On the cover-The Soil Moisture Active Passive (SMAP) mission will provide global measurements of ... more On the cover-The Soil Moisture Active Passive (SMAP) mission will provide global measurements of soil moisture and its freeze/thaw state from a 685-km, near-polar, sun-synchronous orbit for a period of 3 years. The SMAP observatory's instrument suite includes a radiometer and a synthetic aperture radar to make coincident measurements of surface emission and backscatter. SMAP data will be used to enhance understanding of processes that link the water, energy, and carbon cycles, and to extend the capabilities of weather and climate prediction models.
Soil moisture is an important hydrologic state variable critical to successful hydroclimatic and ... more Soil moisture is an important hydrologic state variable critical to successful hydroclimatic and environmental predictions. Soil moisture varies both in space and time because of spatio-temporal variations in precipitation, soil properties, topographic features, and vegetation characteristics. In recent years, air-and space-borne remote sensing campaigns have successfully demonstrated the use of passive microwave remote sensing to map soil moisture status near the soil surface (»0-0.05 m below the ground) at various spatial scales. In this study root zone (e.g., »0-0.6 m below the ground) soil moisture distributions were estimated across the Little Washita watershed (Oklahoma) by assimilating near-surface soil moisture data from remote sensing measurements using the Electronically Scanned Thinned Array Radiometer (ESTAR) with an ensemble Kalman filter (EnKF) technique coupled with a numerical one-dimensional vadose zone flow model (HYDRUS-ET). The resulting distributed root zone soil moisture assessment tool (SMAT) is based on the concept of having parallel noninteracting streamtubes (hydrologic units) within a geographic information system (GIS) platform. The simulated soil moisture distribution at various depths and locations within the watershed were compared with measured profile soil moisture data using time domain reflectometry (TDR). A reasonable agreement was found under favorable conditions between footprint-scale model estimations and point-scale field soil moisture measurements in the root zone. However, uncertainties introduced by precipitation and soil hydraulic properties caused suboptimal performance of the integrated model. The SMAT holds great promise and offers flexibility to incorporate various data assimilation techniques, scaling, and other hydrological complexities across large landscapes. The integrated model can be useful for simulating profile soil moisture estimation and for predicting transient soil moisture behavior for a range of hydrological and environmental applications.
In this study, we examined the characteristics of soil moisture dynamics of wet and dry fields ac... more In this study, we examined the characteristics of soil moisture dynamics of wet and dry fields across hierarchical spatial scales within the region of Soil Moisture Experiment 2002 (SMEX02) hydrology campaign in Iowa. The Polarimetric Scanning Radiometer (PSR)-based remotely sensed surface (∼ 0-5 cm) soil moisture at 800 m × 800 m resolution was used in this study. Wavelet-based multiresolution technique decomposed the soil moisture into large-scale mean soil moisture fields and fluctuations in horizontal, diagonal, and vertical directions at hierarchical spatial resolutions. Results suggested linearity in the log-log dependency of the variance of soil moisture up to a resolution of 6400 m × 6400 m on PSR sampling dates during SMEX02. The wet fields (with high soil moisture) show almost similar variance for all the resolutions signifying the strong spatial correlation. Analysis of the dry fields (with low soil moisture) indicated a log-log linearity of moments with various scales, a...
IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018
SMAP project released a new enhanced high-resolution (3km) soil moisture active-passive product. ... more SMAP project released a new enhanced high-resolution (3km) soil moisture active-passive product. This product is obtained by combining the SMAP radiometer data and the Sentinel-IA and -IB Synthetic Aperture Radar (SAR) data. The approach used for this product draws heavily from the heritage SMAP active-passive algorithm. Modifications in the SMAP active-passive algorithm are done to accommodate the Copernicus Program's Sentinel-IA and -IB multi-angular C-band SAR data. Assessment of the SMAP and Sentinel active-passive algorithm has been conducted and results show feasibility of estimating surface soil moisture at high-resolution in regions with low vegetation density $(< 3\ \mathrm{kg}\ \mathrm{m}^{-2})$. The beta version of this product is released to public on Nov 1st, 2017. This high resolution (3 km) soil moisture product is useful for agriculture, flood mapping, watershed/rangeland management, and ecological/hydrological applications.
