Bo-Hui Tang

A Review of Current Methodologies for Regional Evapotranspiration Estimation from Remotely Sensed Data

An overview of the commonly applied evapotranspiration (ET) models using remotely sensed data is given to provide insight into the estimation of ET on a regional scale from satellite data. Generally, these models vary greatly in inputs, main assumptions and accuracy of results, etc. Besides the generally used remotely sensed multi-spectral data from visible to thermal infrared bands, most remotely sensed ET models, from simplified equations models to the more complex physically based two-source energy balance models, must rely to a certain degree on ground-based auxiliary measurements in order to derive the turbulent heat fluxes on a regional scale. We discuss the main inputs, assumptions, theories, advantages and drawbacks of each model. Moreover, approaches to the extrapolation of instantaneous ET to the daily values are also briefly presented. In the final part, both associated problems and future trends regarding these remotely sensed ET models were analyzed to objectively show the limitations and promising aspects of the estimation of regional ET based on remotely sensed data and ground-based measurements.

Land surface emissivity retrieval from satellite data

As an intrinsic property of natural materials, land surface emissivity (LSE) is an important surface parameter and can be derived from the emitted radiance measured from space. Besides radiometric calibration and cloud detection, two main problems need to be resolved to obtain LSE values from space measurements. These problems are often referred to as land surface temperature (LST) and emissivity separation from radiance at ground level and as atmospheric corrections in the literature. To date, many LSE retrieval methods have been proposed with the same goal but different application conditions, advantages, and limitations. The aim of this article is to review these LSE retrieval methods and to provide technical assistance for estimating LSE from space. This article first gives a description of the theoretical basis of LSE measurements and then reviews the published methods. For clarity, we categorize these methods into (1) (semi-)empirical or theoretical methods, (2) multi-channel temperature emissivity separation (TES) methods, and (3) physically based methods (PBMs). This article also discusses the validation methods, which are of importance in verifying the uncertainty and accuracy of retrieved emissivity. Finally, the prospects for further developments are given.

Inversion of the PROSAIL model to estimate leaf area index of maize, potato, and sunflower fields from unmanned aerial vehicle hyperspectral data

Leaf area index (LAI) is a key variable for modeling energy and mass exchange between the land surface and the atmosphere. Inversion of physically based radiative transfer models is the most established technique for estimating LAI from remotely sensed data. This study aims to evaluate the suitability of the PROSAIL model for LAI estimation of three typical row crops (maize, potato, and sunflower) from unmanned aerial vehicle (UAV) hyperspectral data. LAI was estimated using a look-up table (LUT) based on the inversion of the PROSAIL model. The estimated LAI was evaluated against in situ LAI measurements. The results indicated that the LUT-based inversion of the PROSAIL model was suitable for LAI estimation of these three crops, with a root mean square error (RMSE) of approximately 0.62 m(2) m(-2), and a relative RMSE (RRMSE) of approximately 15.5%. Dual-angle observations were also used to estimate LAI and proved to be more accurate than single-angle observations, with an RMSE of approximately 0.55 m(2) m(-2) and an RRMSE of approximately 13.6%. The results demonstrate that additional directional information improves the performance of LAI estimation

Surface Soil Water Content Estimation from Thermal Remote Sensing based on the Temporal Variation of Land Surface Temperature

Soil water content (SWC) is a crucial variable in the thermal infrared research and is the major control for land surface hydrological processes at the watershed scale. Estimating the surface SWC from remotely sensed data using the triangle method proposed by Price has been demonstrated in previous studies. In this study, a new soil moisture index (Temperature Rising Rate Vegetation Dryness IndexTRRVDI) is proposed based on a triangle constructed using the mid-morning land surface temperature (LST) rising rate and the vegetation index to estimate the regional SWC. The temperature at the dry edge of the triangle is determined by the surface energy balance principle. The temperature at the wet edge is assumed to be equal to the air temperature. The mid-morning land surface temperature rising rate is calculated using Meteosat Second GenerationSpinning Enhanced Visible and Infrared Imager (MSG-SEVIRI) LST products over 4 cloud-free days (day of year: 206, 211, 212, 242) in 2007. The developed TRRVDI is validated by in situ measurements from 19 meteorological stations in Spain. The results indicate that the coefficient of determination (R-2) between the TRRVDI derived using the theoretical limiting edges and the in situ SWC measurements is greater than that derived using the observed limiting edges. The R-2 values are 0.46 and 0.32; respectively (p < 0.05). Additionally, the TRRVDI is much better than the soil moisture index that was developed using one-time LST and fractional vegetation cover (FVC) with the theoretically determined limiting edges.

