Pei Leng

Bare surface soil moisture retrieval from the synergistic use of optical and thermal infrared data

Land surface soil moisture (SSM) is crucial to research and applications in hydrology, ecology, and meteorology. To develop a SSM retrieval model for bare soil, an elliptical relationship between diurnal cycles of land surface temperature (LST) and net surface shortwave radiation (NSSR) is described and further verified using data that were simulated with the Common Land Model (CoLM) simulation. In addition, with a stepwise linear regression, a multi-linear model is developed to retrieve daily average SSM in terms of the ellipse parameters x0 (horizontal coordinate of the ellipse centre), y0 (vertical coordinate of the ellipse centre), a (semi-major axis), and θ (rotation angle), which were acquired from the elliptical relationship. The retrieval model for daily average SSM proved to be independent of soil type for a given atmospheric condition. Compared with the simulated daily average SSM, the proposed model was found to be of higher accuracy. For eight cloud-free days, the root mean square error (RMSE) ranged from 0.003 to 0.031 m3 m−3, while the coefficient of determination (R2) ranged from 0.852 to 0.999. Finally, comparison and validation were conducted using simulated and measured data, respectively. The results indicated that the proposed model showed better accuracy than a recently reported model using simulated data. A simple calibration decreased RMSE from 0.088 m3 m−3 to 0.051 m3 m−3 at Bondville Companion site, and from 0.126 m3 m−3 to 0.071 m3 m−3 at the Bondville site. Coefficients of determination R2 = 0.548 and 0.445 were achieved between the estimated daily average SSM and the measured values at the two sites, respectively. This paper suggests a promising avenue for retrieving regional SSM using LST and NSSR derived from geostationary satellites in future developments.

Retrieval of daily evolution of soil moisture from satellite-derived land surface temperature and net surface shortwave radiation

Soil moisture is a key parameter in water balance, and it serves as the core and link in atmosphere–vegetation–soil–groundwater systems. Soil moisture directly affects the accuracy of the simulation and prediction conducted by hydrological and atmospheric models. This article aims to develop a new model to retrieve the daily evolution of soil moisture with time series of land surface temperature (LST) and net surface shortwave radiation (NSSR). First, for the time series of soil moisture, LST and NSSR daytime data were simulated by the common land model (CoLM) with different soil types in bare soil areas. Based on these data, the variations between soil moisture and LST-NSSR during the daytime with different soil types were analysed, and a plane function was used to fit the daily evolution of soil moisture and the time series of LST and NSSR data. Further study proved that the coefficients of the soil moisture retrieval model are not sensitive to soil type. Then, a relationship model between the daily evolution of soil moisture and the time series of LST-NSSR was developed and validated using the data simulated by CoLM with different soil types and different atmospheric conditions. To demonstrate the feasibility of the soil moisture retrieval method proposed in this study, it was applied to the African continent with data from the METEOSAT Second Generation Spinning Enhanced Visible and Infrared Imager (MSG–SEVIRI) geostationary satellite. The results show that the variation of soil moisture content can be quantitatively estimated directly by the method at the regional scale with some reasonable assumptions. This study can provide a new method for monitoring the variation of soil moisture, and it also indicates a new direction for deriving the daily variation of soil moisture using the information from the time series of the land surface variables.

Generation of continuous surface soil moisture dataset using combined optical and thermal infrared images

Surface soil moisture (SSM) is a critical variable for understanding water and energy flux between the atmosphere and the Earths surface. An easy to apply algorithm for deriving SSM time series that primarily uses temporal parameters derived from simulated and in situ datasets has recently been reported. This algorithm must be assessed for different biophysical and atmospheric conditions by using actual geostationary satellite images. In the present study, two currently available coarse-scale SSM datasets (microwave and reanalysis product) and aggregated in situ SSM measurements were implemented to calibrate the time-invariable coefficients of the SSM retrieval algorithm for conditions in which conventional observations are rare. These coefficients were subsequently used to obtain SSM time series directly from Meteosat Second Generation (MSG) images over the study area of a well-organized soil moisture network named REMEDHUS in Spain. The results show a high degree of consistency between the estimated and actual SSM time series values when using the three SSM dataset-calibrated time-invariable coefficients to retrieve SSM, with coefficients of determination (R2) varying from 0.304 to 0.534 and root mean square errors (RMSE) ranging from 0.020 m3/m3 to 0.029 m3/m3. Further evaluation with different land use types results in acceptable debiased RMSE between 0.021 m3/m3 and 0.048 m3/m3 when comparing the estimated MSG pixel-scale SSM with in situ measurements. These results indicate that the investigated method is practical for deriving time-invariable coefficients when using publicly accessed coarse-scale SSM datasets, which is beneficial for generating continuous SSM dataset at the MSG pixel scale.