Over land the vegetation canopy affects the microwave brightness temperature by emission, scatter... more Over land the vegetation canopy affects the microwave brightness temperature by emission, scattering and attenuation of surface soil emission. The questions addressed in this study are: 1) what is the transparency of the vegetation canopy for different biomes around the Globe at the low-frequency L-band?, 2) what is the seasonal amplitude of vegetation microwave optical depth for different biomes?, 3) what is the effective scattering at this frequency for different vegetation types?, 4) what is the impact of imprecise characterization of vegetation microwave properties on retrieval of soil surface conditions? These questions are addressed based on the recently completed one full annual cycle measurements by the NASA Soil Moisture Active Passive (SMAP) measurements.
Soil moisture impacts exchanges of water, energy and carbon fluxes between the land surface and t... more Soil moisture impacts exchanges of water, energy and carbon fluxes between the land surface and the atmosphere. Passive microwave remote sensing at L-band can capture spatial and temporal patterns of soil moisture in the landscape. Both ESA and NASA have launched L-band radiometers, in the form of the SMOS and SMAP satellites respectively, to monitor soil moisture globally, every 3-day at about 40 km resolution. However, their coarse scale restricts the range of applications. While SMAP included an L-band radar to downscale the radiometer soil moisture to 9 km, the radar failed after 3 months and this initial approach is not applicable to developing a consistent long term soil moisture product across the two missions anymore. Existing optical-, radiometer-, and oversampling-based downscaling methods could be an alternative to the radar-based approach for delivering such data. Nevertheless, retrieval of a consistent high resolution soil moisture product remains a challenge, and there...
NASA’s Soil Moisture Active Passive (SMAP) mission has been validating its soil moisture (SM) pro... more NASA’s Soil Moisture Active Passive (SMAP) mission has been validating its soil moisture (SM) products since the start of data production on March 31, 2015. Prior to launch, the mission defined a set of criteria for core validation sites (CVS) that enable the testing of the key mission SM accuracy requirement (unbiased root-mean-square error <0.04 m3/m3). The validation approach also includes other (“sparse network”) in situ SM measurements, satellite SM products, model-based SM products, and field experiments. Over the past six years, the SMAP SM products have been analyzed with respect to these reference data, and the analysis approaches themselves have been scrutinized in an effort to best understand the products’ performance. Validation of the most recent SMAP Level 2 and 3 SM retrieval products (R17000) shows that the L-band (1.4 GHz) radiometer-based SM record continues to meet mission requirements. The products are generally consistent with SM retrievals from the ESA Soil ...
