Juan Manuel Sánchez

Validation of Landsat-7/ETM+ Thermal-Band Calibration and Atmospheric Correction With Ground-Based Measurements

Ground-based measurements of land-surface temperature (LST) performed in a homogeneous site of rice crops close to Valencia, Spain, were used for the validation of the calibration and the atmospheric correction of the Landsat-7 Enhanced Thematic Mapper Plus (ETM+) thermal band. Atmospheric radiosondes were launched at the test site around the satellite overpasses. Field-emissivity measurements of the near-full-vegetated rice crops were also performed. Seven concurrences of Landsat-7 and ground data were obtained in July and August 2004-2007. The ground measurements were used with the MODTRAN-4 radiative transfer model to simulate at-sensor radiances and brightness temperatures, which were compared with the calibrated ETM+ observations over the test site. For the cases analyzed here, the differences between the simulated and ETM+ brightness temperatures show an average bias of 0.6 K and a rootmean-square difference (rmsd) of ±0.8 K. The ground-based measurements were also used for the validation of LSTs derived from ETM+ at-sensor radiances with atmospheric correction calculated from the following: 1) the local-radiosonde profiles and 2) the operational atmospheric-correction tool available at http://atmcorr.gsfc.nasa.gov. For the first case, the differences between the ground and satellite LSTs ranged from -0.6 to 1.4 K, with a mean bias of 0.7 K and an rmsd = ±1.0 K. For the second case, the differences ranged between -1.8 and 1.3 K, with a zero average bias and an rmsd = ±1.1 K. Although the validation cases are few and limited to one land cover at morning and summer, results show the good LST accuracy that can be achieved with ETM+ thermal data.

Soil Moisture Effect on Thermal Infrared (8–13-μm) Emissivity

Thermal infrared (TIR) emissivities of soils with different textures were measured for several soil moisture (SM) contents under controlled conditions using the Box method and a high-precision multichannel TIR radiometer. The results showed a common increase of emissivity with SM at water contents lower than the field capacity. However, this dependence is negligible for higher water contents. The highest emissivity variations were observed in sandy soils, particularly in the 8-9-μm range due to water adhering to soil grains and decreasing the reflectance in the 8-9-μm quartz doublet region. Thus, in order to model the emissivity dependence on soil water content, different approaches were studied according to the a priori soil information. Soil-specific relationships were provided for each soil texture and different spectral bands between 8 and 13 μm, with determination coefficients up to 0.99, and standard estimation errors in emissivity lower than ± 0.014. When considering a general relationship for all soil types, standard estimation errors up to ±0.03 were obtained. However, if other soil properties (i.e., organic matter, quartz, and carbonate contents) were considered, along with soil water content, the general relationship predicted TIR emissivities with a standard estimation error of less than ±0.008. Furthermore, the study showed the possibility of retrieving SM from TIR emissivities with a standard estimation error of about ±0.08 m3 . m-3.

Integrated satellite data fusion and mining for monitoring lake water quality status of the Albufera de Valencia in Spain

Lake eutrophication is a critical issue in the interplay of water supply, environmental management, and ecosystem conservation. Integrated sensing, monitoring, and modeling for a holistic lake water quality assessment with respect to multiple constituents is in acute need. The aim of this paper is to develop an integrated algorithm for data fusion and mining of satellite remote sensing images to generate daily estimates of some water quality parameters of interest, such as chlorophyll a concentrations and water transparency, to be applied for the assessment of the hypertrophic Albufera de Valencia. The Albufera de Valencia is the largest freshwater lake in Spain, which can often present values of chlorophyll a concentration over 200 mg m(-3) and values of transparency (Secchi Disk, SD) as low as 20 cm. Remote sensing data from Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat Thematic Mapper (TM) and Enhance Thematic Mapper (ETM+) images were fused to carry out an integrative near-real time water quality assessment on a daily basis. Landsat images are useful to study the spatial variability of the water quality parameters, due to its spatial resolution of 30 m, in comparison to the low spatial resolution (250/500 m) of MODIS. While Landsat offers a high spatial resolution, the low temporal resolution of 16 days is a significant drawback to achieve a near real-time monitoring system. This gap may be bridged by using MODIS images that have a high temporal resolution of 1 day, in spite of its low spatial resolution. Synthetic Landsat images were fused for dates with no Landsat overpass over the study area. Finally, with a suite of ground truth data, a few genetic programming (GP) models were derived to estimate the water quality using the fused surface reflectance data as inputs. The GP model for chlorophyll a estimation yielded a R(2) of 0.94, with a Root Mean Square Error (RMSE) = 8 mg m(-3), and the GP model for water transparency estimation using Secchi disk showed a R(2) of 0.89, with an RMSE = 4 cm. With this effort, the spatiotemporal variations of water transparency and chlorophyll a concentrations may be assessed simultaneously on a daily basis throughout the lake for environmental management.

