Develop techniques and procedures, using multispectral systems, to identify from remotely sensed data the physical and thermal characteristics of plants and soil
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There are no author-identified significant results in this report.
There are no author-identified significant results in this report.
There are no author-identified significant results in this report.
There are no author-identified significant results in this report.
Surface soil samples from a wide range of naturally occurring soils were obtained for the purpose of studying the characteristic variations in soil reflectance as these variations relate to other soil properties and soil classification. A total 485 soil samples from the U.S. and Brazil representing 30 suborders of the 10 orders of 'Soil Taxonomy' was examined. The spectral bidirectional reflectance factor was measured on uniformly moist soils over the 0.52 to 2.32 micron wavelength range with a spectroradiometer adapted for indoor use. Five distinct soil spectral reflectance curve forms were identified according to curve shape, the presence or absence of absorption bands, and the predominance of soil organic matter and iron oxide composition. These curve forms were further characterized according to generically homogeneous soil properties in a manner similar to the subdivisions at the suborder level of 'Soil Taxonomy'. Results indicate that spectroradiometric measurements of soil spectral bidirectional reflectance factor can be used to characterize soil reflectance in terms that are meaningful to soil classification, genesis, and survey.
The sampling scheme for soil characterization within the GLOBE program is uniquely different from the sampling methods of the other protocols. The strategy is based on an understanding of the 5 soil forming factors (parent material, climate, biota, topography, and time) at each study site, and how each of these interact to produce a soil profile with unique characteristics and unique input and control into the atmospheric, biological, and hydrological systems. Soil profile characteristics, as opposed to soil moisture and temperature, vegetative growth, and atmospheric and hydrologic conditions, change very slowly, depending on the parameter being measured, ranging from seasonally to many thousands of years. Thus, soil information, including profile description and lab analysis, is collected only one time for each profile at a site. These data serve two purposes: 1) to supplement existing spatial information about soil profile characteristics across the landscape at local, regional, and global scales, and 2) to provide specific information within a given area about the basic substrate to which elements within the other protocols are linked. Because of the intimate link between soil properties and these other environmental elements, the static soil properties at a given site are needed to accurately interpret and understand the continually changing dynamics of soil moisture and temperature, vegetation growth and phenology, atmospheric conditions, and chemistry and turbidity in surface waters. Both the spatial and specific soil information can be used for modeling purposes to assess and make predictions about global change.
Investigations have been conducted with truck-mounted radiometers to study the variation of microwave emissivity from a soil. It was found that the longer wavelength radiometers, (21 cm), are preferable for the remote sensing of soil moisture. Aircraft observations indicated a nonlinear dependence of microwave brightness temperature on soil moisture. The dielectric constants of soils are considered along with the radiative transfer in soils, and soil water characteristics. A description is presented of test flights conducted with a NASA aircraft, taking into account soil moisture measurements and instrumentation. The obtained results show that the surface emissivity of a soil is determined by the dielectric properties of the surface soil layer a few tenths of a wavelength thick while the thermal sampling depths are much greater. The capability of the 21-cm radiometer to sense soil-moisture variations through a moderate vegetation canopy, and the promising Skylab results encourage consideration of a radiometer operating at this wavelength for routine soil moisture observations.
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.
Regional quantification of energy and water balance fluxes depends inevitably on the estimation of surface and rootzone soil moisture. The simulation of soil moisture depends on the soil retention characteristics, which are difficult to estimate at a regional scale. Thus, the present study proposes a new method to estimate high-resolution Soil Hydraulic Parameters (SHPs) which in turn help to provide high-resolution (spatial and temporal) rootzone soil moisture (RZSM) products. The study is divided into three phases—(I) involves the estimation of finer surface soil moisture (1 km) from the coarse resolution satellite soil moisture. The algorithm utilizes MODIS 1 km Land Surface Temperature (LST) and 1 km Normalized difference vegetation Index (NDVI) for downscaling 25 km C-band derived soil moisture from AMSR-2 to 1 km surface soil moisture product. At one of the test sites, soil moisture is continuously monitored at 5, 20, and 50 cm depth, while at 44 test sites data were collected randomly for validation. The temporal and spatial correlation for the downscaled product was 70% and 83%, respectively. (II) In the second phase, downscaled soil moisture product is utilized to inversely estimate the SHPs for the van Genuchten model (1980) at 1 km resolution. The numerical experiments were conducted to understand the impact of homogeneous SHPs as compared to the three-layered parameterization of the soil profile. It was seen that the SHPs estimated using the downscaled soil moisture (I-d experiment) performed with similar efficiency as compared to SHPs estimated from the in-situ soil moisture data (I-b experiment) in simulating the soil moisture. The normalized root mean square error (nRMSE) for the two treatments was 0.37 and 0.34, respectively. It was also noted that nRMSE for the treatment with the utilization of default SHPs (I-a) and AMSR-2 soil moisture (I-c) were found to be 0.50 and 0.43, respectively. (III) Finally, the derived SHPs were used to simulate both surface soil moisture and RZSM. The final product, RZSM which is the daily 1 km product also showed a nearly 80% correlation at the test site. The estimated SHPs are seen to improve the mean NSE from 0.10 (I-a experiment) to 0.50 (I-d experiment) for the surface soil moisture simulation. The mean nRMSE for the same was found to improve from 0.50 to 0.31.