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Irrigation is not well represented in land surface, hydrological, and climate models. One way to ... more Irrigation is not well represented in land surface, hydrological, and climate models. One way to account for irrigation is by assimilating satellite soil moisture data that contains irrigation signal with land surface models. In this study, the irrigation detection ability of SMAP enhanced 9km and SMAP-Sentinel1 3km and 1km soil moisture products are evaluated using the first moment (mean) and the second moment (variability) of soil moisture data. The SMAP enhanced 9km soil moisture product lacks irrigation signals in an irrigated plain south of Urmia Lake, whereas SMAP-Sentinel1 products record irrigation signal in soil moisture variability. Despite observing higher variability over irrigated areas, there are only small and inconsistent wet biases observed over irrigated pixels relative to nearby non-irrigated pixels during the irrigation season. This is partly attributable to the climatology vegetation water content used in the SMAP-Sentinel 1 soil moisture retrieval algorithm that is not accounting for crop rotation and land management. Thus, in the second part of this study, we updated the retrieval algorithm to use dynamic vegetation water content. The update increased vegetation water content up to 1 kg/m 2, which corresponds with a 0.05 cm 3 /cm 3 increase in soil moisture during irrigation season. The update does not notably change soil moisture retrievals off season. This study shows that irrigation signals are present in both the first and second moment of soil moisture time series, and employing dynamic vegetation water content in the SMAP-Sentinel 1 algorithm can enhance the irrigation signal over agricultural regions.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
NASA's Soil Moisture Active Passive (SMAP) mission potential to produce high-resolution soil mois... more NASA's Soil Moisture Active Passive (SMAP) mission potential to produce high-resolution soil moisture suffered adversely due to its L-band synthetic-aperture radar (SAR) failure. Other satellite-based L-/C-band SAR observations can be used within the SMAP active-passive algorithm. In this study, we evaluated the capability of ingesting ISRO's Radar Imaging Satellite-1 (RISAT-1) C-band SAR observations in the SMAP active-passive algorithm to obtain soil moisture at 1, 3, and 9 km over the agricultural region dominant by paddy that experiences seasonal flooding. We also improved the SMAP mission activepassive algorithm with a dynamic surface water bodies (ponding conditions) masking approach using the native RISAT-1 observations. The study shows that the use of surface water masks helps in mitigating the negative impact of surface water bodies in the active-passive disaggregation process. The SMAP-RISAT soil moisture retrievals at 1 km and 3 km resolutions are found to have high unbiased root-mean-square error (ubRMSE) greater than 0.06 m 3 /m 3 during very wet and high vegetative conditions. However, at low and moderate soil moisture states the ubRMSE is below 0.06 m 3 /m 3. Comparison of soil moisture retrievals at 9 km resolution with upscaled ground-based soil moisture measurements shows ubRMSE less than 0.04 m 3 /m 3. This research work is a precursor for estimating soil moisture for the upcoming RISAT-1A dataset over India. The findings will further help in the implementation of a microwave active-passive algorithm to retrieve soil moisture for future satellite missions involving radiometer-SAR instruments, and challenging geophysical conditions (i.e., dynamic surface water bodies).
Recent advances in L-band passive microwave remote sensing provide an unprecedented opportunity t... more Recent advances in L-band passive microwave remote sensing provide an unprecedented opportunity to monitor soil moisture at~40 km spatial resolution around the globe. Nevertheless, retrieval of the accurate high spatial resolution soil moisture maps that are required to satisfy hydro-meteorological and agricultural applications remains a challenge. Currently, a variety of downscaling, otherwise known as disaggregation techniques have been proposed as the solution to disaggregate the coarse passive microwave soil moisture into high-to-medium resolutions. These techniques take advantage of the strengths of both the passive microwave observations of soil moisture having low spatial resolution and the spatially detailed information on land surface features that either influence or represent soil moisture variability. However, such techniques have typically been developed and tested individually under differing weather and climate conditions, meaning that there is no clear guidance on which technique performs the best. Consequently, this paper presents a quantitative assessment of the existing radar-, optical-, radiometer-, and oversampling-based downscaling techniques using a singular extensive data set collected specifically for that purpose, being the Soil Moisture Active Passive Experiment (SMAPEx)-4 and-5 airborne field campaigns, and the OzNet in situ stations, to determine the relative strengths and weaknesses of their performances. The oversampling-based soil moisture product best captured the temporal and spatial variability of the reference soil moisture overall, though the radar-based products had a better temporal agreement with airborne soil moisture during the short SMAPEx-4 period. Moreover, the difference between temporal analysis of products against in situ and airborne soil moisture reference data sets pointed to the fact that relying on in situ measurements alone is not appropriate for validation of spatially enhanced soil moisture maps.
