def annual_gm_mads_evi_training(ds): dc = datacube.Datacube(app='training') # grab gm+tmads gm_mads=dc.load(product='ga_s2_gm',time='2019',like=ds.geobox, measurements=['red', 'blue', 'green', 'nir', 'swir_1', 'swir_2', 'red_edge_1', 'red_edge_2', 'red_edge_3', 'SMAD', 'BCMAD','EMAD']) gm_mads['SMAD'] = -np.log(gm_mads['SMAD']) gm_mads['BCMAD'] = -np.log(gm_mads['BCMAD']) gm_mads['EMAD'] = -np.log(gm_mads['EMAD']/10000) #calculate band indices on gm gm_mads = calculate_indices(gm_mads, index=['EVI','LAI','MNDWI'], drop=False, collection='s2') #normalise spectral GM bands 0-1 for band in gm_mads.data_vars: if band not in ['SMAD', 'BCMAD','EMAD', 'EVI', 'LAI', 'MNDWI']: gm_mads[band] = gm_mads[band] / 10000 #calculate EVI on annual timeseries evi = calculate_indices(ds,index=['EVI'], drop=True, normalise=True, collection='s2') # EVI stats gm_mads['evi_std'] = evi.EVI.std(dim='time') gm_mads['evi_10'] = evi.EVI.quantile(0.1, dim='time') gm_mads['evi_25'] = evi.EVI.quantile(0.25, dim='time') gm_mads['evi_75'] = evi.EVI.quantile(0.75, dim='time') gm_mads['evi_90'] = evi.EVI.quantile(0.9, dim='time') gm_mads['evi_range'] = gm_mads['evi_90'] - gm_mads['evi_10'] #rainfall climatology chirps_S1 = xr_reproject(assign_crs(xr.open_rasterio('/g/data/CHIRPS/cumulative_alltime/CHPclim_jan_jun_cumulative_rainfall.nc'), crs='epsg:4326'), ds.geobox,"bilinear") chirps_S2 = xr_reproject(assign_crs(xr.open_rasterio('/g/data/CHIRPS/cumulative_alltime/CHPclim_jul_dec_cumulative_rainfall.nc'), crs='epsg:4326'), ds.geobox,"bilinear") gm_mads['rain_S1'] = chirps_S1 gm_mads['rain_S2'] = chirps_S2 #slope url_slope = "https://deafrica-data.s3.amazonaws.com/ancillary/dem-derivatives/cog_slope_africa.tif" slope = rio_slurp_xarray(url_slope, gbox=ds.geobox) slope = slope.to_dataset(name='slope')#.chunk({'x':2000,'y':2000}) result = xr.merge([gm_mads,slope],compat='override') return result.squeeze()
def fun(ds, era): #geomedian and tmads gm_mads = xr_geomedian_tmad(ds) gm_mads = calculate_indices(gm_mads, index=['NDVI','LAI','MNDWI'], drop=False, normalise=False, collection='s2') gm_mads['sdev'] = -np.log(gm_mads['sdev']) gm_mads['bcdev'] = -np.log(gm_mads['bcdev']) gm_mads['edev'] = -np.log(gm_mads['edev']) #rainfall climatology if era == '_S1': chirps = assign_crs(xr.open_rasterio('/g/data/CHIRPS/cumulative_alltime/CHPclim_jan_jun_cumulative_rainfall.nc'), crs='epsg:4326') if era == '_S2': chirps = assign_crs(xr.open_rasterio('/g/data/CHIRPS/cumulative_alltime/CHPclim_jul_dec_cumulative_rainfall.nc'), crs='epsg:4326') chirps = xr_reproject(chirps,ds.geobox,"bilinear") gm_mads['rain'] = chirps for band in gm_mads.data_vars: gm_mads = gm_mads.rename({band:band+era}) return gm_mads
def fun(ds, era): # six-month geomedians gm_mads = xr_geomedian(ds) gm_mads = calculate_indices( gm_mads, index=["NDVI", "LAI", "MNDWI"], drop=False, normalise=False, collection="s2", ) # rainfall climatology if era == "_S1": chirps = assign_crs( xr.open_rasterio( "/g/data/CHIRPS/cumulative_alltime/CHPclim_jan_jun_cumulative_rainfall.nc" ), crs="epsg:4326", ) if era == "_S2": chirps = assign_crs( xr.open_rasterio( "/g/data/CHIRPS/cumulative_alltime/CHPclim_jul_dec_cumulative_rainfall.nc" ), crs="epsg:4326", ) chirps = xr_reproject(chirps, ds.geobox, "bilinear") gm_mads["rain"] = chirps for band in gm_mads.data_vars: gm_mads = gm_mads.rename({band: band + era}) return gm_mads
