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. 

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The first polarimetric GNSS-Reflectometer instrument in space improves the SMAP mission’s sensitivity over densely vegetated areas

The relationships of nitrous oxide (N2O) and methane (CH4) emissions to other environmental parameters have been studied extensively in Antarctic terrestrial ecosystems. However, the effects of sunlight on soil N2O and CH4 fluxes are neglected across the Antarctic tundra. Here, fluxes of N2O and CH4 from maritime Antarctic tundra soils were measured in the absence and presence of sunlight during three summers. The N2O fluxes averaged −4.6±1.2 μg•m−2•h−1 in the absence of sunlight and 5.7±1.5 μg•m−2•h−1 in its presence; CH4 fluxes averaged 119.8±24.5 μg•m−2•h−1 (absence) and −40.5±28.3 μg•m−2•h−1 (presence). The correlations between N2O and CH4 fluxes and other environmental variables (e.g., soil moisture, temperature, organic and inorganic material) were not statistically significant (P>0.05) at all sites. On average, sunlight significantly increased N2O emissions and CH4 uptake by 10.3 μg•m−2•h−1 and 160.3 μg•m−2•h−1, respectively. This study indicates that sunlight is critical for accurately estimating N2O and CH4 budgets from maritime Antarctica and necessary for constraining the role of their emissions from tundra soil. Citation: Bao T, Zhu R B, Ye W J, et al. Effects of sunlight on tundra nitrous oxide and methane fluxes in maritime Antarctica. Adv Polar Sci, 2020, 31(3): 178-191, doi: 10.13679/j.advps.2020.0005

Effects of sunlight on tundra nitrous oxide and methane fluxes in maritime Antarctica

It is well documented that soil moisture can be retrieved from passive microwave (PMW) observations. A basic assumption of most PMW-based SM retrieval algorithms is that vegetation temperatures (<inline-formula> <tex-math notation="LaTeX">$T_{v}$ </tex-math></inline-formula>) and soil temperatures (<inline-formula> <tex-math notation="LaTeX">$T_{s}$ </tex-math></inline-formula>) are equal (i.e., <inline-formula> <tex-math notation="LaTeX">$T_{v} = T_{s}$ </tex-math></inline-formula>), which however is not well satisfied in some cases, especially during daytime. In this study, we proposed a soil-vegetation temperature decomposition (SVTD) approach to avoid such an assumption, which can improve the accuracy of SM retrievals from the Advanced Microwave Scanning Radiometer 2 (AMSR2) data. First, the SVTD was used to decompose the vegetation and soil temperatures of the soil-vegetation mixed pixels in the Tibetan Plateau (TP). Subsequently, the decomposed temperature was integrated into the SM retrieval algorithm to correct the effects of soil and vegetation temperatures, and SM is then retrieved following the same strategy adopted in the land parameter retrieval model (LPRM). Finally, the algorithm was validated against densely instrumented SM networks (Maqu, Naqu, and Ngari) built in the TP and was also compared with the LPRM AMSR2 SM product. Results indicate that the proposed algorithm performs much better than the original LPRM in soil-vegetation mixed areas. The proposed SVTD method is promising for SM retrieval from PMW satellites, especially in the daytime when the difference between soil and vegetation temperatures is relatively large.

Improvement of AMSR2 Soil Moisture Retrieval Using a Soil-Vegetation Temperature Decomposition Algorithm

