Irrigation requirements are mostly determined by estimating the atmospheric evaporative demand, in combination with precipitation data to estimate the irrigation need per field. However, in case of a high groundwater table, the contribution of capillary rise is often not taken into account. Nevertheless, this flux can contribute significantly to the actual evaporation. Ignoring this flux in irrigation practices might lead to over-irrigation and reduced yields. The significance of the groundwater flux in a furrow irrigated sugarcane plantation in Mozambique with a shallow groundwater table is presented here. Groundwater levels in a sugarcane plantation in Xinavane in Mozambique were recorded in several fields for a duration of six months. The groundwater recordings, potential evaporation estimates from satellite remote sensing, and field data were combined in the Vegetation-AtMosPhere-Soil water model (VAMPS) that was set up to understand the effect of groundwater contribution on the actual evaporation of a sugarcane field. With the hydrological field representations set up, we analyzed whether the current furrow irrigation requirement in the plantation, of 1350 mm/year for furrow irrigation, is efficient. The results show that groundwater contribution to the transpiration flux reduces the need for irrigation in the study area. As such, we conclude that the current irrigation requirement is leading to over-irrigation. The incorporation of the groundwater contribution is needed to provide adequate estimations for irrigation. A reduction in irrigation for these fields will lead to a higher water productivity in the study area.
The detection of buried objects with remote sensing techniques mainly relies on thermal infrared, ground penetrating radar, and metal detectors. However, nowadays people also start to use low frequency passive microwave radiometry for the same purpose. The detection performance of passive microwave radiometry is influenced by the depth and size of the object, environmental factors, and soil properties. Soil moisture is a key variable here, due to its strong influence on the observed dielectric constant. Through digging activities will the hydrological conditions of the soil change significantly that can be detected by remotely sensing systems. A study was designed to examine the influence of the hydrological changes caused by the direct placement of an object in the ground. Simulations in a soil moisture model and field observations revealed the development of a wetter part above and a drier part underneath an object. The observations were converted to brightness temperatures with a coherent model in combination with a dielectric mixing model. Development of a drier area underneath an object generally increases the brightness temperature after a precipitation event. As a results are brightness temperature anomalies of low dielectric constant objects raised during the first 36 hours after a rain event. Ground observations of soil moisture and porosity revealed an increase in porosity and loss in soil moisture for the part that was excavated. Knowledge of past weather conditions could therefore improve buried object detection by passive microwave sensors.
The framework for the development of a 30 year global record of remotely sensed vegetation optical depth is presented.
The vegetation data set is derived from passive microwave observations and spans the period from November 1978
through the end of 2008. Different satellite sensor observations (i.e. Nimbus-7 SMMR, DMSP SSM/I, TRMM TMI, and
AQUA AMSR-E). are used in a radiative transfer model to derive vegetation optical depth. Vegetation optical depth can
directly be related to vegetation water content and is a function of biomass. The retrieval model is described and the
quality of the retrieved vegetation optical depth is discussed. The new dataset will be merged into one consistent global
product for the entire period of data record. To explore the potential to use this new product for long term vegetation
modeling, the product was compared to total biomass from the biogeochemical model CASA. The results indicate that
the vegetation optical depth can be an important contribution to the derivation of biophysical properties like biomass. It
can also increase the reliability of optical sensor derived vegetation indices, because the microwave vegetation optical
depth can be derived under cloudy conditions. This unique feature could create the possibility to improve the temporal
resolution of other biophysical data products. The entire vegetation density dataset will be made available for download
by the general science community and could give a significant contribution in climate research.
Historically the atmospheric and meteorological communities are separate worlds with their own data formats and tools
for data handling making sharing of data difficult and cumbersome. On the other hand, these information sources are
becoming increasingly of interest outside these communities because of the continuously improving spatial and temporal
resolution of e.g. model and satellite data and the interest in historical datasets. New user communities that use
geographically based datasets in a cross-domain manner are emerging. This development is supported by the progress
made in Geographical Information System (GIS) software. The current GIS software is not yet ready for the wealth of
atmospheric data, although the faint outlines of new generation software are already visible: support of HDF, NetCDF
and an increasing understanding of temporal issues are only a few of the hints.
