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  1. Article ; Online: Multi-resolution soil moisture retrievals by disaggregating SMAP brightness temperatures with RADARSAT-2 polarimetric decompositions

    Wang, Hongquan / Magagi, Ramata / Goïta, Kalifa / Colliander, Andreas

    International Journal of Applied Earth Observation and Geoinformation. 2022 Dec., v. 115 p.103114-

    2022  

    Abstract: Mapping soil moisture (SM) at high spatial resolution assists to trigger important agricultural management, such as irrigation, to enhance crop yields. This study investigates disaggregation of SMAP brightness temperature (TB) using RADARSAT-2 ... ...

    Abstract Mapping soil moisture (SM) at high spatial resolution assists to trigger important agricultural management, such as irrigation, to enhance crop yields. This study investigates disaggregation of SMAP brightness temperature (TB) using RADARSAT-2 polarimetric decompositions to retrieve high-resolution SM. Compared to Sentinel-1 backscattering coefficients used in the SMAP baseline active–passive SM retrieval algorithms, the RADARSAT-2 surface scattering power Pₛ with a reduced vegetation influence was hypothesized to be more relevant to disaggregate the SMAP TB. Different polarimetric decompositions were evaluated to extract an optimal Pₛ, followed by an incidence angle normalization. Then, the optimal Pₛ parameter was aggregated to the same spatial resolution as the SMAP TB to develop empirical relationships between Pₛ and TB. Furthermore, the airborne TB data collected by Passive Active l-band Sensor (PALS) were analyzed in terms of the Pₛ across multiple spatial resolutions, to account for the scale effect on the Pₛ/TB relationships. Finally, the τ-ω emission model was used to retrieve SM at multiple spatial resolutions (10 km, 1 km, 500 m, 100 m, and 50 m). The impacts of spatial resolution on retrieval accuracy were analyzed to determine the best spatial resolution for SM retrievals. The results indicated that the An polarimetric decomposition with the de-orientation provided the highest surface scattering powers, which may benefit the SM estimation. In contrast to the traditional cosine algorithms, the incidence angle normalization of Pₛ with span resulted in a temporally decreasing surface scattering power, because of the increasing vegetation attenuation as the crop grows. The sensitivity of TB to Pₛ decreases as the resolution scale varies from 36 km to 50 m. The SM retrievals across multiple resolutions obtained marginal differences in retrieval accuracy. Although slightly better results were obtained with 1 km spatial resolution which is close to the nominal size of agricultural fields in the study area (R = 0.68–0.8 and RMSE = 0.039–0.062 m³/m³), the retrievals at 50 m spatial resolution (R = 0.63–0.76 and RMSE = 0.046–0.067 m³/m³) capture the spatial heterogeneity of SM within and across different fields which could be very helpful for the precision agriculture.
    Keywords angle of incidence ; irrigation ; models ; polarimetry ; precision agriculture ; soil water ; spatial data ; spatial variation ; temperature ; vegetation ; SMAP disaggregation ; RADARSAT-2 decomposition ; Surface scattering power ; Multi-resolution ; Soil moisture retrieval
    Language English
    Dates of publication 2022-12
    Publishing place Elsevier B.V.
    Document type Article ; Online
    Note Use and reproduction
    ISSN 1569-8432
    DOI 10.1016/j.jag.2022.103114
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  2. Article ; Online: Spatial and temporal differences in surface and subsurface meltwater distribution over Greenland ice sheet using multi-frequency passive microwave observations

    Colliander, Andreas / Mousavi, Mohammad / Kimball, John S. / Miller, Julie Z. / Burgin, Mariko

    Remote Sensing of Environment. 2023 Sept., v. 295 p.113705-

    2023  

    Abstract: Increasingly larger portions of the Greenland ice sheet are undergoing seasonal melting-refreeze cycles due to global climate warming. The cycle begins with the arrival of high temperatures and increased solar radiation in the spring and summer seasons ... ...