The present study carried out to parameterize the single channel soil moisture active passive (SMAP) passive soil moisture (SM) retrieval algorithm, over Indian conditions. The moderate resolution imaging spectroradiometer (MODIS) data products and soil texture data were used for an improved parameterization of the algorithm. The bias correction was applied to the MODIS leaf area index (LAI) for accurate computation of vegetation optical depth. The necessary vegetation and roughness parameter were calibrated through minimization of the error between model retrieved and ground measured SM. The value of root mean square error (RMSE) for retrieved SM was found as <inline-formula> <tex-math notation="LaTeX">$0.059\,\,m^{3}m^{-3}$ </tex-math></inline-formula> with bias and correlation coefficients of <inline-formula> <tex-math notation="LaTeX">$0.036\,\,m^{3}m^{-3}$ </tex-math></inline-formula> and 0.724 for ascending overpass, respectively, while a lower value was recorded (RMSE = <inline-formula> <tex-math notation="LaTeX">$0.059\,\,m^{3}m^{-3}$ </tex-math></inline-formula>, bias = <inline-formula> <tex-math notation="LaTeX">$0.024\,\,m^{3}m^{-3}$ </tex-math></inline-formula>, and correlation coefficients = 0.752) for descending overpass. The same method is also implemented on two other test sites in different regions of India to check the model robustness, which indicates that the current parameterization provides a better estimate of SM over croplands in India. The overall performance of new parameterized model is found as (RMSE = 0.052 and bias = 0.034) for ascending and descending (RMSE = 0.048 and bias = 0.026) satellite overpasses for all the three test sites. Additionally, the intercomparing of various operational SM products SMAP SM (L2_SM_P), Soil Moisture and Ocean Salinity (SMOS) SM (SMOS_L3_SM), and SMOS-IC data products was carried out with the SAC-ISRO PAN India SM network, which showed a significant RMSE, dry and wet biases over all three test sites as compared to the developed improved parameterized algorithm.

Passive Only Microwave Soil Moisture Retrieval in Indian Cropping Conditions: Model Parameterization and Validation

It is well documented that soil moisture can be retrieved from passive microwave observations. A basic assumption of most passive microwave-based soil moisture retrieval algorithms is that vegetation temperatures (Tv) and soil temperatures (Ts) are equal (i.e., Tv=Ts), which however is not well satisfied in some cases, especially during daytime. In this study, we proposed a soil-vegetation temperature decomposition (SVTD) approach to avoid such an assumption, which can improve the accuracy of soil moisture retrievals from the Advanced Microwave Scanning Radiometer 2 (AMSR2) data. First, the SVTD was used to decompose the vegetation and soil temperatures of the soil-vegetation mixed pixels in the Tibetan Plateau (TP). Subsequently, the decomposed temperature was integrated into the soil moisture retrieval algorithm to correct the effects of soil and vegetation temperatures, and soil moisture is then retrieved following the same strategy adopted in the land parameter retrieval model (LPRM). Finally, the algorithm was validated against densely-instrumented soil moisture networks (Maqu, Naqu, and Ngari) built in the Tibetan Plateau, and was also compared with the LPRM AMSR2 soil moisture product. Results indicate the proposed algorithm performs much better than the original LPRM in soil-vegetation mixed areas. The proposed SVTD method is promising for soil moisture retrieval from passive microwave satellites, especially in the daytime when the difference between soil and vegetation temperatures is relatively large. 

Multi-frequency radiometer-based soil moisture retrieval and algorithm parameterization using in situ sites

The greenhouse gas (CH4, N2O and CO2) fluxes from Shengjin Lake Marsh (SLM) were in situ investigated at the undisturbed sites and the disturbed sites by animal colonization and human activities. Overall the CH4 emissions from the wet sites were one order of magnitude higher than those from the mesic or dry sites, whereas higher N2O emissions and net CO2 uptake occurred at dry sites rather than at mesic and wet sites. Soil moisture was the predominant factor controlling the spatial variability in marsh CH4 and N2O fluxes and NEE at undisturbed sites. Mean CH4 fluxes from migrating water bird and poultry active areas showed no statistically significant differences from the undisturbed sites, but their activities significantly increased N2O emissions and decreased marsh CO2 sink, with the highest N2O fluxes (mean 49.5±11.9 μg N2O m h) in poultry active areas. The conversion of the marsh into agricultural field and landfill did not significantly change CH4 and N2O fluxes, but significantly decreased CO2 sink. Our results revealed that the accurate evaluation of GHG budget for the SLM and other similar temperate wetlands should involve soil water regimes, water bird colonization and human-induced

Y.S. Gao

Papers

Multi-frequency radiometer-based soil moisture retrieval and algorithm parameterization using in situ sites

Spatial variability in greenhouse gas fluxes of a temperate freshwater marsh in china: effects of soil moisture, animal activities and land use