KEYWORDS: Soil science, Data modeling, Satellites, Floods, Data centers, Data conversion, Remote sensing, Geographic information systems, Microwave radiation, Visualization
The remote sensing and GIS communities are still separate worlds with their own tools and data formats. It is extremely
difficult to easily share data among scientists representing these communities without performing some cumbersome
conversions. This paper shows in a case study how these two worlds can benefit from each other by implementing online
satellite derived soil moisture in a GIS based operational flood early warning system. We obtained near real time satellite
data from the currently active satellite microwave sensor AQUA AMSR-E from the National Snow and Ice Data Center
data pool and converted the data to soil moisture maps with the Land Parameter Retrieval Model. The soil moisture
maps, with a spatial resolution of 0.1 degree and temporal resolution of approximately 1 day, were converted in a
gridded format and directly added to an operational Flood Early Warning System. The developed opportunity to directly
visualize soil moisture in such a system appears to be a powerful tool, because it creates the ability to study both the
spatial and temporal evolution of soil moisture within the river basin. Furthermore, near real time qualitative information
on soil moisture conditions prior to rainfall events, such as generated by our system, can even lead to more accurate
estimations for flood hazard conditions. Finally, the current and future role and value of remote sensing products in flood
forecasting systems are discussed.
A historical data set of continuous satellite derived global land surface moisture and land surface temperature is being developed jointly by the NASA Goddard Space Flight Center and the Vrije Universiteit Amsterdam. The data will consist of surface soil moisture retrievals from observations of both historical and currently active satellite microwave sensors, including Nimbus-7 SMMR, DMSP SSM/I, TRMM TMI, and AQUA AMSR-E. The data sets will span the period from November 1978 through December 2005. The soil moisture retrievals are made with the Land Parameter Retrieval Model, which was developed jointly by researchers from the above institutions. The various sensors have some different technical specifications, including primary wavelength, radiometric resolution, and frequency of coverage. Consequently, the soil moisture sensing depth also varies between the different sensors. It is expected that the data will be made available for download by the general science community within about six months. Specifications and capabilities of different sensors and how they affect soil moisture retrievals are discussed.
A methodology for retrieving land surface properties from passive microwave observations is presented. Dual polarization microwave brightness temperature data, together with a simple radiative transfer model are used to derive surface soil moisture and vegetation optical depth simultaneously, in a non linear optimization procedure using a forward modeling approach. Soil temperature is derived off-line with a common heat flow model, driven by high frequency vertical polarization microwave data and remotely sensed observations of net radiation. The methodology does not require any field observations of soil moisture or canopy biophysical properties for calibration purposes and is independent of wavelength. Remote sensing provides an excellent opportunity to monitor and gather environmental data in regions that have little or no instrumentation. Moreover, microwave technology provides a more all-weather capability than is typically afforded with visible and near infrared wavelengths. The model was developed for regional- to global-scale monitoring and related environmental applications such as surface energy balance modelling, numerical weather prediction, flood and drought forecasting, and climate change studies. However, at higher spatial resolutions, which would be possible with aircraft, especially unmanned vehicles, tactical applications may be realized as well. Retrieval results compare well with field observations of soil moisture and satellite-derived vegetation index data from optical sensors.
A physically-based soil temperature model using remote sensing inputs is being developed. The model uses the standard soil heat transfer equation together with remote sensing-based estimates of the surface temperature and incoming radiation to calculate soil temperature at various depths in the profile. Vertical polarization microwave brightness temperatures at a frequency of 37 GHz are used to estimate the near-surface soil temperature. Incoming radiation is derived from surface solar irradiance values acquired from the International Satellite Cloud Climatology Project (ISCCP) data archives. Experimental field observations were used first to develop the temperature model.
Recent field experiments showed that dew has a significant effect on L-band (1.4 GHz) microwave observations. At an experimental grass site in the Netherlands (ELBARA2003), and at an experimental fallow site in France (SMOSREX) several dew events were able to increase the horizontal polarized brightness temperature up to 10 K. The Microwave Polarization Difference Index (MPDI) was shown to be a powerful index to describe the effect of dew.
Current satellite missions (i.e. TRMM and SSM/I) but also future missions (i.e. HYDROS and SMOS) observe the Earth surface when dew is likely, between 6-8 AM. These observations are used in soil moisture retrieval methodologies, and ignoring of the dew effect may lead to a significant underestimation of soil moisture.
Therefore we started, as a follow up of the previous field studies, an investigation of the effect of dew on microwave observations at satellite scale.
Two months of TRMM data were selected to study the diurnal variations of the microwave signal and their relation to morning dew. Between February and March 1998 distinct diurnal MPDI patterns were detected from space. The MPDI values at X band (~10 GHz) were significantly higher in the afternoon, compared to the morning for several agricultural regions in the northern part of the state of Oklahoma in the United States. These diurnal MPDI variations from space were similar as the patterns as detected by the dew affected field observations at L-band, leading us to conclude that TRMM data at X-band is as well affected by dew.
