GPP) products. Rooted in hydrology and water resources, our goal is to provide essential data support for monitoring large-scale hydrological processes, water-carbon coupling, and climate change impacts.",
     "type": "PUBLISHER",
-    "link": "https://scholar.google.com/citations?user=t5iGFeAAAAAJ&hl=en",
+    "link": "https://zhang-hydrolab.github.io/",
     "imageLink": "https://storage.googleapis.com/lshl_public/ee-publisher/lshl_ee_publisher_image.png",
     "logo": "https://storage.googleapis.com/lshl_public/ee-publisher/lshl_logo.png",
     "contactDisplay": "Large Scale Hydrology Lab",