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Article

Leveraging Soil Moisture Assimilation in Permafrost Affected Regions

by
Ankita Pradhan
1,
Akhilesh S. Nair
2,
J. Indu
1,3,*,
Olga Makarieva
4 and
Nataliia Nesterova
4
1
Department of Civil Engineering, Indian Institute of Technology Bombay, Mumbai 400076, India
2
Geophysical Institute, University of Bergen and Bjerknes Center for Climate Research, 5007 Bergen, Norway
3
Interdisciplinary Center for Climate Studies, Indian Institute of Technology Bombay, Mumbai 400076, India
4
Institute of Earth Sciences, Saint Petersburg State University, 199034 St. Petersburg, Russia
*
Author to whom correspondence should be addressed.
Remote Sens. 2023, 15(6), 1532; https://doi.org/10.3390/rs15061532
Submission received: 29 January 2023 / Revised: 26 February 2023 / Accepted: 1 March 2023 / Published: 10 March 2023
(This article belongs to the Special Issue Remote Sensing of Dynamic Permafrost Regions Ⅱ)
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Abstract

:
The transfer of water and energy fluxes between the ground and the atmosphere is influenced by soil moisture (SM), which is an important factor in land surface dynamics. Accurate representation of SM over permafrost-affected regions remains challenging. Leveraging blended SM from microwave satellites, this study examines the potential for satellite SM assimilation to enhance LSM (Land Surface Model) seasonal dynamics. The Ensemble Kalman Filter (EnKF) is used to integrate SM data across the Iya River Basin, Russia. Considering the permafrost, only the summer months (June to August) are utilized for assimilation. Field data from two sites are used to validate the study’s findings. Results show that assimilation lowers the dry bias in Noah LSM by up to 6%, which is especially noticeable in the northern regions of the Iya Basin. Comparison with in situ station data demonstrates a considerable improvement in correlation between SM after assimilation (0.94) and before assimilation (0.84). The findings also reveal a significant relationship between SM and surface energy balance.

1. Introduction

Permafrost conditions have a strong influence on the regional moisture flow through the vadose zone and groundwater dynamics [1,2,3,4]. The development, distribution, and evolution of permafrost are significantly impacted by soil moisture, notably by heat convection and advection along flow trajectories via influencing surface runoff [1,5,6]. This interaction, together with feedback from physical [7,8,9], chemical [10,11,12], and biogeochemical processes [13,14], generates complicated permafrost–soil moisture-groundwater dynamics in regions with permafrost. Permafrost controls drainage, routing, storage of water, as well as surface and sub-surface distribution, which has a substantial impact on terrestrial hydrological processes. Furthermore, permafrost conditions prevent the vertical flow of water through the vadose zone [15], which results in flooding conditions, in case of extreme precipitation events [16].
Soil Moisture (SM) estimates are crucial components for regional hydrology, as they convert incoming precipitation to infiltration and runoff [17,18,19,20,21]. SM remains a crucial land surface state estimate which, when used as a forcing variable in a land surface model, is capable of contributing to improved prediction in different fields, such as weather [22], climate studies [23], hydrology [24,25], hazard mitigation of hydrological extreme events [26,27,28,29], and water resources management [30,31,32,33]. The physics of the model, the quality of the forcing data, the input land surface parameters, and the initialization of the land surface all contribute to the Land Surface Model’s (LSM) prediction accuracy of land surface states [34,35,36]. By coupling the model’s prediction with remote sensing measurements, Data Assimilation (DA) provides enormous potential for enhancing LSM predictive powers [37,38,39,40].
Prior research has examined the impact of SM assimilation on LSM forecasting [19,21,35,41,42]. The Global Land Data Assimilation System (GLDAS) is a global implementation of the concept of data assimilation on multiple LSMs (GLDAS; [43]). GLDAS offers global land surface data at three-hourly intervals from different LSMs and two distinct spatial resolutions [21]. Data assimilation incorporates complications connected with data processing, computing load for a geographically extensive research area, and inputs from remotely sensed observations for the status of the land surface [44,45,46,47]. Previous research has largely concentrated on the effectiveness of SM assimilation in non-permafrost environments. Moreover, there is a lack of detail in global studies on SM DA on the strategy to assimilate satellite SM when soil is covered in snow and moisture is frozen. We address this issue in our study by explicitly carrying out SM assimilation over permafrost-affected areas. Furthermore, we investigate how SM assimilation influences SM dynamics and its impact on streamflow estimates during the next season.
Despite the importance of permafrost to hydrology, our understanding of the processes that influence water flow and storage during permafrost thaw is limited [48,49,50]. Previous research has attributed changes in hydrological regimes to a thickening active layer and melting permafrost, but few have explored the response of runoff mechanisms and genesis to active layer variation [51,52].
The SM products from different satellite sensors vary in terms of the sensor frequency, the retrieval algorithm used to derive each SM product, and the temporal and spatial resolution [53,54]. The SM assimilation capability may be adversely impacted by the temporal resolution of the observation data [55,56,57]. Blended SM products can considerably improve the effect of assimilation on SM prediction [58,59]. Despite studies pertaining to the assimilation of SM from a single satellite sensor, the efficiency of assimilating blended SM in permafrost-affected basins is still not well understood. To overcome this, we designed the present study to assimilate blended SM from the European Space Agency (ESA) and check its impact model simulation over a basin subjected to permafrost in Russia.