Thermal Infrared Emissivity Dependence on Soil Moisture in Field Conditions

An accurate estimate of land surface temperature, which is a key parameter in surface energy balance models, requires knowledge of surface emissivity. Emissivity dependence on soil water content has been already reported and modeled under controlled conditions at the laboratory. This paper completes and extends that previous work by providing emissivity measurements under field conditions without elimination of impurities, local heterogeneities, or soil cracks appearing in the drying process. The multispectral radiometer CE312-2, with five narrow bands and a broad band in the 8-13-μm range, was used, and surface emissivity values were determined through a temperature-emissivity separation algorithm. A bare soil plot of 10 ×17 m2 was selected for this study in the framework of a camelina 2010 experiment. This experiment was carried out during March and April 2010 at The University of Arizona Maricopa Agricultural Center in central Arizona, USA. The soil plot was flood irrigated every two to three days and left to dry. Field emissivity measurements were collected under cloud-free skies, around noon, for different values of soil water content. Soil samples were collected to estimate the soil moisture (SM) using the gravimetric method. An overall increase of emissivity with SM was obtained in all channels. However, when wetted soils subsequently dried, the final minimum emissivity was greater than the initial minimum emissivity. This hysteresis could be due to cavity effects produced by soil cracks not originally present. Thus, the deterioration of soil surface tends to reduce the emissivity spectral contrast. Soil-specific and general relationships obtained by Mira et al. were tested and compared with the field measurements. Field emissivities agree within 2% with the modeled values for all bands under noncracked surface conditions, whereas differences reach 5% in the 8-9- μm range when cracks are present.

Evaluation of the B-method for determining actual evapotranspiration in a boreal forest from MODIS data

Boreal forests occupy about 11% of the terrestrial surface and represent an important contribution to global energy balance. The ground measurement of daily evapotranspiration (LEd) is very difficult due to the limitations on experiments. The objective of this paper is to present and explore the applicability of the B‐method for monitoring actual LEd in these ecosystems. The method shown in this paper allows us to determine the surface fluxes over boreal forests on a daily basis from instantaneous information registered in a conventional meteorological tower, as well as the canopy temperature (T c) retrieved by satellite. Images collected by the MODIS (moderate resolution imaging spectroradiometer) on board EOS‐Terra have been used for this study. The parameters of the model were calibrated from the SIFLEX‐2002 (Solar Induced Fluorescence Experiment 2002) campaign dataset in a northern boreal forest in Finland. A study of these parameters was made on an hourly basis in order to make the method applicable, not only at midday but within an interval of 7 h around it. This is an important advance with respect to the original formulation of this approach since the overpass time of satellites can be very variable. The comparison between T c ground measured with a thermal infrared radiometer, and T c retrieved from land surface temperature (LST) MODIS data, showed an estimation error of ±1.4°C for viewing angles from 5 to 60°. A complete sensitivity analysis was carried out and an estimation error of about ±35%, corresponding to the interval 10.00–11.00 h UTC, was shown as the lowest in LEd retrieval. Finally, the method was validated over the study site using 21 MODIS images for 2002 and 2003. The results were compared with eddy‐correlation ground measurements. An accuracy of ±1.0 mm/day and an overestimation of 0.3 mm/day were shown in the LEd retrieval.

Application of artificial neural networks and logistic regression to the prediction of forest fire danger in Galicia using MODIS data

Fire danger models are a very useful tool for the prevention and extinction of forest fires. Some inputs of these models, such as vegetation status and temperature, can be obtained from remote sensing images, which offer higher spatial and temporal resolution than direct ground measures. In this paper, we focus on the Galicia region (north-west of Spain), and MODIS (Moderate Resolution Imaging Spectroradiometer) images are used to monitor vegetation status and to obtain land surface temperature as essential inputs in forest fire danger models. In this work, we tested the potential of artificial neural networks and logistic regression to estimate forest fire danger from remote sensing and fire history data. Remote sensing inputs used were the land surface temperature and the Enhanced Vegetation Index. A classification into three levels of fire danger was established. Fire danger maps based on this classification will facilitate fire prevention and extinction tasks.