Soil Moisture Active Passive (SMAP) mission of NASA was launched in January 2015. Currently, SMAP... more Soil Moisture Active Passive (SMAP) mission of NASA was launched in January 2015. Currently, SMAP has an Lband radiometer and a defunct L-band radar with a rotating 6-m mesh reflector antenna. On July 7th, 2015, the SMAP radar malfunctioned and became inoperable. Consequently, the production of high-resolution activepassive soil moisture product got hampered, and only~2.5 months (April 15th, 2015 to July 7th, 2015) of data remain available. Therefore, during the SMAP post-radar phase, many ways were examined to restart the highresolution soil moisture product generation of the SMAP mission. One of the feasible approaches was to substitute the SMAP radar with other available SAR data. Sentinel-1A/Sentinel-1B SAR data was found most suitable for combining with the SMAP radiometer data because of its nearly similar orbit configuration that allows overlapping of their swaths with a minimal time difference, a key feature/requirement for the SMAP activepassive algorithm. The Sentinel interferometric wide swath (IW) mode acquisition also provides the co-polarized and cross-polarized observations required for the SMAP active-passive algorithm. However, some differences do exist between the SMAP and Sentinel SAR data. They are mainly: 1) Sentinel has a C-band SAR whereas SMAP operates at L-band; 2) Sentinel has multiple incidence angles within its swath, and SMAP has one single incidence angle; and 3) Sentinel 1A/B Interferometric Wide (IW) swath width is~250 km as compared to SMAP with 1000 km swath width. On any given day, the narrow swath width of the Sentinel observations significantly reduces the overlap spatial coverage between SMAP and Sentinel as compared to the original SMAP radar and radiometer swath coverage. Hence, the temporal resolution (revisit interval) suffers due to narrow overlapped swath width and degrades from 3 days to 12 days. One advantage of using very high-resolution resolution Sentinel-1A/Sentinel-1B data in the SMAP active-passive algorithm is the potential of obtaining the disaggregated brightness temperature and thus soil moisture at a much finer spatial resolution of 3 km and 1 km at
The NASA Soil Moisture Active Passive (SMAP) mission was launched on January 31st, 2015. The spac... more The NASA Soil Moisture Active Passive (SMAP) mission was launched on January 31st, 2015. The spacecraft was to provide high-resolution (3 km and 9 km) global soil moisture estimates at regular intervals by combining for the first time L-band radiometer and radar observations. On July 7th, 2015, a component of the SMAP radar failed and the radar ceased operation. However, before this occurred the mission was able to collect and process 2.5 months of the SMAP high-resolution active-passive soil moisture data (L2SMAP) that coincided with the Northern Hemisphere's vegetation green-up and crop growth season. In this study, we evaluate the SMAP highresolution soil moisture product derived from several alternative algorithms against in situ data from core calibration and validation sites (CVS), and sparse networks. The baseline algorithm had the best comparison statistics against the CVS and sparse networks. The overall unbiased root-mean-square-difference is close to the 0.04 m 3 /m 3 the SMAP mission requirement. A 3 km spatial resolution soil moisture product was also examined. This product had an unbiased root-mean-square-difference of~0.053 m 3 /m 3. The SMAP L2SMAP product for 2.5 months is now validated for use in geophysical applications and research and available to the public through the NASA Distributed Active Archive Center (DAAC) at the National Snow and Ice Data Center (NSIDC). The L2SMAP product is packaged with the geo-coordinates, acquisition times, and all requisite ancillary information. Although limited in duration, SMAP has clearly demonstrated the potential of using a combined Lband radar-radiometer for proving high spatial resolution and accurate global soil moisture.
Global food production depends upon many factors that Earth observing satellites routinely measur... more Global food production depends upon many factors that Earth observing satellites routinely measure about water, energy, weather, and ecosystems. Increasingly sophisticated, publicly available satellite data products can improve efficiencies in resource management and provide earlier indication of environmental disruption. Satellite remote sensing provides a consistent, long-term record that can be used effectively to detect large-scale features over time, such as a developing drought. Accuracy and capabilities have increased along with the range of Earth observations and derived products that can support food security decisions with actionable information. This paper highlights major capabilities facilitated by satellite observations and physical models that have been developed and validated using remotely sensed observations. Although we primarily focus on variables relevant to agriculture, we also include a brief description of the growing use of Earth observations in support of aquaculture and fisheries.
