SMAP Satellite Research Articles

Assessment of long-term multisource surface and subsurface soil moisture products and estimate methods on the Tibetan Plateau

This research is the first attempt to extensively assess the accuracy of commonly used surface and subsurface soil moisture (SM) datasets in reproducing temporal dynamics and trend changes SM observations for a long period on the Tibetan Plateau (TP). To this end, 12 satellite-based and 10 model-based SM products are evaluated using a 10-year upscaled SM dataset obtained from two in situ monitoring networks situated respectively in humid and arid climate zones over the TP. Moreover, the ability of using the outperforming satellite-based surface SM data in combination with cumulative distribution function (CDF) matching and exponential filter (ExpF) methods to improve model-based SM profile estimations is investigated for the first time. Among the 12 satellite-based products, SMAP_L3 provides the best surface SM retrievals, with the DCA and SCA-V products showing superior performance in the humid and arid areas, respectively. Consequently, the harmonized products produced based on the SMAP data (i.e., SMOSMAP and NNsm) also give relatively higher accuracy. It is surprised to find that the latest version of both CCI passive and active products presents degraded performance compared to its predecessor (i.e. v5.3). Among the 10 model-based products, SMAP_L4 with assimilation of L-band brightness temperature (TB) measurements provides relatively better estimations of surface and subsurface SM on the TP. Above findings highlight the superiority of utilizing the L-band TB measurements especially from the SMAP satellite to generate surface SM and improve the model-based SM estimations via data assimilation. Alternatively, it is further demonstrated that incorporating surface SM data from SMAP_L3, in conjunction with both CDF matching and ExpF methods, significantly enhances the precision of model-based SM estimations. This may open up a new possibility for improving the model-based SM profile data across the TP.

Watershed scale soil moisture estimation model using machine learning and remote sensing in a data-scarce context

Soil moisture content can be used to predict drought impact on agricultural yield better than precipitation. Remote sensing is viable source of soil moisture data in instrument-scarce areas. However, space-based soil moisture estimates lack suitability for daily and high-resolution agricultural, hydrological, and environmental applications. This study aimed to assess the potential of the random forest machine learning technique to enhance the spatial resolution of remote soil moisture products from the SMAP satellite. Models were built using random forest for spatial downscaling of SMAP-L3-E, then visually and statistically evaluated for disaggregation quality. The impact of topography, soil properties, and precipitation on the downscaled soil moisture was examined. The relationship between downscaled soil moisture and in-situ soil moisture was analyzed. The results indicate that the proposed method demonstrated spatial and hydrological coherence, along with a satisfactory downscaling quality. Statistical validation indicated suitable generalization error for scientific and practical use (RMSE < 0.05 cm3 cm-3). Random forest effectively achieved spatial downscaling of SMAP-L3-E in the study area. Principal component and spatial analysis revealed dependence of downscaled soil moisture on elevation, soil organic carbon content, clay content, and saturated hydraulic conductivity, mainly under near-saturation conditions. Regarding validation against in-situ data, downscaled soil moisture explained in-situ soil moisture well under low soil water content ( = 0.624). Downscaling performance deteriorates for water contents between 0.40 to 0.50 cm3 cm-3, suggesting inadequacy under near saturation conditions at a daily temporal frequency. However, coarser temporal aggregations (7 to 10 days) yielded an average 0.98 correlation coefficient, regardless of saturation conditions. These results could potentially be applied in irrigation planning, soil physics studies and hydrology monitoring, to forecasting the occurrence of droughts, leaching of contaminants, surface runoff modeling, carbon cycle studies, soil's capacity to store and provide nutrients.

Quality Evaluation of Satellite Sea Surface Salinity Products in the Pacific Ocean

Sea surface salinity (SSS) is one of the most important variables in ocean dynamics and atmospheric climate. The launch of three salinity satellites, Aquarius, SMAP and SMOS, has greatly expanded the global sea surface salinity data field. The latest ocean surface salinity (CCI+SSS) fusion project fully utilizes the satellite data from SMOS, supplemented by SMAP and Aquarius satellite data, to merge into the CCI fusion product. In this paper, the data accuracy of the four SSS products, Aquarius, SMAP, SMOS and CCI, is evaluated in terms of both average deviation and root mean square error (RMSE) by comparing with the in situ SSS monthly gridded EN4.2.2 dataset in the Pacific Ocean. The results show that compared with the in situ EN4.2.2 data, the average deviation and root mean square error of the four SSS products are relatively small in the Pacific subtropical region, while the errors are generally higher in the cold water and offshore regions. Overall, the CCI product has the smallest average deviation and RMSE in the Pacific Ocean, which is the best performance among the four products.