2. Study Area

The Iya River watershed (Figure 1) belongs to one of Russia’s most flood-prone mountainous regions. The extent of seasonally flooded areas in the Irkutsk region reaches 25,000 km2, about 4% of its total area. A snowmelt-flood often mixes with a rainfall-flood, and in certain years, debris flows exacerbate rainfall floods. Rainfall-flooding in the Iya catchment’s southern areas poses the biggest concern. They occur more often, resulting in greater flooded regions and substantial economic damage. In 1984, 1996, and 2001, floods wreaked havoc in the southern section of the Iya watershed, with the July 2001 flood being the most catastrophic [60]. Extreme rainfall-floods devastated the city of Tulun and other villages on the river floodplain in the summer of 2019, on the rivers of the Iya catchment, becoming the most devastating floods in the area [61,62]. The Iya—Tulun stream gauge registered the largest rise in water level during June 2019 (from 474 to 1387 cm over three days), which was the first flood of the year. The Iya River had a second significant flood from 26 July to 4 August 2019, with the maximum water level reaching a mark of 1121 cm. The 1984 flood, which was considered the biggest flood on record until 2019, is similar in size. Numerous variables, including the impacts of climate change, deforestation, logging and fires, and the melting of snow and glaciers, etc., contributed to the June 2019 devastating flood.

3. Model and Data Products

3.1. Noah Land Surface Models

During the 1990s, a comprehensive collaboration of different agencies worked together to improve land surface modeling for the Global Energy and Water Cycle Experiment. The National Center for Atmospheric Research, the National Environmental Satellite Data and Information Service, the U.S. Air Force, NASA, Oregon State University (OSU), and other colleges have engaged in an extensive community effort to create and deploy a standardized Noah LSM. The Noah land surface model is based on the OSU Land Surface Model, which consists of two soil layers with thermal conduction equations for soil temperature and a modified version of Richard’s equation for soil moisture [63,64]. The original OSU LSM relied on a plant coefficient to compute the stomatal control of plants to estimate evapotranspiration [63]. Over the years, through continuous community efforts, the latest version of Noah LSM has emerged as a more advanced version of its predecessor. The stand-alone, unified Noah [18,19,20,21] used in our study is a medium–complexity model that depends on external meteorological forcing to simulate surface states and surface-to-atmosphere fluxes using physically based equations. The Noah model primarily relies on the conservation of mass and diffusion in Richard’s equation to estimate the vertical flow of water in its four-layered soil column. It represents snow as a bulk single layer and represents soil as four layers, with 2 m being the maximum depth. The model assumes downward percolation of water to 2 m depth as the bottom boundary condition of soil moisture.

