Kebiao Mao

A practical split‐window algorithm for retrieving land‐surface temperature from MODIS data

This paper presents a practical split‐window algorithm utilized to retrieve land‐surface temperature (LST) from Moderate‐resolution Imaging Spectroradiometer (MODIS) data, which involves two essential parameters (transmittance and emissivity), and a new method to simplify Planck function has been proposed. The method for linearization of Planck function, how to obtain atmosphere transmittance from MODIS near‐infrared (NIR) bands and the method for estimating of emissivity of ground are discussed with details. Sensitivity analysis of the algorithm has been performed for the evaluation of probable LST estimation error due to the possible errors in water content and emissivity. Analysis indicates that the algorithm is not sensitive to these two parameters. Especially, the average LST error is changed between 0.19–1.1°C when the water content error in the simulation standard atmosphere changes between −80 and 130%. We confirm the conclusion by retrieving LST from MODIS image data through changing retrieval water content error. Two methods have been used to validate the proposed algorithm. Results from validation and comparison using the standard atmospheric simulation and the comparison with the MODIS LST product demonstrate the applicability of the algorithm. Validation with standard atmospheric simulation indicates that this algorithm can achieve the average accuracy of this algorithm is about 0.32°C in LST retrieval for the case without error in both transmittance and emissivity estimations. The accuracy of this algorithm is about 0.37°C and 0.49°C respectively when the transmittance is computed from the simulation water content by exponent fit and linear fit respectively.

Progresses on microwave remote sensing of land surface parameters

Highly accurate observations at various scales on the land surface are urgently needed for the studies of many areas, such as hydrology, meteorology, and agriculture. With the rapid development of remote sensing techniques, remote sensing has had the capacity of monitoring many factors of the Earth’s land surface. Especially, the space-borne microwave remote sensing systems have been widely used in the quantitative monitoring of global snow, soil moisture, and vegetation parameters with their all-weather, all-time observation capabilities and their sensitivities to the characteristics of land surface factors. Based on the electromagnetic theories and microwave radiative transfer equations, researchers have achieved great successes in the microwave remote sensing studies for different sensors in recent years. This article has systematically reviewed the progresses on five research areas including microwave theoretical modeling, microwave inversion on soil moisture, snow, vegetation and land surface temperatures. Through the further enrichment of remote sensing datasets and the development of remote sensing theories and inversion techniques, remote sensing including microwave remote sensing will play a more important role in the studies and applications of the Earth systems.

A Neural Network Technique for Separating Land Surface Emissivity and Temperature From ASTER Imagery

Four radiative transfer equations for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) bands 11, 12, 13, and 14 are built involving six unknowns (average atmospheric temperature, land surface temperature, and four band emissivities), which is a typical ill-posed problem. The extra equations can be built by using linear or nonlinear relationship between neighbor band emissivities because the emissivity of every land surface type is almost constant for bands 11, 12, 13, and 14. The neural network (NN) can make full use of potential information between band emissivities through training data because the NN simultaneously owns function approximation, classification, optimization computation, and self-study ability. The training database can be built through simulation by MODTRAN4 or can be obtained from the reliable measured data. The average accuracy of the land surface temperature is about 0.24 K, and the average accuracy of emissivity in bands 11, 12, 13, and 14 is under 0.005 for test data. The retrieval result by the NN is, on average, higher by about 0.7 K than the ASTER standard product (AST08), and the application and comparison indicated that the retrieval result is better than the ASTER standard data product. To further evaluate self-study of the NN, the ASTER standard products are assumed as measured data. After using AST09, AST08, and AST05 (ASTER Standard Data Product) as the compensating training data, the average relative error of the land surface temperature is under 0.1 K relative to the AST08 product, and the average relative error of the emissivity in bands 11, 12, 13, and 14 is under 0.001 relative to AST05, which indicates that the NN owns a powerful self-study ability and is capable of suiting more conditions if more reliable and high-accuracy ASTER standard products can be compensated.

A method for retrieving soil moisture in Tibet region by utilizing microwave index from TRMM/TMI data

According to simulation analysis of the advanced integral equation model (AIEM), there is a good linear relationship between emissivity and soil moisture under conditions of given roughness. The normalized difference of emissivities at 19.35 GHz and 10.65 GHz with vertical polarization can partly eliminate the influence of roughness and the squared correlation coefficient is about 0.985. This paper uses the normalized brightness temperature for retrieving soil moisture in Tibet from TRMM/TMI data. This method avoids parametrizing the land surface temperature which is a key parameter for the computation of emissivity. We make some sensitivity analysis for the atmosphere which is the main influence factor for our method. The analysis results indicate that our method is very good for clear days but is not very good when there is rainfall. We evaluate our algorithm by using the ground truth data obtained from GAME‐Tibet and the retrieval error of soil moisture is about 0.04m3 m−3 relative to experimental data. The analysis indicates that the relationship obtained from the theoretical model should be revised through the local ground measurement data because the method is still influenced by roughness and vegetation. After making a regression revision, the retrieval error of soil moisture is under 0.02m3 m−3. Finally, we retrieve the soil moisture in Tibet from TRMM/TMI data, and the distribution trend of retrieval results is consistent with the real world.

Near-surface air temperature estimation from ASTER data based on neural network algorithm

An algorithm based on the radiance transfer model (MODTRAN4) and a dynamic learning neural network for estimation of near‐surface air temperature from ASTER data are developed in this paper. MODTRAN4 is used to simulate radiance transfer from the ground with different combinations of land surface temperature, near surface air temperature, emissivity and water vapour content. The dynamic learning neural network is used to estimate near surface air temperature. The analysis indicates that near surface air temperature cannot be directly and accurately estimated from thermal remote sensing data. If the land surface temperature and emissivity were made as prior knowledge, the mean and the standard deviation of estimation error are both about 1.0 K. The mean and the standard deviation of estimation error are about 2.0 K and 2.3 K, considering the estimation error of land surface temperature and emissivity. Finally, the comparison of estimation results with ground measurement data at meteorological stations indicates that the RM‐NN can be used to estimate near surface air temperature from ASTER data.