Evaluation of Disaggregation Methods for Downscaling MODIS Land Surface Temperature to Landsat Spatial Resolution in Barrax Test Site

Thermal infrared (TIR) data are usually acquired at a coarser spatial resolution (CR) than visible and near infrared (VNIR). Several disaggregation methods have been recently developed to enhance the TIR spatial resolution using VNIR data. These approaches are based on the retrieval of a relation between TIR and VNIR data at CR, or training of a neural network, to be applied at the fine resolution afterward. In this work, different disaggregation methods are applied to the combination of two different sensors in the experimental test site of Barrax, Spain. The main objective is to test the feasibility of these techniques when applied to satellites provided with no TIR bands. Landsat and moderate imaging spectroradiometer (MODIS) images were used for this work. Land surface temperature (LST) from MODIS images was disaggregated to the Landsat spatial resolution using Landsat VNIR data. Landsat LST was used for the validation and comparison of the different techniques. Best results were obtained by the method based on a linear regression between normalized difference vegetation index (NDVI) and LST. An average RMSE = ±1.9 K was observed between disaggregated and Landsat LST from four different dates in a study area of 120 km2.

Empirical Relationships for Monitoring Water Quality of Lakes and Reservoirs Through Multispectral Images

Remote sensing techniques can be used to estimate water quality variables such as chlorophyll\mbi a, total suspended particles, and water transparency. This paper describes empirical algorithms for the estimation of these variables using Landsat Thematic Mapper (TM) data. Ground data were taken from several Spanish lakes covering a variety of trophic statuses, ranging from oligotrophic to hypereutrophic. The studied lakes were the Albufera de Valencia and lakes and ponds of the Southeast Regional Park in Madrid. Empirical equations were obtained to estimate chlorophyll\mbi a from the ratio in reflectance values between bands 2 and 4 of TM ( R2\mmb = 0.66, p\lt 0.001), transparency [Secchi disk (SD)] from reflectance in band 2 ( R2\mmb = 0.80, pbf \lt 0.001), and total suspended particles from reflectance in band 4 ( R2 \mmb = 0.92, p\lt 0.001). The spectral equivalence between TM and the recent satellite Deimos-1 was also tested. By applying the proposed algorithms to this new sensor, the temporal resolution is improved by up to 3 days, which increases spatial resolution to 22 m. The algorithms were validated using three Deimos-1 scenes of the Albufera de Valencia together with ground measurements. Results of this validation showed root-mean-square errors (RMSEs) of 40\nbspmg·m\mmb-3 for Chl-\mbi a (data range: 32\mmb - 238\nbspmg·m-3), 10\nbspmg·L\mmb -1 for total suspended solid (TSS) (data range: 25\mmb -89\nbspmg·L\mmb -1), and 0.10 m for SD (data range: 0.17-0.40 m). In any case, these results show the potential of Deimos-1 as a substitute of TM in water quality monitoring in small/medium water bodies, providing continuity to three decades of TM imagery.

A simple equation for determining sea surface emissivity in the 3-15 µm region

The high level of accuracy demanded for the sea surface temperature retrieval from infrared data requires an accurate determination of directional sea surface emissivity (SSE). Previous models have permitted calculating SSEs using a physical characterization of sea surface roughness and emission. However, these result in complex equations, and make an operational application difficult. This paper presents a simple SSE algorithm based on a parametrization of one of these models, which was selected as a reference since it reproduces SSE experimental data to a reasonable level of accuracy. The parametrization provides the SSE variation with observation angle and wind speed from a given nadir SSE value, using only one channel-dependent coefficient. This coefficient and the nadir SSE value are given for the IR bands of several current satellite sensors: ENVISAT‐AATSR, EOSTerra/Aqua‐MODIS, NOAA17‐AVHRR and MSG‐SEVIRI. The average standard error of the SSE estimate using the proposed equation is±0.0009.

Modeling Fire Danger in Galicia and Asturias (Spain) from MODIS Images

Forest fires are one of the most dangerous natural hazards, especially when they are recurrent. In areas such as Galicia (Spain), forest fires are frequent and devastating. The development of fire risk models becomes a very important prevention task for these regions. Vegetation and moisture indices can be used to monitor vegetation status; however, the different indices may perform differently depending on the vegetation species. Eight different spectral indices were selected to determine the most appropriate index in Galicia. This study was extended to the adjacent region of Asturias. Six years of MODIS (Moderate Resolution Imaging Spectroradiometer) images, together with ground fire data in a 10 × 10 km grid basis were used. The percentage of fire events met the variations suffered by some of the spectral indices, following a linear regression in both Galicia and Asturias. The Enhanced Vegetation Index (EVI) was the index leading to the best results. Based on these results, a simple fire danger model was established, using logistic regression, by combining the EVI variation with other variables, such as fire history in each cell and period of the year. A seventy percent overall concordance was obtained between estimated and observed fire frequency.