Soil moisture and VOD retrievalsfrom SMAP contain independent information that can be exploited within
data assimilation. Soil moisture information is more useful for developing improvements in water budget fluxes over water limited domains and VOD is more useful over
be used to estimate opticaldepth across the study region from the measured NDVI and, given these estimates of opticaldepth, surface soil moisture can be estimated from the retrieval algorithm (Fig. 3). The absolute values of the retrieved soil moisture range from around 20 to 60 %, with the lower values in the west of the image, where there were several areas of wheat stubble, and the higher values towards the east, an area dominated by rangeland. In addition, there is significant variability in the retrieved soil moisture, with very high values sometimes close to much lower values, and it is difficult to know whether this variability is real or an artifact of the retrieval process. Again, the lower resolution data have the lower variability. The retrieved soil moisture depends strongly on the value of NDVI and, consequently, any change in land-surface conditions between the SLFMR data collection and the satellite overpass from which NDVI is derived will influence strongly
[ 6 ] The ASCAT and AMSR-E soil moisture data were
assimilated over the maximum available coincident data record, from January 2007 to May 2010. Using a nearest neighbor approach, the satellite observations were interpo- lated from their native resolutions to the 25 km land mod- eling grid used in this experiment (Section 2.3). The quality control applied prior to the assimilation differed for each data set, according to the particularities of passive and active microwave observations, and the ancillary data provided with each. The occurrence of dense vegetation was initially screened using information provided with each data set. For ASCAT the Estimated Soil Moisture Error (ESME) flag includes a signal of dense vegetation, and an upper limit of 14% (in SDS units) for the ESME was applied (V. Naeimi, personal communication, 2011). For AMSR-E a vegetationopticaldepth upper limit of 0.8 was used [Owe et al., 2001]. Additionally, AMSR-E and ASCAT data were discarded where MODIS land cover data from Boston University [Friedl et al., 2002] indicated forests (> 60% trees or woody vegetation).
Abstract. The large observation footprint of low-frequency satellite microwave emissions complicates the interpretation of near-surface soil moisture retrievals. While the effect of sub-footprint lateral heterogeneity is relatively limited under unsaturated conditions, open water bodies (if not accounted for) cause a strong positive bias in the satellite-derived soil moisture retrieval. This bias is generally assumed static and associated with large, continental lakes and coastal areas. Temporal changes in the extent of smaller water bodies as small as a few percent of the sensor footprint size, however, can cause significant and dynamic biases. We analysed the influence of such small open water bodies on near-surface soil moisture products derived from actual (non-synthetic) data from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) for three areas in Oklahoma, USA. Differences between on-ground observa- tions, model estimates and AMSR-E retrievals were related to dynamic estimates of open water fraction, one retrieved from a global daily record based on higher frequency AMSR- E data, a second derived from the Moderate Resolution Imag- ing Spectroradiometer (MODIS) and a third through inver- sion of the radiative transfer model, used to retrieve soil moisture. The comparison demonstrates the presence of rel- atively small areas (<0.05) of open water in or near the sen- sor footprint, possibly in combination with increased, below- critical vegetation density conditions (optical density <0.8), which contribute to seasonally varying biases in excess of 0.2 (m 3 m −3 ) soil water content. These errors need to be ad- dressed, either through elimination or accurate characterisa- tion, if the soil moisture retrievals are to be used effectively in a data assimilation scheme.
generally used (McKague et al. 2011; Berg et al. 2012; Sapiano et al. 2013; Wilheit 2013). RTMs consist of parameterized modules such as atmospheric absorption and surface emissivity that always have some level of uncertainty associated with them. The geophysical data are often from numerical weather prediction analysis or reanalysis fields. There are always errors in these data since they cannot perfectly represent the real geophysical conditions (Bengtsson et al. 2004; Dee et al. 2011). These errors in RTM and reanalysis data affect calibration. For instance, the observed radiance from a radiometer is a function of geophysical parameters such as water vapor. The water vapor profile in reanalysis data may have a bias with respect to the actual profile, and this bias will propagate into simulations and therefore into final calibration results. Additionally, the temporal and spatial variability of water vapor will result accordingly in variability of calibration and in particular of collocation based intercalibration. These challenges have not been investigated previously. As a first step, we need to examine whether calibration variability exists, how it behaves temporally and spatially, how it is related to geophysical parameters, and what the primary and secondary factors are in causing said variability. By addressing these questions, we can move toward understanding and mitigating calibration variability and the effects of model parameterizations.
