Si-Bo Duan

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

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.

Land surface reflectance retrieval from hyperspectral data collected by an unmanned aerial vehicle over the Baotou test site.

To evaluate the in-flight performance of a new hyperspectral sensor onboard an unmanned aerial vehicle (UAV-HYPER), a comprehensive field campaign was conducted over the Baotou test site in China on 3 September 2011. Several portable reference reflectance targets were deployed across the test site. The radiometric performance of the UAV-HYPER sensor was assessed in terms of signal-to-noise ratio (SNR) and the calibration accuracy. The SNR of the different bands of the UAV-HYPER sensor was estimated to be between approximately 5 and 120 over the homogeneous targets, and the linear response of the apparent reflectance ranged from approximately 0.05 to 0.45. The uniform and non-uniform Lambertian land surface reflectance was retrieved and validated using in situ measurements, with root mean square error (RMSE) of approximately 0.01–0.07 and relative RMSE of approximately 5%–12%. There were small discrepancies between the retrieved uniform and non-uniform Lambertian land surface reflectance over the homogeneous targets and under low aerosol optical depth (AOD) conditions (AOD = 0.18). However, these discrepancies must be taken into account when adjacent pixels had large land surface reflectance contrast and under high AOD conditions (e.g. AOD = 1.0).

Evaluation of temporal variations in soil moisture based on the microwave polarization difference index using in situ data over agricultural areas in China

Soil moisture plays a critical role in the energy exchange and water redistribution of the land-atmosphere system. Knowledge of the temporal variations in soil moisture is vital in agricultural applications. Microwave indices are often used to characterize the temporal variations in soil moisture. In this study, we evaluate the temporal variations in soil moisture based on the microwave polarization difference index (MPDI) using ground-based measurements in China. In situ soil moisture at six test sites during the crop-growing season from 2009 to 2011 is obtained. The consistency of the temporal variations between the MPDI values and the in situ soil moisture is analysed in terms of (1) microwave frequencies, (2) satellite overpass times, and (3) measurement depths of soil moisture. The results show that the accuracies of the consistency vary from approximately 40% to 90%. Compared with the in situ soil moisture at 0–10 cm, the temporal variations in soil moisture are best characterized by the 6.9 GHz MPDI values from the ascending overpasses (MPDI_06A). Furthermore, the accuracies of the consistency between MPDI_06A and the in situ soil moisture at 0–10 cm are greater than those between MPDI_06A and the in situ soil moisture at 10–20 cm.

Algorithm Development for Land Surface Temperature Retrieval: Application to Chinese Gaofen-5 Data

Land surface temperature (LST) is a key variable in the study of the energy exchange between the land surface and the atmosphere. Among the different methods proposed to estimate LST, the quadratic split-window (SW) method has achieved considerable popularity. This method works well when the emissivities are high in both channels. Unfortunately, it performs poorly for low land surface emissivities (LSEs). To solve this problem, assuming that the LSE is known, the constant in the quadratic SW method was calculated by maintaining the other coefficients the same as those obtained for the black body condition. This procedure permits transfer of the emissivity effect to the constant. The result demonstrated that the constant was influenced by both atmospheric water vapour content (W) and atmospheric temperature (T0) in the bottom layer. To parameterize the constant, an exponential approximation between W and T0 was used. A LST retrieval algorithm was proposed. The error for the proposed algorithm was RMSE = 0.70 K. Sensitivity analysis results showed that under the consideration of NEΔT = 0.2 K, 20% uncertainty in W and 1% uncertainties in the channel mean emissivity and the channel emissivity difference, the RMSE was 1.29 K. Compared with AST 08 product, the proposed algorithm underestimated LST by about 0.8 K for both study areas when ASTER L1B data was used as a proxy of Gaofen-5 (GF-5) satellite data. The GF-5 satellite is scheduled to be launched in 2017.

Atmospheric correction for retrieving ground brightness temperature at commonly-used passive microwave frequencies