Vegetation moisture estimation in the Western United States using radiometer-radar-lidar synergy

Monitoring vegetation moisture conditions is paramount to better understand and assess drought impacts on vegetation, enhance crop yield predictions, and improve ecosystem models. Passive microwave remote sensing allows retrievals of the vegetation optical depth (VOD; [unitless]), which is directly proportional to the vegetation water content (VWC; in units of water mass per unit area [kg/m2]). However, VWC is largely dependent on the dry biomass and structure imprints on the VOD signal. Previously, statistical models have been used to isolate the water component from the biomass and structure components. Physically-based approaches have not yet been proposed for this goal. In this study, we present a multi-sensor semi-physical approach to retrieve the vegetation moisture from the VOD and express it as Live Fuel Moisture Content (LFMC [%]; the percentage of water mass per dry biomass unit). The study is performed in the western United States for the period April 2015 – December 2018. There, in situ LFMC samples are available for assessment. We rely on a VOD model based on vegetation height data from GEDI/Sentinel-2 and radar backscatter from Sentinel-1, which account for the biomass and structure components. Vegetation moisture is retrieved at L-, X- and Ku-bands by minimizing the difference between the modeled VOD and the VOD estimates from SMAP (L-band) and AMSR-2 (X- and Ku-band) satellites. Results show that the LFMC retrievals are independent of canopy height, land cover, and radar backscatter, demonstrating the capability of the proposed algorithm to separate water dynamics from the biomass/structure component in VOD. LFMC estimates at X- and Ku-bands reproduce well the expected spatio-temporal dynamics of in situ LFMC. Results show good agreement with in situ at a regional scale, with Pearson's correlations (r) between in situ LFMC samples and LFMC estimates of 0.64 (Ku-band), 0.60 (X-band) and 0.47 (L-band). Similar results are obtained independently for shrub and forest sites at X- and Ku-bands. In most comparisons between in situ and estimated LFMC, biases are below 10% of the dynamic range of LFMC. Performance at L-band is limited by the fact that this frequency senses the full vertical extent of the canopy, while in situ samples are taken only from top of canopy leaves to which X- and Ku-bands are much more sensitive. More insight will be needed for grasslands (r = 0.44 at X-band) using time-dynamic canopy height data. Furthermore, a pixel-scale assessment is conducted, showing a good agreement in most sites (r > 0.6). The proposed method can be tailored to exploit the synergies of past (e.g., AMSR-E), current (e.g., AMSR-2) and future satellite sensors such as CIMR and ROSE-L for global vegetation moisture mapping at different canopy layers.

Quantifying and valuing irrigation in energy and water limited agroecosystems

Agriculture in regions with limited water availability is possible because of irrigation. Irrigated croplands are expanding, and irrigation water demand is increasing. Nevertheless, there is a limited understanding of how much water is consumed for irrigation and how effective irrigation increases crop productivity in various climates. In this study, we aim to understand how irrigation water affects crop productivity in different climates. To achieve this goal, we developed a simple approach to quantify irrigation quantities from SMAP satellite soil moisture observations based on a zero-dimensional bucket-type hydrology model. The central assumption is that irrigation quantities can be estimated from the gap between the modeled and observed soil moisture by iteratively providing irrigation as a model input until the soil moisture simulations agree well with the observations. We then used the estimated amount of irrigation to simulate water, energy, and carbon fluxes at two agricultural sites on the west coast of the US: one that was water-limited (Central Valley, CA) and one that was energy-limited (Eugene, OR). An agroecosystem model, AgroIBIS-VSF, was used to conduct simulations. To verify our simulations, we used data from two AmeriFlux Eddy covariance towers at each site. We found that incorporating estimated irrigation amounts into our simulations improved the accuracy of energy balance components and soil moisture predictions, reducing the root-mean-square error of soil moisture predictions by up to 22%. We also discovered that the irrigation value, in terms of increased productivity of actual irrigation water used, is more than five times more valuable at the energy-limited site than at the water-limited site. Soil hydraulic properties have a strong influence on irrigation water valuation. Our study highlights the potential of satellite soil moisture observations to improve our understanding of water productivity in different climates. By better understanding the efficiency of resources used for crop production, we can ensure the sustainability and resilience of agricultural systems, leading to better management practices.

