sensor: add UAVSAR-L (#1320)

+ `objects/sensor`: add UAVSAR-L info

+ `utils.network.py`:
   - refactor `critical_perp_baseline()` to incorporate different near_range value of different sensors
   - `simulate_coherence_v2()`: add SNR as an input argument

+ `utils.ptime.get_date_range()`: ensure output dt_list is within the input `[dmin, dmax]`

src/mintpy/objects/sensor.py

@@ -18,6 +18,7 @@
     'env'   : ['env', 'envisat', 'asar'],
     'ers'   : ['ers', 'ers1', 'ers2', 'ers12'],
     'gf3'   : ['gfen3', 'gaofen3', 'g3', 'gaofen'],
+    'hj1c'  : ['hj1', 'huanjing1c'],
     'jers'  : ['jers', 'jers1'],
     'ksat5' : ['ksat5', 'kompsat5', 'kompsat', 'kmps5'],
     'lt1'   : ['lt1', 'lt', 'lutan', 'lutan1'],
@@ -569,11 +570,39 @@ def get_unavco_mission_name(meta_dict):
     'pulse_repetition_frequency' : 2934,      # Hz
     'chirp_bandwidth'            : 80.0e6,    # Hz
     'azimuth_pixel_size'         : 2.35,      # m
-    'range_pixel_size'           : 1.67,      # m
     'azimuth_resolution'         : 7.15,      # m
+    'range_pixel_size'           : 1.67,      # m
     'range_resolution'           : 2.50,      # m
 }
 
+# UAVSAR-L
+# Reference:
+#   https://earth.jpl.nasa.gov/estd-missions/airborne/uavsar/
+#   https://airbornescience.nasa.gov/instrument/Uninhabited_Aerial_Vehicle_Synthetic_Aperture_Radar
+#   https://www.eoportal.org/other-space-activities/uavsar
+#   Fore et al. (2015) at https://doi.org/10.1109/TGRS.2014.2377637
+#   Hu et al. (2020) at https://doi.org/10.1038/s41467-020-16617-7
+# Parameters:
+#   swath width = 16e3 m
+#   fly path accuracy <= 10 m
+#   instrument power = 3.1e3 W
+#   noise equivalent sigma zero < -50 dB
+#   operating altitude range = 2e3 - 18e3 m
+#   ground speed range = 100 - 250 m/s
+UAV_L = {
+    # orbit
+    'altitude'                   : 13.8e3,    # m, 2 - 18 km
+    # sar / antenna
+    'carrier_frequency'          : 1.2575e9,  # Hz
+    'antenna_length'             : 1.6,       # m
+    'antenna_width'              : 0.5,       # m
+    'chirp_bandwidth'            : 80e6,      # Hz
+    'range_resolution'           : 1.8,       # m
+    'range_pixel_size'           : 1.67,      # m
+    'azimuth_resolution'         : 0.8,       # m
+    'azimuth_pixel_size'         : 0.6,       # m
+}
+
 # NISAR
 # https://nisar.jpl.nasa.gov/system/documents/files/26_NISAR_FINAL_9-6-19.pdf
 NISAR_L = {
@@ -608,11 +637,14 @@ def get_unavco_mission_name(meta_dict):
     'rsat2' : RSAT2,
     'rcm'   : RCM,
     'gf3'   : GF3,
+    # S-band
+    'hj1c'  : HJ1C,
     # L-band
     'jers'  : JERS,
     'alos'  : ALOS,
     'alos2' : ALOS2,
     'alos4' : ALOS4,
     'lt1'   : LT1,
+    'uav'   : UAV_L,
     'ni'    : NISAR_L,
 }

src/mintpy/utils/network.py

@@ -17,6 +17,7 @@
 from matplotlib.tri import Triangulation
 from scipy import sparse
 
+from mintpy.constants import SPEED_OF_LIGHT
 from mintpy.objects import ifgramStack, sensor
 from mintpy.utils import ptime, readfile
 
@@ -183,19 +184,37 @@ def get_date12_list(fname, dropIfgram=False):
     return date12_list
 
