The MORERA program has recently been selected as one of the “Missions Science and Innovation” from the Spanish CDTI, an innovative program targeting solutions for deep social problems through innovation. The main Spanish industry is Agriculture (11% GDP), but this sector is threatened by climate change, as 34% of the Spanish irrigated surface is considered out of balance. Difficulty of providing useful and fully processed information to the end-users for supporting their decisions severely affect the optimization of the resources. Well informed decisions optimize resources and costs, maximizing productivity. To solve this problem, MORERA involves in a unique project the complete value chain, from sensor to user, thanks to a solid consortium, and it is based on three pillars:
- Final personalized irrigation requirements that will be directly provided to the user using a mobile device.
- Artificial intelligence techniques will be used to combine all relevant data to build a final watering recommendation.
- A compact and highly specific freeform optical instrument will be used to estimate evapotranspiration data at farm level with required TIR bandwidth and spatial resolution. Since no present instrument fulfills these requirements, it will be developed in the framework of the project.
The MORERA concept can be extrapolated to many remote sensing applications, and to take advantage of this, it has been conceived as a modular system, where each module may be adapted with minor impact. This first system is focused on providing precise irrigation and fertilization recommendations, as well as self-learning yield estimations.
A large number of remote sensing data sets have been collected in recent years by Earth observation instruments such as the moderate resolution imaging spectroradiometer (MODIS) aboard the Terra/Aqua satellite and the spinning enhanced visible and infrared imager (SEVIRI) aboard the geostationary platform Meteosat Second Generation. The advanced remote sensing products resulting from the analysis of these data are useful in a wide variety of applications but require significant resources in terms of storage, retrieval, and analysis. Despite the wide availability of these MODIS/SEVIRI products, the data coming from these instruments are spread among different locations and retrieved from different sources, and there is no common data repository from which the data or the associated products can be retrieved. We take a first step toward the development of a geo-portal for storing and efficiently retrieving MODIS/SEVIRI remote sensing products. The products are obtained using an automatic system that processes the data as soon as they are provided by the collecting antennas, and then the final products are uploaded with a one day delay in the geo-portal. Our focus in this work is on describing the design and efficient implementation of the geo-portal, which allows for a user-friendly and effective access to a full repository of MODIS/SEVIRI advanced products (comprising tens of terabytes of data) using geolocation retrieval capabilities. The geo-portal has been implemented as a web application composed of different layers. Its modular design provides quality of service and scalability (capacity for growth without any quality losing), allowing for the addition of components without the need to modify the entire system. On the client layer, an intuitive web browser interface provides users with remote access to the system. On the server layer, the system provides advanced data management and storage capabilities. On the storage layer, the system provides a secure massive storage service. An experimental evaluation of the geo-portal in terms of efficiency and product retrieval accuracy is also presented and discussed.
The paper reports a critical analysis of the thermal inertia approach to map surface soil water content on bare and sparsely vegetated soils by means of remotely sensed data. The study area is an experimental area located in Barrax
(Spain). Field data were acquired within the Barrax 2011 research project. AHS airborne images including VIS/NIR and
TIR bands were acquired both day and night time by the INTA (Instituto Nacional de Técnica Aeroespacial) between the
11th and 13rd of June 2011. Images cover a corn pivot surrounded by bare soil, where a set of in situ data have been collected previously and simultaneously to overpasses. To validate remotely sensed estimations, a preliminary proximity sensing set up has been arranged, measuring spectra and surface temperatures on transects by means of ASD hand-held spectroradiometer and an Everest Interscience radiometric thermometer respectively. These data were collected on two transects: the first one on bare soil and the second from bare to sparsely vegetated soil; soil water content in both transects ranged approximately between field and saturation values. Furthermore thermal inertia was measured using a KD2Pro probe, and surface water content of soil was measured using FDR and TDR probes. This ground dataset was used: 1) to verify if the thermal inertia method can be applied to map water content also on soil covered by sparse vegetation, and 2) to quantify a correction factor of the downwelling shortwave radiation taking into account sky cloudiness effects on thermal inertia assessment. The experiment tests both Xue and Cracknell approximation to retrieve the thermal inertia from a dumped value of the phase difference and the three-temperature approach of Sobrino to estimate the phase difference spatial distribution. Both methods were then applied on the remotely sensed airborne images collected during the following days, in order to obtain the spatial distribution of the surface soil moisture on bare soils and sparse vegetation coverage. Results verify that the thermal inertia method can be applied on sparsely vegetated soil characterized by fractional cover up to ~0.25 (maximum value within this experiment); a lumped value of the phase difference allows a good estimate of the thermal inertia, whereas the comparison with the three-temperature approach did not give conclusive responses because ground radiometric temperatures were not acquired in optimal conditions. Results also show that clear sky only at the time of the remote sensing acquisitions is not a sufficient condition to apply the thermal inertia method. A corrective coefficient taking into account the actual sky cloudiness throughout the day allows accurate estimates of the spatial distribution of the thermal inertia (r2 ~ 0.9) and soil water content (r2 ~ 0.7).
