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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Research Project #422787

Research Project: Leveraging Remote Sensing, Land Surface Modeling and Ground-based Observations ... Variables within Heterogeneous Agricultural Landscapes

Location: Hydrology and Remote Sensing Laboratory

2017 Annual Report


Objectives
Objective 1: Develop and verify new observational tools (both remote sensing- and ground observation-based) and scaling techniques for characterizing water balance components, from plot (~10 m) to regional scales (~100 km). Objective 2: Develop remote sensing and modeling approaches for monitoring the magnitude of agricultural drought and its subsequent impact on agricultural crop condition and yield. Objective 3: Develop remote sensing and modeling approaches for characterizing the multi-scale impacts of conservation practices on water quality variables.


Approach
Ground measurements, remote sensing observations, and modeling each provide a partial description of hydrologic variables required at different spatial scales for agricultural applications. This project seeks to integrate these various sources of information into true multi-scale assessments and leverage their mutual strengths.


Progress Report
This is the final report for 8042-13610-028-00D. A number of significant milestones were reached in this project. For example, a rigorous validation was completed for the primary soil moisture product obtained from the NASA Soil Moisture Active/Passive (SMAP) mission. Likewise, a new suite of enhanced resolution SMAP soil moisture products were developed and validated. These products provide improved temporal and spatial resolution and enhance the value of SMAP soil moisture data products for agricultural applications requiring high-resolution soil moisture data. SMAP soil moisture data products were also successfully integrated into the USDA Foreign Agricultural Services (FAS) global drought monitoring system and demonstrated to improve the ability of FAS to anticipate region-scale variability in commodity crop production. Finally, the project demonstrated that SMAP soil moisture products provide a better description of pre-storm soil saturation conditions than other existing soil moisture products within the United States. As a result, its incorporation into operational stream flow forecasting systems is expected to significantly enhance the monitoring of flash flood risk in agricultural regions. These advances in soil moisture remote sensing were complemented by analogous advances in multi-scale surface evapotranspiration (ET) products. The first microwave-based global ET product was developed, validated, and compared to a thermal infrared (TIR) ET product. This new product provides improves all-weather coverage and supports the key project goal of integrating microwave and TIR remote sensing for agricultural drought monitoring. Advances in TIR-based products focused primarily on the development of a multi-scale tool which fuses remote sensing observations acquired from multiple satellite platforms to produce ET estimates at a daily time scale and a 30-m resolution. Obtaining ET estimates at such a fine time/space resolution represents an important breakthrough in the routine remote sensing of ET for field-scale agricultural applications. These methods were used to develop multi-scale modeling system for generating ET estimates to improve water use efficiency in operational vineyard irrigation systems. For example, the Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) project, collected micrometeorogical, biophysical and remote sensing data in adjacent vineyards at different levels of maturity near Lodi, CA from 2013 to 2016. The aim of GRAPEX is to combine in-situ and remotely-sensed data to investigate the effects of canopy structure and row orientation on energy and water exchange processes both within and above the vine canopy. Using these ground observations, the remote sensing model was evaluated and refined to improve ET estimates for highly-structured canopies such as vineyards, as well as the ability to separate vine water use from evaporation from the inter-row cover crop. In addition to the characterization of water quantity variables, significant progress was also made in terms of our ability to characterize basin-scale water quality impacts of field- and farm-scale conservation practices. In particular, a key project goal is the evaluation of winter cover crop (WCC) performance as a best management practice for maximizing water quality. Using the Soil and Water Assessment Tool (or SWAT) model, land use and soil characteristics were identified as having the strongest impact on water and nutrient transport mechanisms and pathways which determine the effectiveness of WCCs within the Chesapeake Bay Watershed. This work was paired with the development of remote sensing maps for the regional-scale mapping of WCCs and the successful demonstration of these maps for the detection of wintertime vegetative cover crops. This capacity to monitor wide-scale conservation practice implementation enhances the ability of watershed managers to achieve environmental outcomes required for restoration of the Chesapeake Bay ecosystem.