 
-def critical_perp_baseline(sensor_name, inc_angle, print_msg=False):
-    """Critical Perpendicular Baseline for each satellite"""
-    # Jers: 5.712e3 m (near_range=688849.0551m)
-    # Alos: 6.331e3 m (near_range=842663.2917m)
-    # Tsx : 8.053e3 m (near_range=634509.1271m)
-    sensor_dict = sensor.SENSOR_DICT[sensor_name.lower()]
-    wvl = SPEED_OF_LIGHT / sensor_dict['carrier_frequency']
-    near_range = 688849  # Yunjun 5/2016, case for Jers, need a automatic way to get this number
-    rg_bandwidth = sensor_dict['chirp_bandwidth']
-    bperp_c = wvl * (rg_bandwidth / SPEED_OF_LIGHT) * near_range * np.tan(inc_angle * np.pi / 180.0)
-    if print_msg:
-        print(f'Critical Perpendicular Baseline: {bperp_c} m')
-    return bperp_c
+def critical_perp_baseline(sensor_name, inc_angle=30, print_msg=False):
+    """Calculate the critical Perpendicular Baseline for each satellite.
+
+    Parameters: sensor_name - str, SAR sensor name
+                inc_angle   - float, LOS incidence angle in degrees
+    Returns:    bperp_crit  - float, critical perpendicular baseline in meters
+                              typical values: LT1 : 26.6 km
+                                              TSX :  8.1 km
+                                              ALOS:  6.3 km
+                                              JERS:  5.7 km
+    """
+    sensor_name = sensor_name.lower()
+    if sensor_name not in sensor.SENSOR_DICT.keys():
+        raise ValueError(f'Un-recognized sensor_name: {sensor_name}')
+
+    # sensor parameters
+    near_range = {
+        # typical values
+        'lt1' : 725702,
+        'tsx' : 634509,
+        'alos': 842663,
+        'jers': 688849,
+    }[sensor_name]
+    wvl = SPEED_OF_LIGHT / sensor.SENSOR_DICT[sensor_name]['carrier_frequency']
+    range_bandwidth = sensor.SENSOR_DICT[sensor_name]['chirp_bandwidth']
+
+    # calculate critical perpendicular baseline
+    bperp_crit = wvl * (range_bandwidth / SPEED_OF_LIGHT) * near_range * np.tan(np.deg2rad(inc_angle))
+    print(f'critical perpendicular baseline: {bperp_crit} m') if print_msg else None
+
+    return bperp_crit
 
 
 def calculate_doppler_overlap(dop_a, dop_b, bandwidth_az):
@@ -227,14 +246,16 @@ def calculate_doppler_overlap(dop_a, dop_b, bandwidth_az):
     return dop_overlap
 
 
-def simulate_coherence_v2(date12_list, decor_time=200.0, coh_resid=0.2, inc_angle=40, sensor_name='Sen',
-                          display=False):
+def simulate_coherence_v2(date12_list, decor_time=200.0, coh_resid=0.2, inc_angle=40,
+                          SNR=22, sensor_name='Sen', display=False):
     """Simulate coherence version 2 (without using bl_list.txt file).
     Parameters: date12_list - list of string in YYYYMMDD_YYYYMMDD format, indicating pairs configuration
                 decor_time  - float, decorrelation rate in days, time for coherence to drop to 1/e of its initial value
                 coh_resid   - float, long-term coherence, minimum attainable coherence value
                 inc_angle   - float, incidence angle in degrees
                 sensor_name - string, SAR sensor name
+                SNR         - float, signal-to-noise ratio in dB
+                              NESZ = -22 dB from Table 1 in https://sentinels.copernicus.eu/web/sentinel
                 display     - bool, display result as matrix or not
     Returns:    coh         - 2D np.array in size of (ifgram_num)
     """
@@ -244,8 +265,7 @@ def simulate_coherence_v2(date12_list, decor_time=200.0, coh_resid=0.2, inc_angl
     date_list = sorted(list(set(date1s + date2s)))
     tbase_list = ptime.date_list2tbase(date_list)[0]
 
-    SNR = 10 ** (22 / 10)  # NESZ = -22 dB from Table 1 in https://sentinels.copernicus.eu/web/sentinel/
-    coh_thermal = 1. / (1. + 1./SNR)
+    coh_thermal = 1. / ( 1. + 1. / 10**(SNR / 10) )
 
     # bperp
     rng = np.random.default_rng(2)

src/mintpy/utils/ptime.py

@@ -524,6 +524,12 @@ def get_date_range(dmin, dmax, dstep=1, dunit='D', out_fmt='%Y%m%d'):
 
     # prepare date range
     dt_objs = np.arange(t1, t2+tstep, tstep, dtype='datetime64').astype('O')
+
+    # ensure output dt_list is within [dmin, dmax]
+    if dt_objs[-1] > t2:
+        dt_objs = dt_objs[:-1]
+
+    # convert datetime.datetime object into given string format
     dt_list = [obj.strftime(out_fmt) for obj in dt_objs]
 
     return dt_list