Water availability and atmospheric dryness controls on spaceborne sun-induced chlorophyll fluorescence yield

Climate change is amplifying the duration, frequency, and intensity of droughts, harming global ecosystems. During droughts, plants can close their stomata to save water, at the expense of a reduced carboxylation rate. When in a carboxylation-limited regime, plants benefit from an increase in water availability, as it increases their photosynthetic rate. The sun-induced chlorophyll fluorescence (SIF) signal, measurable from satellites, is mechanistically linked to this rate. Like canopy photosynthesis, SIF carries an imprint from the available irradiation (PAR) as well as the canopy structure and the efficiency of the photosynthesis at the photosystem level. Normalizing the global TROPOMI SIF observations with TROPOMI reflectance and MODIS Normalised Difference Vegetation Index (NDVI) data, we extracted the fluorescence quantum yield (ϕF), which lab-scale experiments have found to be linked to the photosynthetic electron transport. Plant physiologists have long proved the photosynthetic electron transport to be sensitive to plant water status. Here, the plant water status is controlled by the soil moisture (SM) and the vapour pressure deficit (VPD). Combining data from the TROPOMI, AIRS and SMAP satellite sensors, this study describes how SM and VPD control the ϕF at the global scale. We identify a VPD range (VPD<1.5 kPa) in which the ϕF is mainly controlled by VPD, and another (VPD>1.5 kPa) in which the ϕF is co-regulated by SM and VPD. The precise values of this range, as well as the magnitude of ϕF values, are modulated by the plant isohydricity. To gain a deeper understanding of the link between ϕF and photosynthetic efficiency at large scale, we used the link between ϕF and data on the canopy conductance (Gs), which were calculated using remote sensing data-driven models. A comparison found that the ϕF-Gs relationship at large scale is in line with the ϕF-Gs relationship described in plant-level studies.

A spatial and temporal evaluation of the SMAP cropland b-parameter across the U.S. Corn Belt

The liquid water contained within plant tissue per ground area is an indicator of plant biomass and related to plant transpiration. In the U.S. Corn Belt this plant water is dominated by water in crop tissue. Microwave remote sensing provides an opportunity to measure crop water at a 30 to 40-km spatial scale across the U.S. Corn Belt. Crop water has been shown to be directly proportional to L-band vegetation optical depth (VOD) through a proportionality constant called the b-parameter. NASA’s SMAP satellite uses a static b-parameter of 0.11 for cropland areas, however the b-parameter varies with crop type. This study answers the following research questions. First, does the value of the b-parameter value vary across the U.S. Corn Belt given the observed differences in the proportions of corn and soybeans grown across the region? Secondly, does the SMAP b-parameter value vary throughout the growing season given the significant change in the distribution of crop water within tissue types throughout the growing season? Since in-situ measurements of the b-parameter are higher for corn, we hypothesize that as the relative fraction of corn in a SMAP pixel increases, the b-parameter will increase. We test this hypothesis using satellite scale modeling of crop water and SMAP L2 DCA VOD measurements at 18 sites across the U.S. Corn Belt for the years 2015, 2016, and 2017. For each site year, the crop water for the satellite pixel is simulated using the Agro-IBIS crop model and is weighted according to the proportion of corn and soybeans grown in that location. We find that there is no statistically significant correlation between the relative corn fraction and the SMAP b-parameter across the U.S. Corn Belt and that the current SMAP cropland value of 0.11 is appropriate for the beginning of the growing season (growing degree days(GDD)<100̄0∘C⋅day). However, we find that the b-parameter in the second half of the growing season (GDD≥100̄0∘C⋅day), during the crop reproductive stages, has an average value of 0.17. The results from this study indicate that while there is no change in the b-parameter due to the proportion of crops growing in a satellite pixel, there may be a difference in b-parameter due to changing water distribution within the crop canopy due to the development of crop reproductive structures (ears and pods) during the second half of the growing season.

