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NASA NTRS2017

Estimating Surface Soil Moisture from SMAP Observations Using a Neural Network Technique

A Neural Network (NN) algorithm was developed to estimate global surface soil moisture for April 2015 to March 2017 with a 2-3 day repeat frequency using passive microwave observations from the Soil Moisture Active Passive (SMAP) satellite, surface soil temperatures from the NASA Goddard Earth Observing System Model version 5 (GEOS-5) land modeling system, and Moderate Resolution Imaging Spectroradiometer-based vegetation water content. The NN was trained on GEOS-5 soil moisture target data, making the NN estimates consistent with the GEOS-5 climatology, such that they may ultimately be assimilated into this model without further bias correction. Evaluated against in situ soil moisture measurements, the average unbiased root mean square error (ubRMSE), correlation and anomaly correlation of the NN retrievals were 0.037 m(exp. 3)m(exp. -3), 0.70 and 0.66, respectively, against SMAP core validation site measurements and 0.026 m(exp. 3)m(exp. -3), 0.58 and 0.48, respectively, against International Soil Moisture Network (ISMN) measurements. At the core validation sites, the NN retrievals have a significantly higher skill than the GEOS-5 model estimates and a slightly lower correlation skill than the SMAP Level-2 Passive (L2P) product. The feasibility of the NN method was reflected by a lower ubRMSE compared to the L2P retrievals as well as a higher skill when ancillary parameters in physically-based retrievals were uncertain. Against ISMN measurements, the skill of the two retrieval products was more comparable. A triple collocation analysis against Advanced Microwave Scanning Radiometer 2 (AMSR2) and Advanced Scatterometer (ASCAT) soil moisture retrievals showed that the NN and L2P retrieval errors have a similar spatial distribution, but the NN retrieval errors are generally lower in densely vegetated regions and transition zones.

Soil Moisture Remote Sensing↗

NASA NTRS2018

Polarization Decomposition and Temperature Bias Resolution for SMAP Passive Soil Moisture Retrieval Using Time Series Brightness Temperature Observations

In passive microwave remote sensing of soil moisture, the tau-omega (τ-ω) model has often been used to provide soil moisture estimates at a spatial scale representative of the satellite footprint dimensions. For modeling simplicity, model parameters such as the single scattering albedo (ω) and vegetation opacity (τ) that go into the geophysical inversion process are often assumed to be independent of polarizations. Although this absence of polarization dependence can often be justified in special cases as in low-frequency remote sensing or under dense vegetation conditions, it is not a robust assumption in general. Additional model parameterization errors arising from this assumption are possible, leading to degradation in soil moisture estimation accuracy. In this paper, we propose a time series approach to try to resolve the polarization dependence of several τ-ω model parameters as well as the temperature bias arising from the ancillary temperature data. The Version 4 of the Soil Moisture Active Passive (SMAP) Level 1B brightness temperature time series observations were used to illustrate the mechanics of this approach, with an emphasis on a comparison between resulting satellite soil moisture retrievals and in situ data collected at several core validation sites. It was found that this time series approach resulted in significant reduction of the dry bias exhibited in the current SMAP passive soil moisture data products, while retaining the same performance in other metrics of the current baseline passive soil moisture retrieval algorithm.

time series↗

NASA NTRS2021

Asymmetry in Subseasonal Surface Air Temperature Forecast Error with Respect to Soil Moisture Initialization

Soil moisture (W) helps control evapotranspiration (ET), and ET variations can in turn have a distinct impact on 2-m air temperature (T2M), given that increases in evaporative cooling encourage reduced temperatures. Soil moisture is accordingly linked to T2M, and realistic soil moisture initialization has, in previous studies, been shown to improve the skill of subseasonal T2M forecasts. The relationship between soil moisture and evapotranspiration, however, is distinctly nonlinear, with ET tending to increase with soil moisture in drier conditions and to be insensitive to soil moisture variations in wetter conditions. Here, through an extensive analysis of subseasonal forecasts produced with a state-of-the-art seasonal forecast system, this nonlinearity is shown to imprint itself on T2M forecast error in the conterminous United States in two unique ways: (i) the T2M forecast bias (relative to independent observations) induced by a negative precipitation bias tends to be larger for dry initializations, and (ii) on average, the unbiased root-mean-square error (ubRMSE) tends to be larger for dry initializations. Such findings can aid in the identification of forecasts of opportunity; taken a step further, they suggest a pathway for improving bias correction and uncertainty estimation in subseasonal T2M forecasts by conditioning each on initial soil moisture state.