On the cover-The Soil Moisture Active Passive (SMAP) mission will provide global measurements of ... more On the cover-The Soil Moisture Active Passive (SMAP) mission will provide global measurements of soil moisture and its freeze/thaw state from a 685-km, near-polar, sun-synchronous orbit for a period of 3 years. The SMAP observatory's instrument suite includes a radiometer and a synthetic aperture radar to make coincident measurements of surface emission and backscatter. SMAP data will be used to enhance understanding of processes that link the water, energy, and carbon cycles, and to extend the capabilities of weather and climate prediction models.
Soil moisture is an important hydrologic state variable critical to successful hydroclimatic and ... more Soil moisture is an important hydrologic state variable critical to successful hydroclimatic and environmental predictions. Soil moisture varies both in space and time because of spatio-temporal variations in precipitation, soil properties, topographic features, and vegetation characteristics. In recent years, air-and space-borne remote sensing campaigns have successfully demonstrated the use of passive microwave remote sensing to map soil moisture status near the soil surface (»0-0.05 m below the ground) at various spatial scales. In this study root zone (e.g., »0-0.6 m below the ground) soil moisture distributions were estimated across the Little Washita watershed (Oklahoma) by assimilating near-surface soil moisture data from remote sensing measurements using the Electronically Scanned Thinned Array Radiometer (ESTAR) with an ensemble Kalman filter (EnKF) technique coupled with a numerical one-dimensional vadose zone flow model (HYDRUS-ET). The resulting distributed root zone soil moisture assessment tool (SMAT) is based on the concept of having parallel noninteracting streamtubes (hydrologic units) within a geographic information system (GIS) platform. The simulated soil moisture distribution at various depths and locations within the watershed were compared with measured profile soil moisture data using time domain reflectometry (TDR). A reasonable agreement was found under favorable conditions between footprint-scale model estimations and point-scale field soil moisture measurements in the root zone. However, uncertainties introduced by precipitation and soil hydraulic properties caused suboptimal performance of the integrated model. The SMAT holds great promise and offers flexibility to incorporate various data assimilation techniques, scaling, and other hydrological complexities across large landscapes. The integrated model can be useful for simulating profile soil moisture estimation and for predicting transient soil moisture behavior for a range of hydrological and environmental applications.
In this study, we examined the characteristics of soil moisture dynamics of wet and dry fields ac... more In this study, we examined the characteristics of soil moisture dynamics of wet and dry fields across hierarchical spatial scales within the region of Soil Moisture Experiment 2002 (SMEX02) hydrology campaign in Iowa. The Polarimetric Scanning Radiometer (PSR)-based remotely sensed surface (∼ 0-5 cm) soil moisture at 800 m × 800 m resolution was used in this study. Wavelet-based multiresolution technique decomposed the soil moisture into large-scale mean soil moisture fields and fluctuations in horizontal, diagonal, and vertical directions at hierarchical spatial resolutions. Results suggested linearity in the log-log dependency of the variance of soil moisture up to a resolution of 6400 m × 6400 m on PSR sampling dates during SMEX02. The wet fields (with high soil moisture) show almost similar variance for all the resolutions signifying the strong spatial correlation. Analysis of the dry fields (with low soil moisture) indicated a log-log linearity of moments with various scales, a...
IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018
SMAP project released a new enhanced high-resolution (3km) soil moisture active-passive product. ... more SMAP project released a new enhanced high-resolution (3km) soil moisture active-passive product. This product is obtained by combining the SMAP radiometer data and the Sentinel-IA and -IB Synthetic Aperture Radar (SAR) data. The approach used for this product draws heavily from the heritage SMAP active-passive algorithm. Modifications in the SMAP active-passive algorithm are done to accommodate the Copernicus Program's Sentinel-IA and -IB multi-angular C-band SAR data. Assessment of the SMAP and Sentinel active-passive algorithm has been conducted and results show feasibility of estimating surface soil moisture at high-resolution in regions with low vegetation density $(< 3\ \mathrm{kg}\ \mathrm{m}^{-2})$. The beta version of this product is released to public on Nov 1st, 2017. This high resolution (3 km) soil moisture product is useful for agriculture, flood mapping, watershed/rangeland management, and ecological/hydrological applications.