Accomplishments
1. A multi-scale data fusion remote sensing toolkit for daily evapotranspiration (ET). A fundamental requirement for effective agricultural water management is acquiring the means to accurately measure crop water use at appropriate temporal and spatial scales. To address this need, ARS scientists in Beltsville, Maryland, have developed and distributed a novel evapotranspiration (ET) mapping toolkit based on the fusion of remote sensing observations obtained from multiple satellite platforms. The fusion allows for the production of daily crop water use estimates at an unprecedented 30-m spatial resolution. Due to its significant resolution advantages, the toolkit has already been used to address many water resource issues in agriculture, including: groundwater depletion via irrigation in Central Wisconsin (Wisconsin-Department of Natural Resources, University of Wisconsin), the impact of expanding agricultural drainage on regional hydrology in the Corn Belt (U.S. Geological Survey; USGS), water use in managed forest plantations (U.S. Forest Service, Virginia Polytechnic Institute and State University), calibration of hydrologic/water quality models for the Chesapeake Bay Watershed (ARS, University of Maryland), irrigation management decision making in vineyards (in collaboration with E&J Gallo), consumptive use assessments for the U.S. Water Census and California’s new Sustainable Groundwater Management Act (USGS, University of California-Davis), and drought and water information delivery for the Near-East North African region (Daugherty Water for Food Institute and U.S. Agency for International Development). The toolkit will also be used to generate ET and water stress products for NASA’s ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission. Through these projects, this ET mapping tool has greatly expanded our ability to monitor, and therefore optimize, water use and availability across a broad range of agricultural systems.

2. Dating and tracking agricultural nitrogen. Determining the age of nitrate in groundwater and stream networks is critical for assessing the effectiveness of conservation practices designed to reduce off-site nitrogen pollution from farms. ARS scientists in Beltsville, Maryland, have demonstrated how Metolachlor ethane sulfonic acid (MESA), a commonly used crop herbicide, can be used to measure the mean residence time of groundwater and surface water and provide an age estimate for dissolved agricultural nitrogen. This age estimate gives scientists important new information about watershed residence times and how nutrients are being transported off fields and into streams and rivers. In particular, the approach can be applied to assess the effectiveness of field and farm-scale conservation practices for reducing agricultural nitrogen pollution. This approach has substantial advantages over other water dating methods because it can be applied to both surface and groundwater samples while previous techniques did not allow the water sample to be exposed to the open atmosphere.

3. The Soil Moisture Active/Passive Validation Experiment in 2016 (SMAPVEX16). ARS scientists in Beltsville, Maryland, contributed to an international validation experiment for the NASA Soil Moisture Active/Passive (SMAP) satellite mission in the Red River Basin of the Northern Plains that was completed in 2016. The satellite mission is the first mission to use both active and passive L-band sensors to monitor surface soil moisture at a high resolution. A ground and aircraft validation campaign was conducted in and around the South Fork Experimental Watershed near Iowa City, Iowa and the Carman Study region near Winnipeg, Ontario to provide a valuable ground truth dataset to verify the accuracy of the mission product. This was conducted from May 2016 to August of 2016, providing a range of conditions for a critical agricultural region and the results of this experiment are providing datasets for the revision of algorithms for monitoring soil moisture in row crop domains, such as the central and northern plains of the United States. This will ultimately improve the understanding of climate and weather dynamics in a drought-prone region of significant interest to the USDA.


Review Publications
Wang, Z., Schaaf, C., Sun, Q., Kim, J., Erb, A., Gao, F.N., Roman, M., Yang, Y., Petroy, S., Taylor, J., Masek, J., Morisette, J., Zhang, X. 2017. Monitoring land surface albedo and vegetation dynamics using high spatial and temporal resolution synthetic data from Landsat and MODIS BRDF/albedo product. Geophysical Research Letters. 59:104-117.
Gao, F., Anderson, M.C., Zhang, X., Yang, Z., Alfieri, J.G., Kustas, W.P., Mueller, R., Johnson, D., Prueger, J.H. 2016. Mapping crop progress at field scales using Landsat and MODIS. Remote Sensing of Environment. 188:9-25.
Renkenberger, J., Montas, H., Leisnham, P., Chanse, V., Shirmohammadi, A., Sadeghi, A.M., Brubaker, K., Rockler, A., Hutson, T., Lansing, D. 2017. Climate change impact on critical source area (CSAs) identification in a Maryland watershed. Transactions of the ASABE. doi:10.13031/trans.59.11677.
