In this study, optical and microwave satellite observations are integrated to estimate soil moisture at the same spatial resolution as the optical sensors (5km here) and applied for drought analysis in the continental United States. A new refined model is proposed to include auxiliary data like soil texture, topography, surface types, accumulated precipitation, in addition to Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) used in the traditional universal triangle method. It is found the new proposed soil moisture model using accumulated precipitation demonstrated close agreements with the U.S. Drought Monitor (USDM) spatial patterns. Currently, the USDM is providing a weekly map. Recently, “flash” drought concept appears. To obtain drought map on daily basis, LST is derived from microwave observations and downscaled to the same resolution as the thermal infrared LST product and used to fill the gaps due to clouds in optical LST data. With the integrated daily LST available under nearly all weather conditions, daily soil moisture can be estimated at relatively higher spatial resolution than those traditionally derived from passive microwave sensors, thus drought maps based on soil moisture anomalies can be obtained on daily basis and made the flash drought analysis and monitoring become possible.
more »
« less
Land Surface Temperature Derivation under All Sky Conditions through Integrating AMSR-E/AMSR-2 and MODIS/GOES Observations
Land surface temperature (LST) is an important input to the Atmosphere–Land Exchange Inverse (ALEXI) model to derive the Evaporative Stress Index (ESI) for drought monitoring. Currently, LST inputs to the ALEXI model come from the Geostationary Operational Environmental Satellite (GOES) and Moderate Resolution Imaging Spectroradiometer (MODIS) products, but clouds affect them. While passive microwave (e.g., AMSR-E and AMSR-2) sensors can penetrate non-rainy clouds and observe the Earth’s surface, but usually with a coarse spatial resolution, how to utilize multiple instruments’ advantages is an important methodology in remote sensing. In this study, we developed a new five-channel algorithm to derive LST from the microwave AMSR-E and AMSR-2 measurements and calibrate to the MODIS and GOES LST products. A machine learning method is implemented to further improve its performance. The MODIS and GOES LST products still show better performance than the AMSR-E and AMSR-2 LSTs when evaluated against the ground observations. Therefore, microwave LSTs are only used to fill the gaps due to clouds in the MODIS and GOES LST products. A gap filling method is further applied to fill the remaining gaps in the merged LSTs and downscale to the same spatial resolution as the MODIS and GOES products. With the daily integrated LST at the same spatial resolution as the MODIS and GOES products and available under nearly all sky conditions, the drought index, like the ESI, can be updated on daily basis. The initial implementation results demonstrate that the daily drought map can catch the fast changes of drought conditions and capture the signals of flash drought, and make flash drought monitoring become possible. It is expected that a drought map that is available on daily basis will benefit future drought monitoring.
more »
« less
- Award ID(s):
- 1841520
- NSF-PAR ID:
- 10139182
- Date Published:
- Journal Name:
- Remote Sensing
- Volume:
- 11
- Issue:
- 14
- ISSN:
- 2072-4292
- Page Range / eLocation ID:
- 1704
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
In this study, optical and microwave satellite observations are integrated to estimate soil moisture at high spatial resolution and applied for drought analysis in the continental United States. To estimate soil moisture, a new refined model is proposed to estimate soil moisture (SM) with auxiliary data like soil texture, topography, surface types, accumulated precipitation, in addition to Normalized Difference Vegetation Index and Land Surface Temperature (LST) used in the traditional universal triangle method. It is found the new proposed SM model using accumulated precipitation demonstrated close agreements with the U.S. Drought Monitor (USDM) spatial patterns. Currently, the USDM is providing a weekly map. Recently, “flash” drought concept appears. To obtain drought map on daily basis, LST is derived from microwave observations and downscaled to the same resolution as the thermal infrared LST product and used to fill the gaps due to clouds in optical LST data. With the integrated daily LST available under nearly all weather conditions, daily soil moisture can be estimated at relatively high spatial resolution, thus drought maps based on soil moisture anomalies can be obtained at high spatial resolution on daily basis and made the flash drought analysis and monitoring become possible.more » « less
-
Abstract. Land surface temperature (LST) is one of the most important and widely used parameters for studying land surface processes. Moderate ResolutionImaging Spectroradiometer (MODIS) LST products (e.g., MOD11A1 and MYD11A1) can provide this information with moderate spatiotemporal resolution withglobal coverage. However, the applications of these data are hampered because of missing values caused by factors such as cloud contamination,indicating the necessity to produce a seamless global MODIS-like LST dataset, which is still not available. In this study, we used a spatiotemporalgap-filling framework to generate a seamless global 1 km daily (mid-daytime and mid-nighttime) MODIS-like LST dataset from 2003 to 2020based on standard MODIS LST products. The method includes two steps: (1) data pre-processing and (2) spatiotemporal fitting. In the datapre-processing, we filtered pixels with low data quality and filled gaps using the observed LST at another three time points of the same day. In thespatiotemporal fitting, first we fitted the temporal trend (overall mean) of observations based on the day of year (independent variable) in eachpixel using the smoothing spline function. Then we spatiotemporally interpolated residuals between observations and overall mean values for eachday. Finally, we estimated missing values of LST by adding the overall mean and interpolated residuals. The results show that the missing values inthe original MODIS LST were effectively and efficiently filled with reduced computational cost, and there is no obvious block effect caused by largeareas of missing values, especially near the boundary of tiles, which might exist in other seamless LST datasets. The cross-validation withdifferent missing rates at the global scale indicates that the gap-filled LST data have high accuracies with the average root mean squared error(RMSE) of 1.88 and 1.33∘, respectively, for mid-daytime (13:30) and mid-nighttime (01:30). The seamless global daily (mid-daytime andmid-nighttime) LST dataset at a 1 km spatial resolution is of great use in global studies of urban systems, climate research and modeling,and terrestrial ecosystem studies. The data are available at Iowa State University's DataShare at https://doi.org/10.25380/iastate.c.5078492 (T. Zhanget al., 2021).more » « less
