Vicente García-Santos

Test of the MODIS Land Surface Temperature and Emissivity Separation Algorithm With Ground Measurements Over a Rice Paddy

The Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) and emissivity separation (MODTES) algorithm is the basis of the MOD21 product, which provides 1-km LSTs and emissivities for bands 29 (8.55 μm), 31 (11 μm), and 32 (12 μm). The MODTES algorithm uses the TES method with the water vapor scaling (WVS) method for refined atmospheric correction. The performance of the MODTES algorithm was tested with a set of MODIS data concurrent with ground LST and emissivity measurements. The test site is a large area of homogeneous full-cover rice crops (graybody), with high atmospheric water vapor. The data included LSTs measured along transects with multiple calibrated radiometers and emissivity measurements in different bands within 8-13 μm. We applied the full MODTES algorithm and that without the WVS method for comparison. For the data used here, MODTES minus in situ LSTs yielded a median difference of 0.5 K and a robust standard deviation (RSD) of 0.4 K. When the WVS method was not applied, we obtained a median bias of 1.3 K and an RSD of 0.9 K. MODTES retrieved emissivities were (median ± RSD) 0.966 ± 0.011 for band 29, 0.976 ± 0.004 for band 31, and 0.981 ± 0.003 for band 32 (0.961 ± 0.029, 0.971 ± 0.013, and 0.978 ± 0.007 without WVS), underestimating the ground values by 0.019, 0.009, and 0.001, respectively. Emissivities from the full MODTES algorithm presented less dispersion and were closer to the ground data, particularly for lower water vapor cases. These results show that MODTES performs within the predicted algorithm uncertainty and the importance of the WVS method for reducing residual errors in atmospheric correction.

Evaluation of Different Methods to Retrieve the Hemispherical Downwelling Irradiance in the Thermal Infrared Region for Field Measurements

The thermal infrared hemispherical downwelling irradiance (HDI) emitted by the atmosphere and surrounding elements contributes through reflection to the signal measured over an observed surface by remote sensing. This irradiance must be estimated in order to obtain accurate values of land-surface temperature (LST). There are some fast methods to measure the HDI with a single measurement pointing to the sky at a specified viewing direction, but these methods require completely cloud-free or cloudy skies, and they do not account for the radiative contribution of surrounding elements. Another method is the use of a diffuse reflectance panel (usually, a rough gold-coated surface) with near-Lambertian behavior. This method considers the radiative contribution of surrounding elements and can be used under any sky condition. A third possibility is the use of atmospheric profiles and a radiative transfer code (RTC) in order to simulate the atmospheric signal and to calculate the HDI by integration. This study compares the HDI estimations with these approaches, using measurements made on four different days with a completely clear sky and two days with a partially cloudy sky. The measurements were made with a four-channel CIMEL Electronique radiometer working in the 8-14-μm spectral range. The HDI was also estimated by means of National Centers for Environmental Prediction atmospheric profiles introduced in the MODTRAN RTC. Additionally, the measurements were made at two different places with very different environments to quantify the effect of the contributing surroundings. Results showed that, for a clear-sky day with a minimal contribution of the surroundings, all methods differed from each other between 5% and 11%, depending on the spectral range, and any of them could be used to estimate HDI in these conditions. However, in the case of making surface measurements in an area with significant surrounding elements (buildings, trees, etc.), HDI values retrieved from the panel present an increase of +3 W·m-2·μm-1 compared with the other methods; this increase, if ignored, implies to make an error in LST ranging from +0.5 °C to +1.5 °C, depending on the spectral range and on surface emissivity and temperature. Comparison under heterogeneous skies with changing cloud coverage showed also large differences between the use of panel and the other methods, reaching a maximum difference of +4.6 W·m-2·μm-1, which implies to make an error on LST of +2.2 °C. In these cases, the use of the diffuse reflectance panel is proposed, since it is the unique way to capture the contribution of the surroundings and also to adequately measure HDI for sky changing conditions.

Effect of Soil Moisture on the Angular Variation of Thermal Infrared Emissivity of Inorganic Soils

Emissivity is influenced by different factors. This study deals with the effect of the soil moisture (SM) content on the zenithal (θ) variation of ratio-to-nadir emissivity (ε_r), for a wide variety of inorganic bare soils. To retrieve ε_r, a goniometer assembly was used, together with two identical CIMEL Electronique CE312-2 radiometers working at six spectral bands within 7.7-14.3 μm, performing simultaneous radiance measurements at different combinations of zenith and azimuth angles. The results showed that the effect of SM upon ε_r(θ) is different depending on the spectral range and textural composition of the sample. Sandy soils showed a decrease of ε_r(θ) from nadir up to 0.132 for θ ≥ 40^° at 8-9.4 μm under dry conditions, but this decrease was reduced to 0.093 with the increase of SM. Clayey samples did not present dependence of ε_r(θ) on SM. Loamy texture samples presented a more sharp decrease of ε_r(θ) with the increase of SM, reaching differences between nadir values and 70 ^° up to 6%, at all spectral ranges studied. Finally, a parameterization of ε_r with SM and θ was derived allowing to obtain ratio-to-nadir emissivities with an accuracy of ± 0.011.