3.2. Data Products

The stand-alone Noah model needs external forcing for its simulation process. The external forcing mainly consists of near-surface atmospheric parameters, such as temperature, precipitation, wind, pressure and radiation components. Furthermore, the model requires inputs of geostationary parameters defining the soil and vegetation properties.
In this analysis, we use the Global Data Assimilation System meteorological forcing (GDAS). It is a National Centers for Environmental Prediction (NCEP) atmospheric assimilation system [65] that uses a four-dimensional multivariate approach to assimilate in situ and satellite observations into a 3D gridded model space [43]. Four synoptic hours are analyzed for the GDAS operational analysis: 00, 06, 12, and 18 UTC. In addition, each study includes a 3-h and 6-h backdrop forecast. For the present experiment, Noah LSM was used in conjunction with GDAS forcing data updated by the GLDAS team. The specifics of the forcing and geostationary parameters used in this investigation are given in Table 1 and Table 2 below.
This research employs ESA’s CCI V4.2 [66,67,68] to evaluate the performance of assimilating blended SM products. Various microwave products from active (e.g., ASCAT, SCAT) and passive (e.g., AMSR-E, SMMR, SMAP, SSM/I, SMOS (LPRM), WindSat, Aquarius, TMI) systems are combined to produce them. The combined results are available on the GLDAS Noah climatology with a quarter-degree spatial resolution.

4. Land Data Assimilation Framework and Runoff Routing

The LSM are advanced physics-based numerical models with the ability to predict temporal and spatial variations of land surface state variables. However, these models are subjected to uncertainties from model initialization, parameter, forcing, and physics errors [69,70,71,72,73]. Therefore, through DA, we combine the strengths of hydrologic models as well as observation data to provide a superior surface state estimate. In this study, we particularly evaluate the efficiency of assimilating blended SM products over the Iya Basin in Russia. Our research employs the Land Information System (LIS) [74] architecture established by NASA’s Goddard Space Flight Center for absorbing blended SM outputs in Noah LSM. This study is divided into two stages. In the first stage, we implemented a 1-D Ensemble Kalman Filter (EnKF) algorithm [55] to assimilate blended SM in Noah LSM. This research used a thirty-member ensemble because the improvement in outcomes is negligible when the ensemble size exceeds twelve [74]. For further details on the assimilation approach it is recommended to refer to [19,21]. In the second stage of the experiment, runoff estimates from the Noah LSM are routed to the outlet point of catchment to estimate the total river discharge at stations having in situ observations.

4.1. Assimilation Setup for the Iya River Basin in Russia

We assimilate the blended SM from the European Space Agency (ESA) Climate Change Initiative (CCI) in the Noah LSM for a river basin in Russia. The Iya River Basin (Figure 1) is located in southeastern Siberia, between 98.85°E to 101.17°E longitude and 52.94°N to 54.62°N latitude with a total area of 14,500 km2. It is susceptible to four primary weather conditions: spring (April to May), summer (June to August), fall (September to October), and winter (November to March). This basin is subjected to permafrost, which is one of the major challenges in SM simulation due to its high heterogeneity. In this study, we explore the ability of satellite SM assimilation to improve LSM-simulated seasonal dynamics of the Iya Basin. The details of the methodology flowchart are shown in Figure 2.
The assimilation setup adopted for this study is 1-D Ensemble Kalman Filter (EnKF) for integrating ESA CCI SM into Noah. However, the major challenge in this study region is permafrost. Owing to this, the soil temperature in the basin remains below freezing point during major parts of the year. In such conditions, assimilating satellite SM can lead to uncertainty due to frozen moisture components. Therefore, for this experiment, we only assimilate SM when the temperature is above freezing point (i.e., during the summer season), as shown in Figure 3. Such a simulation setup will also help to evaluate the memory of SM assimilation, as the improvement in other seasons will be only due to improved states during summer.

4.2. Routing of Runoff Estimates from LSM

Noah LSM provides runoff estimates for each grid cell. These grid cells from the LSM simulation are routed to the outlet point of the catchment to estimate the total river discharge at stations having in situ observations. The individual grid cells, which are independently modeled, do not have horizontal water flow. Due to the fact that the Noah LSM only models non-uniformly distributed runoff time-series for each cell, a separate model was required to transport cell surface runoff and base flow to the outlet. Research was conducted by [75] to build a horizontal routing model that could be connected to grid-based LSM. This routing concept prohibits water from flowing back into grid cells from channels, because this water will no longer be included in the water budget. A linearized Saint-Venant equation is used to route the discharge at the basin outflow. The fundamental assumption is that runoff from a cell flows in a single direction. In this study, in situ river discharge observations from Tulun and Inga are used to calibrate the routing model. Figure 4 illustrates the routing procedure in its entirety.

