This repository will contain the Surface Biology and Geology Thermal Infrared (SBG-TIR) OTTER level 2 land-surface temperature and emissivity (L2 LSTE) data product algorithm.
The SBG collection 1 level 2 surface temperature data product is being developed based on the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) collection 3 level 2 surface temperature data product.
Glynn C. Hulley (he/him)
[email protected]
Lead developer and designer
NASA Jet Propulsion Laboratory 329G
This document outlines the theory and methodology for generating the OTTER Level-2 (L2) land surface temperature and emissivity (LST&E) products. The LST product is derived from the six TIR spectral bands between 8 and 12.5 µm, while the emissivity is retrieved for all 6 TIR and 2 MIR bands. The LST&E products are retrieved from the surface spectral radiance that is obtained by atmospherically correcting the at-sensor spectral radiance. Knowledge of the surface emissivity is critical for accurately recovering the surface temperature, a key climate variable in many scientific studies from climatology to hydrology, modeling the greenhouse effect, drought monitoring, and land surface models (Anderson et al. 2007; French et al. 2005; Jin and Dickinson 2010).
In addition to surface energy balance, LST&E products are essential for a wide range of other Earth system studies. For example, emissivity spectral signatures are important for geologic studies and mineral mapping studies (Hook et al. 2005; Vaughan et al. 2005). This is because emissivity features in the TIR region are unique for many different types of materials that make up the Earth's surface, for example, quartz, which is ubiquitous in most of the arid regions of the world. Emissivities are also used for land use and land cover change mapping since vegetation fractions can often be inferred if the background soil is observable (French et al. 2008).
Maximum radiometric emission for the typical range of Earth surface temperatures, excluding fires and volcanoes, is found in two infrared spectral "window" regions: the midwave infrared (3.5–5 µm) and the thermal infrared (8–13 µm). The radiation emitted in these windows for a given wavelength is a function of both temperature and emissivity. Determining the separate contribution from each component in a radiometric measurement is an ill-posed problem since there will always be more unknowns—N emissivities and a single temperature—than the number of measurements, N, available. For SBG, we will be solving for one temperature and eight emissivities. Therefore, an additional constraint is needed, independent of the data. There have been numerous theories and approaches over the past two decades to solve for this extra degree of freedom. For example, the ASTER Temperature Emissivity Working Group (TEWG) analyzed ten different algorithms for solving the problem (Gillespie et al. 1999). Most of these relied on a radiative transfer model to correct at-sensor radiance to surface radiance and an emissivity model to separate temperature and emissivity. Other approaches include the split-window (SW) algorithm, which extends the SST SW approach to land surfaces, assuming that land emissivities in the window region (10.5–12 µm) are stable and well known. However, this assumption leads to unreasonably large errors over barren regions where emissivities have large variations both spatially and spectrally. The ASTER TEWG finally decided on a hybrid algorithm, termed the temperature emissivity separation (TES) algorithm, which capitalizes on the strengths of previous algorithms with additional features (Gillespie et al. 1998).
The remainder of the document will discuss the SBG instrument characteristics, provide a background on TIR remote sensing, give a full description and background on the atmospheric correction and the TES algorithm, provide quality assessment, discuss numerical simulation studies and, finally, outline a validation plan.
SBG standards incorporate additional metadata that describe each HDF5 Dataset within the HDF5 file. Each of these metadata elements appear in an HDF5 Attribute that is directly associated with the HDF5 Dataset. Wherever possible, these HDF5 Attributes employ names that conform to the Climate and Forecast (CF) conventions. Table 2-3 lists the CF names for the HDF5 Attributes that SBG products typically employ.