microwave radiometer (RPG-HATPRO, Radiometer Physics GmbH). This instrument performs measurements of the sky brightness temperature in a continuous and automated way with a radiometric resolution between 0.3 and 0.4 K root mean square error at 1.0 s integration time. The radiometer uses direct detection receivers within two bands: 22–31 and 51–58 GHz. The first band contains channels providing infor- mation about the humidity profile of the troposphere, while the second band contains information about the temperature profile. The retrievals of both temperature and humidity pro- files from brightness temperature are done by the inversion algorithms described in Rose et al. (2005). Temperature data are provided with 0.1 K precision and the accuracy of the temperature retrievals has a mean value of up to 0.8 K within the boundary layer. Tropospheric profiles are obtained from the surface up to 10 km using 39 heights with vertical reso- lution ranging from 10 m near the surface to 1000 m for alti- tudes higher than 7 km. For heights below 3 km (a.s.l.), where the planetary boundary layer (PBL) is usually located over Granada (Granados-Muñoz et al., 2012), data at 25 points with resolution between 10 and 200 m are provided.
Optical remote sensing can be used to identify snow cover extent accurately using the normalized difference of snow index (NDSI) method due to its high reflectance in the op- tical band and low reflectance in the near-infrared band (Hall et al., 2002; Hall and Riggs, 2007). However, the drawback of optical remote sensing is that clouds mask snow data on most the days during the snow season. Therefore, 8-day and 16-day composite snow cover products are produced to elim- inate cloud cover (Hall et al., 2002; Hall and Riggs, 2007). Daily cloud-free snow cover products were also produced us- ing temporal or spatial interpolation algorithms (Tang et al., 2013; Hall et al., 2010; Gafurov and Bárdossy, 2009; Para- jka et al., 2010). However, for the strong spatial heterogene- ity and rapid snow cover changes across the QTP, interpola- tion algorithms do not work under conditions of continuous multi-day cloud cover or for large areas. Therefore, in the cloud-covered areas, snow cover derived frompassive mi- crowave (PMW) remote sensing, which is independent of sunlight, has been used to supplement optical remote sens- ing (Liang et al., 2008; Gao et al., 2012; Deng et al., 2015). The data from the combination of these two techniques pro- vide information masked by clouds and improves the tempo- ral resolution of snow cover products. Many combined snow cover products have been used in climate change and hy- drological analysis (Barnett et al., 2005; Wang et al., 2015; Brown and Robinson, 2011; Choi et al., 2010). However, the accuracy of snow cover from PMW directly influences the accuracy of the combined snow cover product. In addition, although optical remote sensing is an efficient way to mon- itor spatial snow cover with high resolution, it cannot pene- trate snowpack and obtain snow depth.