Over land the vegetation canopy affects the microwave brightness temperature by emission, scatter... more Over land the vegetation canopy affects the microwave brightness temperature by emission, scattering and attenuation of surface soil emission. The questions addressed in this study are: 1) what is the transparency of the vegetation canopy for different biomes around the Globe at the low-frequency L-band?, 2) what is the seasonal amplitude of vegetation microwave optical depth for different biomes?, 3) what is the effective scattering at this frequency for different vegetation types?, 4) what is the impact of imprecise characterization of vegetation microwave properties on retrieval of soil surface conditions? These questions are addressed based on the recently completed one full annual cycle measurements by the NASA Soil Moisture Active Passive (SMAP) measurements.
Soil moisture impacts exchanges of water, energy and carbon fluxes between the land surface and t... more Soil moisture impacts exchanges of water, energy and carbon fluxes between the land surface and the atmosphere. Passive microwave remote sensing at L-band can capture spatial and temporal patterns of soil moisture in the landscape. Both ESA and NASA have launched L-band radiometers, in the form of the SMOS and SMAP satellites respectively, to monitor soil moisture globally, every 3-day at about 40 km resolution. However, their coarse scale restricts the range of applications. While SMAP included an L-band radar to downscale the radiometer soil moisture to 9 km, the radar failed after 3 months and this initial approach is not applicable to developing a consistent long term soil moisture product across the two missions anymore. Existing optical-, radiometer-, and oversampling-based downscaling methods could be an alternative to the radar-based approach for delivering such data. Nevertheless, retrieval of a consistent high resolution soil moisture product remains a challenge, and there...
NASA’s Soil Moisture Active Passive (SMAP) mission has been validating its soil moisture (SM) pro... more NASA’s Soil Moisture Active Passive (SMAP) mission has been validating its soil moisture (SM) products since the start of data production on March 31, 2015. Prior to launch, the mission defined a set of criteria for core validation sites (CVS) that enable the testing of the key mission SM accuracy requirement (unbiased root-mean-square error <0.04 m3/m3). The validation approach also includes other (“sparse network”) in situ SM measurements, satellite SM products, model-based SM products, and field experiments. Over the past six years, the SMAP SM products have been analyzed with respect to these reference data, and the analysis approaches themselves have been scrutinized in an effort to best understand the products’ performance. Validation of the most recent SMAP Level 2 and 3 SM retrieval products (R17000) shows that the L-band (1.4 GHz) radiometer-based SM record continues to meet mission requirements. The products are generally consistent with SM retrievals from the ESA Soil ...
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Irrigation is not well represented in land surface, hydrological, and climate models. One way to ... more Irrigation is not well represented in land surface, hydrological, and climate models. One way to account for irrigation is by assimilating satellite soil moisture data that contains irrigation signal with land surface models. In this study, the irrigation detection ability of SMAP enhanced 9km and SMAP-Sentinel1 3km and 1km soil moisture products are evaluated using the first moment (mean) and the second moment (variability) of soil moisture data. The SMAP enhanced 9km soil moisture product lacks irrigation signals in an irrigated plain south of Urmia Lake, whereas SMAP-Sentinel1 products record irrigation signal in soil moisture variability. Despite observing higher variability over irrigated areas, there are only small and inconsistent wet biases observed over irrigated pixels relative to nearby non-irrigated pixels during the irrigation season. This is partly attributable to the climatology vegetation water content used in the SMAP-Sentinel 1 soil moisture retrieval algorithm that is not accounting for crop rotation and land management. Thus, in the second part of this study, we updated the retrieval algorithm to use dynamic vegetation water content. The update increased vegetation water content up to 1 kg/m 2, which corresponds with a 0.05 cm 3 /cm 3 increase in soil moisture during irrigation season. The update does not notably change soil moisture retrievals off season. This study shows that irrigation signals are present in both the first and second moment of soil moisture time series, and employing dynamic vegetation water content in the SMAP-Sentinel 1 algorithm can enhance the irrigation signal over agricultural regions.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