def fun(ds, chirps, chpclim, era): ds = calculate_indices(ds, index=['EVI'], drop=False, normalise=False, collection='s2') #geomedian and tmads gm_mads = xr_geomedian_tmad(ds) gm_mads = calculate_indices(gm_mads, index=['EVI', 'NDVI', 'LAI', 'MNDWI'], drop=False, normalise=False, collection='s2') gm_mads['sdev'] = -np.log(gm_mads['sdev']) gm_mads['bcdev'] = -np.log(gm_mads['bcdev']) gm_mads['edev'] = -np.log(gm_mads['edev']) # EVI stats gm_mads['evi_10'] = ds.EVI.quantile(0.1, dim='time') gm_mads['evi_50'] = ds.EVI.quantile(0.5, dim='time') gm_mads['evi_90'] = ds.EVI.quantile(0.9, dim='time') gm_mads['evi_range'] = gm_mads['evi_90'] - gm_mads['evi_10'] gm_mads['evi_std'] = ds.EVI.std(dim='time') # rainfall actual gm_mads['rain_min'] = chirps.min(dim='time') gm_mads['rain_mean'] = chirps.mean(dim='time') gm_mads['rain_max'] = chirps.max(dim='time') gm_mads['rain_range'] = gm_mads['rain_max'] - gm_mads['rain_min'] gm_mads['rain_std'] = chirps.std(dim='time') # rainfall climatology gm_mads['rainclim_min'] = chpclim.min(dim='time') gm_mads['rainclim_mean'] = chpclim.mean(dim='time') gm_mads['rainclim_max'] = chpclim.max(dim='time') gm_mads['rainclim_range'] = gm_mads['rainclim_max'] - gm_mads[ 'rainclim_min'] gm_mads['rainclim_std'] = chpclim.std(dim='time') for band in gm_mads.data_vars: gm_mads = gm_mads.rename({band: band + era}) return gm_mads
def fun(ds, chirps, chpclim, era): ds = calculate_indices( ds, index=["EVI"], drop=False, normalise=False, collection="s2" ) # geomedian and tmads gm_mads = xr_geomedian_tmad(ds) gm_mads = calculate_indices( gm_mads, index=["EVI", "NDVI", "LAI", "MNDWI"], drop=False, normalise=False, collection="s2", ) gm_mads["sdev"] = -np.log(gm_mads["sdev"]) gm_mads["bcdev"] = -np.log(gm_mads["bcdev"]) gm_mads["edev"] = -np.log(gm_mads["edev"]) # EVI stats gm_mads["evi_10"] = ds.EVI.quantile(0.1, dim="time") gm_mads["evi_50"] = ds.EVI.quantile(0.5, dim="time") gm_mads["evi_90"] = ds.EVI.quantile(0.9, dim="time") gm_mads["evi_range"] = gm_mads["evi_90"] - gm_mads["evi_10"] gm_mads["evi_std"] = ds.EVI.std(dim="time") # rainfall actual gm_mads["rain_min"] = chirps.min(dim="time") gm_mads["rain_mean"] = chirps.mean(dim="time") gm_mads["rain_max"] = chirps.max(dim="time") gm_mads["rain_range"] = gm_mads["rain_max"] - gm_mads["rain_min"] gm_mads["rain_std"] = chirps.std(dim="time") # rainfall climatology gm_mads["rainclim_min"] = chpclim.min(dim="time") gm_mads["rainclim_mean"] = chpclim.mean(dim="time") gm_mads["rainclim_max"] = chpclim.max(dim="time") gm_mads["rainclim_range"] = gm_mads["rainclim_max"] - gm_mads["rainclim_min"] gm_mads["rainclim_std"] = chpclim.std(dim="time") for band in gm_mads.data_vars: gm_mads = gm_mads.rename({band: band + era}) return gm_mads
def fun(ds, era): # geomedian and tmads # gm_mads = xr_geomedian_tmad(ds) gm_mads = xr_geomedian_tmad_new(ds).compute() gm_mads = calculate_indices( gm_mads, index=["NDVI", "LAI", "MNDWI"], drop=False, normalise=False, collection="s2", ) gm_mads["sdev"] = -np.log(gm_mads["sdev"]) gm_mads["bcdev"] = -np.log(gm_mads["bcdev"]) gm_mads["edev"] = -np.log(gm_mads["edev"]) gm_mads = gm_mads.chunk({"x": 2000, "y": 2000}) # rainfall climatology if era == "_S1": chirps = assign_crs( xr.open_rasterio( "/g/data/CHIRPS/cumulative_alltime/CHPclim_jan_jun_cumulative_rainfall.nc" ), crs="epsg:4326", ) if era == "_S2": chirps = assign_crs( xr.open_rasterio( "/g/data/CHIRPS/cumulative_alltime/CHPclim_jul_dec_cumulative_rainfall.nc" ), crs="epsg:4326", ) chirps = xr_reproject(chirps, ds.geobox, "bilinear") chirps = chirps.chunk({"x": 2000, "y": 2000}) gm_mads["rain"] = chirps for band in gm_mads.data_vars: gm_mads = gm_mads.rename({band: band + era}) return gm_mads