Some of the information concerning soils that a multispectral scanner system may provide is discussed, and a practical approach for realizing this potential is suggested. Some reflectance and emittance characteristics of soil surfaces are reviewed, and the feasibility of selectively enhancing contrasts associated with differing soil conditions is demonstrated. The simplicity of the proposed ratio technique, its relative economy, and the compatibility of the image output with conventional survey methods commends it for consideration as an operational method.
A simple method of estimating moisture content (W) of a bare soil from the observed brightness temperature (T sub B) at 1.4 GHz is discussed. The method is based on a radiative transfer model calculation, which has been successfully used in the past to account for many observational results, with some modifications to take into account the effect of surface roughness. Besides the measured T sub B's, the three additional inputs required by the method are the effective soil thermodynamic temperature, the precise relation between W and the smooth field brightness temperature T sub B and a parameter specifying the surface roughness characteristics. The soil effective temperature can be readily measured and the procedures of estimating surface roughness parameter and obtaining the relation between W and smooth field brightness temperature are discussed in detail. Dual polarized radiometric measurements at an off-nadir incident angle are sufficient to estimate both surface roughness parameter and W, provided that the relation between W and smooth field brightness temperature at the same angle is known. The method of W estimate is demonstrated with two sets of experimental data, one from a controlled field experiment by a mobile tower and the other, from aircraft overflight. The results from both data sets are encouraging when the estimated W's are compared with the acquired ground truth of W's in the top 2 cm layer. An offset between the estimated and the measured W's exists in the results of the analyses, but that can be accounted for by the presently poor knowledge of the relationship between W and smooth field brightness temperature for various types of soils. An approach to quantify this relationship for different soils and thus improve the method of W estimate is suggested.
Nitrous oxide flux and soil nutrient characteristics were measured in three undisturbed tropical ecosystem types, in cleared and burned areas, and in areas of forest converted to pasture near Manaus, Brazil. Nitrogen mineralization, nitrification, and soil nitrogen pools were high in upland forests on clay soils (terra firme) and low in the sand-type and floodplain (varzea) soils. Nitrous oxide flux followed the same pattern, with an average flux of 1.9 ng/sq cm per hr in terra firme, 0.3 in sand types, and 0.1 in varzea. Flux from recently cleared and burned areas did not differ from terra firme forest, but pastures had significantly elevated fluxes (10.3 ng/sq cm per hr). These data were combined with satellite data-based areal estimates of land cover classes to estimate total N2O-N flux from the intensive study area used by the Amazon Boundary Layer Experiment. Total N2O-N flux from the area was 22.9 kg/h; pastures covered 11 percent of the area but accounted for over 40 percent of the flux.
Thematic Mapper signal counts in the six reflective bands (i.e., excluding the thermal band) are simulated using field and laboratory spectrometer measurements of a variety of crops, crop conditions, and soil types. The Dave atmospheric model and prelaunch sensor characteristics comprise the other components of the simulation. The simulated data are found to occupy essentially three dimensions, two of which are equivalent to the MSS Tasseled Cap Greennes and Brightness features, and a third which is substantially influenced by the mid-infrared bands of the TM. This new dimension is primarily related to soil characteristics, including soil moisture. The nature and characteristics of each dimension are discussed, as are some of the expected information gains (over MSS data) resulting from the additional dimensionality of the data.
