Drought is considered to be the most severe natural hazard in terms of impact, duration, and spatial extent [1] .
The sparse spatial distribution of weather stations makes it daunting for drought monitoring and predicting. Satellite remote sensing capabilities have been greatly improved for decades and served as the main method for drought monitoring. Drought may occur unnoticeably and varyingly. Lack of information to drought may lead to severe disaster. The damage was extensive and the impact to livestock and farm production is uncountable [2] .
Government agencies within National Oceanic and Atmospheric Administration (NOAA) and United States Department of Agriculture (USDA) have teamed up with the National Drought Mitigation Center (NDMC) to produce a weekly drought monitor (DM) map that incorporates climate data and professional input from all levels and is well known as the U.S. Drought Monitor (USDM). The USDM maps are consensus product based on several indicators and key variables, and the final maps are adjusted manually by numerous experts throughout the country to reflect the real-world conditions as reported (Svoboda et al. 2002). The USDM drought conditions are classified into five classes based on a ranking percentile approach: (1) D0 -abnormally, (2) D1 -moderate, (3) D2 -severe, (4) D3 -extreme, and (5) D4 -exceptional dry conditions. They are utilized as (1) D0-D4 (percentile≤30%), (2) D1-D4 (percentile≤20%),
(3) D2-D4 (percentile ≤ 10%), (4) D3-D4 (percentile ≤ 5%), and (5) D4 (percentile ≤ 2%) [3][4][5] .
The USDM maps are currently distributed online (http://droughtmonitor.unl.edu/) with relatively coarse resolution. They served as one of the criteria to determine the eligibility for relief of aggravation due to drought condition.
Agricultural interest in drought is important in much of the U.S. In fact, there is considerable interest in indices that can monitor agricultural drought. The hydrological condition of agricultural drought is closely linked to soil moisture [6] , which is dependent on precipitation, water infiltration, and soil water holding capacity. Since it's hard to measure soil moisture over large area directly, Leese et al. [7] concluded it's better to monitor soil moisture with combination of in-situ model and remote sensed variables respond to soil moisture. Satellite remote sensing data with large area coverage is a promising and economical tool to estimate soil moisture and enables drought monitoring based on surface parameters, such as NDVI, LST, evaportranspiration, and soil moisture. The microwave-optical/IR synergistic approach is an efficient method to improve the current drought-related soil moisture products with several advantages including higher spatial and temporal resolutions. Zhan et al. [8] described a synergistic technique using optical/infrared frequency products to overcome the coarse spatial resolution of the MW satellite products. This method was later enhanced by Chauhan et al. [9] . They built the statistical relationships between near-surface soil moisture and optical-derived soil moisture indices. Merlin et al. [10] applied these relations and transferred this method to a wider range of conditions. However, this method requires many surface parameters and micrometeorological data, which may not be available over large areas. It's desirable to find a simple and reasonable model for drought monitoring comparable to the USDM drought classifications, and to explore the possibility for linking a real-time index with surface wetness condition in a fine resolution. In this study, a new approach to build a drought indicator at fine resolution are implemented with near real time microwave and optical satellite observations. After introduction of the study area and data used, specifics of these approaches and their results in analyzing drought conditions in the continental United States (CONUS, the latitude and longitude range is about 20~50 N, and -125 ~ -75W) during the recent years are presented in the following sections.
A comprehensive data set is collected and processed for deriving soil moisture at optical sensor resolution (5 km in this study) from satellite observations and evaluating drought conditions in the CONUS. These data include:
-MODIS LST and emissivity daily L3 global climate modeling grid (CMG) product (short name: MYD11C1) with a resolution of 0.05° [ 11] .
-MODIS LST/emissivity 8-Day L3 CMG product (short name: MYD11C2) with a resolution of 0.05° [ 11] -NDVI data is extracted from the MODIS 16-day composite NDVI product (short name: MYD13C1) with a resolution of 0.05° [ 12] .
-Precipitation data are obtained from the TRMM (Tropical Rainfall Measuring Mission) Multi-satellite Precipitation Analysis (TMPA) with 0.25° spatial resolution and 3-hourly temporal resolution [13] .
-Elevation data are derived from the National Elevation Dataset (NED) data at a resolution of 100 meters [14] .
-MODIS land cover Climate Modeling Grid (CMG) product (Short Name: MCD12C1) provides the dominant land cover types at a spatial resolution of 0.05°.