In practice the tie points are defined from analysis of satellite data and given as brightness temperatures. A retrieval of, for example, 110% is understood as a mixture of 110% sea ice of tie point radiative properties and 10% open water. Wherever possible, we have applied the most recent tie points provided by the authors of a given algorithm; Table 1 provides the pertinent references. Many of these tie points have been tuned to daily average brightness temperature data and our use of swath data may therefore introduce a slight inconsistency that may minimally affect the error standard deviations, but not the correlations, in subsequent comparisons. This possible in- consistency must be weighed against the possible large error from an increased time offset between SSM/I and reference observation. In addition, the diurnal variation in tie point emissivity during winter is minimal and the comparison statistics will only be slightly affected by a minor change in tie points. It is common to limit the range of the algorithms to the physically meaningful range in a postprocessing step, which makes good sense for most users of sea ice concen- trations. However, ‘‘saturated’’ ice concentration estimates will have reduced sensitivity to real openings in the ice and this practice additionally complicates comparisons of algo- rithm statistics as part of the true bias and variance are hidden in the cut off portion of the retrievals. In the present study we avoid all such postprocessing with the exception of NT2 and N90. For N90, the problem is that the smooth interpolation between the ice and water points is only strictly valid inside the 0 to 100% concentration interval. In fact, outside the interval and depending on the tie points, the concentration is not generally monotonously increasing with decreasing polarization [Spreen, 2004]. Our choice of tie points results in a well-behaved characteristic in the domain of interest in this study. However, to avoid errors, pixels where the polarization falls below the ice tie point and the retrieved concentration is below 100% are dis- carded. This error is only found in very few cases. For the NT2 algorithm tie points are integrated in tables of simulated brightness temperatures that are used in a mini- mization scheme to find the combination of sea ice and atmospheric contributions matching the satellite observa- tions. Since only solutions for ice concentrations up to 100% are allowed, the variability of the NT2 algorithm is not directly comparable to the remaining algorithms. We have therefore extended these tables to enable solutions to be found in the range between 0 and 120%; we name this version of the algorithm ‘‘unconstrained’’ and refer to it as NT2U henceforth. Owing to the dynamic scheme used to
Calibration of CALIOP signals (e.g., Powell et al., 2009; Rogers et al., 2011) and verification/validation of value- added (i.e., Level 2.0 and higher) NASA data products (e.g., Liu et al., 2009; Kacenelenbogen et al., 2011) ensure that high-accuracy data are available for researchers and that the archive is consistent for legacy study long after the mission is completed. Kittaka et al. (2011) recently compare 0.532 µm aerosol opticaldepth (AOD) retrievals reported in the NASA Version 2.01 CALIOP 5-km Aerosol Layer product versus 0.550 µm AOD retrievals based on measurements made by the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Aqua satellite and, thus, collected in formation and approximately 5 min before CALIOP as part of the NASA “A-Train” constellation (e.g., Stephens et al., 2002). Naturally, this work relates only to half of all available CALIOP AOD data, since MODIS retrievals are based on scattered solar radiances. Given that the lidar is duly capable of nighttime measurements, additional validation is neces- sary to fully evaluate CALIOP aerosol retrieval performance. Furthermore, given that particle layer identification and the accuracy of the CALIOP-derived 0.532 µm extinction coef- ficient and, thus, its column-integrated sum AOD, are each a function of the amount of ambient solar background light measured in any given scattering profile (i.e., noise; Hunt et al., 2009; Liu et al., 2009; Vaughan et al., 2009; Young and Vaughan, 2009), nighttime AOD retrievals should exhibit
A spatial-temporal approach based on Ichoku (2002a) was employed to validate MODIS AOD retrievals in this study. MODIS retrieved AODs for 3 wavelengths (405 - 420 nm, 450 - 479 nm and 620 - 670 nm) were plotted against temporally coincident AERONET retrieved AODs. Figures 3 (a)-(d) below shows the graphs for the 3 sites for the year 2005. Spatial and temporal trends of aerosol retrievalsfrom MODIS and AERONET for Canberra, Birdsville and Tinga Tingana sites in Australia for year 2005 are described in the following paragraphs. A generally observation is that MODIS retrievals overes- timates for low aerosols and underestimates for higher AOD. AODs were extracted for variable spatial windows of 50 km by 50 km and 10 km by 10 km from the MODIS scenes. The solar zenith angle (sza) of each scenes were recorded to provide a qualitative estimate of the angular variations of AOD retrievals. The statistics (mean, median, standard deviation) were generated for
December 1999, provide aerosol information at up to seven spectral bands, ranging from visible to near-IR wavelengths, and cover the entire globe every 1 to 2 days. Another advanced satellite sensor —the Visible Infrared Imaging Radiometer Suites [ 4 ]—was launched onboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite in October 2011. It collects images and measurements of land and atmosphere in 22 spectral bands, covering wavelengths from 412 to 12,050 nm, and provides routine retrievals of land, aerosol and cloud properties. Sophisticated algorithms have been developed to retrieve aerosol property, in particular, aerosol opticaldepth, from these various sensors. The overall accuracy of Moderate Resolution Spectroradiometer (MODIS)-retrieved Aerosol OpticalDepth (AOD) is expected to be ±(0.05% + 15% × AOD) over land [ 3 ]. Although global validation proves that the current sensors largely meet these accuracy requirements, regionally, the uncertainties can still be large. For example, over China, only 50.6% of Visible Infrared Imaging Radiometer Suite (VIIRS) AOD fall within the expected accuracy interval, with an overall bias of 0.13 (or ~28%) [ 5 ]. The MODIS latest Collection 6.1 data also show unsatisfactory performance over Asia. The percentage of AOD retrievals falling within the expected error envelope is ~57% compared to 66% globally, and the root mean square error can be as large as 0.176~0.194 over different surfaces [ 6 ]. According to previous studies, the AOD errors can be attributed to several main factors, including cloud screening [ 3 ], surface reflectance parameterization [ 7 ], aerosol model assumptions [ 7 – 10 ], and aerosol vertical profile assumptions [ 10 , 11 ].