Air Temperature↗

NASA NTRSDate not supplied

T2M Forecasts at Subseasonal Leads: Do Different Soil Moisture Initial States Have Different Impacts?

2-meter Temperature↗

NASA NTRS2020

How Satellite Soil Moisture Data Can Help to Monitor the Impacts of Climate Change: SMAP Case Studies

Socially and economically costly extreme weather events have become more prevalent in the last decade. Monitoring and early warning systems could help mitigate the impact of such events by allowing people to better prepare themselves to manage their responses to these events. One significant element of an effective warning system is soil moisture because it is a key determinant of the exchange of water and heat energy between the land and atmosphere, the partitioning of precipitation between infiltration and runoff, and therefore has an influence on weather patterns and streamflow. In addition, soil moisture governs plant water availability - the key to crop yield forecasting. For these reasons, a wide range of organizations use soil moisture information to better predict and monitor climate and weather phenomena such as floods and droughts. By improving soil moisture estimates, it may be possible to improve the monitoring and early warning systems upon which these organizations rely, and hence better mitigate the impacts of extreme weather events. Through case studies, this article discusses several uses of soil moisture data products from NASA's Soil Moisture Active Passive (SMAP) mission to help improve soil moisture-related monitoring and early warning systems.

agriculture↗

NASA NTRS2022

Effect of Assimilating SMAP Soil Moisture on CO2 and CH4 Fluxes through Direct Insertion in a Land Surface Model

Soil moisture impacts the biosphere–atmosphere exchange of CO2 and CH4 and plays an important role in the terrestrial carbon cycle. A better representation of soil moisture would improve coupled carbon–water dynamics in terrestrial ecosystem models and could potentially improve model estimates of large-scale carbon fluxes and climate feedbacks. Here, we investigate using soil moisture observations from the Soil Moisture Active Passive (SMAP) satellite mission to inform simulated carbon fluxes in the global terrestrial ecosystem model LPJ-wsl. Results suggest that the direct insertion of SMAP reduces the bias in simulated soil moisture at in situ measurement sites by 40%, with a greater improvement at temperate sites. A wavelet analysis between the model and measurements from 26 FLUXNET sites suggests that the assimilated run modestly reduces the bias of simulated carbon fluxes for boreal and subtropical sites at 1–2-month time scales. At regional scales, SMAP soil moisture can improve the estimated responses of CO2 and CH4 fluxes to extreme events such as the 2018 European drought and the 2019 rainfall event in the Sudd (Southern Sudan) wetlands. The simulated improvements to land–surface carbon fluxes using the direct insertion of SMAP are shown across a variety of timescales, which suggests the potential of SMAP soil moisture in improving the model representation of carbon–water coupling.

SMAP↗

NASA NTRSDate not supplied

Skillful Forecasts of Basic Hydrological Quantities Through the Application of SMAP-Based Soil Moisture Retrievals

The top five centimeters of soil lie at the interface between the atmosphere and land; hydrological variations in the atmosphere communicate themselves to the land largely through this layer, and vice-versa. The estimates of near-surface soil moisture provided by the SMAP mission are thus central to studies of hydrological variability. In fact, recent analyses show that the hydrological variability captured in the SMAP soil moisture retrievals can be parlayed into useful hydrological predictions at various leads (weekly out to seasonal). Specifically, at a given location, using the antecedent time series of SMAP Level 2 soil moisture retrievals up to the start of a forecast, skillful predictions can be made of surface soil moisture anomalies at a 1-week lead, of evapotranspiration stress anomalies at a ~1 month lead, and of streamflow anomalies at a multi-month lead. The skill is derived in part from soil moisture memory (along with the interpretation of deeper soil moisture from the surface measurements) and from joint analyses, outside the forecast period, of the SMAP retrievals with existing observational hydrological datasets.

soil moisture retrievals↗

NASA NTRS2024

Soil Salinity Level Assessment and Prediction Integrating UAV-borne Hyperspectral Imaging and Machine Learning Algorithms to Combat Desertification