Colliander, A., Njoku, E., Jackson, T.J., Chazanoff, S., Mcnairn, H., Powers, J., Cosh, M.H. 2016. Retrieving soil moisture for non-forested areas using PALS radiometer measurements in SMAPVEX12 field campaign. Remote Sensing of Environment. 184:086-100.
Tuttle, S., Cho, E., Restrepo, P., Jia, X., Vuyovich, C., Cosh, M.H., Jacobs, J. 2016. Remote sensing of drivers of spring snowmelt flooding in the North Central US. In: Lakshmi, V., editor. Remote Sensing of Hydrological Extremes. Switzerland: Springer International Publishing. p. 21-45.
Leroux, D., Das, N., Entekhabi, D., Colliander, A., Njoku, E., Jackson, T.J., Yueh, S. 2017. Active–passive soil moisture retrievals during the SMAP validation experiment 2012. Geoscience and Remote Sensing Letters. 13:475-479.
Kim, S., Van Zyl, J., Johnson, J., Moghaddam, M., Tsang, L., Colliander, A., Dunbar, R., Jackson, T.J., Jarauwatanadilok, S., West, R., Berg, A., Caldwell, T., Cosh, M.H., Goodrich, D.C., Livingston, S.J., Lopez, B., Rowlandson, T., Thibeault, M., Walker, J., Entekhabi, D., Njoku, E., O'Neill, P., Yueh, S. 2017. Surface soil moisture retrieval using the L-band synthetic aperture radar onboard the Soil Moisture Active Passive satellite and evaluation at core validation sites. IEEE Transactions on Geoscience and Remote Sensing. 55(4):1897-1914.
Colliander, A., Jackson, T.J., Bindlish, R., Chan, S., Das, N., Kim, S., Cosh, M.H., Dunbar, R., Dang, L., Pashaian, L., Asanuma, J., Aida, K., Berg, A., Rowlandson, T., Bosch, D.D., Caldwell, T., Caylor, K., Goodrich, D.C., Jassar, H., Lopez-Baeza, E., Martinez-Fernandez, J., Gonzalez-Zamora, Livingston, M.S., McNairn, H., Pacheco, A., Moghaddam, M., Montzka, C., Notarnicola, C., Niedrist, G., Pellarin, T., Prueger, J.H., Pulliainen, J., Rautiainen, K., Ramo, J., Seyfried, M.S., Starks, P.J., Su, Z., Zeng, Y., Velde, R., Thibeault, M., Dorigo, W., Vreugdenhil, M., Walker, J., Wu, X., Monerris, A., O'Neill, P., Entekhabi, D., Njoku, E., Yueh, S. 2017. Validation of SMAP surface soil moisture products with core validation sites. Remote Sensing of Environment. 192:238-262.
Coopersmith, E., Cosh, M.H., Bell, J., Kelly, V., Hall, M., Palecki, M., Temimi, M. 2016. Deploying temporary networks for upscaling of sparse network stations. International Journal of Applied Earth Observation and Geoinformation. 52:433-444.
Dong, J., Steele-Dunne, S., Ochsner, T., Hatch, C., Sayde, C., Selker, J., Tyler, S., Cosh, M.H., Van De Giesen, N. 2016. Mapping high-resolution soil moisture and properties using distributed temperature sensing data and an adaptive particle batch smoother. Water Resources Research. 52(10):7690-7710.
Sun, L., Gao, F.N., Anderson, M.C., Kustas, W.P., Alsina, M., Sanchez, L., Sams, B., Mckee, L.G., Dulaney, W.P., White, W.A., Alfieri, J.G., Prueger, J.H., Melton, F. 2017. Daily mapping of 30m LAI and NDVI for grape yield prediction in California vineyards. Remote Sensing. doi:10.3390/rs9040317.
Coopersmith, E., Cosh, M.H., Bell, J., Boyles, R. 2016. Using machine learning to produce near surface soil moisture estimates from deeper in situ records at U.S. Climate Reference Network (USCRN) locations: Analysis and applications to AMSR-E satellite validation. Advances in Water Resources. 98:122-131.
Ran, Y., Li, X., Jin, R., Kang, J., Cosh, M.H. 2016. Strengths and weaknesses of temporal stability analysis for monitoring and estimating grid-mean soil moisture in a high-intensity irrigated agricultural landscape. Water Resources Research. 53(1):283-301. doi:10.1002/2015/RWR018182.