def fun(ds, era): # normalise SR and edev bands for band in ds.data_vars: if band not in ["sdev", "bcdev"]: ds[band] = ds[band] / 10000 gm_mads = calculate_indices( ds, index=["NDVI", "LAI", "MNDWI"], drop=False, normalise=False, collection="s2", ) gm_mads["sdev"] = -np.log(gm_mads["sdev"]) gm_mads["bcdev"] = -np.log(gm_mads["bcdev"]) gm_mads["edev"] = -np.log(gm_mads["edev"]) # rainfall climatology if era == "_S1": chirps = assign_crs( xr.open_rasterio( "/g/data/CHIRPS/cumulative_alltime/CHPclim_jan_jun_cumulative_rainfall.nc" ), crs="epsg:4326", ) if era == "_S2": chirps = assign_crs( xr.open_rasterio( "/g/data/CHIRPS/cumulative_alltime/CHPclim_jul_dec_cumulative_rainfall.nc" ), crs="epsg:4326", ) chirps = xr_reproject(chirps, ds.geobox, "bilinear") gm_mads["rain"] = chirps for band in gm_mads.data_vars: gm_mads = gm_mads.rename({band: band + era}) return gm_mads
def get_training_data_for_shp(gdf, index, row, out_arrs, out_vars, products, dc_query, custom_func=None, field=None, calc_indices=None, reduce_func=None, drop=True, zonal_stats=None): """ Function to extract data from the ODC for training a machine learning classifier using a geopandas geodataframe of labelled geometries. This function provides a number of pre-defined methods for producing training data, including calcuating band indices, reducing time series using several summary statistics, and/or generating zonal statistics across polygons. The 'custom_func' parameter provides a method for the user to supply a custom function for generating features rather than using the pre-defined methods. Parameters ---------- gdf : geopandas geodataframe geometry data in the form of a geopandas geodataframe products : list a list of products to load from the datacube. e.g. ['ls8_usgs_sr_scene', 'ls7_usgs_sr_scene'] dc_query : dictionary Datacube query object, should not contain lat and long (x or y) variables as these are supplied by the 'gdf' variable field : string A string containing the name of column with class labels. Field must contain numeric values. out_arrs : list An empty list into which the training data arrays are stored. out_vars : list An empty list into which the data varaible names are stored. custom_func : function, optional A custom function for generating feature layers. If this parameter is set, all other options (excluding 'zonal_stats'), will be ignored. The result of the 'custom_func' must be a single xarray dataset containing 2D coordinates (i.e x, y - no time dimension). The custom function has access to the datacube dataset extracted using the 'dc_query' params, along with access to the 'dc_query' dictionary itself, which could be used to load other products besides those specified under 'products'. calc_indices: list, optional If not using a custom func, then this parameter provides a method for calculating a number of remote sensing indices (e.g. `['NDWI', 'NDVI']`). reduce_func : string, optional Function to reduce the data from multiple time steps to a single timestep. Options are 'mean', 'median', 'std', 'max', 'min', 'geomedian'. Ignored if 'custom_func' is provided. drop : boolean, optional , If this