String: Unable to convert object to string. Predicted pH: Layer error: String: Unable to convert object to string.

[safariant]

Hi People, I'm trying to model some soil parameters using random forest in google earth engine. However I am having a problem going past this error code in my work: Number (Error) String: Unable to convert object to string. Predicted pH: Layer error: String: Unable to convert object to string. Kindly assist.

This is my code: // Define the study area boundary using your shapefile var studyArea = ee.FeatureCollection('projects/ee-evanomondi/assets/Study_area');

// Load the pH and CaCO3 data from your shapefile var soilData = ee.FeatureCollection('projects/ee-evanomondi/assets/Data_Points2');

// Load the Sentinel-1 SAR, Digital Elevation Model, and climatic data for 2018 var sentinel1 = ee.ImageCollection('COPERNICUS/S1_GRD_FLOAT') .filterDate('2018-01-01', '2018-12-31') .filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VV')) .median();

// Load the Digital Elevation Model (DEM) data var dem = ee.Image('USGS/SRTMGL1_003');

// Print the DEM image object print('DEM:', dem);

// Visualize the DEM var demVis = { min: 0, max: 4000, palette: ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5'] }; Map.addLayer(dem, demVis, 'DEM');

// Optional: Center the map on the DEM Map.centerObject(dem, 10);

// Assign the DEM data to the 'dem' variable var dem = ee.Image('CGIAR/SRTM90_V4');

// Define the date range for the CHIRPS data var startDate = '2018-01-01'; var endDate = '2018-12-31';

// Load the CHIRPS precipitation data for the specified date range var chirps = ee.ImageCollection('UCSB-CHG/CHIRPS/DAILY') .filterDate(startDate, endDate) .select('precipitation') .sum();

// Print the CHIRPS image object print('CHIRPS Data:', chirps);

// Visualize the precipitation var precipitationVis = { min: 0, max: 200, palette: ['white', 'lightblue', 'blue', 'purple', 'pink'] }; Map.addLayer(chirps, precipitationVis, 'Precipitation');

// Optional: Center the map on the CHIRPS data Map.centerObject(chirps, 6);

// Assign the CHIRPS data to the climaticData variable and use the correct band name var climaticData = chirps.select('precipitation');

// Prepare the training data by combining the soil data with the auxiliary variables var trainingData = soilData.map(function(feature) { var point = feature.geometry(); var pH = feature.getNumber('pH'); var CaCO3 = feature.getNumber('CaCO3');

var sentinel1Values = sentinel1.sampleRegions({ collection: ee.FeatureCollection(point), scale: 10, tileScale: 16 }).first();

var demValue = dem.reduceRegion({ reducer: ee.Reducer.first(), geometry: point, scale: 10, maxPixels: 1e9 }).get('elevation');

var climaticDataValue = climaticData.reduceRegion({ reducer: ee.Reducer.first(), geometry: point, scale: 10, maxPixels: 1e9 }).get('precipitation');

return ee.Feature(point, { pH: pH, CaCO3: CaCO3, sentinel1: sentinel1Values, dem: demValue, climaticData: climaticDataValue }); });

// Split the training data into training and validation sets var split = 0.7; // Change this value to adjust the split ratio var randomSeed = 123; // Change this value to set a specific random seed for reproducibility var trainingSet = trainingData.randomColumn('random', randomSeed).filter(ee.Filter.lt('random', split)); var validationSet = trainingData.randomColumn('random', randomSeed).filter(ee.Filter.gte('random', split));

// Prepare the training features and target var features = ee.List(['sentinel1', 'dem', 'climaticData']); var target = 'pH'; // Change this to 'CaCO3' for modeling CaCO3

var trainingFeatures = trainingSet.map(function(feature) { var values = ee.Image(feature.get('sentinel1')).sampleRegions({ collection: ee.FeatureCollection(feature.geometry()), scale: 10, tileScale: 16 }); return feature.set('sentinel1', values); });

var trainingFeaturesArray = trainingFeatures.aggregate_array(features.slice(0, features.size()));

trainingFeaturesArray = ee.Array(trainingFeaturesArray).toList().map(function(row) { return ee.Feature(null, ee.Dictionary.fromLists(features, row)); });

var trainingFeatures = ee.FeatureCollection(trainingFeaturesArray); var trainingTarget = trainingSet.select(target);

// Prepare the validation features and target var validationFeaturesList = validationSet.map(function(feature) { var values = ee.Image(feature.get('sentinel1')).sampleRegions({ collection: ee.FeatureCollection(feature.geometry()), scale: 10, tileScale: 16 }); return feature.set('sentinel1', values); });

var validationFeaturesArray = validationFeaturesList.aggregate_array(features.slice(0, features.size()));

validationFeaturesArray = ee.Array(validationFeaturesArray).toList().map(function(row) { return ee.Feature(null, ee.Dictionary.fromLists(features, row)); });

var validationFeatures = ee.FeatureCollection(validationFeaturesArray); var validationTarget = validationSet.select(target);

// Train the model using a Random Forest algorithm var nTrees = 100; // Change this value to adjust the number of trees in the model var rf = ee.Classifier.smileRandomForest({ numberOfTrees: nTrees, variablesPerSplit: 0, bagFraction: 0.5, seed: randomSeed }).train({ features: trainingFeatures, classProperty: target, inputProperties: features });

// Make predictions on the validation set var validationPredictions = validationFeatures.map(function(feature) { var prediction = ee.Image(rf).classify(feature); return feature.set('prediction', prediction); });

// Compute the confusion matrix for the validation set var validationAccuracy = ee.ConfusionMatrix(validationPredictions.aggregate_array(target), validationPredictions.aggregate_array('prediction'));

// Print the validation accuracy print('Validation Accuracy:', validationAccuracy.accuracy());

// Visualize the predicted pH values var pHVis = { min: 0, max: 14, palette: ['red', 'yellow', 'green'] }; Map.addLayer(validationPredictions.select(['prediction']), pHVis, 'Predicted pH');

// Optional: Center the map on the study area Map.centerObject(studyArea, 8);

// Define a function to export the pH prediction map var exportMap = function() { // Make predictions on the entire study area var studyAreaFeatures = studyArea.map(function(feature) { var values = ee.Image(sentinel1).sampleRegions({ collection: eeCollection(feature.geometry()), scale: 10, tileScale: 16 }); return feature.set('sentinel1', values); });

var studyAreaFeaturesArray = studyAreaFeatures.aggregate_array(features.slice(0, features.size()));

studyAreaFeaturesArray = ee.Array(studyAreaFeaturesArray).toList().map(function(row) { return ee.Feature(null, ee.Dictionary.fromLists(features, row)); });

studyAreaFeatures = ee.FeatureCollection(studyAreaFeaturesArray);

var predictionMap = studyAreaFeatures.map(function(feature) { var prediction = ee.Image(rf).classify(feature); return feature.geometry().set('prediction', prediction); });

// Export the prediction map as a GeoTIFF file to your Google Drive Export.image.toDrive({ image: predictionMap.select(['prediction']), description: 'pH_prediction_map', folder: 'GEE_Project', scale: 10, region: studyArea.geometry() }); };