SMOS Level-2 Soil Moisture Product Evaluation in Rain-Fed Croplands of the Pampean Region of Argentina

A field campaign was carried out to evaluate the Soil Moisture (SM) MIR_SMUDP2 product (v5.51) generated from the data of the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) aboard the Soil Moisture and Ocean Salinity (SMOS) mission. The study area was the Pampean Region of Argentina, which was selected because it is a vast area of flatlands containing quite homogeneous rain-fed croplands, which are considered SMOS nominal land uses and hardly affected by radio-frequency interference contamination. Transects of ground handheld SM measurements were performed using ThetaProbe ML2x probes within four Icosahedral Snyder Equal Area Earth (ISEA) grid nodes, where permanent SM stations are located. The campaign results showed a negative bias of -0.02 m3m-3 between concurrent SMOS data and ground SM measurements, which means a slight SMOS underestimation, and a standard deviation of ±0.06 m3m-3. Additionally, a good correlation was obtained between the handheld SM measurements taken during the campaign and the permanent SM station data within a node, which pointed out that the station data could be used as reference data to evaluate the SMOS product over a longer temporal period. SMOS-retrieved data were also compared with station mean SM values from 2012 to 2014. A general SMOS underestimation of -0.05 m3m-3 was observed, with a standard deviation of ±0.04 m3m-3, which yields an uncertainty of ±0.07 m3m-3 for the SMOS product. Although the random error meets the SMOS missions goal of ±0.04 m3m-3, the product overall uncertainty is higher than that due to the significant dry bias, which is also found in other regions of the world.

Landsat and Local Land Surface Temperatures in a Heterogeneous Terrain Compared to MODIS Values

Land Surface Temperature (LST) as provided by remote sensing onboard satellites is a key parameter for a number of applications in Earth System studies, such as numerical modelling or regional estimation of surface energy and water fluxes. In the case of Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra or Aqua, pixels have resolutions near 1 km 2 , LST values being an average of the real subpixel variability of LST, which can be significant for heterogeneous terrain. Here, we use Landsat 7 LST decametre-scale fields to evaluate the temporal and spatial variability at the kilometre scale and compare the resulting average values to those provided by MODIS for the same observation time, for the very heterogeneous Campus of the University of the Balearic Islands (Mallorca, Western Mediterranean), with an area of about 1 km 2 , for a period between 2014 and 2016. Variations of LST between 10 and 20 K are often found at the sub-kilometre scale. In addition, MODIS values are compared to the ground truth for one point in the Campus, as obtained from a four-component net radiometer, and a bias of 3.2 K was found in addition to a Root Mean Square Error (RMSE) of 4.2 K. An indication of a more elaborated local measurement strategy in the Campus is given, using an array of radiometers distributed in the area.

Estimation of atmospheric water vapour content from direct measurements of radiance in the thermal infrared region

Atmospheric water vapour content is a required parameter in thermal infrared (TIR) to carry out processes such as atmospheric correction or retrieving atmospheric factors (downwelling or upwelling irradiance, transmittance of the atmosphere and so on). This study proposes an alternative method to the ones already in use to measure water vapour content from direct measurements of downwelling atmospheric radiance in the TIR range. It was possible to estimate a linear relationship between atmospheric water vapour and downwelling atmospheric radiance using a simulated study, based on data from a radiosounding database. A subsequent validation concludes that it is possible to obtain water vapour content with an uncertainty of 0.5 cm using in situ measurements of downwelling atmospheric radiance in the TIR range of 11.5–12.5 μm.

Comparison of Three Methods for Estimating Land Surface Temperature from Landsat 8-TIRS Sensor Data

After Landsat 8 was launched in 2013, it was observed that for Thermal Infrared sensor (TIRS) bands, radiance from outside of an instrument’s field-of-view produced a non-uniform ghost signal across the focal plane that varied depending on the out-of-scene content (i.e., the stray light effect). A new stray light correction algorithm (SLCA) is currently operational and has been implemented into the United States Geological Survey (USGS) ground system since February 2017. The SLCA has also been applied to reprocess historical Landsat 8 scenes. After approximately two years of SLCA implementation, more land surface temperature (LST) validation studies are required to check the effect of correction in the estimation of LST from different retrieval algorithms. For this purpose, three different LST estimation method algorithms (i.e., the radiative transfer equation (RTE), single-channel algorithm (SCA), and split-window algorithm (SWA)) have been assessed. The study site is located on the campus of the University of Balearic Islands on the island of Mallorca (Spain) in the western Mediterranean Sea. The site is considered a heterogeneous area that is composed of different types of surfaces, such as buildings, asphalt roads, farming areas, sloped terrains, orange fields, almond trees, lawns, and some natural vegetation regions. Data from 21 scenes, which were acquired by the Landsat 8-TIRS sensor and extracted from a 100 × 100 m2 pixel, were used to retrieve the LST with different algorithms; then, they were compared with in situ LST measurements from a broadband thermal infrared radiometer located on the same Landsat 8 pixel. The results show good performances of the three methods, with the SWA showing the lowest observed RMSE (within 1.6–2 K), whereas the SCA applied to the TIRS band 10 (10 μm) was also appropriate, with a RMSE ranging within 2.0–2.3 K. The LST estimates using the RTE algorithm display the highest observed RMSE values (within 2.0–3.6 K) of all of the compared methods, but with an almost unbiased value of −0.1 K for the case of techniques applied to band 10 data. The SWAs are the preferred method to estimate the LST in our study area. However, further validation studies around the world are required.