Ouellette, J., Johnson, J., Balenzano, A., Mattia, F., Satalinio, G., Kim, S., Dunbar, R., Colliander, A., Cosh, M.H., Caldwell, T., Walker, J., Berg, A. 2017. A time-series approach to estimating soil moisture from vegetated surfaces using L-band radar backscatter. IEEE Transactions on Geoscience and Remote Sensing. 99:1-8
Mohanty, B., Cosh, M.H., Lakshmi, V., Montzkka, C. 2017. Soil moisture remote sensing: State of the science. Vadose Zone Journal. doi:10.2136/vzj2016.10.0105.
Cai, X., Pan, M., Chaney, N., Colliander, A., Misra, S., Cosh, M.H., Crow, W.T., Jackson, T.J., Woodd, E. 2017. Validation of SMAP soil moisture for the SMAPVEX15 field campaign using a hyper-resolution model. Water Resources Research. 53(4):3013-3028.
Hashemian, M., Ryu, D., Crow, W.T., Kustas, W.P. 2015. Improving root-zone soil moisture estimations using dynamic root growth and crop phenology. Advances in Water Resources. 86(A):170-183.
Kustas, W.P., Anderson, M.C., Alfieri, J.G., Hipps, L. 2016. Using radiometric surface temperature for surface energy flux estimation in Mediterranean drylands from a two-source perspective. Remote Sensing of Environment. doi: 10.1016/j.rse.2016.07.024.
Lee, S., Yeo, I., Sadeghi, A.M., Lang, M., Mccarty, G.W., Hively, D. 2016. Impacts of watershed characteristics and crop rotations on winter cover crop nitrate uptake capacity within agricultural watersheds in the Chesapeake Bay region. PLoS One. 11(6):e0157637.
Sharif, A., Lang, M., Mccarty, G.W., Sadeghi, A.M., Lee, S., Yen, Jeong, J. 2016. Enhancing model prediction reliability through improved soil representation and constrained model auto calibration - A paired waterhsed study. Journal of Hydrology. 541:1088-1103.
Rice, C., Mccarty, G.W., Bialek Kalinski, K.M., Zabetakis, K., Torrents, A., Hapeman, C.J. 2016. Analysis of metolachlor ethane sulfonic acid chirality in groundwater: A tool for dating groundwater movement in agricultural settings. Science of the Total Environment. 560-561:36-43.
Wilusz, D., Zaitchik, B., Anderson, M.C., Hain, C., Yilmaz, M., Mladenova, I. 2017. Monthly flooded area classification using low resolution SAR imagery in the Sudd wetland from 2007-2011. Remote Sensing of Environment. 194:205-218.
Song, L., Liu, S., Kustas, W.P., Zhou, J., Xu, Z., Xia, T., Li, M. 2016. Application of remote sensing-based two-source energy balance model for mapping field surface fluxes with composite and component surface temperatures. Agricultural and Forest Meteorology. 230–231:8–19.
Chan, S., Bindlish, R., O'Neill, P., Njoku, E., Jackson, T.J., Colliander, A., Chen, F., Burgin, M., Dunbar, R., Peipmeier, J., Yueh, S., Entekhabi, D., Cosh, M.H., Caldwell, T., Walker, J., Wu, X., Berg, A., Rowlandson, T., Pacheco, A., McNairn, H., Thibeault, M., Martinez-Fernandez, J., Gonzalez-Zamora, A., Seyfried, M.S., Bosch, D.D., Starks, P.J., Goodrich, D.C., Prueger, J.H., Palecki, M., Small, E., Zreda, M., Calvet, J., Crow, W.T., Kerr, Y. 2016. Assessment of the SMAP level 2 passive soil moisture product. IEEE Transactions on Geoscience and Remote Sensing. 54(8):1-14. doi:10.1109/TGRS.2016.2561938.
Yang, Y., Anderson, M.C., Gao, F.N., Hain, C., Semmens, K., Kustas, W.P., Noormets, A., Wynne, R., Thomas, V., Sun, G. 2017. Daily Landsat-scale evapotranspiration estimation over a managed pine plantation in North Carolina, USA using multi-satellite data fusion. Hydrology and Earth System Sciences. 21:1017-1037. doi:10.5194/hess-21-1017-2017.