Downscaling and validating SMAP soil moisture using a machine learning algorithm over the Awash River basin, Ethiopia.

Microwave remote sensing instrument like Soil Moisture Active Passive ranging from 1 cm to 1 m has provided spatial soil moisture information over the entire globe. However, Soil Moisture Active Passive satellite soil moisture products have a coarse spatial resolution (36km x 36km), limiting its application at the basin scale. This research, subsequently plans to; (1) Evaluate the capability of SAR for the retrieval of surface roughness variables in the Awash River basin; (2) Measure the performance of Random Forest (RF) regression model to downscale SMAP satellite soil moisture over the Awash River basin; (3) validate downscaled soil moisture data with In-situ measurements in the river basin. Random Forest (RF) based downscaling approach was applied to downscale satellite-based soil moisture product (36km x 36km) to fine resolution (1km x 1km). Fine spatial resolution (1km) soil moisture data for the Awash River basin was generated. The downscaled soil moisture product also has a strong spatial correlation with the original one, allowing it to deliver more soil moisture information than the original one. In-situ soil moisture and downscaled soil moisture had a 0.69 Pearson correlation value, compared to a 0.53 correlation between the original and In-situ soil moisture. In-situ soil moisture measurements were obtained from the Middle and Upper Awash sub-basins for validation purposes. In the case of Upper Awash, downscaled soil moisture shows a variation of 0.07 cm3 /cm3, -0.036 cm3 /cm3, and 0.112 cm3 /cm3 with Root Mean Square Error, Bias error, and Unbiased Root Mean Square Error respectively. Following that, the accuracy of downscaled soil moisture against the Middle Awash Sub-basin reveals a variance of 0.1320 cm3 /cm3, -0.033 cm3 /cm3, and 0.148 cm3 /cm3 with Root Mean Square Error, Bias error, and Unbiased Root Mean Square Error respectively. Future studies should take into account the temporal domain of Soil Moisture Active Passive satellite soil moisture product downscaling over the study region.

DOWNSCALING OF SOIL MOISTURE PRODUCT OF SMAP

Abstract. Soil moisture as a variable parameter of the Earth's surface plays a very important role in many applications such as meteorology, climatology, water resources management and hydrology. Therefore, access to soil surface moisture product with high spatial resolution is very important. Due to the lack of access to soil moisture information with high spatial resolution, the main goal in this article is to downscale the existing soil moisture products and improve their spatial resolution into 1 square kilometer. For this purpose, two methods based on regression and neural network have been used for downscaling the 3 km soil moisture products of SMAP satellite. To this end, other available satellite data and products including various combinations of land surface temperature (LST), normalized difference vegetation index (NDVI), brightness temperature in different polarizations of (TBH and TBV) passive microwave sensor data, digital elevation model (DEM) and short-wavelength infrared (SWIR) data of MODIS and Sentinel 3 have been used. In this study, two regions in the north and south of Iran, Golestan and Fars provinces, have been examined, due to the lack of ground measurements of soil moisture, the SMAP product with the resolution of 1 km which has been already downscaled by exploiting the Sentinel-1 radar data, was used to evaluate the results. The evaluation results in Golestan and Fars provinces showed the correlation coefficient of 0.82 to 0.93 and 0.72 to 0.77, respectively, and the average percentage of absolute error in both regression and neural network methods was less than 21 to 30 and 42 to 46 percent.

Evaluation of Six Satellite-Based Soil Moisture Products Based on in situ Measurements in Hunan Province, Central China