Each SBG product bundle contains at least two sets of product metadata:
- ProductMetadata
- StandardMetadata
Information on the StandardMetadata is included on the SBG-TIR github landing page
| CF Compliant Attribute Name | Description | Required? |
|---|---|---|
| Units | Units of measure. Appendix A lists applicable units for various data elements in this product. | Yes |
| valid_max | The largest valid value for any element in the Dataset. The data type in valid_max matches the type of the associated Dataset. Thus, if the associated Dataset stores float32 values, the corresponding valid_max will also be float32. | No |
| valid_min | The smallest valid value for any element in the Dataset. The data type in valid_min matches the type of the associated Dataset. Thus, if the associated Dataset stores float32 values, the corresponding valid_min will also be float32. | No |
| _FillValue | Specification of the value that will appear in the Dataset when an element is missing or undefined. The data type of _FillValue matches the type of the associated Dataset. Thus, if the associated Dataset stores float32 values, the corresponding _FillValue will also be float32. Datasets that do not have a fill value will omit this attribute. | No |
| long_name | A descriptive name that clearly describes the content of the associated Dataset. | Yes |
Table 1. SBG Specific Local Attributes
| Group | Type | Size | Example |
|---|---|---|---|
| QAPercentCloudCover | Int | 4 | 80 |
| CloudMeanTemperature | LongFloat | 8 | 231 |
| CloudMaxTemperature | LongFloat | 8 | 275 |
| CloudMinTemperature | LongFloat | 8 | 221 |
| CloudSDevTemperature | LongFloat | 8 | 0.45 |
| QAFractionGoodQuality | Int | 4 | 0.7 |
| LSTGoodAvg | LongFloat | 8 | 285.4 |
| Emis3GoodAvg | LongFloat | 8 | 0.95 |
| Emis4GoodAvg | LongFloat | 8 | 0.95 |
| Emis5GoodAvg | LongFloat | 8 | 0.95 |
| Emis6GoodAvg | LongFloat | 8 | 0.95 |
| Emis7GoodAvg | LongFloat | 8 | 0.95 |
| Emis8GoodAvg | LongFloat | 8 | 0.95 |
| Emis9GoodAvg | LongFloat | 8 | 0.95 |
| Emis10GoodAvg | LongFloat | 8 | 0.95 |
| AncillaryGEOS5 | String | 255 | |
| BandSpecification | Float32 | μm |
Table 2. L2 LSTE Product Metadata Definitions
| Name | Type | Size | Example |
|---|---|---|---|
| QAPercentCloudCover | Int | 4 | 80 |
| CloudMeanTemperature | LongFloat | 8 | 231 |
| CloudMaxTemperature | LongFloat | 8 | 275 |
| CloudMinTemperature | LongFloat | 8 | 221 |
| CloudSDevTemperature | LongFloat | 8 | 0.45 |
Table 3. L2 CLOUD Product Metadata Definitions
| SDS | Long Name | Data Type | Units | Valid Range | Fill Value | Scale Factor | Offset |
|---|---|---|---|---|---|---|---|
| LST | Land Surface Temperature | uint16 | K | 7500-65535 | 0 | 0.02 | 0 |
| QC | Quality control for LST and emissivity | uint16 | N/A | 0-65535 | N/A | N/A | N/A |
| Emis3 | Band 3 emissivity | uint8 | N/A | 1-255 | 0 | 0.002 | 0.49 |
| Emis4 | Band 4 emissivity | uint8 | N/A | 1-255 | 0 | 0.002 | 0.49 |
| Emis5 | Band 5 emissivity | uint8 | N/A | 1-255 | 0 | 0.002 | 0.49 |
| Emis6 | Band 6 emissivity | uint8 | N/A | 1-255 | 0 | 0.002 | 0.49 |
| Emis7 | Band 7 emissivity | uint8 | N/A | 1-255 | 0 | 0.002 | 0.49 |
| Emis8 | Band 8 emissivity | uint8 | N/A | 1-255 | 0 | 0.002 | 0.49 |
| Emis9 | Band 9 emissivity | uint8 | N/A | 1-255 | 0 | 0.002 | 0.49 |
| Emis10 | Band 10 emissivity | uint8 | N/A | 1-255 | 0 | 0.002 | 0.49 |
| LST_Err | Land Surface Temperature error | uint8 | K | 1-255 | 0 | 0.04 | 0 |
| Emis3_Err | Band 3 emissivity error | uint16 | N/A | 0-65535 | 0 | 0.0001 | 0 |
| Emis4_Err | Band 4 emissivity error | uint16 | N/A | 0-65535 | 0 | 0.0001 | 0 |
| Emis5_Err | Band 5 emissivity error | uint16 | N/A | 0-65535 | 0 | 0.0001 | 0 |