An RM‐NN algorithm for retrieving land surface temperature and emissivity from EOS/MODIS data

[1] Three radiative transfer equations are built for MODIS bands 29, 31, and 32, which involve six unknown parameters ( average atmospheric temperature, land surface temperature (LST), three band emissivities, and water vapor content). The relationships between geophysical parameters have been analyzed in detail, which indicates that neural network is one of the best methods to resolve these ill-posed problems ( LST and emissivity). Retrieval analysis indicates that the combined radiative transfer model (RM) with neural network (NN) algorithm can be used to simultaneously retrieve land surface temperature and emissivity from Moderate-Resolution Imaging Spectroradiometer ( MODIS) data. Simulation data analysis indicates that the average error of LST is under 0.4 K and the average error of emissivity is under 0.008, 0.006, and 0.006 for bands 29, 31, and 32, respectively. The comparison analysis between retrieval result by RM-NN and MODIS product algorithm indicates that the generalized split window LST overestimates the emissivity and underestimates land surface temperature. The retrieval results by RM-NN lie between the two products provided by NASA and closer to day/night LST algorithm after statistics analysis. The average error is 0.36 K relative to MODIS LST product (MOD11_L2) retrieved by generalized split window algorithm if we make a regression revision. The comparison of retrieval results with ground measurement data in Xiaotangshan also indicates that the RM-NN can be used to retrieve accurately land surface temperature and emissivity.

Estimation of water vapor content in near-infrared bands around 1 μm from MODIS data by using RM–NN

An algorithm based on the radiance transfer model (RM) and a dynamic learning neural network (NN) for estimating water vapor content from moderate resolution imaging spectrometer (MODIS) 1B data is developed in this paper. The MODTRAN4 is used to simulate the sun-surface-sensor process with different conditions. The dynamic learning neural network is used to estimate water vapor content. Analysis of the simulation data indicates that the mean and standard deviation of estimation error are under 0.06 gcm(-2 )and 0.08 gcm(-2). The comparison analysis indicates that the estimation result by RM-NN is comparable to that of a MODIS water vapor content product (MYD05_L2). Finally, validation with ground measurement data shows that RM-NN can be used to accurately estimate the water vapor content from MODIS 1B data, and the mean and standard deviation of the estimation error are about 0.12 gcm(-2 )and 0.18 gcm(-2).

A neural-network technique for retrieving land surface temperature from AMSR-E passive microwave data

It is very difficult to retrieve the land surface temperature (LST) from passive microwave remote sensing because a single multi-frequency thermal measurement with N bands owns n equations in N+1unknowns (N emissivities and LST) which is a typical ill-posed inversion problem. However, the emissivity is mainly influenced by dielectric constant which is a function of physical temperature, salinity, water content, soil texture, and other factors (the structure and types of vegetation). These make it very difficult to develop a general physical algorithm. This paper intends to utilize the multiple- sensor/resolution and neural network to retrieve land surface temperature from AMSR-E data. MODIS LST product is made as ground data which overcomes the difficulty of obtaining large scale land surface temperature data. The retrieval result and analysis indicate that the neural network can be used to accurately retrieve land surface temperature from AMSR-E data.

The Monitoring Analysis for the Drought in China by Using an Improved MPI Method

MPI (microwave polarization index) method can use different frequencies at vertical polarization to retrieve soil moisture from TMI (tropical microwave imager) data, which is mainly suitable for bare soil. This paper makes an improvement for MPI method which makes it suitable for surface covered by vegetation. The MPI by using single frequency at different polarizations is used to discriminate the bare soil and vegetation which overcomes the difficulty in previous algorithms by using optical remote sensing data, and then the revision is made according to the different land surface types. The validation by using ground measurement data indicates that revision for different land surface types can improve the retrieval accuracy. The average error is about 24.5% by using the ground truth data obtained from ground observation stations, and the retrieval error is about 13.7% after making a revision by using ground measurement data from local observation stations for different surface types. The improved MPI method and precipitation are used to analyze the drought in Southwest China, and the analysis indicates the soil moisture retrieved by improved MPI method can be used to monitor the drought.

Study of Atmospheric effects on AMSR-E microwave brightness temperature over Tibetan Plateau

This paper demonstrates a study to the atmospheric influence on the passive microwave brightness temperature (BT) in Tibetan Plateau area at clear-sky condition. The absorption and emission of dry air and water vapor are considered as the main contribution of atmosphere at the fact of cloud-free. We choose the day of Dec. 07, 2005 as an example, and calculated the atmosphere absorption factor and effective atmospheric temperature which are based on an updated atmospheric microwave absorption model. With the help of MODIS-Aqua land surface products (MYD11_L2) and MODIS atmospheric profile (MOD07_L2) products, which can decide a real atmospheric status, a simplified radiative transfer equation (RTE) is employed to estimate the AMSR-E frequencies surface emissivity over Tibetan Plateau. As a result, the surface actual microwave brightness temperature is obtained through the product of retrieved emissivity and MODIS LST, it can be found that the atmospheric contribution to the brightness temperature add up to about 5.56 K at 89.0 GHz and average 0.54 K at 23.8 GHz somewhere Tibetan Plateau in the cloud-free winter days, and the space variation of atmospheric effect to microwave BT has been further discussed.