    Abstract Increasingly larger portions of the Greenland ice sheet are undergoing seasonal melting-refreeze cycles due to global climate warming. The cycle begins with the arrival of high temperatures and increased solar radiation in the spring and summer seasons generating meltwater on the ice sheet's surface. Meltwater percolates to deeper ice layers, either refreezing within the firn, creating longer-term meltwater pockets (firn aquifers), or generating peripheral runoff. Depending on the location and climate, the refreeze duration, the depth of infiltration, and meltwater persistence are temporally and spatially complex. Our recent study showed that multi-frequency passive microwave measurements in the 1.4 GHz to 36.5 GHz range effectively distinguished seasonal meltwater between the immediate surface and deeper firn layers at an experiment site in the accumulation zone of the southwestern Greenland ice sheet. Here, we further explored the vertically and horizontally polarized multi-frequency melt response at the pan-Greenland scale. We employed 1.4 GHz brightness temperature (TB) measurements from the NASA Soil Moisture Active Passive (SMAP) satellite and 6.9, 10.7, 18.9, and 36.5 GHz TB measurements from the JAXA Global Change Observation Mission-Water Shizuku (GCOM-W) satellite. The results show that the frequency-dependent response was consistent across the ice sheet. The multi-frequency melt indications match with lasting seasonal subsurface meltwater with delayed refreezing compared to the surface. These results suggest persistent seasonal subsurface meltwater occurrences that are spatially and temporally significant but concealed from the high-frequency observations. Retrieving the meltwater evolution in snow and firn presents a complex problem; this work represents an initial step toward developing an ice-sheet-wide algorithm for more comprehensive retrieval of the meltwater profile.
    Keywords algorithms ; climate ; environment ; evolution ; global change ; ice ; runoff ; satellites ; snow ; snowmelt ; solar radiation ; spring ; summer ; temperature ; Greenland ; Ice sheet ; Firn ; Melt
    Language English
    Dates of publication 2023-09
    Publishing place Elsevier Inc.
    Document type Article ; Online
    ZDB-ID 431483-9
    ISSN 0034-4257
    ISSN 0034-4257
    DOI 10.1016/j.rse.2023.113705
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  3. Article ; Online: Deep learning estimation of northern hemisphere soil freeze-thaw dynamics using satellite multi-frequency microwave brightness temperature observations.

    Donahue, Kellen / Kimball, John S / Du, Jinyang / Bunt, Fredrick / Colliander, Andreas / Moghaddam, Mahta / Johnson, Jesse / Kim, Youngwook / Rawlins, Michael A

    Frontiers in big data

    2023  Volume 6, Page(s) 1243559

    Abstract: Satellite microwave sensors are well suited for monitoring landscape freeze-thaw (FT) transitions owing to the strong brightness temperature (TB) or backscatter response to changes in liquid water abundance between predominantly frozen and thawed ... ...

    Abstract Satellite microwave sensors are well suited for monitoring landscape freeze-thaw (FT) transitions owing to the strong brightness temperature (TB) or backscatter response to changes in liquid water abundance between predominantly frozen and thawed conditions. The FT retrieval is also a sensitive climate indicator with strong biophysical importance. However, retrieval algorithms can have difficulty distinguishing the FT status of soils from that of overlying features such as snow and vegetation, while variable land conditions can also degrade performance. Here, we applied a deep learning model using a multilayer convolutional neural network driven by AMSR2 and SMAP TB records, and trained on surface (~0-5 cm depth) soil temperature FT observations. Soil FT states were classified for the local morning (6 a.m.) and evening (6 p.m.) conditions corresponding to SMAP descending and ascending orbital overpasses, mapped to a 9 km polar grid spanning a five-year (2016-2020) record and Northern Hemisphere domain. Continuous variable estimates of the probability of frozen or thawed conditions were derived using a model cost function optimized against FT observational training data. Model results derived using combined multi-frequency (1.4, 18.7, 36.5 GHz) TBs produced the highest soil FT accuracy over other models derived using only single sensor or single frequency TB inputs. Moreover, SMAP L-band (1.4 GHz) TBs provided enhanced soil FT information and performance gain over model results derived using only AMSR2 TB inputs. The resulting soil FT classification showed favorable and consistent performance against soil FT observations from ERA5 reanalysis (mean percent accuracy, MPA: 92.7%) and
    Language English
    Publishing date 2023-11-17
    Publishing country Switzerland
    Document type Journal Article
    ISSN 2624-909X
    ISSN (online) 2624-909X
    DOI 10.3389/fdata.2023.1243559
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  4. Article ; Online: The first polarimetric GNSS-Reflectometer instrument in space improves the SMAP mission's sensitivity over densely vegetated areas.