variable is set to True, and 'calc_indices' are supplied, the spectral bands will be dropped from the dataset leaving only the band indices as data variables in the dataset. Default is True. zonal_stats : string, optional An optional string giving the names of zonal statistics to calculate for each polygon. Default is None (all pixel values are returned). Supported values are 'mean', 'median', 'max', 'min', and 'std'. Will work in conjuction with a 'custom_func'. Returns -------- Two lists, a list of numpy.arrays containing classes and extracted data for each pixel or polygon, and another containing the data variable names. """ # prevent function altering dictionary kwargs dc_query = deepcopy(dc_query) # remove dask chunks if supplied as using # mulitprocessing for parallization if 'dask_chunks' in dc_query.keys(): dc_query.pop('dask_chunks', None) # connect to datacube dc = datacube.Datacube(app='training_data') # set up query based on polygon (convert to WGS84) geom = geometry.Geometry(gdf.geometry.values[index].__geo_interface__, geometry.CRS('epsg:4326')) # print(geom) q = {"geopolygon": geom} # merge polygon query with user supplied query params dc_query.update(q) # Identify the most common projection system in the input query output_crs = mostcommon_crs(dc=dc, product=products, query=dc_query) # load_ard doesn't handle geomedians # TODO: Add support for other sensors if 'ga_ls8c_gm_2_annual' in products: ds = dc.load(product='ga_ls8c_gm_2_annual', **dc_query) ds = ds.where(ds != 0, np.nan) else: # load data with HiddenPrints(): ds = load_ard(dc=dc, products=products, output_crs=output_crs, **dc_query) # create polygon mask with HiddenPrints(): mask = xr_rasterize(gdf.iloc[[index]], ds) # mask dataset ds = ds.where(mask) # Use custom function for training data if it exists if custom_func is not None: with HiddenPrints(): data = custom_func(ds) else: # first check enough variables are set to run functions if (len(ds.time.values) > 1) and (reduce_func == None): raise ValueError( "You're dataset has " + str(len(ds.time.values)) + " time-steps, please provide a reduction function," + " e.g. reduce_func='mean'") if calc_indices is not None: # determine which collection is being loaded if 'level2' in products[0]: collection = 'c2' elif 'gm' in products[0]: collection = 'c2' elif 'sr' in products[0]: collection = 'c1' elif 's2' in products[0]: collection = 's2' if len(ds.time.values) > 1: if reduce_func in ['mean', 'median', 'std', 'max', 'min']: with HiddenPrints(): data = calculate_indices(ds, index=calc_indices, drop=drop, collection=collection) # getattr is equivalent to calling data.reduce_func method_to_call = getattr(data, reduce_func) data = method_to_call(dim='time') elif reduce_func == 'geomedian': data = GeoMedian().compute(ds) with HiddenPrints(): data = calculate_indices(data, index=calc_indices, drop=drop, collection=collection) else: raise Exception( reduce_func + " is not one of the supported" + " reduce functions ('mean','median','std','max','min', 'geomedian')" ) else: with HiddenPrints(): data = calculate_indices(ds, index=calc_indices, drop=drop, collection=collection) # when band indices are not required, reduce the # dataset to a 2d array through means or (geo)medians if calc_indices is None: if len(ds.time.values) > 1: if reduce_func == 'geomedian': data = GeoMedian().compute(ds) elif reduce_func in ['mean', 'median', 'std', 