NASA's Soil Moisture Active Passive (SMAP) mission potential to produce high-resolution soil mois... more NASA's Soil Moisture Active Passive (SMAP) mission potential to produce high-resolution soil moisture suffered adversely due to its L-band synthetic-aperture radar (SAR) failure. Other satellite-based L-/C-band SAR observations can be used within the SMAP active-passive algorithm. In this study, we evaluated the capability of ingesting ISRO's Radar Imaging Satellite-1 (RISAT-1) C-band SAR observations in the SMAP active-passive algorithm to obtain soil moisture at 1, 3, and 9 km over the agricultural region dominant by paddy that experiences seasonal flooding. We also improved the SMAP mission activepassive algorithm with a dynamic surface water bodies (ponding conditions) masking approach using the native RISAT-1 observations. The study shows that the use of surface water masks helps in mitigating the negative impact of surface water bodies in the active-passive disaggregation process. The SMAP-RISAT soil moisture retrievals at 1 km and 3 km resolutions are found to have high unbiased root-mean-square error (ubRMSE) greater than 0.06 m 3 /m 3 during very wet and high vegetative conditions. However, at low and moderate soil moisture states the ubRMSE is below 0.06 m 3 /m 3. Comparison of soil moisture retrievals at 9 km resolution with upscaled ground-based soil moisture measurements shows ubRMSE less than 0.04 m 3 /m 3. This research work is a precursor for estimating soil moisture for the upcoming RISAT-1A dataset over India. The findings will further help in the implementation of a microwave active-passive algorithm to retrieve soil moisture for future satellite missions involving radiometer-SAR instruments, and challenging geophysical conditions (i.e., dynamic surface water bodies).
Recent advances in L-band passive microwave remote sensing provide an unprecedented opportunity t... more Recent advances in L-band passive microwave remote sensing provide an unprecedented opportunity to monitor soil moisture at~40 km spatial resolution around the globe. Nevertheless, retrieval of the accurate high spatial resolution soil moisture maps that are required to satisfy hydro-meteorological and agricultural applications remains a challenge. Currently, a variety of downscaling, otherwise known as disaggregation techniques have been proposed as the solution to disaggregate the coarse passive microwave soil moisture into high-to-medium resolutions. These techniques take advantage of the strengths of both the passive microwave observations of soil moisture having low spatial resolution and the spatially detailed information on land surface features that either influence or represent soil moisture variability. However, such techniques have typically been developed and tested individually under differing weather and climate conditions, meaning that there is no clear guidance on which technique performs the best. Consequently, this paper presents a quantitative assessment of the existing radar-, optical-, radiometer-, and oversampling-based downscaling techniques using a singular extensive data set collected specifically for that purpose, being the Soil Moisture Active Passive Experiment (SMAPEx)-4 and-5 airborne field campaigns, and the OzNet in situ stations, to determine the relative strengths and weaknesses of their performances. The oversampling-based soil moisture product best captured the temporal and spatial variability of the reference soil moisture overall, though the radar-based products had a better temporal agreement with airborne soil moisture during the short SMAPEx-4 period. Moreover, the difference between temporal analysis of products against in situ and airborne soil moisture reference data sets pointed to the fact that relying on in situ measurements alone is not appropriate for validation of spatially enhanced soil moisture maps.