-Soil texture data, including sand and porosity, are obtained from the Food and Agriculture Organization / United Nations Educational, Scientific and Cultural Organization (FAO/UNESCO) soil map, with a resolution of about 0.0833°[ 15,16] .
-Soil moisture data used for calibration is obtained from the Soil Moisture Operational Product System (SMOPS) at 0.25° resolution developed by NO-AA-NESDIS. This SMOPS product merges soil moisture retrievals from microwave satellite sensors such as the Advanced Scatterometers (ASCAT) on MetOp-A and B, Soil Moisture and Ocean Salinity of European Space Agency, WindSat of Naval Research Lab based on the Single Channel Algorithm [17,18] .
-Soil moisture outputs at 0.125° resolution from the three land-surface models (LSMs): the community Noah [19] , the Mosaic [20] , and the Variable Infiltration Capacity (VIC) model [21] , are obtained from the North American Land Data Assimilation System (NLDAS) [22] .
The datasets used in this study were obtained at different temporal and spatial resolutions. All the datasets were needed to be resampled to the same resolution.
-For calibration using the SMOPS soil moisture (SM) data, all the datasets were aggregated to 25 km, the same resolution as the SMOPS SM data. The SM models were firstly built at 25 km resluiton, then were applied to optical sensor data to estimate SM at the optical sensor resolution (5 km here).
-In order to compare with the USDM drought condition maps, all the datasets have been resampled or interpolated to uniform weekly (7 days) temporal and 0.0833° (about 12-km) spatial resolutions.
-For "flash" drought study, all the datasets were resampled or downscaled to the same 5 km spatial resolution as the MODIS LST product and estimate SM at 5 km spatial resolution on daily basis.
Land cover data has been resampled via the nearest neighbor assignment due to its discrete value. The bicubic interpolation assignment [23] was used to re-scale the other datasets, assuming that each point value changes consistently during observations.
A close relationship exists between vegetation vigor and soil moisture availability, especially in arid and semiarid areas, thus in many cases satellite derived NDVI and LST products have been used to evaluate drought condition. Carlson et al. found the relationship between measured surface temperature, vegetation fraction, and soil moisture, known as the "Universal Triangle Model" [24] . Chauhan et al. [9] argued that the second or third order polynomial gives a better representation of the data since a single polynomial represents a wide range of surface climate conditions and land surface types. Thus an Universal Triangle Model was developed and can be described as: (1) where , , subscripts max and min refer to the maximum and minimum values. Parameters a 00 , a 10 , …, a 22 are the regression coefficients.
Sun and Kafatos [25] the relationship between soil moisture and measurable land surface parameters [9] . Nevertheless, surface types vary significantly, and therefore, even a combination of NDVI, LST or albedo is not enough to fully describe the surface conditions. Soil moisture is also highly related to precipitation (the land water balance equation indicates the change of soil moisture is highly related to pre-
where "Pr" represents precipitation, "DEM" is for Digital Elevation Model (DEM) data, "Sand" is the individual grains or particles which can be seen with the naked eyes, "Poro" refers to porosity about how many pores/ holes a soil has, and "LC" is for land cover data. b 0 , b 1 , …, b 8 are regression coefficients.
As shown in Figure 1, the black line in Figure 1b is the corresponding normalized monthly accumulated precipitation, and the LOWESS (LOcally Weighted Scatterplot Smoothing) [26] (Cleveland 1979) is applied to describe the nonlinear trends of precipitation (the blue line in Figure 1b). The drought condition may not be directly reflected by temporal variation in precipitation because drought is caused by precipitation deficit during some period of time, usually more than a season. It is found that precipitation has an accumulating and lagging effect on drought condition. For example, the trend of precipitation is reduced in 2006 and 2011 (Figure 1b), yet the USDM drought maps marked these years as normal conditions (Figure 1a
The SMOPS soil moisture products were used for calibration to derive the regression coefficients in equations (1), (2), and (3). The least square regression was applied to estimate the regression coefficients and 50% data were used for training.