'max', 'min']: method_to_call = getattr(ds, reduce_func) data = method_to_call('time') else: data = ds.squeeze() if zonal_stats is None: # If no zonal stats were requested then extract all pixel values flat_train = sklearn_flatten(data) # Make a labelled array of identical size flat_val = np.repeat(row[field], flat_train.shape[0]) stacked = np.hstack((np.expand_dims(flat_val, axis=1), flat_train)) elif zonal_stats in ['mean', 'median', 'std', 'max', 'min']: method_to_call = getattr(data, zonal_stats) flat_train = method_to_call() flat_train = flat_train.to_array() stacked = np.hstack((row[field], flat_train)) else: raise Exception( zonal_stats + " is not one of the supported" + " reduce functions ('mean','median','std','max','min')") # Append training data and labels to list out_arrs.append(stacked) out_vars.append([field] + list(data.data_vars))
def get_training_data_for_shp(polygons, out, products, dc_query, field=None, calc_indices=None, reduce_func='median', drop=True, zonal_stats=None, collection='c1'): """ Function to extract data for training a classifier using a shapefile of labelled polygons. Parameters ---------- polygons : geopandas geodataframe polygon data in the form of a geopandas geodataframe out : list Empty list to contain output data. products : list a list of products ot load from the datacube. e.g. ['ls8_usgs_sr_scene', 'ls7_usgs_sr_scene'] dc_query : dictionary Datacube query object, should not contain lat and long (x or y) variables as these are supplied by the 'polygons' variable field : string A string containing name of column with labels in shapefile attribute table. Field must contain numeric values. calc_indices: list, optional An optional list giving the names of any remote sensing indices to be calculated on the loaded data (e.g. `['NDWI', 'NDVI']`. reduce_func : string, optional Function to reduce the data from multiple time steps to a single timestep. Options are 'mean' drop : booleam, optional , 'median', or 'geomedian' If this variable is set to True, and 'calc_indices' are supplied, the spectral bands will be dropped from the dataset leaving only the band indices as data variables in the dataset. Default is False. zonal_stats: string, optional An optional string giving the names of zonal statistics to calculate for the polygon. Default is None (all pixel values). Supported values are 'mean' or 'median' collection: string, optional to calculate band indices, the satellite collection is required. Options include 'c1' for Landsat C1, 'c2' for Landsat C2, and 's2' for Sentinel 2. Returns -------- A list of numpy.arrays containing classes and extracted data for each pixel or polygon. """ #prevent function altering dictionary kwargs dc_query = deepcopy(dc_query) dc = datacube.Datacube(app='training_data') #set up some print statements i = 0 if calc_indices is not None: print("Calculating indices: " + str(calc_indices)) if reduce_func is not None: print("Reducing data using: " + reduce_func) if zonal_stats is not None: print("Taking zonal statistic: " + zonal_stats) # loop through polys and extract training data for index, row in polygons.iterrows(): print(" Feature {:04}/{:04}\r".format(i + 1, len(polygons)), end='') # set up query based on polygon (convert to WGS84) geom = geometry.Geometry(polygons.geometry.values[0].