Anderson, M.C., Hain, C., Jurecka, F., Trnka, M., Hlavinka, Dulaney, W.P., Otkin, J., Johnson, D., Gao, F.N. 2016. An energy balance approach for mapping crop waterstress and yield impacts over the Czech Republic. Climate Research. 70:215-230.
Su, C., Zhang, J., Gruber, A., Parinussa, R., Ryu, D., Crow, W.T., Wagner, W. 2016. Error sources in passive and active microwave satellite soil moisture over Australia. Remote Sensing of Environment. 182:128-140. doi:10.1016/j.rse.2016.05.008.
Tao, J., Wu, D., Gourley, J., Zhang, S., Crow, W.T., Peters-Lidard, C., Barros, A. 2016. Operational hydrological forecasting during the 2 IPHEx-IOP campaign – meet the challenge. Journal of Hydrology. doi:10.1016/j.jhydrol.2016.02.019.
Chen, F., Crow, W.T., Colliander, A., Cosh, M.H., Jackson, T.J., Bindlish, R., Reichle, R., Chan, S., Starks, P.J., Goodrich, D.C., Seyfried, M.S. 2016. Application of triple collocation in ground-based validation of soil moisture active/passive (SMAP) level 2 data products. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 99:1-14.
Kerr, Y., Al-Yarri, A., Rodriguez-Fernandez, N., Parrens, M., Molero, B., Leroux, D., Bircher, S., Mahmoodi, A., Mialon, A., Richaume, P., Delwart, S., Albitar, A., Pellarin, T., Bindlish, R., Jackson, T.J., Rudiger, C., Waldteufel, P., Mecklenburg, S., Wigneron, J. 2016. Overview of SMOS performance in terms of global soil moisture monitoring after six years in operation. Remote Sensing of Environment. 180:40-63. doi:10.1016/j.rse.2016.01.042.
Molero, B., Merlin, O., Malbeteau, Y., Al Bitar, A., Cabot, F., Stefan, V., Kerr, Y., Bacon, S., Cosh, M.H., Bindlish, R., Jackson, T.J. 2016. SMOS disaggregated soil moisture product at 1 km resolution: processor overview and first validation results. Remote Sensing of Environment. 180:361-376. doi:10.1016/j.rse.2016.02.045
Bian, H., Lu, H., Sadeghi, A.M., Zhu, Y., Yu, Z., Ouyang, F., Su, J. 2017. Impact of climate change on the streamflow hydrology of the Yangtze River in China. Water. 9(1):70. https://doi.org/10.3390/w9010070.
Song, L., Kustas, W.P., Liu, S., Colaizzi, P.D., Nieto, H., Xu, Z., Li, M., Xu, T., Agam, N., Tolk, J.A., Evett, S.R. 2016. Applications of a thermal-based two-source energy balance model using Priestley-Taylor approach for surface temperature partitioning under advective conditions. Journal of Hydrology. 540:574–587.
Koster, R., Brocca, L., Crow, W.T., Burgin, M., De Lannoy, G. 2016. Precipitation estimation using L-Band and C-Band soil moisture retrievals. Water Resources Research. 52:7213–7225.
Mladenova, I., Bolten, J., Crow, W.T., Anderson, M.C., Hain, C., Mueller, R., Johnson, D. 2017. Inter-comparison of soil moisture, water use and vegetation indices for estimating corn and soybean yields over the U.S. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 10(4):1328-1343.
Alvarez, C., Ryu, D., Western, A., Crow, W.T., Su, C., Robertson, D. 2016. Dual assimilation of satellite soil moisture to improve flood prediction in ungauged catchments. Water Resources Research. 52:5357–5375.
Holmes, T., Hain, C., Anderson, M.C., Crow, W.T. 2016. Cloud tolerance of remote sensing technologies to measure land surface temperature. Hydrology and Earth System Sciences. 20:3263-3275. doi:10.5194/hess-20-3263-2016.
Qiu, J., Crow, W.T., Nearing, G. 2017. The impact of vertical measurement depth on the information content of soil moisture for latent heat flux estimation. Journal of Hydrometeorology. 17:2419–2430.
Coopersmith, E.J., Cosh, M.H., Jacobs, J. 2016. Comparison Of In Situ Soil Moisture Measurements: An Examination of the Neutron and Dielectric Measurements within the Illinois Climate Network. Journal of Atmospheric and Ocean Technology. doi:10.1175/JTECH-D-16-0029.1.