Long-term near-surface soil moisture (SM) data can be obtained on a regional scale through microwave remote sensing. Therefore, to quantitatively analyze the accuracy of multisource remote sensing–based observation products, improve the retrieval algorithm, and effectively use in terminal environments, a standardized comprehensive evaluation is imperative. The SM data obtained by the China Meteorological Administration and Ministry of Water Resources were used as reference data to verify the performance of six passive microwave remote sensing–based SM products from the SMOS, SMAP, GCOM-W, FY-3B, and FY-3C satellites in Hunan province, China. These data were also used to analyze the effects of topographical, land cover, and meteorological factors on SM retrieval accuracy. Results show that SMAP shows the best overall performance in Hunan province; furthermore, it exhibits stable accuracy and is not easily affected by environmental factors. The FY series of satellite products shows the worst performance, and some grid remote sensing data are negatively correlated with the ground measurement data. AMSR2 possesses the largest amount of data and the largest deviation, and only this product exhibits significant differences with the fluctuation trend of the measured SM and precipitation. Passive microwave detection technology presents the best performance in the central part of Hunan province and the largest bias in the Dongting Lake area. SMOS-L3 and SMOS-IC, two products of the same satellite, show the lowest bias but present differences in the SM fluctuation range, orbital accuracy, as well as dry or wet bias. Furthermore, FY-3B and FY-3C, two satellites belonging to the same series, exhibit excellent consistency in performance. The evaluation results and accuracy variation between products as well as other factors identified in the study provide a baseline reference for improving the retrieval algorithm. This study provides a quantitative basis for developing improved applications of passive microwave SM products.

Global Shallow Groundwater Patterns From Soil Moisture Satellite Retrievals

Groundwater is the most significant freshwater source and plays a critical role in the earth's water and energy balance. The lack of groundwater observations with a high spatiotemporal resolution at a global scale hinders our ability to study and model the environment when shallow groundwater has a direct impact on surface soil moisture. This study aims to estimate the spatial and temporal distributions of shallow groundwater-influenced areas at a global scale. We trained an ensemble machine learning algorithm, using outputs from a variably saturated soil moisture flux model, to identify the shallow groundwater occurrence. Model simulations spanned various climate zones and soil types across the globe. The overall accuracy of the algorithm in reproducing the soil moisture flux model results was 95.5%. We applied the algorithm to spaceborne soil moisture observations retrieved by NASA's SMAP satellite and present a global-scale shallow groundwater map derived from the SMAP observations. The derived global distribution of shallow groundwater identifies wetlands, large riparian corridors, and seasonally inundated lowlands. The results showed that 19% of terrestrial land cover had been influenced by shallow groundwater at some point in time during the period of interest (2015–2018). Temporally, shallow groundwater follows an annual cyclic pattern with 2% to 6% of the land surface being influenced globally. This study shows that SMAP observations could be used in estimating shallow groundwater in high spatiotemporal resolution at a global scale, potentially providing invaluable inputs for modeling and environmental monitoring studies.

Artificial Neural Network-Based Microwave Satellite Soil Moisture Reconstruction over the Qinghai–Tibet Plateau, China

Soil moisture is a key parameter for land-atmosphere interaction system; however, fewer existing spatial-temporally continuous and high-quality observation records impose great limitations on the application of soil moisture on long term climate change monitoring and predicting. Therefore, this study selected the Qinghai–Tibet Plateau (QTP) of China as research region, and explored the feasibility of using Artificial Neural Network (ANN) to reconstruct soil moisture product based on AMSR-2/AMSR-E brightness temperature and SMAP satellite data by introducing auxiliary variables, specifically considering the sensitivity of different combination of input variables, number of neurons in hidden layer, sample ratio, and precipitation threshold in model building. The results showed that the ANN model had the highest accuracy when all variables were used as inputs, it had a network containing 12 neurons in a hidden layer, it had a sample ratio 80%-10%-10% (training-validation-testing), and had a precipitation threshold of 8.75 mm, respectively. Furthermore, validation of the reconstructed soil moisture product (named ANN-SM) in other period were conducted by comparing with SMAP (April 2019 to July 2021) for all grid cells and in situ soil moisture sites (August 2010 to March 2015) of QTP, which achieved an ideal accuracy. In general, the proposed method is capable of rebuilding soil moisture products by adopting different satellite data and our soil moisture product is promising for serving the studies of long-term global and regional dynamics in water cycle and climate.

Using remote sensing data for environmental monitoring of water objects using GIS and machine learning

The main way to understand variability of any spatial data using remote sensing is calculating spectral indices. For now, some difficulties have receiving water surface temperature due to specific properties for satellite sensors and low spatial resolution. The main sources of receiving salinity data are remote sensing data from ESA SMOS, NASA Aquarius and SMAP satellites. Using different machine learning algorithms, we can get models or equations, representing dependency between studied environmental variable and different spectral channels of remote monitoring data. After receiving and collecting remote sensing data in database this system uses machine learning algorithms to find dependency between collected field data and different spectral bands of the remote sensing data. Our goal was to form an analytical system based on remote sensors and machine learning algorithm to analyse, predict and evaluate water ecosystems for fisheries and environmental protection.