Abstract. The scan geometry of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors, combined with the Earth’s curvature, results in a pixel shape distor- tion known as the “bow-tie effect”. Specifically, sensor pix- els near the edge of the swath are elongated along-track and across-track compared to pixels near the centre of the swath, resulting in an increase of pixel area by up to a factor of ∼ 9 and, additionally, the overlap of pixels acquired from con- secutive scans. The Deep Blue and Dark Target aerosol opti- cal depth (AOD) retrieval algorithms aggregate sensor pixels and provide level 2 (L2) AOD at a nominal horizontal pixel size of 10 km, but the bow-tie distortion means that they also suffer from this size increase and overlap. This means that the spatial characteristics of the data vary as a function of satellite viewing zenith angle (VZA) and, for VZA > 30 ◦ , corresponding to approximately 50 % of the data, are are- ally enlarged by a factor of 50 % or more compared to this nominal pixel area and are not spatially independent of each other. This has implications for retrieval uncertainty and ag- gregated statistics, causing a narrowing of AOD distributions near the edge of the swath, as well as for data comparability from the application of similar algorithms to sensors without this level of bow-tie distortion. Additionally, the pixel over- lap is not obvious to users of the L2 aerosol products because only pixel centres, not boundaries, are provided within the L2 products. A two-step procedure is proposed to mitigate the effects of this distortion on the MODIS aerosol products. The first (simple) step involves changing the order in which pixels are aggregated in L2 processing to reflect geographical location rather than scan order, which removes the bulk of the
The relative uncertainty among the channels varied from 0.7 % (870 nm) to 1.0 % (415 nm) in 2012, and from 0.4 % (870 nm) to 1.0 % (415 nm) in 2015, which is surprisingly satisfactory for conditions diverse from those that are recom- mended (clean mountaintops). Additionally, alternative final extraterrestrial response estimations were calculated based on the median of the set of individual Langley plot calibra- tions. In general, the differences between median and mean based final extraterrestrial response estimations were less than 1 %, which would result in AOD differences lower than 0.01, i.e. half of the typical uncertainty of AOD derived from AERONET field Sun photometer measurements. In our case, extraterrestrial response estimations based on mean were consistent with estimations based on median; therefore, we used mean based values as a reference for estimating MFRSR AOD. Optional techniques may be applied to derive extrater- restrial response calibrations; Michalsky et al. (2001) used Forgan’s (1988) ratio Langley technique, based on ratioing values of individual Langley plot calibrations of the 500 nm channel to those of the 860 nm channel, to select best indi- vidual Langley plot calibration, in order to improve the fi- nal extraterrestrial response estimations. In the current study, the lower stability of the 870 nm channel prevents applying the method of Michalsky et al. (2001). Regarding the relative difference ( − 0.4 %) between mean calibration constants de-
Figure 5a shows the temperature profiles retrieved from radiosonde and from TEMPERA radiometer using and with- out using cloud information in the forward model. The mea- surements were done on 21 November 2012 and an ILW value of 0.47 mm was measured with TROWARA for these cloudy conditions. Figure 5b presents the absolute temper- ature deviation between the radiosondes and the radiome- ter retrievals. For this case the cloud base altitude was de- tected at 2450 m (a.s.l.) and the cloud thickness was 1670 m. From the figure we observe a very good agreement between radiosonde and radiometer retrievals when the cloud was considered. The mean absolute temperature deviation in the first kilometre reached an average value of 0.8 ± 0.6 K. Al- though the discrepancies increased a little bit above this al- titude, the mean absolute deviation was always below 3 K in the whole profile. However, we can observe that the dis- crepancies between the radiosonde and the microwave pro- file retrieved without cloud information are much larger. Al- though the agreement was reasonable in the lower profile, the discrepancies increased considerably above 1300 m (a.s.l.), reaching a maximum absolute deviation of 9.2 K at 4480 m (a.s.l.). Figure 5c shows the corresponding brightness tem- peratures for the different retrievals. A total of 108 mea- sured brightness temperatures are shown corresponding to