| Emis6_Err | Band 6 emissivity error | uint16 | N/A | 0-65535 | 0 | 0.0001 | 0 |
| Emis7_Err | Band 7 emissivity error | uint16 | N/A | 0-65535 | 0 | 0.0001 | 0 |
| Emis8_Err | Band 8 emissivity error | uint16 | N/A | 0-65535 | 0 | 0.0001 | 0 |
| Emis9_Err | Band 9 emissivity error | uint16 | N/A | 0-65535 | 0 | 0.0001 | 0 |
| Emis10_Err | Band 10 emissivity error | uint16 | N/A | 0-65535 | 0 | 0.0001 | 0 |
| EmisWB | Wideband emissivity | uint8 | N/A | 0-255 | 0 | 0.002 | 0.49 |
| PWV | Precipitable Water Vapor | uint16 | cm | 0-65535 | N/A | 0.0001 | 0 |
| water_mask | Land/water mask | uint8 | 1=water, 0=land | 0-1 | 255 | 1 | 0 |
| cloud | Land/water mask | uint8 | 1=cloud, 0=clear | 0-1 | 255 | 1 | 0 |
| height | Ground elevation | uint16 | meters | -1000-10000 | -32768 | 1 | 0 |
| range | Satellite to pixel range | uint16 | meters | 0-32767 | -32768 | 100 | 800000 |
| view_zenith | Sensor zenith angle | uint16 | degrees | 0-18000 | -32768 | 0.01 | 0 |
Table 3-4: Product Data Definitions for the L2 LSTE Product
| Bits | Long Name | Description |
|---|---|---|
| 1&0 | Mandatory QA flags | 00 = Pixel produced, best quality |
| 01 = Pixel produced, nominal quality. Either one or more of the following conditions are met: |
- emissivity in both bands 9 and 10 < 0.95, i.e. possible cloud contamination
- low transmissivity due to high water vapor loading (<0.4), check PWV values and error estimates Recommend more detailed analysis of other QC information 10 = Pixel produced, but cloud detected 11 = Pixel not produced due to missing/bad data, user should check Data quality flag bits | | 3 & 2 | Data quality flag | 00 = Good quality L1B data 01 = not set 10 = not set 11 = Missing/bad L1B data | | 5 & 4 | Cloud/Ocean Flag | Not set. Please check SBG GEO and CLOUD products for this information. | | 7 & 6 | Iterations | 00 = Slow convergence 01 = Nominal 10 = Nominal 11 = Fast | | 9 & 8 | Atmospheric Opacity | 00 = >=3 (Warm, humid air; or cold land) 01 = 0.2 - 0.3 (Nominal value) 10 = 0.1 - 0.2 (Nominal value) 11 = <0.1 (Dry, or high altitude pixel) | | 11 & 10 | MMD | 00 = > 0.15 (Most silicate rocks) 01 = 0.1 - 0.15 (Rocks, sand, some soils) 10 = 0.03 - 0.1 (Mostly soils, mixed pixel) 11 = <0.03 (Vegetation, snow, water, ice) | | 13 & 12 | Emissivity accuracy | 00 = >0.02 (Poor performance) 01 = 0.015 - 0.02 (Marginal performance) 10 = 0.01 - 0.015 (Good performance) 11 = <0.01 (Excellent performance) | | 15 & 14 | LST accuracy | 00 = >2 K (Poor performance) 01 = 1.5 - 2 K (Marginal performance) 10 = 1 - 1.5 K (Good performance) 11 = <1 K (Excellent performance) |
Table 3-5: Bit flags defined in the QC SCS
| SDS | Long Name | Data Type | Units | Valid Range | Fill Value | Scale Factor | Offset |
|---|---|---|---|---|---|---|---|
| Cloud_confidence | Brightness temperature LUT test | uint8 | 3=confident cloudy, 2=probably cloudy, 1=probably clear, 0=confident clear | 0-1 | 255 | 1 | 0 |
| Cloud_final | Final cloud mask | uint8 | 1=cloud, 0=clear | 0-1 | 255 | 1 | 0 |
Table 3-6: Product Data Definitions for the L2 Cloud Product
| Name | Type | Size | Example |
|---|---|---|---|
| QAPercentCloudCover | Int | 4 | 80 |
| CloudMeanTemperature | LongFloat | 8 | 231 |
| CloudMaxTemperature | LongFloat | 8 | 275 |
| CloudMinTemperature | LongFloat | 8 | 221 |
| CloudSDevTemperature | LongFloat | 8 | 0.45 |
Table 3-7: Metadata Definitions for the L2 Cloud Product
A low latency (< 24 hour) product will be created in addition to the standard product. It may or may not be archived. The product contents will be the same as the standard product, although the method to obtain it will be different (see the relevant ATBD).
