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3 edition of Remote measurement of soil moisture over vegetation using infrared temperature measurements found in the catalog.

Remote measurement of soil moisture over vegetation using infrared temperature measurements

Toby N. Carlson

Remote measurement of soil moisture over vegetation using infrared temperature measurements

final technical report to National Aeronautics and Space Administration, NASA grant no. NAG 5-919

by Toby N. Carlson

  • 266 Want to read
  • 36 Currently reading

Published by National Aeronautics and Space Administration, National Technical Information Service, distributor in [Washington, D.C.], [Springfield, Va .
Written in English

    Subjects:
  • Soil moisture -- Remote sensing.

  • Edition Notes

    Statementsubmitted by Toby N. Carlson.
    SeriesNASA contractor report -- NASA CR-187382.
    ContributionsUnited States. National Aeronautics and Space Administration.
    The Physical Object
    FormatMicroform
    Pagination1 v.
    ID Numbers
    Open LibraryOL15392966M

    Mapping Root Zone Soil Moisture Using Remotely Sensed Optical Imagery Christopher A. Scott1; Wim G. M. Bastiaanssen2; and Mobin-ud-Din Ahmad3 Abstract: Field-based soil moisture measurements are cumbersome. Remote sensing techniques based on active or passive microwave data have limitations. A network of in situ soil moisture measurement devices, established to provide validation data for the satellite collections and for long-term estimation of soil moisture conditions throughout the region, provided continuous measurements at 19 sites. Additional soil moisture and temperature validation data were collected daily from 49 field by:

    soil moisture and their relationship to climate and hydrology. In fact, variations in soil moisture may be so great that point measurements--the conventional measurement toolhave little meaning. The practical result is that soil moisture is inadequately represented in current hydrologic, climatic, agricultural and biogeochemical models. variety of techniques for measuring different soil characteristics (moisture, organic matter content, clay matter, sand matter, etc.) across a wide area continuously over time. We focused on the analysis of soil moisture. Summary of remote sensing techniques for near -surface soil moisture estimation (after Engman, ; Moran et al., ).

    The objectives of this research are as follows: (1) to develop a new soil moisture index based on the temporal variation of land surface temperatures in the mid-morning on a clear sky day; and (2) to validate the index using the in situ volumetric soil moisture content measurements and compare it to two other soil moisture indices. introduced to estimate soil moisture content over land surface. Remote sensing techniques give large scale spatially distributed and frequent coverage of a phenomenon [33], but soil moisture estimation from the remote sensing techniques only provides surface layer information and is unable to .


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Remote measurement of soil moisture over vegetation using infrared temperature measurements by Toby N. Carlson Download PDF EPUB FB2

Additionally, remote sensing advances have provided major soil moisture data availability at global scale [20], which facilitates obtaining precise and frequent soil moisture maps globally [21, A Method to Make Use of Thermal Infrared Temperature and NDVI measurements to Infer Surface Soil Water Content and Fractional Vegetation Cover March Remote Sensing Reviews 9(1) One approach to solving the underdetermination of soil moisture from remotely sensed observations is to employ spatial or contextual arrays as a means to increase the measurement domain Chen et al.,Price,Prihodko & Goward, Empirical evidence has repeatedly demonstrated that surface radiometric temperatures, measured in the 8- to μm spectral region, are correlated with Cited by: Get this from a library.

Remote measurement of soil moisture over vegetation using infrared temperature measurements: final technical report to National Aeronautics and Space Administration, NASA grant no. NAG [Toby N Carlson; United States. National Aeronautics and Space Administration.]. In Jiang and Islam () model, ϕ can be estimated through the Ts/vegetation space from remote sensing data.

In this research, ϕ is estimated by three different combinations of temperatures and NDVI is replaced by vegetation fraction.

The schematic presented in Fig. 2 was used to estimate ϕ values. Three different variables were examined to be used as the y axis; (1) Ts−Ta where Ts is Cited by: An extensive dataset of in situ measurements of soil moisture (0–6 cm soil layer), soil temperature (surface, 5 cm depth) soil bulk density, and vegetation water content was collected.

Aircraft-mounted instruments—the passive and active, L- and S-band sensor (PALS) and the NASA/JPL airborne synthetic aperture radar (AIRSAR)—were flown Cited by: mal infrared measurements to estimate near-surface soil moisture conditions (Goward, Cruickshanks, & Hope, ; Price, ). Background One approach to solving the underdetermination of soil moisture from remotely sensed observations is to employ.