KEYWORDS: Geographic information systems, Personal digital assistants, Computing systems, Global Positioning System, Databases, Data processing, Remote sensing, Hydrology, Geology, Analytical research
Environmental studies have always been associated with fieldwork, which is still carried out in rather conventional ways. At the same time it is well known that these fieldworks consume considerable parts of the research budgets while collected data has often not the desired quality and data collection methods tend to interrupt the whole research process. Now the usage of mobile GIS systems is drastically altering the outdoor work and solving or reducing many of the associated problems. This paper describes how the usage of mobile GIS technology during a hydrological fieldwork campaign in the Algarve (Portugal) addresses several of the traditional fieldwork problems and how the involvement of students can have a double positive side-effect. The evaluation showed that the new method enables researchers to collect and record reliable spatial data in a more uniform and efficient way, which saves time that could be used to analyze and process data during the fieldwork. Even more, using the mobile GIS system, the researcher is able to retrieve important hydrological information when he or she is in the field. Further work is planned on the improvement of field communication and data-exchange possibility for guidance and feedback from specialists at the office.
A technique to quantify the amount of dew on grassland with an L-band (1.4 GHz) passive microwave radiometer has been presented. The horizontal polarized brightness temperature is sensitive to dew and morning dew can increase the temperature up to 5 K. This is in contrary to recent published results, where they expect that dew does not have any effect on L band (1.4 GHz) observations. By using both the horizontal and vertical polarized brightness temperature in combination with measured soil moisture conditions we were able to estimate the amount of dew. The results compared well with another remote sensing technique to measure dew using a spectral reflectance sensor. In addition, a simple comparison study was done to study the sensitivity of the microwave emission on dew events and changes in internal water. This study showed that the microwave emission at L band is more sensitive to changes in dew than to changes in internal vegetation water content when the soil is wet. When the soil is dry, the microwave emission is more sensitive to internal vegetation water.
A global data base of daily surface soil moisture has been compiled by applying a recently developed land parameter retrieval algorithm to a nine year historical data set of brightness temperatures from the Scanning Multichannel Microwave Radiometer (SMMR). The instrument, flew on-board the Nimbus-7 satellite, and made daily daytime and nighttime global observations of brightness temperature at five frequencies and two polarizations from 1978 to 1987. Spatial distributions of global soil moisture are examined, and they compare well with corresponding observations of global precipitation and global vegetation indices.
A methodology was recently developed to estimate the land surface parameters soil moisture, soil temperature and vegetation optical depth on a global scale by using passive microwave remote sensing. This methodology is general, in a way that it does not require any field observations of soil moisture or canopy biophysical properties for calibration purposes, and can be used with microwave observations at different wavelengths. However, several algorithms in this approach are somewhat empirical, and the vegetation component in this methodology is still difficult to understand and interpret. A follow up field experiment was planned for April 2003 to address some of these issues. The experiment was conducted at a controlled meteorological field site in Wageningen (The Netherlands). Three different plots, a bare soil, a soil with short grass (reference site), and a site with growing grass vegetation were selected. Several hydro-meteorological parameters were monitored extensively at each site, including the radiobrightness temperatures from the ELBARA 1.4 GHz passive microwave radiometer. This paper gives a description of this field experiment and will demonstrate several effects of vegetation on the radiobrightness temperature.
The Scanning Multichannel Microwave Radiometer (SMMR) provides ground coverage on the average of two times per week during the day and two times per week during the night. However, past problems associated with surface soil temperature estimation have resulted in both systematic and random differences between various applications utilizing both daytime and nighttime observations. This was especially true for SMMR-based soil moisture retrievals. Significant offsets were frequently observed, which prevented the daytime and nighttime soil moisture data from being combined into a single data set. This leaves one with a time series of data with very poor temporal resolution, and limits its usefulness for other applications. Improvements in surface temperature estimation have reduced the differences between day and night estimates significantly. The improved consistency between the two data sets now permits combining them into one, making the data more useful, especially for other land surface processes applications.
Surface soil temperature is an important input parameter to a variety of environment models, such as global circulation models, radiative transfer, and land surface process models. Soil temperature is especially important for normalizing microwave radiobrightness temperatures in inverse radiative transfer modelling for soil moisture and vegetation optical depth retrieval. To ensure maximum accuracy of soil moisture retrieval models on a regional or global scale, spatially averaged temperature data are necessary. Since the variability of surface temperature in time and space is extremely high due to incoming solar radiation, air temperature, vegetation, soil physical properties, and topography, an aggregation of a few point measurements rarely provides a good spatial average. Remote sensing methods typically provide spatially averaged values needed. Thermal infrared sensors (TIR) measure the skin temperature, but usually require some atmospheric correction, and during periods of cloud cover they become unusable. Microwave sensors also have the potential for providing reliable estimates of spatially averaged soil temperature. Microwave instruments are also much less affected by atmospheric conditions and thus require little or no correction. A technique to estimate the effective temperature with vertical polarized high-frequency microwave brightness temperatures is presented. Calibration procedures with field observations are discussed, and a technique to estimate the soil temperature at the soil moisture sampling depth for 6.6 GHz is shown.