The Mars Scout Phoenix lander will land in the north polar region of Mars in May, 2008. One objective of the Phoenix lander is to search for evidence of past life in the form of molecular organics that may be preserved in the subsurface soil. The Thermal Evolved Gas Analyzer (TEGA) was developed to detect these organics by coupling a simultaneous differential thermal analyzer (SDTA) with a mass spectrometer. Martian soil will be heated to approx.1000 C and potential organic decomposition products such as CO2, CH4 etc. will be examined for with the MS. TEGA s SDTA will also assess the presence of endothermic and exothermic reactions that are characteristic of soil organics and minerals as the soil is heated. The MS in addition to detecting organic decompositon products, will also assess the levels of soil inorganic volatiles such as H2O, SO2, and CO2. Organic detection has a high priority for this mission; however, TEGA has the ability to provide valuable insight into the mineralogical composition of the soil. The overall goal of this work is to develop a TEGA database of minerals that will serve as a reference for the interpretation of Phoenix-TEGA. Previous databases for the ill-fated Mars Polar Lander (MPL)-TEGA instrument only went to 725 C. Furthermore, the MPL-TEGA could only detect CO2 and H2O while the Phoenix-TEGA MS can examine up to 144 atomic mass units. The higher temperature Phoenix-TEGA SDTA coupled with the more capable MS indicates that a higher temperature database is required for TEGA interpretation. The overall goal of this work is to develop a differential scanning calorimeter (DSC) database of minerals along with corresponding MS data of evolved gases that can used to interpret TEGA data during and after mission operations. While SDTA and DSC measurement techniques are slightly different (SDTA does not use a reference pan), the results are fundamentally similar and thus DSC is a useful technique in providing comparative data for the TEGA database. The objectives of this work is to conduct DSC and MS analysis up to 1000 C of select minerals that may be found in the martian soil.
Cartographic soils data were digitized, spatially registered, and merged with processed Landsat image data. The Landsat Multispectral Scanner Subsystem (MSS) image data were used to generate a thematic map representing different soil surface characteristics and an enhanced image. The thematic map was generated using supervised and unsupervised classification procedures. The enhanced image was generated by performing a linear contrast stretch on data altered by a principal components transformation. Although both procedures yielded images useful for soil unit delineation, image enhancement was determined to be more suitable because it was more expedient and inexpensive. Enhanced images cost $0.06 per hectare, spectral classifications cost $0.08 per hectare. The overlay of cartographic data on Landsat data facilitates comparisons between the various processing methods used for soil unit boundary determination, delineation, and verification. This technique also provides for accurate and expedient spatial referencing for field observations and cartographic correlation.
A soil water computer model that uses the IBM Continuous System Modeling Program III to solve the dynamic equations representing the soil, plant, and atmospheric physical or physiological processes considered is presented and discussed. Using values describing the soil-plant-atmosphere characteristics, the model predicts evaporation, transpiration, drainage, and soil water profile changes from an initial soil water profile and daily meteorological data. The model characteristics and simulations that were performed to determine the nature of the response to controlled variations in the input are described the results of the simulations are included and a change that makes the response of the model more closely represent the observed characteristics of evapotranspiration and profile changes for dry soil conditions is examined.
The physical and mechanical properties of the lunar soil, as determined by Luna 16 and Lunokhod 1 experiments, are discussed. Data are included for interactions between vehicle wheels and the lunar soil, compressibility, resistance to penetration, and friction characteristics of the soil. The shear strength of the returned lunar soil for various bulk densities is also examined. Several potential spacecraft materials were tested in contact with lunar soil to determine their friction and wear characteristics.
One of the objectives of the Mars Scout mission, Phoenix, is to characterize the present state of water in the martian environment, in a location where water may play a significant role in the present and past habitability of Mars. Given the generally dry and cold climate of Mars today any substantial amount of water is expected to occur in the form of ground ice (subsurface ice) within the regolith. The Mars Odyssey Gamma Ray Spectrometer has indicated abundant subsurface hydrogen and inferred ground ice at high latitudes. Therefore, the Phoenix mission will be targeted to land in the northern high latitudes (approximately 65 degrees N - 75 degrees N) where ground ice is expected to be abundantly available for analysis. The lander will be capable of excavating, sampling, and analyzing, dry and water-rich/icy soils. The location and depth of excavation necessary to achieve the goals of sampling and analysis of icy material become important parameters to assess. In the present work we ask two key questions: 1) At what depth within the regolith do we expect to find ice? 2) How might this depth vary over the region of potential landing sites? Numerous lines of evidence can be employed to provide an indication of the presence or absence of shallow ground ice at the potential landing sites. For example geomorphology, neutrons, gamma rays, and theory each contribute clues to an overall understanding of the distribution of ice. Orbital observations provide information on a variety of spatial scales, typically 10 s of meters (patterned ground) to 100 s of kilometers (gamma rays). While information on all of these scales are important, of particular interest is how the presence and depth of ground ice might vary on spatial scales comparable to the lander and its work area. While ground ice may be stable (and present) on a regional scale, local-scale slopes and changes in the physical characteristics of soils can result in significant variations in the distribution of ice.