In response to the ongoing global food crisis, the United Nations has identified “Zero Hunger” as one of its Sustainable Development Goals. A central contributor to the crisis is the process in which agricultural lands go through desertification. Research has shown a direct correlation between soil salinity and desertification - increased salinity levels indicate a higher risk for desertification. Furthermore, researchers have explored various techniques to map soil salinity, but these methods are oftentimes inefficient and don’t address future salinity predictions. To improve desertification monitoring, soil salinity can be observed via hyperspectral imaging on unmanned aerial vehicles (UAVs) to predict the risk of agricultural desertification using artificial intelligence (AI) and machine learning (ML) techniques. A significant gap exists in past research that applies ML and imaging techniques to soil salinity: convolutional neural networks (CNNs) and regression models are rarely leveraged together, despite the efficiency and accuracy of these models. To compensate for this gap, the proposed system leverages the use of these AI and ML models to improve soil assessment and prediction techniques. This approach involves three steps - data collection, image analysis, and future prediction. Using hyperspectral cameras on UAVs to collect the data from the region, a trained CNN model will output estimated soil salinity levels at a specific time. The estimations will then be analyzed by a regression model to assess the accuracy of future soil salinity predictions. The proposed system will identify regions at risk of desertification to help farmers mitigate agricultural loss, in turn helping alleviate the food crisis.

UAV systems↗

NASA NTRS1971

Mixing models and the recognition of end-member groups in Apollo 11 and 12 soils.

Lunar soils returned from the Apollo 11 and 12 sites appear to consist of more than one source material. Investigators have suggested that the soil can be described by as few as two end members and possibly as many as five or six. In the present study Q-mode factor analysis is used to establish the end members more rigorously and suggests that major-element chemistry of Apollo 11 and 12 lunar soils can be adequately explained in terms of a three-component mixing system. The end members isolated by Q-mode factor analysis can be regarded as basaltic, anorthositic, and noritic (or KREEP) components. Three-component mixing models fitted by least squares to the eight major oxides indicate that six of the Apollo 12 soils investigated are dominated by the basaltic component, whereas the other five are dominated by the noritic or KREEP component. Anorthosite is present in all soils except sample 12033, which is a simple two-component mixture of basalt and KREEP. The Apollo 11 soil is also a three-component mixture but is more basaltic than the Apollo 12 soils. Anorthositic and KREEP materials are important components of the lunar lithosphere and may be distributed on the lunar surface areally in an independent manner.

Lindsay, J. F.↗

NASA NTRS1972

Enzyme activity in terrestrial soil in relation to exploration of the Martian surface

An exploration was made of enzyme activities in soil, including abundance, persistence and localization of these activities. An attempt was made to develop procedures for the detection and assaying of enzymes in soils suitable for presumptive tests for life in planetary soils. A suitable extraction procedure for soil enzymes was developed and measurements were made of activities in extracts in order to study how urease is complexed in soil organic matter. Mathematical models were developed, based on enzyme action and microbial growth in soil, for rates of oxidation of nitrogen as nitrogen compounds are moved downward in soil by water flow. These biogeochemical models should be applicable to any percolating system, with suitable modification for special features, such as oxygen concetrations, and types of hydrodynamic flow.

Ardakani, M. S.↗

NASA NTRS1973

ERTS data user no. 119: Effective use of ERTS multisensor data in the Great Plains. ERTS-1 MSS imagery: A tool for identifying soil associations

The author has identified the following significant results. Soil association maps show the spatial relationships of land units developed in unique climatic, geologic, and topographic environments, and having characteristic slopes, soil depths, textures, available water capacities, permeabilities, and the like. ERTS-1 imagery was found to be a useful tool in the identification of soil associations since it provides a synoptic view of an 8 million acre scene, which is large enough so that the effect can be seen on soils of climate, topography, and geology. A regional view also allows soil associations to be observed over most, if not all, of their extent. ERTS-1 MSS imagery also provides four spectral bands taken every 18 days which give data on relief, hydrology, and vegetation, all of which bear on the delineation and interpretation of soil associations. Enlarged prints derived from the individual spectral bands and shown in gray tones were useful for identifying soil associations.

Myers, V. I.↗

NASA NTRS1973

Identification of soil associations in western South Dakota on ERTS-1 imagery

Soil association maps show the spatial relationships of land units having characteristic soil depths and textures, available water capacities, permeabilities, pH characteristics, plasticity indices, liquid limits, and the like, from which broad interpretations can be made such as how the soil is suited as a source for top soil, and as a source for sand and gravel, and how corrosive the soil is for steel and concrete, and what crop and grass yields can be expected. Film color composites of bands 4, 5 and 7 viewed over a light table with magnification show the soil associations of western South Dakota that are now recognized, and, in addition, several new soil association areas have been brought to light.

Westin, F. C.↗

NASA NTRS1972

Development of soil on the lunar surface.