__geo_interface__, geometry.CRS('epsg:4326')) q = {"geopolygon": geom} # merge polygon query with user supplied query params dc_query.update(q) # Identify the most common projection system in the input query output_crs = mostcommon_crs(dc=dc, product=products, query=dc_query) #load_ard doesn't handle geomedians if 'ga_ls8c_gm_2_annual' in products: ds = dc.load(product='ga_ls8c_gm_2_annual', **dc_query) else: # load data with HiddenPrints(): ds = load_ard(dc=dc, products=products, output_crs=output_crs, **dc_query) # create polygon mask mask = rasterio.features.geometry_mask( [geom.to_crs(ds.geobox.crs) for geoms in [geom]], out_shape=ds.geobox.shape, transform=ds.geobox.affine, all_touched=False, invert=False) mask = xr.DataArray(mask, dims=("y", "x")) ds = ds.where(mask == False) # Check if band indices are wanted if calc_indices is not None: if len(ds.time.values) > 1: if reduce_func == 'geomedian': data = GeoMedian().compute(ds) with HiddenPrints(): data = calculate_indices(data, index=calc_indices, drop=drop, collection=collection) elif reduce_func == 'std': with HiddenPrints(): data = calculate_indices(ds, index=calc_indices, drop=drop, collection=collection) data = data.std('time') elif reduce_func == 'mean': with HiddenPrints(): data = calculate_indices(ds, index=calc_indices, drop=drop, collection=collection) data = data.mean('time') elif reduce_func == 'median': with HiddenPrints(): data = calculate_indices(ds, index=calc_indices, drop=drop, collection=collection) data = data.median('time') else: with HiddenPrints(): data = calculate_indices(ds, index=calc_indices, drop=drop, collection=collection) # when band indices are not required, reduce the # dataset to a 2d array through means or (geo)medians if calc_indices is None: if (len(ds.time.values) > 1) and (reduce_func == None): raise ValueError( "You're dataset has " + str(len(ds.time.values)) + "time-steps, please provide a reduction function, e.g. reduce_func='mean'" ) if len(ds.time.values) > 1: if reduce_func == 'geomedian': data = GeoMedian().compute(ds) if reduce_func == 'mean': data = ds.mean('time') if reduce_func == 'std': data = ds.std('time') if reduce_func == 'median': data = ds.median('time') else: data = ds.squeeze() # compute in case we have dask arrays data = data.compute() if zonal_stats is None: # If no summary stats were requested then extract all pixel values flat_train = sklearn_flatten(data) # Make a labelled array of identical size flat_val = np.repeat(row[field], flat_train.shape[0]) stacked = np.hstack((np.expand_dims(flat_val, axis=1), flat_train)) elif zonal_stats == 'mean': flat_train = data.mean(axis=None, skipna=True) flat_train = flat_train.to_array() stacked = np.hstack((row[field], flat_train)) elif zonal_stats == 'median': flat_train = data.median(axis=None, skipna=True) flat_train = flat_train.to_array() stacked = np.hstack((row[field], flat_train)) # Append training data and label to list out.append(stacked) i += 1 # Return a list of labels for columns in output array return [field] + list(data.data_vars)