Zhang, X., Wang, J., Gao, F.N., Liu, Y., Schaaf, C., Henebry, G., Friedl, M., Yu, Y., Jayavelu, S., Gray, J., Liu, L., Yan, D. 2017. Exploration of scaling effects on coarse resolution land surface phenology. Remote Sensing of Environment. 190:318-330.
Lu, H., Crow, W.T., Zhu, Y., Ouyang, F., Su, J. 2016. Improving streamflow prediction using remotely-sensed soil moisture and snow depth. Remote Sensing. 8(6):503.
Shellito, P., Small, E., Colliander, A., Bindlish, R., Cosh, M.H., Berg, A., Bosch, D.D., Caldwell, T., Goodrich, D.C., Lopez-Baeza, E., McNairn, H., Prueger, J.H., Starks, P.J. 2016. SMAP soil moisture drying more rapid than observed in situ following rainfall events. Geophysical Research Letters. 43(15):8068-8075.
Shelitto, P., Small, E., Cosh, M.H. 2016. Calibration of Noah soil hydraulic property parameters using surface soil moisture from SMOS and basin-wide in situ observations. Journal of Hydrometeorology. doi:10.1175/JHM-D-15-0153.1.
Brocca, Luca, Pellarin, T., Crow, W.T., Ciabattta, L., Massari, C., Ryu,D., Rudiger, C., Kerr, Y. 2016. Rainfall estimation by inverting SMOS soil moisture estimates: a comparison of different methods over Australia. Geophysical Research and Atmosphere. 121:12,062–12,079.
Crow, W.T., Han.E, Ryu, E., Hain, C., Anderson, M.C. 2017. Resolving inter-annual terrestrial water storage variations using microwave-based surface soil moisture retrievals. Water Resources Research. 21:1849–1862.
Yee, M., Walker, J., Monerris, A., Rudiger, C., Jackson, T.J. 2016. On the temporal and spatial variability of near-surface soil moisture for the identification of representative in situ soil moisture monitoring stations. Journal of Hydrology. 537:367-381.
Parinussa, R., De Jeu, R., Van Der Schalke, R., Crow, W.T., Lei, F., Holmes, T. 2016. A quasi-global approach to improve day-time satellite surface soil moisture anomalies through land surface temperature input. Climate Research. 4(4):50. doi:10.3390/cli4040050.
Colliander, A., Cosh, M.H., Misra, S., Jackson, T.J., Crow, W.T., Chan, S., Bindlish, R., Chae, C., Holifield Collins, C.D., Yueh, S. 2017. Validation and scaling of soil moisture in a semi-arid environment: SMAP Validation Experiment 2015 (SMAPVEX15). Remote Sensing of Environment. 196:101-112.
Wang, P., Qui, J., Huo, Z., Anderson, M.C., Zhou, Y., Bai, Y., Liu, T., Ren, S., Feng, R., Chen, P. 2017. Temporal downscaling of crop coefficients for winter wheat in the North China Plain: A case study at the Gucheng ecological-meteorological experimental station. Water. doi:10.3390/w9030155.
Sun, L., Chen, Z., Gao, F.N., Anderson, M.C., Song, L., Wang, L., Hu, B., Yang, Y. 2017. Reconstructing daily clear-sky land surface temperature for cloudy regions from MODIS data. IEEE Transactions on Geoscience and Remote Sensing. 105:10-20. doi:10.1016/j.cageo.2017.04.007.
Burgin, M., Colliander, A., Njoku, E., Chan, S., Kerr, Y., Binglish, R., Jackson, T.J., Entekhabi, D., Yueh, S. 2017. A comparative study of the SMAP passive soil moisture product with existing satellite-based soil moisture products. IEEE Transactions on Geoscience and Remote Sensing. 55(5):2959-2971.
Wigneron, J., Jackson, T.J., O'Neill, P., De Jeu, R., De Rosnay, P., Walker, J., Ferrazoli, P., Mirnov, V., Bircher, S., Grant, J., Kurum, M., Schwank, M., Levine, D., Das, N., Royer, A., Al-Yarri, A., Bitar, A., Fernandez-Moran, R., Lawrence, H., Mialon, A. 2017. Modelling the passive microwave signature from land surfaces: a review of recent results and application to the SMOS & SMAP soil moisture retrieval algorithms. Remote Sensing of Environment. 192:238-262.