Discussion of the dynamic processes involved in the evolution of the lunar soil. Size, shape, and modal analyses of soil returned by Apollo 11, 12, 14, and 15, and Luna 16 indicate that the two most important dynamic processes resulting from meteorite impact are vitrification and comminution of the detrital material. The effects of the two processes are mutually opposed. As the glass content of the soil increases over an extended period of time, the statistical parameters of the mature soil tend to stabilize. Comminution probably plays a dominant role early in the development of the soil by reducing the median grain size and producing a logarithmic-normal grain-size distribution. The evolution of the soil does not necessarily progress in a regular manner. Both introduction of freshly comminuted bedrock material by small impact events, as well as local topographic effects, influence the development of the soil and reduce its maturity.

Lindsay, J. F.↗

NASA NTRS1973

ERTS-1 MSS imagery - A tool for identifying soil associations.

Soil association maps show the spatial relationships of land units developed in unique climatic, geologic, and topographic environments, and having characteristic slopes, soil depths, textures, available water capacities, permeabilities, and the like. From these characteristics of the soil, broad interpretations can be made such as how the soil is suited for various agronomic and engineering uses. ERTS-1 imagery was found to be a useful tool in the identification of soil associations since it provides a synoptic view of an 8 million acre scene, which is large enough so that the effect can be seen on soils of climate, topography, and geology. A regional view also allows soil associations to be observed over most, if not all, of their extent. This aids in selecting typical sampling sites and provides a check on the homogeniety of the associations.

Westin, F. C.↗

NASA NTRS1974

Use of visible, near-infrared, and thermal infrared remote sensing to study soil moisture

Two methods are described which are used to estimate soil moisture remotely using the 0.4- to 14.0 micron wavelength region: (1) measurement of spectral reflectance, and (2) measurement of soil temperature. The reflectance method is based on observations which show that directional reflectance decreases as soil moisture increases for a given material. The soil temperature method is based on observations which show that differences between daytime and nighttime soil temperatures decrease as moisture content increases for a given material. In some circumstances, separate reflectance or temperature measurements yield ambiguous data, in which case these two methods may be combined to obtain a valid soil moisture determination. In this combined approach, reflectance is used to estimate low moisture levels; and thermal inertia (or thermal diffusivity) is used to estimate higher levels. The reflectance method appears promising for surface estimates of soil moisture, whereas the temperature method appears promising for estimates of near-subsurface (0 to 10 cm).

Blanchard, M. B.↗

NASA NTRS1973

Identification of soil associations in South Dakota on ERTS 1 imagery

Westin, F. C.↗

NASA NTRS1973

Apollo 16 soils - Grain size analyses and petrography

Soils from South Ray Crater, North Ray Crater, and the interray area of Station 10 have a similar provenance, containing breccia fragments of low to medium metamorphic grade and low light/dark lithic fragment ratios; these appear to be characteristic of the Cayley Formation. The primary difference between soils possibly derived from North Ray and South Ray craters is in the agglutinate content. A soil from Stone Mountain (Station 4) is characterized by breccia fragments of medium to high metamorphic grade and a high light/dark lithic fragment ratio; this soil may be derived from the Descartes Formation. Differences between the selenomorphic units, the Descartes and Cayley formations, may be lithologic as well as structural. The mean grain size varies from 84 to 280 microns, and all of the samples are poorly to very poorly sorted. There appears to be a relation between the sorting, grain size, and agglutinate content, with the finer-grained, better sorted soils containing more than 30% agglutinates. 'Shadowed' soils, collected close to large boulders, are similar in all respects to the 'reference' soils collected at least 5 m from the boulders.

Heiken, G. H.↗

NASA NTRS1974

Use of visible, near-infrared, and thermal infrared remote sensing to study soil moisture

Two methods are used to estimate soil moisture remotely using the 0.4- to 14.0-micron wavelength region: (1) measurement of spectral reflectance, and (2) measurement of soil temperature. The reflectance method is based on observations which show that directional reflectance decreases as soil moisture increases for a given material. The soil temperature method is based on observations which show that differences between daytime and nighttime soil temperatures decrease as moisture content increases for a given material. In some circumstances, separate reflectance or temperature measurements yield ambiguous data, in which case these two methods may be combined to obtain a valid soil moisture determination. In this combined approach, reflectance is used to estimate low moisture levels; and thermal inertia (or thermal diffusivity) is used to estimate higher levels. The reflectance method appears promising for surface estimates of soil moisture, whereas the temperature method appears promising for estimates of near-subsurface (0 to 10 cm).

Blanchard, M. B.↗