Soil moisture changes slowly, therefore cannot catch the fast change of drought conditions. Soil moisture anomaly is more appropriate to describe drought conditions than the absolute soil moisture [27] . In this study, we averaged daily soil moisture into weekly to match with the UM drought maps temporally. Soil moisture anomaly maps are obtained by the difference between weekly soil moisture and the long-term average soil moisture based on the equation: (4) where the average soil moisture for each pixel is calculated for the same weeks over the 11 years from January 1 2003 to December 31 2014. Negative soil moisture anomalies stand for the observed data are lower than the averaged data, and indicate dry conditions.
The ESI is defined as the anomalies in the ratio of actual-to-potential ET (AET/PET), derived from the thermal remote sensing based on the Atmosphere-Land Exchange Inverse (ALEXI) surface energy balance model [28][29][30][31] . The ALEXI uses measurements of morning land-surface temperature retrieved from geostationary satellite thermal band imagery to solve the Two-Source Energy Balance (TSEB) algorithm [32] in time-differential model. Actual ET (AET) output from the ALEXI is estimated as the potential ET (PET) expected under non-moisture limiting conditions, yielding a non-dimensional ET variable, ESI, ranging from 0 (dry)
to approximately 1 (wet).
-Vegetation Health Index (VHI) Kogan et al. [33] proposed to combine the Vegetation Condition Index (VCI) and the Temperature Condition Index (TCI) to Vegetation Health Index (VHI):
VHI=a*VCI+b*TCI (5) where the coefficient a and b are usually taken as 0.5. The VCI is defined as: (6) where and are the multi years maximum and minimum NDVI in a given area for growing season. The TCI is defined by Kogan [34] as: TCI = 100 x (BT max -BT i )/ (BT max -BT min ) (7) where BT, BT max , and BT min are smoothed brightness temperature, its maximum and minimum, respectively calculated for each pixel and week from multiyear data, and i is the year. -Vegetation Temperature Condition Index (VTCI)
Wang et al. [35] developed Vegetation Temperature Condition Index (VTCI) based on the triangular space of LST and NDVI for monitoring drought stress. It's defined as following: (8) where and are the maximum and minimum land surface temperature of pixels which have the same value, respectively, denotes land surface temperature of one pixel whose NDVI value is . If VTCI(i) < 0.4, then the area (i) is under severe drought condition.
The temporal correlation coefficients are computed between the outputs from the refined soil moisture model and the USDM drought classifications at weekly scales during the growing season from April to October of each year. Recently, "flash" drought concept appears. Flash drought frequently occurred in the central and eastern United States [36] . The 2012 drought over the Northern American demonstrated the worst surface condition since the 1930s Dust Bowl [37] . The drought started in 2011, extended rapidly in 2012 (especially in June and July according to the USDM classifications), and continued in 2013. This event was pervasive in the central regions of the United States due to the absence of rainfall in the growing season. The rapid soil moisture loss led this event as "flash drought" [38] . Unlike the common drought that is caused by external forcing like SST anomalies, the flash drought event was a result of natural weather variations, with little warnings found from the traditional drought metrics or climate model simulations [39] . The flash drought event suggests that the current drought monitoring should enhance its temporal resolution.
In the above drought analyses as shown in Figure 2 and Figure 3, the LST input to the soil moisture model is the weekly composite data. Because thermal infrared (TIR) LST can only be obtained under clear conditions, as shown in Figure 4a, there are a lot of gaps or missing values due to clouds in the daily MODIS LST. Only weekly composite can get a clear LST map. Since microwave sensor can penetrate most non-rainy clouds and observe the Earth surface, so we think about using microwave observations to fill the gaps due to clouds in the thermal IR LST. The microwave observations will be firstly calibrated to thermal IR (MODIS here) LST, and then downscaled to the same spatial resolution as the TIR LST, and then merged with the TIR observations to fill the gaps due to clouds in the TIR LST. The detailed information and processes are described in another paper [40] . Here we show an example in Figure 4. As demonstrated in Figure 4, the original daily MODIS LST exist a lot of gaps due to clouds (Figure 4a), while the LST derived from the AMSR-E with a new proposed five-channel algorithm [40] can get a clear and spatial continuous distribution (Figure 4b
In this study, we integrated microwave and optical sensors to estimate soil moisture at high spatial resolu- There are still some limitations in this study: (1) this application was limited to the warm season, while cold season needs further investigation to fulfill the requirement of surface monitoring. (2) to further improve the applications, more agricultural related data should be examined. Since our model output can also provide the information of wetness level, agricultural related data such as irrigation, should be used as an important evaluation for the outputs.