    Rodriguez-Alvarez, Nereida / Munoz-Martin, Joan Francesc / Bosch-Lluis, Xavier / Oudrhiri, Kamal / Entekhabi, Dara / Colliander, Andreas

    Scientific reports

    2023  Volume 13, Issue 1, Page(s) 3722

    Abstract: The Soil Moisture Active Passive (SMAP) mission has dramatically benefited our knowledge of the Earth's surface processes. The SMAP mission was initially designed to provide complementary L-band measurements from a radiometer and a radar, producing ... ...

    Abstract The Soil Moisture Active Passive (SMAP) mission has dramatically benefited our knowledge of the Earth's surface processes. The SMAP mission was initially designed to provide complementary L-band measurements from a radiometer and a radar, producing geophysical measurements at a finer spatial resolution than the radiometer alone. Both instruments, sensitive to the geophysical parameters in the swath, provided independent measurements at different spatial resolutions. A few months after SMAP's launch, the radar transmitter's high-power amplifier suffered an anomaly, and the instrument could no longer return data. During recovery activities, the SMAP mission switched the radar receiver frequency facilitating the reception of Global Positioning System (GPS) signals scattered off the Earth's surface, and enabling the radar to become the first spaceborne polarimetric Global Navigation Satellite System - Reflectometry (GNSS-R) instrument. With more than 7 years of continued measurements, SMAP GNSS-R data are the most extensive existing GNSS-R dataset and the only one providing GNSS-R polarimetric measurements. We demonstrate that the SMAP polarimetric GNSS-R reflectivity, derived from Stokes parameters mathematical formulation, can enhance the radiometer data over dense vegetation areas, recovering some of the original SMAP radar capability to contribute to the science products and pioneering the first polarimetric GNSS-R mission.
    Language English
    Publishing date 2023-03-06
    Publishing country England
    Document type Journal Article
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-023-30805-7
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  5. Article: Assessing the Spatiotemporal Variability of SMAP Soil Moisture Accuracy in a Deciduous Forest Region

    Abdelkader, Mohamed / Temimi, Marouane / Colliander, Andreas / Cosh, Michael H. / Kelly, Vicky R. / Lakhankar, Tarendra / Fares, Ali

    Remote Sensing. 2022 July 11, v. 14, no. 14

    2022  

    Abstract: The goal of this study is to assess the temporal variability of the performance of the Soil Moisture Active Passive, SMAP, soil moisture retrievals throughout the seasons as surface conditions change. In-situ soil moisture observations from a network ... ...

    Abstract The goal of this study is to assess the temporal variability of the performance of the Soil Moisture Active Passive, SMAP, soil moisture retrievals throughout the seasons as surface conditions change. In-situ soil moisture observations from a network deployed in Millbrook, New York, between 2019 and 2021 are used. The network comprises 25 stations distributed across a 33-km SMAP pixel with a predominantly forest land cover. The in-situ soil moisture observations were collected between 6 and 7 a.m., local time. This article covers the assessment of the temporal accuracy of SMAP soil moisture by incorporating various upscaling methods. Four upscaling methods are used in this study: arithmetic average, Voronoi diagram, topographic wetness index, and land cover weighted average. The agreement between SMAP soil moisture and the upscaled in-situ measurements was gauged using the root-mean-squared difference, the mean difference, and the unbiased root-mean-squared difference. The consistency of the temporal variability of SMAP soil moisture data resulting from the four upscaling methods was analyzed. The results revealed that SMAP retrievals (soil moisture data) are systematically higher than in situ observations during the different seasons. The results indicate that the highest performance of SMAP soil moisture retrievals is in September with an ubRMSD value of 0.03 m³.m⁻³ for the morning and evening overpasses, which can be attributed to a lower vegetation density during the seasonal transition. The agreement with in-situ observations degrades during March–April with ubRMSD values above 0.04 m³.m⁻³, reaching ~0.06 m³.m⁻³ in April, which can be attributed to the non-reliability of in-situ measurements due to freeze\thaw transition and the challenging determination of the soil effective temperature. The ubRMSD is also higher than 0.04 m³.m⁻³ in the months of May–June, which could be due to the introduced vegetation effect during the growth season. These findings are consistent across all the upscaling methods. The average ubRMSD over the study period is 0.055 m³.m⁻³, which falls short of meeting the mission’s performance target. This study proves the need to enhance SMAP retrieval over forest sites.
    Keywords arithmetics ; deciduous forests ; forest land ; land cover ; satellites ; soil water ; temperature ; temporal variation ; topography ; New York
    Language English
    Dates of publication 2022-0711
    Publishing place Multidisciplinary Digital Publishing Institute
    Document type Article
    ZDB-ID 2513863-7
    ISSN 2072-4292
    ISSN 2072-4292
    DOI 10.3390/rs14143329
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  6. Article ; Online: A multi-scale algorithm for the NISAR mission high-resolution soil moisture product