Retraction Note: SMAP satellite ocean surface salinity observations and music classroom effects in coastal areas: environmental numerical prediction

Retraction Note: SMAP satellite ocean surface salinity observations and music classroom effects in coastal areas: environmental numerical prediction

Assessment of the Temperature Effects in SMAP Satellite Soil Moisture Products in Oklahoma

Soil moisture is a notably important component in various studies in water sciences, including hydrology, agriculture, and water management. To achieve extensive or global spatial coverage, satellites focusing on soil moisture observation have been launched, and many satellite products, such as SMAP and SMOS soil moisture products, have been provided. Most of these satellite observations are based on the dielectric properties of wet soil, and most soil moisture retrieval algorithms are calibrated or evaluated using in situ soil moisture. While the in situ data observed by dielectric sensors, which are the most widely used, are reported to include errors caused by the so-called “temperature effects” of these sensors, the temperature dependency of bulk soil dielectric constant has rarely been discussed on satellite data. Since both in situ dielectric measurements and satellite observations are based on the same physical variable, the dielectric constant and the dielectrically measured in situ soil moisture data are also used as ground truth, it is necessary to assess the impact of temperature effects on satellite products. In this work, we attempted to identify the existence of the temperature effects and evaluate the consequences of removing these effects on in situ and satellite soil moisture and the relationships between the brightness temperature at the soil surface and the brightness temperature provided by satellite observation. To achieve the goals of this study, we analyzed the temperature effects on surface soil moisture data provided by a SMAP mission in Oklahoma, the United States. The results show that temperature effects exist in SMAP soil moisture products in Oklahoma, and the removal of these effects will potentially improve the accuracy of these products.

SMAP-HydroBlocks, a 30-m satellite-based soil moisture dataset for the conterminous US

Soil moisture plays a key role in controlling land-atmosphere interactions, with implications for water resources, agriculture, climate, and ecosystem dynamics. Although soil moisture varies strongly across the landscape, current monitoring capabilities are limited to coarse-scale satellite retrievals and a few regional in-situ networks. Here, we introduce SMAP-HydroBlocks (SMAP-HB), a high-resolution satellite-based surface soil moisture dataset at an unprecedented 30-m resolution (2015–2019) across the conterminous United States. SMAP-HB was produced by using a scalable cluster-based merging scheme that combines high-resolution land surface modeling, radiative transfer modeling, machine learning, SMAP satellite microwave data, and in-situ observations. We evaluated the resulting dataset over 1,192 observational sites. SMAP-HB performed substantially better than the current state-of-the-art SMAP products, showing a median temporal correlation of 0.73 ± 0.13 and a median Kling-Gupta Efficiency of 0.52 ± 0.20. The largest benefit of SMAP-HB is, however, the high spatial detail and improved representation of the soil moisture spatial variability and spatial accuracy with respect to SMAP products. The SMAP-HB dataset is available via zenodo and at https://waterai.earth/smaphb.

Effects of meteorological forcings and land surface model on soil moisture simulation over China

Land surface model (LSM) simulations forced by observed meteorological data provide spatially continuous and temporally complete soil moisture estimates, but the influences of models and meteorological forcings are yet to be validated over a large area due to the lack of in situ measurements. In this study, the Conjunctive Surface-Subsurface Process version 2 (CSSPv2) model was driven by three meteorological forcings, namely, CLDASv2.0, ERA5 and GLDASv2.1, to provide soil moisture simulations over China. The validations over 2090 in situ stations during 2012–2017 showed that CLDASv2.0/CSSPv2 soil moisture simulation performed better than ERA5 and GLDASv2.1 reanalysis products, with an increased correlation of 26%–68% and reduced errors of 14%–24% at the daily time scale. The improvements mostly originate from the use of an advanced LSM because CLDASv2.0/CSSPv2 only increased the correlation by 5%–35% and decreased the errors by up to 9% when compared with ERA5/CSSPv2 and GLDASv2.1/CSSPv2. In contrast, ERA5/CSSPv2 and GLDASv2.1/CSSPv2 soil moisture simulations increased the correlations from their alternative reanalysis LSMs by 17%–63%, and decreased the errors by up to 18%. The results are similar when using the SMAP satellite product as the validation data. The influence of the LSM was more obvious over semiarid regions, such as northern China. The influence of meteorological forcing was more significant for soil moisture simulations at the surface layer, while the LSMs played a more critical role for the middle and deep layers, especially during the cold season due to freeze-thaw processes. This study demonstrates the possibility to further improve soil moisture estimates at a large scale with advanced LSMs, even with the emergence of modern reanalyses.