We have applied a Land Surface Temperature algorithm to the whole Pathfinder AVHRR Land (PAL) database, aiming at studying the evolution of the vegetation at a global scale. The Land Surface Temperature parameter, along with NDVI, will allow retrieving vegetation changes between July 1981 and September 2001. We have also built a classification which takes into account both vegetation variations and thermal patterns, from NDVI and Air Temperature at 2 meters height data. This classification allows differentiating areas which present close vegetation changes throughout the year, but totally different climates, as for example in mountainous and semiarid regions. The main quality of this classification is that it does not need any a priori information on the encountered vegetation, and thus can evolve from year to year. Through the 20 years of data, the evolution of Land Surface Temperature shows to be strongly affected by orbital drift and satellite changes. This will require an adequate correction to allow deeper study. On the other hand, NDVI does not show this trend, but aerosol absorption from Mount Pinatubo's eruption in June 1991 seems to corrupt temporarily the data in the northern hemisphere.
WATERMED project contributes to the international efforts in analyzing efficiency in water use, in particular for the Mediterranean Basin countries. The general aim of this project is to develop a comprehensive method for the study of the water use and the resistance to the drought of the natural and irrigated vegetation in the Mediterranean Basin, by means of a combined historical remote sensing database, vegetation models and field measurements. The project has provided regional maps of critical parameters at regional scale, such as land surface temperature, emissivity, and NDVI. A multi-temporal analysis using the PATHFINDER AVHRR land data has been carried out into the frame of this project to map and monitor changes in the biophysical characteristics of land cover over the last 20 years. On the other hand, REANALYSIS data, which is a result of a joint project NCEP/NCAR, have been incorporated to provide mean monthly climate data over the study area.
In this paper, it is shown the importance of thermal measurements to characterize different surfaces carried out in boreal environment in the SIFLEX (Solar Induced and Fluorescence Experiment) campaign. The data was acquired in Sodankyla (Finland), over the boreal forest, from 23rd April to 10th June 2002. Bio-geophysical parameters such as land surface temperature and emissivity were retrieved in relationship with other parameters from fluorescence measurements made with a Passive Multiwavelength Fluorescence Detector (PMFD).
The thermal measurements of different targets (soil, vegetation, sky) under different observation angles have been carried out using a four-band field radiometer (CIMEL CE312) and two single band radiometers (EVEREST 3000.4ZLC and RAYTEK ST6). Angular measurements and transects have been also carried out concurrently to the satellites flights over the region.
12 MODIS (Moderate Resolution Imaging Spectroradiometer) was launched on board the NASA's Terra Earth Observing System (EOS AM-1) Satellite on December 18, 1999. We propose in this work operative split-window algorithms for retrieving sea surface temperature (SST) and land surface temperature (LST) using MODIS data. In order to attain our goal, the MODTRAN 3.5 radiative transfer code was used to predict radiances for MODIS channels 31 and 32. To analyze atmospheric effects, a set of radiosoundings was used to cover the variability of surface temperature and water vapor concentration on a worldwide scale. These simulated data were split into two sets which have very similar distributions in space and time. The first one was used to fit the coefficients of the algorithms for retrieving both SST and LST, while the second one was used to test the fitted coefficients. Later, a sensibility study, including the effects of noise, emissivity and water vapor content uncertainties, has been done using the error theory.
A study has been carried out using MODTRAN 3.5 simulations of the Along-Track Scanning Radiometer-2 (ATSR-2) data at 3.7, 11 and 12 µm wavelengths to give a great range of algorithms for estimating sea surface temperature (SST) and land surface temperature (LST). Algorithms based on split-window, dual-angle and mixed structure have been considered. The coefficients of the algorithms are derived by regression analysis using the MATLAB code. The results show that, in general, dual-angle algorithms give better results than split-window ones, retrieving LST with a standard deviation as low as 0.4 K and 0.6 K respectively if the satellite data are error free. The introduction of 3.7 µm channel involves less error in the estimation of surface temperature. Water vapor dependence supposes an improvement of the accuracy of the results.