-
Abstract. Remote sensing data are a crucial tool for monitoring climatological changes and glacier response in areas inaccessible for in situ measurements. The Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) product provides temperature data for remote glaciated areas where air temperature measurements from weather stations are sparse or absent, such as the St. Elias Mountains (Yukon, Canada). However, MODIS LSTs in the St. Elias Mountains have been found in prior studies to show an offset from available weather station measurements, the source of which is unknown. Here, we show that the MODIS offset likely results from the occurrence of near-surface temperature inversions rather than from the MODIS sensor’s large footprint size or from poorly constrained snow emissivity values used in LST calculations. We find that an offset in remote sensing temperatures is present not only in MODIS LST products but also in Advanced Spaceborne Thermal Emissions Radiometer (ASTER) and Landsat temperature products, both of which have a much smaller footprint (90–120 m) than MODIS (1 km). In all three datasets, the offset was most pronounced in the winter (mean offset >8 ∘C) and least pronounced in the spring and summer (mean offset <2 ∘C). We also find this enhanced seasonal offset in MODIS brightness temperatures, before the incorporation of snow surface emissivity into the LST calculation. Finally, we find the MODIS LST offset to be consistent in magnitude and seasonal distribution with modeled temperature inversions and to be most pronounced under conditions that facilitate near-surface inversions, namely low incoming solar radiation and wind speeds, at study sites Icefield Divide (60.68∘N, 139.78∘ W; 2,603 m a.s.l) and Eclipse Icefield (60.84∘ N, 139.84∘ W; 3017 m a.s.l.). Although these results do not preclude errors in the MODIS sensor or LST algorithm, they demonstrate that efforts to convert MODIS LSTs to an air temperature measurement should focus on understanding near-surface physical processes. In the absence of a conversion from surface to air temperature based on physical principles, we apply a statistical conversion, enabling the use of mean annual MODIS LSTs to qualitatively and quantitatively examine temperatures in the St. Elias Mountains and their relationship to melt and mass balance.more » « less
-
null (Ed.)Surface albedo is a fundamental radiative parameter as it controls the Earth’s energy budget and directly affects the Earth’s climate. Satellite observations have long been used to capture the temporal and spatial variations of surface albedo because of their continuous global coverage. However, space-based albedo products are often affected by errors in the atmospheric correction, multi-angular bi-directional reflectance distribution function (BRDF) modelling, as well as spectral conversions. To validate space-based albedo products, an in situ tower albedometer is often used to provide continuous “ground truth” measurements of surface albedo over an extended area. Since space-based albedo and tower-measured albedo are produced at different spatial scales, they can be directly compared only for specific homogeneous land surfaces. However, most land surfaces are inherently heterogeneous with surface properties that vary over a wide range of spatial scales. In this work, tower-measured albedo products, including both directional hemispherical reflectance (DHR) and bi-hemispherical reflectance (BHR), are upscaled to coarse satellite spatial resolutions using a new method. This strategy uses high-resolution satellite derived surface albedos to fill the gaps between the albedometer’s field-of-view (FoV) and coarse satellite scales. The high-resolution surface albedo is generated from a combination of surface reflectance retrieved from high-resolution Earth Observation (HR-EO) data and moderate resolution imaging spectroradiometer (MODIS) BRDF climatology over a larger area. We implemented a recently developed atmospheric correction method, the Sensor Invariant Atmospheric Correction (SIAC), to retrieve surface reflectance from HR-EO (e.g., Sentinel-2 and Landsat-8) top-of-atmosphere (TOA) reflectance measurements. This SIAC processing provides an estimated uncertainty for the retrieved surface spectral reflectance at the HR-EO pixel level and shows excellent agreement with the standard Landsat 8 Surface Reflectance Code (LaSRC) in retrieving Landsat-8 surface reflectance. Atmospheric correction of Sentinel-2 data is vastly improved by SIAC when compared against the use of in situ AErosol RObotic NETwork (AERONET) data. Based on this, we can trace the uncertainty of tower-measured albedo during its propagation through high-resolution EO measurements up to coarse satellite scales. These upscaled albedo products can then be compared with space-based albedo products over heterogeneous land surfaces. In this study, both tower-measured albedo and upscaled albedo products are examined at Ground Based Observation for Validation (GbOV) stations (https://land.copernicus.eu/global/gbov/), and used to compare with satellite observations, including Copernicus Global Land Service (CGLS) based on ProbaV and VEGETATION 2 data, MODIS and multi-angle imaging spectroradiometer (MISR).more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/rs11141704