The at-sensor measured radiance in the infrared region (4–15 µm, MIR: 3-5 µm, TIR: 8-15 µm) consists of a combination of different terms from surface emission, solar reflection, and atmospheric emission and attenuation. The Earth-emitted radiance is a function of temperature and emissivity and gets attenuated by the atmosphere on its path to the satellite. The emissivity of an isothermal, homogeneous emitter is defined as the ratio of the actual emitted radiance to the radiance emitted from a black body at the same thermodynamic temperature (Norman and Becker 1995), ϵ_λ= R_λ/B_λ. The emissivity is an intrinsic property of the Earth’s surface and is an independent measurement of the surface temperature, which varies with irradiance and local atmospheric conditions. The emissivity of most natural Earth surfaces for the TIR wavelength ranges between 8 and 12 μm and, for a sensor with spatial scales <100 m, varies from ~0.7 to close to 1.0. Narrowband emissivities less than 0.85 are typical for most desert and semi-arid areas due to the strong quartz absorption feature (reststrahlen band) between the 8- and 9.5-μm range, whereas the emissivity of vegetation, water, and ice cover are generally greater than 0.95 and spectrally flat in the 3–15-μm range except for dry and senesced vegetation that can have emissivities range from 0.9-0.95 in the longer wavelengths above 10 μm.
TES is applied to the land-leaving TIR radiances that are estimated by atmospherically correcting the at-sensor radiance on a pixel-by-pixel basis using a radiative transfer model. TES uses an empirical relationship to predict the minimum emissivity that would be observed from a given spectral contrast, or minimum-maximum difference (MMD) (Kealy and Hook 1993; Matsunaga 1994). The empirical relationship is referred to as the calibration curve and is derived from a subset of spectra in the ASTER spectral library (Baldridge et al. 2009). A new calibration curve, applicable to SBG TIR bands, will be computed using the latest ECOSTRESS spectral library v2 (Meerdink et al. 2019), in addition to spectra from 9 pseudo-invariant sand dune sites located in the US Southwest (Hulley et al. 2009a). TES has been shown to accurately recover temperatures within 1 K and emissivities within 0.015 for a wide range of surfaces and is a well established physical algorithm that produces seamless images with no artificial discontinuities such as might be seen in a land classification type algorithm (Gillespie et al. 1998).
NASA has identified a major need to develop long-term, consistent products valid across multiple missions, with well-defined uncertainty statistics addressing specific Earth-science questions. These products are termed Earth System Data Records (ESDRs), and LST&E has been identified as an important ESDR. Currently a lack of understanding of LST&E uncertainties limits their usefulness in land surface and climate models. In this section we present results from the Temperature Emissivity Uncertainty Simulator (TEUSim) that has been developed to quantify and model uncertainties for a variety of TIR sensors and LST algorithms (Hulley et al. 2012b). Using the simulator, uncertainties were estimated for the L2 products of SBG using a 6-band TES approach. These uncertainties will be parameterized according to view angle and estimated total column water vapor for eventual application to real-time SBG L2 data on a pixel by pixel basis.
The OTTER L2 LST product will be validated with a combination of Temperature-based (Coll et al. 2005; Hook et al. 2004) and Radiance-based methods (Hulley and Hook 2012; Wan and Li 2008) using a global set of validation sites. The L2 emissivity product will be validated using a combination of lab-measured samples collected at various sand dune sites, and with the ASTER Global Emissivity Database (ASTER GED) (Hulley and Hook 2009b).
The research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration.
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