Abstract. Microwaveradiometry is a suitable technique to measure atmospheric temperature profiles with high tem- poral resolution during clear sky and cloudy conditions. In this study, we included cloud models in the inversion algo- rithm of the microwave radiometer TEMPERA (TEMPEra- ture RAdiometer) to determine the effect of cloud liquid wa- ter on the temperature retrievals. The cloud models were built based on measurements of cloud base altitude and integrated liquid water (ILW), all performed at the aerological station (MeteoSwiss) in Payerne (Switzerland). Cloud base altitudes were detected using ceilometer measurements while the ILW was measured by a HATPRO (Humidity And Temperature PROfiler) radiometer. To assess the quality of the TEMPERA retrieval when clouds were considered, the resulting temper- ature profiles were compared to 2 years of radiosonde mea- surements. The TEMPERA instrument measures radiation at 12 channels in the frequency range from 51 to 57 GHz, cor- responding to the left wing of the oxygen emission line com- plex. When the full spectral information with all the 12 fre- quency channels was used, we found a marked improvement in the temperature retrievals after including a cloud model. The chosen cloud model influenced the resulting tempera- ture profile, especially for high clouds and clouds with a large amount of liquid water. Using all 12 channels, however, pre- sented large deviations between different cases, suggesting that additional uncertainties exist in the lower, more transpar- ent channels. Using less spectral information with the higher, more opaque channels only also improved the temperature profiles when clouds where included, but the influence of the chosen cloud model was less important. We conclude that tropospheric temperature profiles can be optimized by considering clouds in the microwave retrieval, and that the
model DMRT-ML with Crocus outputs. The DMRT-ML model is well detailed in the literature (Tsang et al., 1992; Tsang and Kong, 2001; Picard et al., 2013, Royer et al., 2017), so only the calibration is described here. Snow grain size, and more generally snow microstructure, are factors that most affect the accuracy of simulated PMW emission from a snowpack as they determine the strength of scattering mech- anisms in the snowpack at the high frequencies used (Roy et al., 2013; Leppänen et al., 2015; Sandells et al., 2017, Larue et al., 2018). In DMRT-ML, snow grains are represented as spheres of ice with variable interactions between them. The potential formation of clusters of grains, which increases the effective snow grain size, is not taken into account, generat- ing uncertainties (Picard et al., 2013). Several studies have shown that DMRT-ML needed an effective scaling factor to represent the stickiness between snow grains and to correct the snow microstructure representation (Brucker et al., 2011; Roy et al., 2013; Royer et al., 2017). Larue et al. (2018) have shown that a mean snow stickiness parameter (τ snow ) of 0.17
For VOD computed at VV polarization, results show that the temporal dynamic of the estimated VOD fits generally well with the temporal dynamics of NDVI. In general, a medium to good correlation between VOD and NDVI temporal dynamics was obtained (R 2 about 0.58, 0.39, 0.46, and 0.61 for barley, fallow, oat, and wheat, respectively). However, during the beginning of the senescence period (from 25/04/2018 to 15/05/2018 and from 20/04/2019 to 10/05/2019), VOD and NDVI values became uncorrelated. VOD started decreasing on 25/04/2018 in the first crop growth cycle and on 20/04/2019 in the second crop cycle due to a decrease in the canopy water content while NDVI continued increasing due to the increase in the vegetation photosynthetic activity. Moreover, results showed that in the presence of a high temperature over a well-developed canopy (NDVI reaching its peak value), VOD values computed from the SAR images acquired at ~18h (Ascending mode) were lower than those computed from the SAR images acquired at ~6h (Descending mode). This observation was attributed to changes in the canopy water status which leads to a decrease in VWC during the hot afternoon. Finally, our results showed that the temporal dynamics of VOD values computed from VH polarization do not perfectly match that of NDVI (R 2 lower than 0.4 for barley, fallow, oat, and wheat). It is likely due to the multiple-scattering mechanisms present in VH polarization and which is not considered in WCM formulation. In future, other more complex modelling approaches will be used to better account for these volume scattering effects.