    Lal, Preet / Gurjeet Singh / Das, Narendra N. / Entekhabi, Dara / Lohman, Rowena / Colliander, Andreas / Pandey, Dharmendra Kumar / Setia, R.K.

    Remote Sensing of Environment. 2023 Sept., v. 295 p.113667-

    2023  

    Abstract: This study proposes a multi-scale soil moisture algorithm for the upcoming NASA-ISRO SAR (NISAR) mission to estimate high-resolution (200 [m]) soil moisture (the water content of the soil). The algorithm takes advantage of the high-resolution (∼10 [m]) ... ...

    Abstract This study proposes a multi-scale soil moisture algorithm for the upcoming NASA-ISRO SAR (NISAR) mission to estimate high-resolution (200 [m]) soil moisture (the water content of the soil). The algorithm takes advantage of the high-resolution (∼10 [m]) synthetic aperture radar (SAR) backscatter and coarse resolution modeled/reanalysis soil moisture products (∼ 9 [km]) to create a high-resolution (200 [m]) soil moisture product at a global extent. The end goal of the algorithm is to remove dependencies on any complex modeling, tedious retrieval steps, or multiple ancillary data needs, and subsequently decrease the degrees of freedom to achieve optimal accuracy in soil moisture retrievals. The use of modeled/reanalysis soil moisture products with high temporal resolution gives an added advantage in reducing the temporal mismatch between the two different inputs used in the algorithm. In this study, the proposed algorithm is tested using L-band UAVSAR backscatter (σ°) data and Advanced Land Observing Satellite −2 (ALOS-2) SAR σ° as a substitute for the NISAR L-band SAR observations. The algorithm uses the L-band SAR σ° to disaggregate coarse resolution (∼9 [km]) reanalysis soil moisture of the European Centre for Medium-Range Weather Forecast (ECMWF) to a high-resolution of ∼200 [m] soil moisture product. The potential of the algorithm is demonstrated over three sites in different hydroclimatic regions of the world, such as India, the USA, and Canada. The high-resolution soil moisture estimates were compared with the in-situ soil moisture measurements available for three sites (North India, Southern California, and Carman, Manitoba, Canada). In North India, in-situ measurements are from paddy crops with high vegetation water content, the unbiased root-mean-square-error (ubRMSE) for the high-resolution soil moisture retrievals was found to be 0.036 [m³/m³] with a bias of −0.051 [m³/m³]. For the southern California site, the validation statistics shows low ubRMSE of 0.027 [m³/m³] and a low bias of 0.016 [m³/m³]. At the Carman, Manitoba test site of Canada, where in-situ soil measurements were available for multiple crop types, the comparison shows that the ubRMSE for all the crop types lies below 0.05 [m³/m³] with an average bias of <0.07 [m³/m³]. The result confirms that the proposed algorithm meets the NISAR mission's accuracy goals, i.e., 0.06 [m³/m³] ubRMSE over areas with vegetation water content (VWC) below 5 [kg/m²]. Rigorous validation work will still need to be carried out in the future based on the availability of L-band SAR datasets and after the launch of the NISAR satellite.
    Keywords algorithms ; data collection ; environment ; multiple cropping ; paddies ; satellites ; soil water ; statistics ; synthetic aperture radar ; vegetation ; water content ; weather forecasting ; California ; India ; Manitoba ; High-resolution ; Soil moisture ; Multi-scale algorithm ; NISAR ; ALOS-2 ; UAVSAR ; ECMWF ; SMAPVEX-12
    Language English
    Dates of publication 2023-09
    Publishing place Elsevier Inc.
    Document type Article ; Online
    ZDB-ID 431483-9
    ISSN 0034-4257
    ISSN 0034-4257
    DOI 10.1016/j.rse.2023.113667
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  7. Article ; Online: Assessment of 9 km SMAP soil moisture: Evidence of narrowing the gap between satellite retrievals and model-based reanalysis