5. Results

5.1. Enhancement after Assimilating ESA CCI SM

This study demonstrates the improvement in Noah LSM simulations over the permafrost region of Russia [76] (Figure 1) after assimilating the SM product from ESA CCI. As discussed in Section 4.1, SM is only assimilated during the summer season to avoid uncertainty due to frozen moisture. To highlight the improvement, we compare the seasonal mean of SM between open loop and DA. Figure 5 indicates SM during the spring season. It should be noted that no assimilation is performed during the spring season. Therefore, improvement in SM during spring is solely due to improved states during assimilation during the previous summer season. It is noticed that SM decreases after assimilation towards the north of the basin, while SM increases in the southern part of the basin after assimilation.
Figure 6 shows SM during the summer season. During this season, the depth of the active soil layer increases, which is suitable to assimilate SM. From this result, it is observed that, during the summer season, open loop simulation in the northern part of the basin has a dry bias compared to DA run. On the other hand, the southern part of the basin follows a negative trend, where the open loop has a wet bias, which is reduced after assimilation. The assimilated ESA CCI observations have less SM compared to Noah model simulations.
Similarly, the results are displayed during the autumn season (Figure 7). It should be noted that SM is not directly assimilated during autumn. Therefore, it can be inferred that the improvement in SM is due to the assimilation conducted during the previous season. Results depict a similar behavior, wherein after assimilation, the SM is increased in the northern part of the basin. Similarly, south of the basin exhibits the opposite tendency, with a drop in SM after assimilation due to the EnKF approach’s properties; the model results are brought closer to the assimilated observations from ESA CCI.

5.2. Soil Moisture Validation with Independent In-Situ Measurements

The simulated surface soil moisture from open loop and after assimilation are validated with the in situ soil moisture data at two stations: (a) Tulun station and (b) Ikey station (Figure 8). In situ soil moisture is determined by the thermostatic-weight method every ten days in the upper soil layer at a depth of 20 cm.
The simulated assimilated surface soil moisture shows promising results as compared to the open loop results. At the Tulun station, the bias for open loop is 2.2%, whereas after assimilation, the bias is reduced to 0.3%. The correlation coefficient improves from 0.88 in the case of open loop to 0.91 in the case of assimilated soil moisture. At the Ikey station, the bias for open loop is 15.5%, whereas after assimilation, the bias is reduced to 11.5% (Table 3). Furthermore, the correlation coefficient improves from 0.84 in the case of open loop to 0.94 in the case of assimilated soil moisture.

5.3. Impact of Assimilation on Streamflow Discharge

Figure 9 shows the spatial plots of surface runoff during 2012, 2013, 2014, and 2015. The top row shows surface runoff for the open loop runs and the second row depicts surface runoff after assimilation. It is noticed that the surface runoff towards the north of basin increases after assimilation. While in the southern part of basin, after assimilation, it moreover remains the same. This is owing to the goltsy rock terrain in the south part, which is less impacted by SM assimilation and, subsequently, runoff. Goltsy rock terrain is a type of bald mountain summit of certain areas in Siberia, Russia. They are barren, rocky, or stony, and only rarely may lichens or stunted small scrubs, such as Siberian pine, grow on them.
These results are consistent with the results of the soil moisture data shown in Figure 5, Figure 6 and Figure 7.
Figure 10 shows the spatial plots of sub-surface runoff during 2012, 2013, 2014, and 2015. The top row shows the surface runoff for the open loop condition and the second row shows the sub-surface runoff from the assimilated data. Similar to Figure 9, this figure also shows similar results. It is noticed that the surface runoff towards the north of basin increases after assimilation. While in the southern part of basin after assimilation, it moreover remains the same because of the goltsy landform in the south as discussed above. However, there are drastic changes in the years 2014 and 2015.
Figure 11 represents the time-series of the streamflow generated from routing the surface and sub-surface runoff with respect to the gauge data over the Tulun gauge station. The gray line represents the in situ gauge streamflow. The streamflow generated from the open loop data is represented in orange color, whereas the streamflow generated from the assimilated soil moisture data is shown in blue color. The time-series represented is from the year 2010 to 2015. From the time-series, it is clearly visible that bias in streamflow is more in the case of open loop compared to the assimilated data. The time-series shows that the streamflow generated from assimilated data is closer to the in situ streamflow rather than the streamflow generated from the open loop data. Thereby, it is clear that the improvement in the soil moisture estimates through data assimilation enhances or improves the streamflow predictions in the permafrost-affected regions.