An Effective Emission Depth Model for Passive Microwave Remote Sensing

Knowing the exact penetration depth for passive microwave will enable us to gain a better understanding of the characteristics of the targets. But now, there is only a penetration depth model that can calculate the specific depth while it is suitable for active microwaves. However, due to the completely different working mechanisms, the penetration depth model is most likely limited when applying to passive microwaves. Therefore, an effective emission depth model for passive microwaves was first proposed based on the radiative transfer theory, assuming that a homogenous double-layer (soil and atmosphere) medium exists between the passive microwave radiometer and a certain soil depth. With simulated data from the advanced integral equation model (AIEM) and subsequent analysis, the effective emission depth model was further simplified to make it more practical with a root-mean-squared error (RMSE) of 0.46 cm and a bias of 0.032 cm. For the simplified model, only the soil moisture content and the noise-equivalent differential temperature and center frequency of the radiometer are required. By comparing the penetration depth model and simplified effective emission depth model, a certain soil moisture content that makes both the models reveal the same results is found.

An Algorithm for Retrieving Precipitable Water Vapor over Land Based on Passive Microwave Satellite Data

Precipitable water vapor (PWV) is one of the most variable components of the atmosphere in both space and time. In this study, a passive microwave-based retrieval algorithm for PWV over land without land surface temperature (LST) data was developed. To build the algorithm, two assumptions exist: (1) land surface emissivities (LSE) at two adjacent frequencies are equal and (2) there are simple parameterizations that relate transmittance, atmospheric effective radiating temperature, and PWV. Error analyses were performed using radiosonde sounding observations from Zhangye, China, and CE318 measurements of Dalanzadgad (43°34′37′′N, 104°25′8′′E) and Singapore (1°17′52′′N, 103°46′48′′E) sites from Aerosol Robotic Network (AERONET), respectively. In Zhangye, the algorithm had a Root Mean Square Error (RMSE) of 4.39 mm and a bias of 0.36 mm on cloud-free days, while on cloudy days there was an RMSE of 4.84 mm and a bias of 0.52 mm because of the effect of liquid water in clouds. The validations in Dalanzadgad and Singapore sites showed that the retrieval algorithm had an RMSE of 4.73 mm and a bias of 0.84 mm and the bigger errors appeared when the water vapor was very dry or very moist.

Toward the Estimation of Surface Soil Moisture Content Using Geostationary Satellite Data over Sparsely Vegetated Area

Based on a novel bare surface soil moisture (SSM) retrieval model developed from the synergistic use of the diurnal cycles of land surface temperature (LST) and net surface shortwave radiation (NSSR) (Leng et al. 2014. “Bare Surface Soil Moisture Retrieval from the Synergistic Use of Optical and Thermal Infrared Data”. International Journal of Remote Sensing 35: 988–1003.), this paper mainly investigated the model’s capability to estimate SSM using geostationary satellite observations over vegetated area. Results from the simulated data primarily indicated that the previous bare SSM retrieval model is capable of estimating SSM in the low vegetation cover condition with fractional vegetation cover (FVC) ranging from 0 to 0.3. In total, the simulated data from the Common Land Model (CoLM) on 151 cloud-free days at three FLUXNET sites that with different climate patterns were used to describe SSM estimates with different underlying surfaces. The results showed a strong correlation between the estimated SSM and the simulated values, with a mean Root Mean Square Error (RMSE) of 0.028 m3·m−3 and a coefficient of determination (R2) of 0.869. Moreover, diurnal cycles of LST and NSSR derived from the Meteosat Second Generation (MSG) satellite data on 59 cloud-free days were utilized to estimate SSM in the REMEDHUS soil moisture network (Spain). In particular, determination of the model coefficients synchronously using satellite observations and SSM measurements was explored in detail in the cases where meteorological data were not available. A preliminary validation was implemented to verify the MSG pixel average SSM in the REMEDHUS area with the average SSM calculated from the site measurements. The results revealed a significant R2 of 0.595 and an RMSE of 0.021 m3·m−3.