    Xing, Zanpin / Li, Xiaojun / Fan, Lei / Colliander, Andreas / Frappart, Frédéric / de Rosnay, Patricia / Liu, Xiangzhuo / Wang, Huang / Zhao, Lin / Wigneron, Jean-Pierre

    Remote Sensing of Environment. 2023, p.113721-

    2023  , Page(s) 113721–

    Abstract: A number of global surface soil moisture (SM) datasets have been retrieved from the L-band frequency Soil Moisture Active Passive (SMAP) and the Soil Moisture and Ocean Salinity (SMOS) missions to study the terrestrial water, energy, and carbon cycles. ... ...

    Abstract A number of global surface soil moisture (SM) datasets have been retrieved from the L-band frequency Soil Moisture Active Passive (SMAP) and the Soil Moisture and Ocean Salinity (SMOS) missions to study the terrestrial water, energy, and carbon cycles. This paper presents the performance of the recently developed 9 km global SMAP product (hereafter SMAP-INRAE-BORDEAUX, SMAP-IB9). The product retrieves SM from the 9 km SMAP radiometric products using the forward model (L-MEB, L-band Microwave Emission of the Biosphere) of SMOS INRA-CESBIO (SMOS-IC) and SMOS L2 algorithms. We inter-compared SMAP-IB9 with two other products with a similar grid resolution (~10 km): the SMAP Enhanced Level-3 SM data set (SMAP-E) and the enhanced global dataset for the land component of the fifth generation of European reanalysis (ERA5-Land) with the main objective of assessing the discrepancy in accuracy between remotely sensed and model SM datasets. We found that ERA5-Land and SMAP-IB9 SM had the overall highest correlations (R = 0.62(±0.15) for ERA5-Land vs. 0.60(±0.17) for SMAP-IB9 and 0.50(±0.15) for SMAP-E) by comparing with the International Soil Moisture Network (ISMN) in-situ measurements from 22 networks. ERA5-Land showed better performances in the forest areas where SMAP-IB9 and SMAP-E still showed high potential in detecting the time variations of the observed SM, particularly in terms of median correlation values (0.62(±0.18) for SMAP-IB9 vs. 0.66(±0.16) for ERA5-and). The discrepancy in R between satellite and model SM products that were reported in some past studies has decreased to statistically insignificant levels over time. For instance, in the non-forest areas, we found that the latest versions of the SMAP SM products (SMAP-E and SMAP-IB9) had relatively comparable performances with ERA5-Land with regard to median ubRMSE (0.07(±0.02) m³/m³ for SMAP-E and ERA5-Land) and R (0.59(±0.16) for SMAP-IB9 vs. 0.61(±0.15) for ERA5-Land), respectively.
    Keywords Soil Moisture and Ocean Salinity satellite ; biosphere ; carbon ; data collection ; energy ; forests ; models ; radiometry ; soil water ; SMAP-IB ; SMAP-E ; ERA5-land ; Remote sensing ; Model simulation ; Soil moisture
    Language English
    Publishing place Elsevier Inc.
    Document type Article ; Online
    Note Pre-press version
    ZDB-ID 431483-9
    ISSN 0034-4257
    ISSN 0034-4257
    DOI 10.1016/j.rse.2023.113721
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  8. Article: Satellite detection of varying seasonal water supply restrictions on grassland productivity in the Missouri basin, USA

    A, Geruo / Colliander, Andreas / Kimball, John S / Velicogna, Isabella / Zhao, Meng

    Remote sensing of environment. 2020 Mar. 15, v. 239

    2020  

    Abstract: Climate observations indicate more frequent drought in recent years, and model predictions suggest that drought occurrence will continue to rise with global warming. Understanding drought impacts on ecosystem functioning requires accurate quantification ... ...

