A Modified Temperature-Vegetation Dryness Index(MTVDI)for Assessment of Surface Soil Moisture Based on MODIS Data

Spatio-temporal dynamic monitoring of soil moisture is highly important to management of agricultural and vegetation eco-systems.The temperature-vegetation dryness index based on the triangle or trapezoid method has been used widely in previous studies.However,most existing studies simply used linear regression to construct empirical models to fit the edges of the feature space.This requires extensive data from a vast study area,and may lead to subjective results.In this study,a Modified Temperature-Vegetation Dryness Index(MTVDI)was used to monitor surface soil moisture status using MODIS(Moderate-resolution Imaging Spectroradiometer)remote sensing data,in which the dry edge conditions were determined at the pixel scale based on surface energy balance.The MTVDI was validated by field measurements at 30 sites for 10 d and compared with the Temperature-Vegetation Dryness Index(TVDI).The results showed that the R^(2) for MTVDI and soil moisture obviously improved(0.45 for TVDI,0.69 for MTVDI).As for spatial changes,MTVDI can also better reflect the actual soil moisture condition than TVDI.As a result,MTVDI can be considered an effective method to monitor the spatio-temporal changes in surface soil moisture on a regional scale.

Utilizing a new soil effective temperature scheme and archived satellite microwave brightness temperature data to estimate surface soil moisture in the Nagqu region, Tibetan Plateau of China

Since the early 2000s, many satellite passive microwave brightness temperature （BT） archives, such as the Advanced Microwave Scanning Radiometer for the Earth Observing System （AMSR-E） BTs, have become the useful resources for assessing the changes in the surface and deep soil moistures over both arid and semi-arid regions. In this study, we used a new soil effective temperature （T scheme and the archived AMSR-E BTs to estimate surface soil moisture （SM） over the Nagqu region in the central Tibetan Plateau, China. The surface and deep soil temperatures required for the calculation of regional-scale T were obtained from outputs of the Community Land Model version 4.5 （CLM4.5）. In situ SM measurements at the CEOP-CAMP/Tibet （Coordinated Enhanced Observing Period Asia-Australia Monsoon Project on the Tibetan Plateau） experimental sites were used to validate the AMSR-E-based SM estimations at regional and single-site scales. Furthermore, the spatial distribution of monthly mean surface SM over the Nagqu region was obtained from 16 daytime AMSR-E BT observations in July 2004 over the Nagqu region. Results revealed that the AMSR-E-based surface SM estimations agreed well with the in situ-based surface SM measurements, with the root mean square error （RMSE） ranging from 0.042 to 0.066 m3/m3 and the coefficient of determination （R2） ranging from 0.71 to 0.92 during the nighttime and daytime. The regional surface soil water state map showed a clear spatial pattern related to the terrain. It indicated that the lower surface SM values occurred in the mountainous areas of the northern, mid-western and southeastern parts of Nagqu region, while the higher surface SM values appeared in the low elevation areas such as the Tongtian River Basin, Namco Lake and bog meadows in the central part of Nagqu region. Our analysis also showed that the new T^scheme does not require special fitting parameters or additional assumptions, which simplifies the data requirements for regional-scale applications. This scheme combined with the archived satellite passive microwave BT observations can be used to estimate the historical surface SM for hydrological process studies over the Tibetan Plateau regions.

Influence of Vegetation Cover on the Oh Soil Moisture Retrieval Model: A Case Study of the Malinda Wetland, Tanzania

Soil moisture is an important parameter that drives agriculture, climate and hydrological systems. In addition, retrieval of soil moisture is important in the analysis as well as its influence on these systems. Radar imagery is best suited for this retrieval due to its all-weather capability and independence from solar irradiation. Soil moisture retrieval was done for the Malinda Wetland, Tanzania, during two time periods, March and September 2013. The aim of this paper was to analyze soil moisture retrieval performance when vegetation contribution is taken into account. Backscatter values were obtained from TerraSAR-X Spotlight mode imagery taken in March and September 2013. The backscatter values recorded by SAR imagery are influenced by vegetation, soil roughness and soil moisture. Thus, in order to obtain the backscatter due to soil moisture, the roughness and vegetation contribution are determined and decoupled from total backscatter. The roughness parameters were obtained from a Digital Surface Model (DSM) from Unmanned Aerial Vehicle (UAV) photographs whereas the vegetation parameter was obtained by inverting the Water Cloud Model (WCM). Lastly, soil moisture was retrieved using the Oh Model. The coefficient of correlation between the observed and retrieved was 0.39 for the month of March and 0.65 in the month of August. When the vegetation contribution was considered, the r2 for March was 0.64 and that in August was 0.74. The results revealed that accounting for vegetation improved soil moisture retrieval.