def load_crophealth_data(lat, lon, buffer): """ Loads Landsat 8 analysis-ready data (ARD) product for the crop health case-study area over the last two years. Last modified: April 2020 Parameters ---------- lat: float The central latitude to analyse lon: float The central longitude to analyse buffer: The number of square degrees to load around the central latitude and longitude. For reasonable loading times, set this as `0.1` or lower. Returns ---------- ds: xarray.Dataset data set containing combined, masked data Masked values are set to 'nan' """ # Suppress warnings warnings.filterwarnings('ignore') # Initialise the data cube. 'app' argument is used to identify this app dc = datacube.Datacube(app='Crophealth-app') # Define area to load latitude = (lat - buffer, lat + buffer) longitude = (lon - buffer, lon + buffer) # Specify the date range # Calculated as today's date, subtract 730 days to collect two years of data # Dates are converted to strings as required by loading function below end_date = dt.date.today() start_date = end_date - dt.timedelta(days=730) time = (start_date.strftime("%Y-%m-%d"), end_date.strftime("%Y-%m-%d")) # Construct the data cube query products = ["ls8_usgs_sr_scene"] query = { 'x': longitude, 'y': latitude, 'time': time, 'measurements': [ 'red', 'green', 'blue', 'nir', 'swir2' ], 'output_crs': 'EPSG:6933', 'resolution': (-30, 30) } # Load the data and mask out bad quality pixels ds = load_ard(dc, products=products, min_gooddata=0.5, **query) # Calculate the normalised difference vegetation index (NDVI) across # all pixels for each image. # This is stored as an attribute of the data ds = calculate_indices(ds, index='NDVI', collection='s2') # Return the data return(ds)
def annual_gm_mads_evi_training(ds): dc = datacube.Datacube(app="training") # grab gm+tmads gm_mads = dc.load( product="ga_s2_gm", time="2019", like=ds.geobox, measurements=[ "red", "blue", "green", "nir", "swir_1", "swir_2", "red_edge_1", "red_edge_2", "red_edge_3", "SMAD", "BCMAD", "EMAD", ], ) gm_mads["SMAD"] = -np.log(gm_mads["SMAD"]) gm_mads["BCMAD"] = -np.log(gm_mads["BCMAD"]) gm_mads["EMAD"] = -np.log(gm_mads["EMAD"] / 10000) # calculate band indices on gm gm_mads = calculate_indices( gm_mads, index=["EVI", "LAI", "MNDWI"], drop=False, collection="s2" ) # normalise spectral GM bands 0-1 for band in gm_mads.data_vars: if band not in ["SMAD", "BCMAD", "EMAD", "EVI", "LAI", "MNDWI"]: gm_mads[band] = gm_mads[band] / 10000 # calculate EVI on annual timeseries evi = calculate_indices( ds, index=["EVI"], drop=True, normalise=True, collection="s2" ) # EVI stats gm_mads["evi_std"] = evi.EVI.std(dim="time") gm_mads["evi_10"] = evi.EVI.quantile(0.1, dim="time") gm_mads["evi_25"] = evi.EVI.quantile(0.25, dim="time") gm_mads["evi_75"] = evi.EVI.quantile(0.75, dim="time") gm_mads["evi_90"] = evi.EVI.quantile(0.9, dim="time") gm_mads["evi_range"] = gm_mads["evi_90"] - gm_mads["evi_10"] # rainfall climatology chirps_S1 = xr_reproject( assign_crs( xr.open_rasterio( "/g/data/CHIRPS/cumulative_alltime/CHPclim_jan_jun_cumulative_rainfall.nc" ), crs="epsg:4326", ), ds.geobox, "bilinear", ) chirps_S2 = xr_reproject( assign_crs( xr.open_rasterio( "/g/data/CHIRPS/cumulative_alltime/CHPclim_jul_dec_cumulative_rainfall.nc" ), crs="epsg:4326", ), ds.geobox, "bilinear", ) gm_mads["rain_S1"] = chirps_S1 gm_mads["rain_S2"] = chirps_S2 # slope url_slope = "https://deafrica-data.s3.amazonaws.com/ancillary/dem-derivatives/cog_slope_africa.tif" slope = rio_slurp_xarray(url_slope, gbox=ds.geobox) slope = slope.to_dataset(name="slope") # .chunk({'x':2000,'y':2000}) result = xr.merge([gm_mads, slope], compat="override") return result.squeeze()