    Abstract Climate observations indicate more frequent drought in recent years, and model predictions suggest that drought occurrence will continue to rise with global warming. Understanding drought impacts on ecosystem functioning requires accurate quantification of vegetation sensitivity to changes in water supply condition. This is complicated by the seasonal variation in plant structural and physiological response to water stress, especially for semi-arid grasslands with characteristic strong spatial and temporal variability in carbon uptake. Here, we use complementary satellite soil moisture (SM) and total water storage (TWS) observations to delineate plant-accessible water supply variations for natural grasslands in the Missouri basin, USA. We evaluate how water supply influences the spatiotemporal variations in grassland productivity as a function of seasonal timing and climate condition. We identify a 128-day period from mid-June to early October when grassland growth is sensitive to soil moisture changes. We find the strongest SM sensitivity after the peak of the growing season associated with high temperature and VPD. SM limitation can extend to early and late growing season under warm conditions, while grassland sensitivity to SM is generally stronger in the late growth stage than in the green-up period given similar temperature and soil moisture. We find that complementary to the surface SM observations, TWS provides plant-available water storage information from the deeper soil, and both SM and TWS exert a lagged impact on grassland productivity. We find that the lag between the inter-annual variation of SM and associated plant response increases through the season, and overall there is a transition from SM-limitation to TWS-limitation on productivity during the late growing period when the TWS level is near the seasonal low. Future global change projections should account for a seasonally varying vegetation-moisture relationship to accurately assess the impact of the water supply constraint on plant productivity in a warming climate.
    Keywords arid lands ; basins ; carbon ; climatic factors ; developmental stages ; drought ; ecological footprint ; environmental impact ; global warming ; grasslands ; growing season ; models ; physiological response ; plant available water ; plant response ; prediction ; remote sensing ; satellites ; seasonal variation ; spatial variation ; temperature ; water storage ; water stress ; water supply ; Missouri
    Language English
    Dates of publication 2020-0315
    Publishing place Elsevier Inc.
    Document type Article
    ZDB-ID 431483-9
    ISSN 0034-4257
    ISSN 0034-4257
    DOI 10.1016/j.rse.2019.111623
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  9. Article: Soil moisture retrieval over a site of intensive agricultural production using airborne radiometer data

    Wang, Hongquan / Magagi, Ramata / Goïta, Kalifa / Colliander, Andreas / Jackson, Thomas / McNairn, Heather / Powers, Jarrett

    International journal of applied earth observation and geoinformation. 2021 May, v. 97

    2021  

    Abstract: This study investigates soil moisture retrievals using airborne passive microwave data at two different resolutions collected during the Soil Moisture Active Passive Validation Experiments in 2012 and 2016 (SMAPVEX12 and SMAPVEX16-MB). Based on the fine- ... ...