A series of validation studies for a recently developed soil moisture retrieval algorithm is presented. The approach is largely theoretical, and uses a non-linear iterative optimisation procedure to solve for soil moisture and vegetation optical depth with a radiative transfer model from satellite microwave observations. The new theoretical approach is not dependent on field observations of soil moisture or canopy biophysical measurements and can be used at any wavelength in the microwave region. Details of the model and its development are discussed. Satellite retrievals were derived from 6.6 GHz Nimbus/SMMR brightness temperatures, and were validated with soil moisture data sets from the U.S., Mongolia, and Turkmenistan. Time series of the satellite-derived surface moisture compared well with the available ground observations and precipitation data. The vegetation optical depth showed similar seasonal patterns as the NDVI.
A new approach for retrieving surface soil moisture from satellite microwave brightness temperature is described. The approach uses radiative transfer theory together with a non- linear optimization routine to partition the observed microwave signal into its soil and vegetation components. Vegetation optical depth is derived directly from the microwave polarization difference index, while the soil component is solved in terms of the soil dielectric constant. A global data base of soil physical properties is then used to derive soil moisture from the soil dielectric constant. The approach is tested with historical SMMR data to produce time series of surface soil moisture over several global test sites. Comparisons with ground observations of soil moisture are made. Preliminary results over several global test sites are provided.
A methodology for deriving spatially averaged emitting layer temperature from high frequency microwave observations is presented. Microwave brightness temperature is a function of the emissivity and the physical temperature of the emitting soil layer, thereby possessing a strong physical basis for estimating soil temperature. Field observations have shown that maximum and minimum daily air temperatures are strongly related to midday and midnight surface soil temperature. Field measurements of surface temperature are also compared to METEOSAT thermal observations. Long term daily maximum and minimum air temperatures are used to derive data sets of daytime and nighttime surface temperatures. Results indicate that 37 GHz vertical polarization brightness temperature provides a reasonable estimate of the emitting layer soil temperature. This technique is especially useful for normalizing microwave brightness temperatures at longer wavelengths for soil moisture retrieval algorithms. It could provide a useful tool for climate modelling, land surface processes investigations, and other energy balance applications by providing consistent and independent long term estimates of daily global surface temperature.
A database of long-term soil moisture was compared to satellite microwave observations over a test site in the Midwestern United States. Ground measurements of average volumetric surface soil moisture in the top ten cm were made several times per month at 19 locations throughout the state of Illinois. Nighttime microwave brightness temperatures were observed at a frequency of 6.6 GHz, by the Scanning Multichannel Microwave Radiometer (SMMR), onboard the Nimbus 7 satellite. At 6.6 GHz, the instrument provides a spatial resolution of approximately 150 km, and a temporal frequency over the test area of about 3 nighttime orbits per week. Vegetation radiative transfer characteristics, such as the canopy transmissivity, were estimated from vegetation indices such as the Normalized Difference Vegetation Index (NDVI) and the 37 GHz Microwave Polarization Difference Index (MPDI). Because the time of satellite coverage does not always coincide with the ground measurements of soil moisture, the existing ground data were used to calibrate a water balance for the top 10 cm surface layer in order to interpolate daily surface moisture values. Such a climate-based approach is often more appropriate for estimating large-area average soil moisture because meteorological data are generally more spatially representative than isolated point measurements of soil moisture. Passive microwave remote sensing presents the greatest potential for providing regular spatially representative estimates of surface soil moisture at global scales. Real time estimates should improve weather and climate modeling efforts, while the development of historical data sets will provide necessary information for simulation and validation of long-term climate and global change studies.
Based on a series of studies conducted in Botswana and preliminary results from an ongoing study in Spain, developments in microwave remote sensing by satellite which can be used to monitor near real-time surface moisture and also study long term soil moisture climatology are described. A progression of methodologies beginning with single polarization studies and leading to both dual polarization and multiple frequency techniques are described. Continuing analysis of a nine year data set of satellite-derived surface moisture in Spain is ongoing. Preliminary results from this study appear to provide some evidence of long term decertification in certain parts of this region. The methodologies developed during these investigations can be applied to other regions, and have the potential for providing modelers with extended data sets of independently derived surface moisture for simulation and validation studies, and climate change studies at the global scale.
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