The main goal of this study was to evaluate four major remote sensing soil moisture (SM) products over the state of Texas. These remote sensing products are: (i) the Advanced Microwave Scanning Radiometer—Earth Observing System (AMSR-E) (–September ); (ii) the Soil Moisture Ocean Salinity system (SMOS, –present); (iii) AMSR2 (–present); and (iv) the Soil Moisture Active.

remote sensing. This paper reviews the potential of applications of microwave remote sensing of soil moisture and vegetation for agricultural application. Microwave remote sensing can be used to estimate soil moisture on the basis of large contrast that exists between the Cited by:   2.

SM Estimation from Optical and Thermal Remote Sensing. Although the signal penetration capabilities of optical and thermal methods are worse than those of thermal infrared and microwave methods, the reflectance domain is the most operational because the images are easily available over a broad range of ground resolutions [77,78,79,80,81,82].

Cited by:   This map shows how soil moisture in Ethiopia, averaged from April 1 to Apdiffered from conditions one year earlier.

The map is based on data from NASA’s Soil Moisture Active Passive (SMAP) satellite, which can estimate soil moisture in the surface layer—the top 5 Author: Elizabeth Borneman.

The real-time surface temperature measurements were carried out on Aug (– AM, GMT+2) over crusted saline soil, bare saline soil, salt, and fallow area during daytime, cloud-free concurrently to the overpasses time of Landsat-5 at AM (GMT+2) over the Salt Lake, Turkey (Path/Row = /).

Model Infrared Cited by:   A large number of research papers have introduced a variety of methods to retrieve soil-moisture information from different types of remote sensing data, such as optical data or radar data.

We evaluate the most robust methods for retrieving soil-moisture information of Cited by: This paper aims to explore the potential of Sentinel-1 for estimating surface soil moisture using a multi-temporal approach.

To this end, a linear mixed effects (LME) model was developed over Poyang Lake ungauged zone, using time series Sentinel 1A and 1B images and soil moisture ground measurements from 15 automatic observation sites. During both studies, measurement of soil temperature at 10 cm depth was not possible when the soil was dry, as probes could not be driven to that depth (without breaking) to carry out measurements.

In both field studies, soil moisture was quantified using the gravimetric technique. Wang L, Qu JJ () NMDI: a normalized multi‐band drought index for monitoring soil and vegetation moisture with satellite remote sensing.

Geophys Res Lett 34(20):L CrossRef Google Scholar Wang J, Price KP, Rich PM () Spatial patterns of NDVI in response to precipitation and temperature in the central Great by: RADAR REMOTE SENSING FOR ESTIMATION OF SURFACE SOIL MOISTURE AT THE WATERSHED SCALE 5 and R is a surface roughness term (Engman and Chauhan, ).

Considering this, many algorithms using single-wavelength, single-polarization SAR for estimatingm sfollow a standard two-step approach, where the first step is to estimate and remove the signal due to backscatterFile Size: 92KB.

2 DROUGHT AND SOIL MOISTURE 5 3 MONITORING SOIL MOISTURE USING THE WATER BALANCE 7 4 USING REMOTELY SENSED INFORMATION 9 5 SIMPLE INVERTIBLE MODELS RELATING DAYTIME SURFACE TEMPERATURE AND EVAPOTRANSPIRATION 13 One Layer Models 13 Two-Layer REBM Model 16 Constrained Two-Layer Models 19 Available Cited by: Jackson RD () Soil moisture inferences from thermal-infrared measurements of vegetation temperatures.

IEEE Transactions on Geoscience and Remote Sensing – CrossRef Google Scholar Jackson T, Schmugge T, Wang J () Passive microwave remote sensing of soil moisture under vegetation by: 2.

Integrating decades of research conducted by leading scientists in the field, Remote Sensing of Energy Fluxes and Soil Moisture Content provides an overview of state-of-the-art methods and modeling techniques employed for deriving spatio-temporal estimates of energy fluxes and soil surface moisture from remote sensing.

It also underscores the range of such techniques available nowadays as well Format: Hardcover. Firstly, the planned instruments can only measure soil moisture in the top 0- 5cm, but we really need to know soil moisture in the root zone because it determines how much water is available to transpiring plants.

Secondly, even surface soil moisture can only be measured if the overlying vegetation is sparse enough to allow microwaves through.Microwave radiometry has been used for the remote sensing of soil moisture in a series of aircraft flights over an agricultural test area in the vicinity of Phoenix, Arizona, The radiometers covered the wavelength range –21 cm.

Ground truth in the form of gravimetric measurements of the soil moisture in the top 15 cm were obtained for fields at this site.Fig. Soil moisture changes over 5 days. Dashed blue line obtained by adding the bias between the averaged sensed and estimated soil moisture values over day 1, 3, 10, 17 to the Hydraprobe initial values.

32 Fig Plot with accumulated NDVI percentage for the 7 th and 23 rd of November,File Size: 3MB.