A performance evaluation of remotely sensed sea surface salinity products in combination with other surface measurements in reconstructing three-dimensional salinity fields

Several remotely sensed sea surface salinity（SSS） retrievals with various resolutions from the soil moisture and ocean salinity（SMOS） and Aquarius/SAC-D missions are applied as inputs for retrieving salinity profiles（S） using multilinear regressions. The performance is evaluated using a total root mean square（RMS） error, different error sources, and the feature resolutions of the retrieved S fields. In the mixed layer of the salinity, the SSS-S regression coefficients are uniformly large. The SSS inputs yield smaller RMS errors in the retrieved S with respect to Argo profiles as their spatial or temporal resolution decreases. The projected SSS errors are dominant, and the retrieved S values are more accurate than those of climatology in the tropics except for the tropical Atlantic, where the regression errors are abnormally large. Below that level, because of the influence of a sea level anomaly, the areas of high-accuracy S values shift to higher latitudes except in the high-latitude southern oceans, where the projected SSS errors are abnormally large. A spectral analysis suggests that the CATDS-0.25° results are much noisier and that the BEC-L4-0.25° results are much smoother than those of the other retrievals. Aquarius-CAP-1° generates the smallest RMS errors, and Aquarius-V2-1° performs well in depicting large-scale phenomena. BEC-L3-0.25°,which has small RMS errors and remarkable mesoscale energy, is the best fit for portraying mesoscale features in the SSS and retrieved S fields. The current priority for retrieving S is to improve the reliability of satellite SSS especially at middle and high latitudes, by developing advanced algorithms, combining both sensors, or weighing between accuracy and resolutions.

Water Resources of the South Asian Region in a Warmer Atmosphere

The global mean surface temperature may rise by about 0.3t per decade during the next few decades as a result o f anthropogenic greenhouse gas emissions in the earth's atmosphere. The data generated in the greenhouse warming simulations (Business-as-Usual scenario of IPCC) with the climate models developed at Max Planck Institute for Meteorology, Hamburg have been used to assess future plausible hydrological scenario for the South Asian region.The model results indicate enhanced surface warming (2.7) for summer and 3.6℃ for winter) over the land reginos of South Asia during the next hundred years. While there is no significant change in the precipitation over most of the land regions during winter, substantial increase in precipitation is likely to occur during summer. As a result, an increase in soil moisture is likely over central india, Bangladesh and South China during summer but a statistically significant decline in soil moisture is expected over central China in winter. A moderate decrease in surface runoff may occur over large areas of central China during winter while the flood prone areas of NE--India, Bangladesh and South China are likely to have an increase ill surface runoff during summer by the end of next century.

Digitizing the thermal and hydrological parameters of land surface in subtropical China using AMSR-E brightness temperatures

Digitizing the land surface temperature(T_(s))and surface soil moisture(m _(v))is essential for developing the intelligent Digital Earth.Here,we developed a two parameter physical-based passive microwave remote sensing model for jointly retrieving T_(s) and m_(v) using the dual-polarized T_(b) of Aqua satellite advanced microwave scanning radiometer(AMSR-E)C-band(6.9 GHz)based on the simplified radiative transfer equation.Validation using in situ T_(s) and m_(v) in southern China showed the average root mean square errors(RMSE)of T s and m_(v) retrievals reach 2.42 K(R^(2)=0.61,n=351)and 0.025 g cm^(−3)(R^(2)=0.68,n=663),respectively.The results were also validated using global in situ T_(s)(n=2362)and m_(v)(n=1657)of International Soil Moisture Network.The corresponding RMSE are 3.44 k(R 2=0.86)and 0.039 g cm^(−3)(R^(2)=0.83),respectively.The monthly variations of model-derived Ts and mv are highly consistent with those of the Moderate Resolution Imaging Spectroradiometer T_(s)(R^(2)=0.57;RMSE=2.91 k)and ECV_SM m_(v)(R^(2)=0.51;RMSE=0.045 g cm^(−3)),respectively.Overall,this paper indicates an effective way to jointly modeling T_(s) and m_(v) using passive microwave remote sensing.