In the present paper it is presented a methodology to calculate the surface temperature (ST) from the combination of the radiometric temperature in two different DAIS (Digital Airborne Imaging Spectrometer) thermal bands using split-window (sw) method. To get this objective the MODTRAN 3.5 radiative transfer code was used to predict radiance for DAIS channels 74 (8.75 µm), 75 (9.65 µm), 76 (10.48 µm), 77 (1 1.27 µm), 78 (12.00 µm) and 79 (12.67 µm) at different aircraft altitudes with the appropriate channel filter functions. In order to analyse atmospheric effects a set of radiosoundings that cover the variability of surface temperature and water vapour concentration on a world-wide scale was used. Once the algorithm has been obtained, an application to DAIS images obtained in Colmar (France) and Barrax (Spain) during the DAISEX'98 and '99 ( Data Airborne Imaging Spectrometer Experiment ) campaigns framework has been made. Finally a comparison between the surface temperature obtained from DAIS data, previously corrected from the atmospheric and emissivity effect, and the simultaneous in-situ measurements, is included. The results show that the proposed theoretical sw are able to produce land surface temperature with a standard deviation lower than 1 K. This is in good agreement with the validation results obtained from DAISEX campaign.
KEYWORDS: Solar radiation, Satellites, Atmospheric modeling, Meteorology, Atmospheric corrections, Data modeling, Temperature metrology, Shortwaves, Sensors, Solar radiation models
In this work, we present a methodology to obtain the daily net radiation flux from NOAA-AVHRR data. To get this objective we need firstly to obtain shortwave net radiation flux from the solar global radiation flux and the albedo map. Secondly, we need to obtain the upward longwave radiation flux from surface temperature and emissivity and the downward longwave radiation flux from air temperature. Like an example of application of this methodology a daily net radiation flux image of the Iberian Peninsula is presented, in which we show that daily net radiation flux can be obtained with a satisfactory precision lower than 1.0 mmday-1.
Estimation of Sea Surface Temperature (SST) from split- window algorithms for NOAA-AVHRR data can be determined with rms values of 0.7 K on a global basis. However, this figure is not compatible with the stringent accuracy of 0.3 K required by climate studies. Among the different sources of errors, the presence of tropospheric aerosols in the satellite field of view prevents the retrieval of accurate satellite SSTs. Still, the effect of aerosols on temperature measurements derived from remote sensing techniques has been traditionally overlooked. Very few studies have addressed the problem of giving split-window algorithms which incorporate aerosol correction, although retrieving algorithms of the aerosol loading from the images do exist. The aim of this study is the evaluation of the effect of the aerosols on the SST MODTRAN code. Such code was used to compute the upwelling radiances and, subsequently k, the brightness temperatures under cloud-free conditions. The filter response functions for the NOAA14 instrument are used to produce theoretical brightness temperatures for the zenith angles: 0 degrees, 30 degrees and 55 degrees. The results show that for most of all the atmospheres that we have considered, deviations as far as 0.8 K are reached compared with the case in which the aerosols are not considered. It is important to point up that deviations higher than 0.4K are able to mask the improvement introduced by a diminution of the Noise Equivalent Temperature in the new sensors as a consequence of error propagation.
The thermal inertia, P, is defined as a measure of the resistance offered by materials to change their temperature. P is the most important single thermal property which governs surface temperature variation. Therefore thermal inertia is of great interest to geological and hydrological studies and climate modeling. An attractive and unique way to map and monitoring this parameter over large scale is to use space observation from satellite in the visible and thermal infrared bands. In this paper we present a new algorithm, based on Xue and Cracknell's model, which allows to obtain the thermal inertia combining afternoon and morning NOAA satellites. The algorithm was tested with a set of measurements made on a region of Niger in the frame of HAPEX-Sahel experiment. The behavior of the model was analyzed by comparing the predicted surface temperatures with the measured ones every ten minutes along the daytime, and by comparing the predicted and measured maximum and minimum surface temperature values as well as their times in the daytime. Our results indicate that for the 90 per cent of the cases the absolute difference between predicted and measured surface temperature is lower than 2 K, with a standard deviation of 1.5 K that improves to 1 K when predicting the maximum and minimum surface temperatures. In this situation the FTM predicts also their respective times with a standard deviation lower than 30 minutes, this makes possible building images of minimum surface temperature and their date from NOAA data. This fact is of great interest in the case of frosting with clear sky conditions. Following the proposed algorithm a map of thermal inertia of the Iberian peninsula is presented. The results are consistent with the known properties of this area.
Multi-angle algorithms for estimating sea and land surface temperature with ATSR data require a precise knowledge of the angular variation of surface emissivity in the thermal infrared. Nowadays, very few measurements do exist of this variation. In this work an experimental investigation of the angular variation of the infrared emissivity in the thermal infrared (8 - 14 micrometer) band of some representative samples has been made at angles of 0 degrees - 65 degrees (at 5 degree increments) to the surface normal. The results show a decrease of the emissivity with increasing viewing angles, being water the substance with highest angular dependence (about 7% from 0 degree to 65 degree views). Clay, sand, slimy and gravel show variations about 1 - 3% in the same range of views while an homogeneous grass cover does not show angular dependence. Finally, we include an evaluation of the impact that these data can produce in the algorithms for determining land and sea surface temperature from double angle views.
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