Estimation of broadband surface emissivity from narrowband emissivities

This work analyzed and addressed the estimate of the broadband emissivities for the spectral domains 3-14μm (ε(3-14)) and 3-∞μm (ε(3-∞). Two linear narrow-to-broadband conversion models were proposed to estimate broadband emissivities ε(3-14) and ε(3-∞) using the Moderate Resolution Imaging Spectroradiometer (MODIS) derived emissivities in three thermal infrared channels 29 (8.4-8.7μm), 31 (10.78-11.28μm) and 32 (11.77-12.27μm). Two independent spectral libraries, the Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) spectral library and the MODIS UCSB (University of California, Santa Barbara) emissivity library, were used to calibrate and validate the proposed models. Comparisons of the estimated broadband emissivities using the proposed models and the calculated values from the spectral libraries, showed that the proposed method of estimation of broadband emissivity has potential accuracy and the Root Mean Square Error (RMSE) between estimated and calculated broadband emissivities is less than 0.01 for both ε(3-14) and ε(3-∞).

Estimation of Diurnal Cycle of Land Surface Temperature at High Temporal and Spatial Resolution from Clear-Sky MODIS Data

The diurnal cycle of land surface temperature (LST) is an important element of the climate system. Geostationary satellites can provide the diurnal cycle of LST with low spatial resolution and incomplete global coverage, which limits its applications in some studies. In this study, we propose a method to estimate the diurnal cycle of LST at high temporal and spatial resolution from clear-sky MODIS data. This method was evaluated using the MSG-SEVIRI-derived LSTs. The results indicate that this method fits the diurnal cycle of LST well, with root mean square error (RMSE) values less than 1 K for most pixels. Because MODIS provides at most four observations per day at a given location, this method was further evaluated using only four MSG-SEVIRI-derived LSTs corresponding to the MODIS overpass times (10:30, 13:30, 22:30, and 01:30 local solar time). The results show that the RMSE values using only four MSG-SEVIRI-derived LSTs are approximately two times larger than those using all LSTs. The spatial distribution of the modeled LSTs at the MODIS pixel scale is presented from 07:00 to 05:00 local solar time of the next day with an increment of 2 hours. The diurnal cycle of the modeled LSTs describes the temporal evolution of the LSTs at the MODIS pixel scale.

Estimation and Validation of Land Surface Temperatures from Chinese Second-Generation Polar-Orbit FY-3A VIRR Data

This work estimated and validated the land surface temperature (LST) from thermal-infrared Channels 4 (10.8 µm) and 5 (12.0 µm) of the Visible and Infrared Radiometer (VIRR) onboard the second-generation Chinese polar-orbiting FengYun-3A (FY-3A) meteorological satellite. The LST, mean emissivity and atmospheric water vapor content (WVC) were divided into several tractable sub-ranges with little overlap to improve the fitting accuracy. The experimental results showed that the root mean square errors (RMSEs) were proportional to the viewing zenith angles (VZAs) and WVC. The RMSEs were below 1.0 K for VZA sub-ranges less than 30° or for VZA sub-ranges less than 60° and WVC less than 3.5 g/cm2, provided that the land surface emissivities were known. A preliminary validation using independently simulated data showed that the estimated LSTs were quite consistent with the actual inputs, with a maximum RMSE below 1 K for all VZAs. An inter-comparison using the Moderate Resolution Imaging Spectroradiometer (MODIS)-derived LST product MOD11_L2 showed that the minimum RMSE was 1.68 K for grass, and the maximum RMSE was 3.59 K for barren or sparsely vegetated surfaces. In situ measurements at the Hailar field site in northeastern China from October, 2013, to September, 2014, were used to validate the proposed method. The result showed that the RMSE between the LSTs calculated from the ground measurements and derived from the VIRR data was 1.82 K.