Soil Moisture Active Passive (SMAP) mission of NASA was launched in January 2015. Currently, SMAP... more Soil Moisture Active Passive (SMAP) mission of NASA was launched in January 2015. Currently, SMAP has an Lband radiometer and a defunct L-band radar with a rotating 6-m mesh reflector antenna. On July 7th, 2015, the SMAP radar malfunctioned and became inoperable. Consequently, the production of high-resolution activepassive soil moisture product got hampered, and only~2.5 months (April 15th, 2015 to July 7th, 2015) of data remain available. Therefore, during the SMAP post-radar phase, many ways were examined to restart the highresolution soil moisture product generation of the SMAP mission. One of the feasible approaches was to substitute the SMAP radar with other available SAR data. Sentinel-1A/Sentinel-1B SAR data was found most suitable for combining with the SMAP radiometer data because of its nearly similar orbit configuration that allows overlapping of their swaths with a minimal time difference, a key feature/requirement for the SMAP activepassive algorithm. The Sentinel interferometric wide swath (IW) mode acquisition also provides the co-polarized and cross-polarized observations required for the SMAP active-passive algorithm. However, some differences do exist between the SMAP and Sentinel SAR data. They are mainly: 1) Sentinel has a C-band SAR whereas SMAP operates at L-band; 2) Sentinel has multiple incidence angles within its swath, and SMAP has one single incidence angle; and 3) Sentinel 1A/B Interferometric Wide (IW) swath width is~250 km as compared to SMAP with 1000 km swath width. On any given day, the narrow swath width of the Sentinel observations significantly reduces the overlap spatial coverage between SMAP and Sentinel as compared to the original SMAP radar and radiometer swath coverage. Hence, the temporal resolution (revisit interval) suffers due to narrow overlapped swath width and degrades from 3 days to 12 days. One advantage of using very high-resolution resolution Sentinel-1A/Sentinel-1B data in the SMAP active-passive algorithm is the potential of obtaining the disaggregated brightness temperature and thus soil moisture at a much finer spatial resolution of 3 km and 1 km at
The NASA Soil Moisture Active Passive (SMAP) mission was launched on January 31st, 2015. The spac... more The NASA Soil Moisture Active Passive (SMAP) mission was launched on January 31st, 2015. The spacecraft was to provide high-resolution (3 km and 9 km) global soil moisture estimates at regular intervals by combining for the first time L-band radiometer and radar observations. On July 7th, 2015, a component of the SMAP radar failed and the radar ceased operation. However, before this occurred the mission was able to collect and process 2.5 months of the SMAP high-resolution active-passive soil moisture data (L2SMAP) that coincided with the Northern Hemisphere's vegetation green-up and crop growth season. In this study, we evaluate the SMAP highresolution soil moisture product derived from several alternative algorithms against in situ data from core calibration and validation sites (CVS), and sparse networks. The baseline algorithm had the best comparison statistics against the CVS and sparse networks. The overall unbiased root-mean-square-difference is close to the 0.04 m 3 /m 3 the SMAP mission requirement. A 3 km spatial resolution soil moisture product was also examined. This product had an unbiased root-mean-square-difference of~0.053 m 3 /m 3. The SMAP L2SMAP product for 2.5 months is now validated for use in geophysical applications and research and available to the public through the NASA Distributed Active Archive Center (DAAC) at the National Snow and Ice Data Center (NSIDC). The L2SMAP product is packaged with the geo-coordinates, acquisition times, and all requisite ancillary information. Although limited in duration, SMAP has clearly demonstrated the potential of using a combined Lband radar-radiometer for proving high spatial resolution and accurate global soil moisture.
Global food production depends upon many factors that Earth observing satellites routinely measur... more Global food production depends upon many factors that Earth observing satellites routinely measure about water, energy, weather, and ecosystems. Increasingly sophisticated, publicly available satellite data products can improve efficiencies in resource management and provide earlier indication of environmental disruption. Satellite remote sensing provides a consistent, long-term record that can be used effectively to detect large-scale features over time, such as a developing drought. Accuracy and capabilities have increased along with the range of Earth observations and derived products that can support food security decisions with actionable information. This paper highlights major capabilities facilitated by satellite observations and physical models that have been developed and validated using remotely sensed observations. Although we primarily focus on variables relevant to agriculture, we also include a brief description of the growing use of Earth observations in support of aquaculture and fisheries.
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Papers by Narendra Das