Figure 5 compares (a) ZWD and (b) PW observed by WVR, GPS, and radiosondes for March 18 to 24, 1998. Gen- erally speaking, observations from all of the three techniques match each other reasonably well except for the part where radiosonde observables appear to be somewhat higher than the other two as listed in Tables 4 and 5. Note that radiosonde soundings were collected twice daily, while GPS observa- tions were taken two times per minute and WVR observa- tions were taken once per couple of minutes. We therefore interpolated the GPS and WVR data to provide hourly ob- servations. We found that the average difference between GPS and WVR measurements of ZWD is − 0.35 cm with a standard deviation of 1.70 cm. The corresponding aver- age difference in PW is − 0.04 cm with a standard deviation of 0.27 cm. This result is considerably higher than 1–2 mm agreement reported by others at higher latitudes (e.g., Rocken et al., 1993, 1995, 1997; Duan et al., 1996; Emardson et al., 1998; Tregoning et al., 1998), roughly in proportion to the differences in PW. This suggests that the agreement between GPS- and WVR-sensed PW may be dependent on the total water vapor burden.
Overall, L-band soil moisture retrievals showed the best agreement with field-measured data over the study sites (Fig. 3), which is consistent with previous studies (Al-Yaari et al., 2014; Holgate et al., 2016). Interestingly, errors did not increase with increasing frequency, and the X-band re- trievals performed better than C-band retrievals. This may be typical of the sites studied here or of the AMSR-2 sen- sor, as previous studies have found that LPRM C-band re- trievals from AMSR-2’s predecessor, AMSR-E, slightly out- perform X-band retrievals (Gruhier et al., 2010; Parinussa et al., 2011). As expected, datasets with smaller errors were generally more informative in assimilation, especially for top-layer soil moisture. This can be attributed in part to the differences in the magnitude of the errors, as this affects the weight given to the observations in the assimilation proce- dure. On average, triple collocation errors for C-band re- trievals were 0.24 (AWRA-L wetness units), compared to 0.18 for the other retrievals. Further research is needed to evaluate whether these differences in errors are due to the trade-off between spatial resolution and sensitivity to vege- tation and/or the atmosphere or whether they are the result of other factors. For the root zone, differences between the assimilation experiments are much less pronounced (Fig. 4). The similar information content in L- and X-band retrievals, especially, implies that data assimilation systems can substi- tute one retrieval for the other without substantially affecting model performance. This is especially important for model- ing systems that cover a relatively long time period that need to transition between microwave sensors and missions.
The ANN method has proved to be a very useful tool for the reconstruction of daily AOD values at 500 nm from meteorological input data, such as the horizontal visibil- ity, fraction of clear sky, and relative humidity, recorded at IZO. ANN AOD estimates adequately capture the day-to- day AOD variations and the long-term trends when compared to coincident AOD measurements from Mark-I solar spec- trometer (1984–2009) and AERONET (2004–2009). The re- sults show a good agreement for the daily values, with Pear- son coefficients of 0.97 (AERONET/ANN) and 0.93 (Mark- I/ANN). At the longest timescale (1941–2009), we found a good agreement between ANN AOD monthly medians and the percentage of time the wind blows from the Sahara desert (SE) (R = 0.86), and also a good correlation between the number of days with AOD ≥ 0.20 and the number of days in which synoptical observations reported mineral dust events (R = 0.89). These results show the reliability of the recon- structed ANN AOD series, confirming its consistency in this long period (1941–2009), and capability for tracking inter- annual variations of dust-laden Saharan air mass outbreaks.