An analysis of the atmospheric impact on ground brightness temperature (Tg) is performed for numerous land surface types at commonly-used frequencies (i.e., 1.4 GHz, 6.93 GHz, 10.65 GHz, 18.7 GHz, 23.8 GHz, 36.5 GHz and 89.0 GHz). The results indicate that the atmosphere has a negligible impact on Tg at 1.4 GHz for land surfaces with emissivities greater than 0.7, at 6.93 GHz for land surfaces with emissivities greater than 0.8, and at 10.65 GHz for land surfaces with emissivities greater than 0.9 if a root mean square error (RMSE) less than 1 K is desired. To remove the atmospheric effect on Tg, a generalized atmospheric correction method is proposed by parameterizing the atmospheric transmittance τ and upwelling atmospheric brightness temperature Tba↑. Better accuracies with Tg RMSEs less than 1 K are achieved at 1.4 GHz, 6.93 GHz, 10.65 GHz, 18.7 GHz and 36.5 GHz, and worse accuracies with RMSEs of 1.34 K and 4.35 K are obtained at 23.8 GHz and 89.0 GHz, respectively. Additionally, a simplified atmospheric correction method is developed when lacking sufficient input data to perform the generalized atmospheric correction method, and an emissivity-based atmospheric correction method is presented when the emissivity is known. Consequently, an appropriate atmospheric correction method can be selected based on the available data, frequency and required accuracy. Furthermore, this study provides a method to estimate τ and Tba↑ of different frequencies using the atmospheric parameters (total water vapor content in observation direction Lwv, total cloud liquid water content Lclw and mean temperature of cloud Tclw), which is important for simultaneously determining the land surface parameters using multi-frequency passive microwave satellite data.

Combining thermal inertia and a diurnal temperature difference cycle model to estimate thermal inertia from MSG-SEVIRI data

Thermal inertia is an important parameter in geological and agricultural applications. In this study, we present a method that combines models of thermal inertia and the diurnal temperature difference cycle to estimate the thermal inertia from Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI) data. This method can directly derive thermal inertia from MSG-SEVIRI brightness temperatures without the need to include the land surface temperature and emissivity. Two important parameters (the time of the maximum temperature and the diurnal temperature difference) that were input into the thermal inertia model were obtained by fitting the diurnal temperature difference cycle model to the diurnal cycle of land surface temperatures. The spatial distribution of thermal inertia shows that high thermal inertia values occur over vegetated areas, whereas low thermal inertia values occur over bare areas. The uncertainty in thermal inertia is investigated in terms of the uncertainties in the surface albedo, the time of the maximum temperature, and the diurnal temperature difference. The results indicate that the uncertainty in thermal inertia over vegetated areas is greater than that over bare areas. The consistency of the thermal inertia model is evaluated by analysing the difference in thermal inertia values on two consecutive days. The root mean square error of the thermal inertia differences under nearly identical surface and atmospheric conditions on two consecutive days is considered to be the error of the thermal inertia model.

Generation of an all-weather land surface temperature product from MODIS and AMSR-E data

Land surface temperature (LST) is widely used in a variety of applications, such as meteorology, climatology, and ecology. Up to now, there are no all-weather LST products at high spatial resolution. In this study, we propose a method to generate an all-weather LST product by merging MODIS and AMSR-E data. Two main processes are performed in this method, including retrieving AMSR-E LST and downscaling AMSR-E LST to MODIS pixel resolution. After the implement of these two processes, MODIS LSTs under clear-sky conditions and AMSR-E LSTs under cloudy conditions are merged to generate an all-weather LST product. Results indicate that the merged LSTs filled up the missing data in the original MODIS LSTs due to the effects of cloud when compared with the original MODIS LSTs.

Temporal-spatial variations monitoring of soil moisture using microwave polarization difference index

Soil moisture is a key variable that influences the redistribution of the radiant energy and the runoff generation and percolation of water in soil. Knowledge of soil moisture temporal-spatial variations is important in a wide range of studies. This study aims to investigate the temporal-spatial variations of soil moisture using microwave polarization difference index (MPDI). The AMSR-E/Aqua Daily Global Quarter-Degree Gridded Brightness Temperature at 10.65 GHz channel was used to calculate the MPDI. In addition, the AMSR-E/Aqua Daily L3 Surface Soil Moisture was used in this study. The temporal and spatial patterns between the MPDI and soil moisture were analyzed. The results indicate that the temporal and spatial patterns of the MPDI are consistent with those of soil moisture. The MPDI reflects the temporal and spatial variations of soil moisture.

Preliminary results of temporal normalization of MODIS land surface temperature

MODIS land surface temperature (LST) products have been widely used in numerous applications. Each pixel within the MODIS LST products is acquired at different local solar time even though they are in the same granule. A temporal consistency and spatial comprehensiveness data set will benefit us in the utilization of the LST products in related applications and researches. In this study, a diurnal temperature cycle (DTC) model was employed to normalize the MODIS LSTs to the same local solar time. The MODIS LSTs were derived from the Terra/MODIS and Aqua/MODIS LST products (MOD11_L2 and MYD11_L2, respectively). The results at daytime only are presented because the larger LSTs heterogeneity makes the comparison of LSTs before and after the temporal normalization much clearer. The preliminary results indicate that the spatial variations of the MODIS LSTs caused by different local solar time are removed after the temporal normalization. The temporal normalized LSTs may become more suitable for the analysis of land surface processes.