RETRACTED ARTICLE: SMAP satellite ocean surface salinity observations and music classroom effects in coastal areas: environmental numerical prediction

The ocean surface salinity is an important indicator for monitoring the changes in the chemical composition of the sea surface. It can be used to evaluate and optimize the quality of the seawater and then analyze the process of marine fluid mechanics and predict the ocean. Therefore, it has attracted the attention of relevant government departments, and it is the focus of research by experts and scholars. Satellite remote sensing technology, as an effective means to obtain a large amount of continuous observation of sea surface salt data, overcomes the time and space limitations of conventional observation methods. SMAP’s ocean surface salinity data shows quality advantages, excellent application value, and considerable research potential due to its own characteristics. However, since the emergence of applied technology, due to slow progress and mainly carried out in foreign countries, research results have been few. This paper analyzes the research situation of SMAP data, with the purpose of providing reference for the application of remote sensing data of SMAP in China; reviews the progress of the main research of SMAP; and provides support for the development of research in China. So as to summarize the problems existing in the sound environment of university music classrooms, from the perspectiive of the sound environment in campus planning, the architectural design problems of music classrooms and the problems of music classroom teaching are considered. Then use the sound environment of the music classroom to analyze and summarize the problems, and propose simple and effective improvement measures to the music classroom, construct reasonable proposals, and try to avoid the same problem from happening again, which can not only optimize the service to users but also provide a good sound environment for the normal development of university music classroom teaching.

Data for Identification and Forecast of Forest Fire Severity in Spain during Dry Season using SMAP Satellite Soil Moisture

Data for Identification and Forecast of Forest Fire Severity in Spain during Dry Season using SMAP Satellite Soil Moisture

Winter surface salinity in the northeastern Gulf of Maine from five years of SMAP satellite data

Wintertime freshwater transport into the Gulf of Maine (GoM) is typically controlled by a seasonal velocity increase in the fresh upstream Nova Scotia Current (NSC). Repeat satellite observations from the Soil Moisture Active Passive mission have mapped significant GoM surface salinity anomalies in four of five recent winters. These satellite data are used in combination with Jordan basin buoy and model datasets to investigate the likelihood that variable wind-forcing of the NSC contributed to these anomalies. This stems from regional ocean circulation studies suggesting that strengthening of alongshore southwesterly winds on the Scotian shelf weakens NSC transport into the GoM, while cross-shore southeasterly winds may also contribute to NSC weakening, and vice versa. A 17-year time series analysis of GoM buoy and satellite data shows that near-surface salinity in the eastern GoM can indeed be modulated by both alongshore and cross-shore winds through their impact on the NSC. The NSC geostrophic current modulation correlates with buoy-observed surface salinity anomalies when using a one month advective lead time. For a shorter 5-year period, a SMAP-derived salinity anomaly index in the eastern GoM indicates a similar relation to NSC variation and also correlates with Scotian Shelf wind anomalies. The relationships between winter wind and NSC transport variability are confirmed using output from a 19-year high resolution global ocean simulation. Attribution of these local wind anomalies to basin-scale atmospheric patterns shows that most years with strong winter GoM freshening coincide with weakening of a North Atlantic Oscillation-like wind pattern. But its reversals do not always correspond with a saline GoM. This contrast suggests that wind forcing more directly controls fresh winter GoM anomalies but not salty. Instead, they are partly due to density-driven advection from neighboring warm and saline Atlantic slope water including episodic Gulf Stream instabilities. • SMAP detects significant eastern GoM SSSA (up to 1 psu) in four of five winters. • Jordan basin SSSA correlation with Nova Scotia Current geostrophic anomaly is ~0.7. • Only half of the above correlation is explained by wind-driven current variations. • Eastern GoM satellite SSSA index is better indicator of fresh Scotian Shelf inflows.