6. Conclusions

The blended SM from the European Space Agency’s Climate Change Initiative (ESA CCI) was assimilated over a permafrost basin in Russia. This experiment helps to understand the improvement in Noah model simulation after assimilating blended SM in the permafrost region. The major constraints of permafrost reduced the assimilation window to only the summer season (June, July, August) in this experiment. Therefore, through this study, we demonstrated the impact of assimilating SM in one season and its impact on subsequent seasons. Further analysis on the impacts of DA on permafrost regions warrants attention. This study highlights the efficiency of DA in improving Noah model simulations using blended SM.
Using 1-D Ensemble Kalman Filter (EnKF), this work proposes an effective assimilation configuration for integrating ESA CCI SM into Noah LSM across permafrost-impacted regions. This configuration aids in overcoming the primary obstacles of SM incorporation in permafrost locations. Therefore, the soil temperatures in these places stay below the freezing point for the majority of the year, resulting in permafrost. Due to the frozen moisture component, absorbing satellite SM under such circumstances might lead to ambiguity. Therefore, we only executed SM assimilation in this experiment when the temperature was above freezing (i.e., during the summer season). Such a simulated scenario will also aid in evaluating the memory of SM assimilation, because improvements in future seasons will be solely attributable to summer state improvements. Our study’s findings demonstrate the capability of this assimilation setup to enhance SM and, therefore, its influence on streamflow models. The approach used in this study can be applied to any study region where soil moisture data are available. There were no specific region-based limitations used in the study.

Author Contributions

Conceptualization, A.P., A.S.N. and J.I.; formal analysis, A.P.; data curation for soil moisture station observations, O.M. and N.N.; manuscript draft preparation, A.P.; manuscript review and editing, A.S.N., J.I. and O.M.; supervision, J.I. All authors have read and agreed to the published version of the manuscript.

Funding

The study was carried out with the support of RFBR (19-55-80028) and St. Petersburg State University (project 75295876).

Data Availability Statement

All data is available by the request to corresponding author.

Acknowledgments

The authors acknowledge the support from DST supported BRICS Project DST/IMRCD/BRICS/PilotCall3/CCAFRITPOA/2019.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