Retrieval of atmospheric and land surface parameters from satellite-based thermal infrared hyperspectral data using a neural network technique

Land surface temperature (LST), land surface emissivity (LSE), and atmospheric profiles are of great importance in many applications. Radiances observed by satellites depend not only on land surface parameters (LST and LSE) but also on atmospheric conditions, and it is difficult to retrieve these parameters simultaneously from multispectral measurements with high accuracies. This work aims to establish a neural network (NN) to retrieve atmospheric profiles, LST, and LSE simultaneously from hyperspectral thermal infrared data suitable for various air mass types and surface conditions. The distributions of surface materials, LST, and atmospheric profiles were elaborated carefully to generate the simulated data. The simulated at-sensor radiances were divided into two sub-ranges in the spectral domain: one in the atmospheric window and the other in the water absorption band. Subsequently, the radiances were transformed in the eigen-domain in each sub-range, and then the transformed coefficients were used as the inputs for the network. Similarly, the atmospheric profiles, LST, and LSE were used as outputs after the eigen-domain transformation. The validation of the NN using the simulated data indicated that the root mean square error (RMSE) of LST is approximately 1.6 K, and the RMSE of the temperature profiles is approximately 2 K in the troposphere. Meanwhile, the RMSE of total water content is approximately 0.3 g cm−2, and that of LSE is less than 0.01 in the spectral interval where the wave number is less than 1000 cm−1. Two experiments using actual thermal hyperspectral satellite data were carried out to further validate the proposed NN. All of these studies showed that the proposed NN is capable of retrieving atmospheric and land surface parameters with compromised accuracies. Because of its simplicity, the proposed NN can be used to yield preliminary results employed as first estimates for physics-based retrieval models.

Determination of snow cover from MODIS data for the Tibetan Plateau region

This paper addresses a snow-mapping algorithm for the Tibetan Plateau region using Moderate Resolution Imaging Spectroradiometer (MODIS) data. Accounting for the effects of the atmosphere and terrain on the satellite observations at the top of the atmosphere (TOA), particularly in the rugged Tibetan Plateau region, the surface reflectance is retrieved from the TOA reflectance after atmospheric and topographic corrections. To reduce the effect of the misclassification of snow and cloud cover, a normalized difference cloud index (NDCI) model is proposed to discriminate snow/cloud pixels, separate from the MODIS cloud mask product MOD35. The MODIS land surface temperature (LST) product MOD11_L2 is also used to ensure better accuracy of the snow cover classification. Comparisons of the resulting snow cover with those estimated from high spatial-resolution Landsat ETM+ data and obtained from MODIS snow cover product MOD10_L2 for the Mount Everest region for different seasons in 2002, show that the MODIS snow cover product MOD10_L2 overestimates the snow cover with relative error ranging from 20.1% to 55.7%, whereas the proposed algorithm estimates the snow cover more accurately with relative error varying from 0.3% to 9.8%. Comparisons of the snow cover estimated with the proposed algorithm and those obtained from MOD10_L2 product with in situ measurements over the Hindu Kush-Himalayan (HKH) region for December 2003 and January 2004 (the snowy seasons) indicate that the proposed algorithm can map the snow cover more accurately with greater than 90% agreement

Modeling of Day-to-Day Temporal Progression of Clear-Sky Land Surface Temperature

This letter presents a method to calculate the width ω over the half-period of the cosine term in a diurnal temperature cycle (DTC) model. ω deduced from the thermal diffusion equation (TDE) is compared with ω obtained from solar geometry. The results demonstrate that ω deduced from the TDE describes the shape of the DTC model more adequately around sunrise and the time of maximum temperature than ω obtained from solar geometry. Additionally, taking into account the physical continuity of land surface temperature (LST) variation, a day-to-day temporal progression (DDTP) model of LST is developed to model several days of DTCs. The results indicate that the DDTP model fits in situ [or Spinning Enhanced Visible and Infrared Imager (SEVIRI)] LST well with a root-mean-square error (RMSE) less than 1 K. Compared with the DTC model, the DDTP model slightly increases the quality of LST fits around sunrise. Assuming that only six LST measurements corresponding to the NOAA/AVHRR and MODIS overpass times for each day are available, several days of DTCs can be predicted by the DDTP model with an RMSE less than 1.5 K.