Effects of vegetation and soil texture on surface soil moisture retrieval using multi-temporal optical and thermal infrared observations

To retrieve surface soil moisture (SSM) content over natural surfaces quantitatively, the effects of vegetation and soil texture on a previously developed bare SSM retrieval model are evaluated using simulated data from the common land model (CoLM). The results indicate that (1) both the accuracy and the five model parameters of the previous SSM retrieval model show relatively consistent variations when the fractional vegetation cover (FVC) varies from 0 to 0.7; and (2) the SSM exhibits a generally significant and exponential relationship with the rotation angle when the clay content is lower than 30%, with the FVC ranging from 0 to 0.7. These findings make it possible to estimate SSM directly under the conditions that the underlying surface is in the presence of spatially variable FVC and soil texture. On this basis, we further confirm the feasibility of using the previous bare SSM retrieval model to estimate SSM for FVC varying from 0 to 0.7 with a clay content lower than 30%. For the simulated data on eight cloud-free days, the total root mean square error (RMSE) of the retrieved SSM and the coefficient of determination (R2) are 0.033 m3m−3 and 0.758, respectively. Ultimately, a preliminary validation is conducted using the ground measurements at the Bondville site; an R2 = 0.328 and a RMSE = 0.058 m3m−3 are obtained for 14 cloud-free days.

First Results of Estimating Surface Soil Moisture in the Vegetated Areas Using ASAR and Hyperion Data: The Chinese Heihe River Basin Case Study

This study introduces a new approach to estimate surface soil moisture in vegetated areas using Synthetic Aperture Radar (SAR) and hyperspectral data. To achieve this, the Michigan Microwave Canopy Scattering (MIMICS) model was initially used to simulate backscatter from vegetated surfaces containing various canopy water contents, across three frequency bands (i.e., L, S, and C). Using this simulated dataset, the influence of the canopy water content on the backscattered signals was further analyzed. In addition, we developed a modified Water-Cloud model which adds in the crown-ground interaction term. Finally, a soil moisture retrieval model for an agricultural region was developed. Alternating polarization data with ASAR and Hyperion hyperspectral data were used to retrieve soil moisture and validate the feasibility of the retrieval model. The field measured data from the Heihe river basin was used to confirm the proposed model. Results revealed an average absolute deviation (AAD) and average absolute relative deviation (AARD) of 0.051 cm3∙cm−3 and 19.7%, respectively, between the estimated soil moisture and the field measurements.

Preliminary validation of two temporal parameter-based soil moisture retrieval models using a satellite product and in situ soil moisture measurements over the REMEDHUS network

ABSTRACT This study aims to preliminarily validate two newly developed temporal parameter-based surface soil moisture (SSM) retrieval models, namely the mid-morning model and daytime model, using both microwave satellite soil moisture product and in situ SSM measurements over a well-organized soil moisture network named REd de MEDición de la HUmedad del Suelo (REMEDHUS) in Spain. Ground SSM measurements and geostationary satellite observations were primarily implemented to obtain the model coefficients for the two SSM retrieval models for each cloud-free day. These model coefficients were subsequently used to estimate SSM using the Meteosat Second Generation products over the study area. Preliminary verification using both a satellite product and in situ SSM measurements demonstrated that SSM variation can be well detected by both SSM retrieval models. Specifically, a generally similar accuracy (coefficient of determination R2: 0.419–0.379, root mean square error: 0.046–0.051 m3 m−3, Bias: −0.020 to −0.025 m3 m−3) was found for the mid-morning model and the daytime model with the microwave missions based climate change initiative SSM product, respectively. Moreover, except for the comparable R2 (0.614–0.675), a better accuracy (Bias: 0.032–0.044 m3 m−3, RMSE: 0.043–0.050 m3 m−3) are achieved for the daytime model and the mid-morning model with network SSM measurements, respectively. These results indicate that the daytime model exhibited generally comparable or better accuracy than that of the mid-morning model over the study area. This study has strengthened the feasibility of using multi-temporal information derived from the geostationary satellites to estimate SSM in future research.

Evaluation of the Effects of Soil Layer Classification in the Common Land Model on Modeled Surface Variables and the Associated Land Surface Soil Moisture Retrieval Model

Land surface soil moisture (SSM) is crucial in research and applications in hydrology, ecology, and meteorology. A novel SSM retrieval model, based on the diurnal cycles of land surface temperature (LST) and net surface shortwave radiation (NSSR), has recently been reported. It suggests a promising avenue for the retrieval of regional SSM using LST and NSSR derived from geostationary satellites in a future development. As part of a further improvement of previous work, effects of soil layer classification in the Common Land Model (CoLM) on modeled LST, NSSR and the associated SSM retrieval model in particular, have been evaluated. To address this issue, the soil profile has been divided in to three layers, named upper layer (0–0.05 m), root layer (0.05–1.30 m) and bottom layer (1.30–2.50 m). By varying the number of soil layers with the three layer zones, nine different soil layer classifications have been performed in the CoLM to produce simulated data. Results indicate that (1) modeled SSM is less sensitive to soil layer classification while modeled LST and NSSR are sensitive, especially under wet conditions and (2) the simulated data based SSM retrieval model is stable for a fixed upper layer with varying classifications of root and bottom layers. It also concludes an optimal soil layer classification for the CoLM while producing simulated data to develop the SSM retrieval model.