def fun(ds, era): # normalise SR and edev bands for band in ds.data_vars: if band not in ["sdev", "bcdev"]: ds[band] = ds[band] / 10000 gm_mads = calculate_indices( ds, index=["NDVI", "LAI", "MNDWI"], drop=False, normalise=False, collection="s2", ) gm_mads["sdev"] = -np.log(gm_mads["sdev"]) gm_mads["bcdev"] = -np.log(gm_mads["bcdev"]) gm_mads["edev"] = -np.log(gm_mads["edev"]) # rainfall climatology if era == "_S1": chirps = assign_crs( xr.open_rasterio( "/g/data/CHIRPS/cumulative_alltime/CHPclim_jan_jun_cumulative_rainfall.nc" ), crs="epsg:4326", ) if era == "_S2": chirps = assign_crs( xr.open_rasterio( "/g/data/CHIRPS/cumulative_alltime/CHPclim_jul_dec_cumulative_rainfall.nc" ), crs="epsg:4326", ) # Clip CHIRPS to ~ S2 tile boundaries so we can handle NaNs local to S2 tile xmin, xmax = ds.x.values[0], ds.x.values[-1] ymin, ymax = ds.y.values[0], ds.y.values[-1] inProj = Proj("epsg:6933") outProj = Proj("epsg:4326") xmin, ymin = transform(inProj, outProj, xmin, ymin) xmax, ymax = transform(inProj, outProj, xmax, ymax) # create lat/lon indexing slices - buffer S2 bbox by 0.05deg if (xmin < 0) & (xmax < 0): x_slice = list(np.arange(xmin + 0.05, xmax - 0.05, -0.05)) else: x_slice = list(np.arange(xmax - 0.05, xmin + 0.05, 0.05)) if (ymin < 0) & (ymax < 0): y_slice = list(np.arange(ymin + 0.05, ymax - 0.05, -0.05)) else: y_slice = list(np.arange(ymin - 0.05, ymax + 0.05, 0.05)) # index global chirps using buffered s2 tile bbox chirps = assign_crs(chirps.sel(x=y_slice, y=x_slice, method="nearest")) # fill any NaNs in CHIRPS with local (s2-tile bbox) mean chirps = chirps.fillna(chirps.mean()) chirps = xr_reproject(chirps, ds.geobox, "bilinear") gm_mads["rain"] = chirps for band in gm_mads.data_vars: gm_mads = gm_mads.rename({band: band + era}) return gm_mads
def features(ds, era): #normalise SR and edev bands for band in ds.data_vars: if band not in ['sdev', 'bcdev']: ds[band] = ds[band] / 10000 gm_mads = calculate_indices(ds, index=['NDVI', 'LAI', 'MNDWI'], drop=False, normalise=False, collection='s2') gm_mads['sdev'] = -np.log(gm_mads['sdev']) gm_mads['bcdev'] = -np.log(gm_mads['bcdev']) gm_mads['edev'] = -np.log(gm_mads['edev']) #rainfall climatology if era == '_S1': chirps = assign_crs(xr.open_rasterio( '/g/data/CHIRPS/cumulative_alltime/CHPclim_jan_jun_cumulative_rainfall.nc' ), crs='epsg:4326') if era == '_S2': chirps = assign_crs(xr.open_rasterio( '/g/data/CHIRPS/cumulative_alltime/CHPclim_jul_dec_cumulative_rainfall.nc' ), crs='epsg:4326') #Clip CHIRPS to ~ S2 tile boundaries so we can handle NaNs local to S2 tile xmin, xmax = ds.x.values[0], ds.x.values[-1] ymin, ymax = ds.y.values[0], ds.y.values[-1] inProj = Proj('epsg:6933') outProj = Proj('epsg:4326') xmin, ymin = transform(inProj, outProj, xmin, ymin) xmax, ymax = transform(inProj, outProj, xmax, ymax) #create lat/lon indexing slices - buffer S2 bbox by 0.05deg if (xmin < 0) & (xmax < 0): x_slice = list(np.arange(xmin + 0.05, xmax - 0.05, -0.05)) else: x_slice = list(np.arange(xmax - 0.05, xmin + 0.05, 0.05)) if (ymin < 0) & (ymax < 0): y_slice = list(np.arange(ymin + 0.05, ymax - 0.05, -0.05)) else: y_slice = list(np.arange(ymin - 0.05, ymax + 0.05, 0.05)) #index global chirps using buffered s2 tile bbox chirps = assign_crs(chirps.sel(x=y_slice, y=x_slice, method='nearest')) #fill any NaNs in CHIRPS with local (s2-tile bbox) mean chirps = chirps.fillna(chirps.mean()) #reproject to match satellite data chirps = xr_reproject(chirps, ds.geobox, "bilinear") gm_mads['rain'] = chirps for band in gm_mads.data_vars: gm_mads = gm_mads.rename({band: band + era}) return gm_mads