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Figure 1. Indicates geographical extent of the Iya River Basin overlaid on SRTM DEM.
Figure 1. Indicates geographical extent of the Iya River Basin overlaid on SRTM DEM.
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Figure 2. Methodology flowchart showing Stage 1 (Blue box) and Stage 2 (Red box).
Figure 2. Methodology flowchart showing Stage 1 (Blue box) and Stage 2 (Red box).
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Figure 3. Time frame indicating the assimilation window of ESA CCI SM.
Figure 3. Time frame indicating the assimilation window of ESA CCI SM.
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Figure 4. Routing Methodology Flowchart. The grid cells from the LSM simulation are routed to the outlet point of the catchment to estimate the total river discharge at stations having in situ observations. The accumulated flow generates a hydrograph, which is validated with field in situ data.
Figure 4. Routing Methodology Flowchart. The grid cells from the LSM simulation are routed to the outlet point of the catchment to estimate the total river discharge at stations having in situ observations. The accumulated flow generates a hydrograph, which is validated with field in situ data.
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Figure 5. Represents spatial pattern of SM over the Iya Basin during the spring season. Annual mean from open loop are shown in top row (a–e), from DA in bottom row (f–j).
Figure 5. Represents spatial pattern of SM over the Iya Basin during the spring season. Annual mean from open loop are shown in top row (a–e), from DA in bottom row (f–j).
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Figure 6. Represents spatial pattern of SM over the Iya Basin during summer season. Annual mean from open loop are shown in top row (a–e), from DA in bottom row (f–j).
Figure 6. Represents spatial pattern of SM over the Iya Basin during summer season. Annual mean from open loop are shown in top row (a–e), from DA in bottom row (f–j).
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Figure 7. Represents spatial pattern of SM over Iya Basin during autumn season. Annual mean from open loop are shown in top row (a–e), from DA in bottom row (f–j).
Figure 7. Represents spatial pattern of SM over Iya Basin during autumn season. Annual mean from open loop are shown in top row (a–e), from DA in bottom row (f–j).
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Figure 8. Surface soil moisture validation of open loop and after data assimilation with respect to in situ measurement over (a) Tulun station, (b) Ikey station.
Figure 8. Surface soil moisture validation of open loop and after data assimilation with respect to in situ measurement over (a) Tulun station, (b) Ikey station.
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Figure 9. Represents spatial pattern of surface runoff over Iya Basin. Annual mean from open loop are shown in top row (a–d), from DA in bottom row (e–h).
Figure 9. Represents spatial pattern of surface runoff over Iya Basin. Annual mean from open loop are shown in top row (a–d), from DA in bottom row (e–h).
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Figure 10. Represents spatial pattern of sub-surface runoff over Iya Basin. Annual mean from open loop are shown in top row (a–d), from DA in bottom row (e–h).
Figure 10. Represents spatial pattern of sub-surface runoff over Iya Basin. Annual mean from open loop are shown in top row (a–d), from DA in bottom row (e–h).
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Figure 11. Time-series of streamflow with respect to gauge data over Tulun.
Figure 11. Time-series of streamflow with respect to gauge data over Tulun.
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Table
Table 1. Specification of forcing variables used for Noah LSM.
Table 1. Specification of forcing variables used for Noah LSM.
VariableSpatial ResolutionTemporal ResolutionSource
Near surface air temperature0.47° × 0.47°3 HourlyGDAS (Source: https://www.ncdc.noaa.gov/dataaccess/model-data/modeldatasets/global-dataassimilation-system-gdas (accessed on 26 April 2021)
Near surface specific humidity
Total incident shortwave radiation
Incident Longwave Radiation
Eastward wind
Northward wind
Surface pressure
Rainfall rate
Convective rainfall rate
Table
Table 2. Details of geostationary parameters used in Noah L.SM.
Table 2. Details of geostationary parameters used in Noah L.SM.
VariableSpatial ResolutionSource
Landcover0.01° × 0.01°Moderate Resolution Imaging Spectroradiometer (MODIS) [ https://modis.gsfc.nasa.gov/data/dataprod/mod12.php], (accessed on 26 April 2021)
Soil Texture0.25° × 0.25°Food and Agriculture Organization (FAO) [http://www.fao.org/soils-portal/soilsurvey/soil-properties/physical-properties/en/], (accessed on 26 April 2021)
Soil Fraction (clay, sand, silt)0.25° × 0.25°FAO [http://www.fao.org/soils-portal/soilsurvey/soil-properties/physical-properties/en/], (accessed on 26 April 2021)
Slope type0.01° × 0.01°NCEP_LIS [Source: https://lis.gsfc.nasa.gov/], (accessed on 26 April 2021)
Elevation Data Shuttle Radar Topographic Mission (SRTM) [http://srtm.csi.cgiar.org/], (accessed on 26 April 2021)
Albedo (Monthly)0.01° × 0.01°NCEP_LIS [https://lis.gsfc.nasa.gov/], (accessed on 26 April 2021)
Greenness fraction0.01° × 0.01°NCEP_LIS [https://lis.gsfc.nasa.gov/], (accessed on 26 April 2021)
Table
Table 3. Validation of open loop (OL) and after assimilation (PA) with respect to in situ measurement.
Table 3. Validation of open loop (OL) and after assimilation (PA) with respect to in situ measurement.
Index\StationTulunIkey
PAOLPAOL
Bias0.3%2.2%11.5%15.5%
Correlation0.910.880.940.84
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Pradhan, A.; Nair, A.S.; Indu, J.; Makarieva, O.; Nesterova, N. Leveraging Soil Moisture Assimilation in Permafrost Affected Regions. Remote Sens. 2023, 15, 1532. https://doi.org/10.3390/rs15061532

AMA Style

Pradhan A, Nair AS, Indu J, Makarieva O, Nesterova N. Leveraging Soil Moisture Assimilation in Permafrost Affected Regions. Remote Sensing. 2023; 15(6):1532. https://doi.org/10.3390/rs15061532

Chicago/Turabian Style

Pradhan, Ankita, Akhilesh S. Nair, J. Indu, Olga Makarieva, and Nataliia Nesterova. 2023. "Leveraging Soil Moisture Assimilation in Permafrost Affected Regions" Remote Sensing 15, no. 6: 1532. https://doi.org/10.3390/rs15061532

APA Style

Pradhan, A., Nair, A. S., Indu, J., Makarieva, O., & Nesterova, N. (2023). Leveraging Soil Moisture Assimilation in Permafrost Affected Regions. Remote Sensing, 15(6), 1532. https://doi.org/10.3390/rs15061532

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