    Abstract This study investigates soil moisture retrievals using airborne passive microwave data at two different resolutions collected during the Soil Moisture Active Passive Validation Experiments in 2012 and 2016 (SMAPVEX12 and SMAPVEX16-MB). Based on the fine-resolution passive data (500 m), we integrate the surface roughness parameters which are traditionally used in the radar backscatter models into the passive emission models. To parameterize the effective roughness Hᵣ in L-band Microwave Emission of the Biosphere (L-MEB) model, we analyze two different functions which include only surface Root Mean Square height (s), as well as both s and the autocorrelation length l (zₛ = s²/l). For each of the two roughness functions, the b vegetation parameters are optimized for canola, soybean and wheat. The transferability of the b parameters between 2012 and 2016 is also evaluated by comparing the L-MEB model simulated and the measured brightness temperature. Then, the calibrated L-MEB model is applied to the subpixels of the coarse-resolution passive data (1500 m) given the vegetation heterogeneity, to map the soil moisture over the SMAPVEX entire experimental site. The results indicated the Hᵣ model with the zₛ parameter outperformed that with the s parameter. This suggests that the inclusion of the roughness autocorrelation length in the L-MEB model improved the accuracy of modeling the brightness temperature. The vegetation attenuation on the brightness temperature at V-polarization was stronger than that at H-polarization, due to the dominant vertical structure of the crop canopy. Since the airborne passive observations exhibited remarkable consistency between the 2012 and 2016 measurements, the b parameters obtained in 2012 can be transferred to the 2016. Based on the obtained Hᵣ and b parameters, the soil moisture maps were retrieved using the calibrated L-MEB model applied to the sub-pixel of the coarse-resolution passive data, implying a Root Mean Square Errors (RMSEs) of 0.049–0.058 m³/m³ and correlation coefficients of 0.82–0.87. This paper suggests that the physical roughness zₛ in the radar domain can be coupled into the L-MEB model to refine the soil moisture retrievals from passive brightness temperature.
    Keywords autocorrelation ; biosphere ; canola ; canopy ; models ; radar ; roughness ; satellites ; soil water ; soybeans ; spatial data ; surface roughness ; temperature ; vegetation ; wheat
    Language English
    Dates of publication 2021-05
    Publishing place Elsevier B.V.
    Document type Article
    ISSN 1569-8432
    DOI 10.1016/j.jag.2020.102287
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  10. Article: Passive/active microwave soil moisture change disaggregation using SMAPVEX12 data

    Fang, Bin / Bindlish, Rajat / Colliander, Andreas / Jackson, Thomas J / Lakshmi, Venkat

    Journal of hydrology. 2019 July, v. 574

    2019  

    Abstract: The SMAPVEX12 (Soil Moisture Active Passive (SMAP) Validation Experiment 2012) experiment was conducted during June-July 2012 in Manitoba, Canada with the goal of collecting remote sensing data and ground measurements for the development and testing of ... ...

    Abstract The SMAPVEX12 (Soil Moisture Active Passive (SMAP) Validation Experiment 2012) experiment was conducted during June-July 2012 in Manitoba, Canada with the goal of collecting remote sensing data and ground measurements for the development and testing of soil moisture retrieval algorithms under varying vegetation and soil conditions for the SMAP satellite. The aircraft based soil moisture data provided by the passive/active microwave sensor PALS (Passive and Active L-band System) has a nominal spatial resolution of 1600 m. However, this resolution is not compatible with agricultural, meteorological and hydrological studies that require high spatial resolutions and this issue can be solved by soil moisture disaggregation. The soil moisture disaggregation algorithm integrates radiometer soil moisture retrievals and high-resolution radar observations and it can provide soil moisture estimates at a finer scale than the radiometer data alone. In this study, a change detection algorithm was used for disaggregation of coarse resolution passive microwave soil moisture retrievals with radar backscatter coefficients obtained from the higher spatial resolution UAVSAR (Unmanned Air Vehicle Synthetic Aperture Radar) at crop field scale. The accuracy of the disaggregated change in soil moisture was evaluated using ground based soil moisture measurements collected during SMAPVEX12 campaign. The results showed that soil moisture spatial variabilities were better characterized by the disaggregated change in soil moisture estimates at 5 m/800 m resolution as well as a good agreement between disaggregated estimates and in situ measurements. It also showed that VWC (Vegetation Water Content) did not have a big impact on disaggregation algorithm performance, with R2 of the disaggregated results ranging 0.628–0.794. The 5 m and 800 m resolution disaggregated soil moisture did no show significant difference in statistical performance variables.
    Keywords algorithms ; satellites ; soil quality ; soil water ; spatial data ; synthetic aperture radar ; unmanned aerial vehicles ; vegetation ; water content ; Manitoba
    Language English
    Dates of publication 2019-07
    Size p. 1085-1098.
    Publishing place Elsevier B.V.
    Document type Article
    ZDB-ID 1473173-3
    ISSN 1879-2707 ; 0022-1694
    ISSN (online) 1879-2707
    ISSN 0022-1694
    DOI 10.1016/j.jhydrol.2019.04.082
    Database NAL-Catalogue (AGRICOLA)

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