Land Surface Temperature Product from the GOES-R Series

C H A P T E R

12 Land Surface Temperature Product from the GOES-R Series ⁎

⁎

Yunyue Yu , Peng Yu† NOAA/NESDIS Center for Satellite Applications and Research, Environmental Monitoring Branch, College Park, MD, United States, †Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, United States

12.1 INTRODUCTION Land surface temperature (LST) is one of the key variables in the weather and climate system controlling surface heat and water exchange at the land-atmosphere interface. Satellite measured LST, however, is mostly based on thermal infrared band observations, which theoretically give the temperature at some nominal skin depth of the surface. In the Geostationary Operational Environmental Satellites (GOES)-R Series mission, LST is measured from its onboard Advanced Baseline Imager (ABI). Knowledge of LST gives critical information on temporal and spatial variations of the surface equilibrium state and is of fundamental importance to many aspects of geoscience. Remotely sensed LST can be assimilated into weather and climate models to optimize weather and climate predictions (Meng et al., 2009; Zheng et al., 2012), be applied as input data for mesoscale atmospheric and land surface models to estimate sensible heat flux and latent heat flux, or be utilized to evaluate model prediction performance. It has been widely used in commercial applications including the evaluation of water requirements for crops in summer, and to estimate where and when damaging frost may occur in winter. LST can also provide warning signs for possible forest and grass fires, as well as an indicator of possible drought, just to name a few (Karnieli et al., 2010; Zhang et al., 2014; Quintano et al., 2015). In 2016, the World Meteorological Organization included LST as one of the essential climate variables (ECVs) in the Global Climate Observing System (GCOS, 2017). While accurate in situ measurement of LST can be obtained, such observations are very expensive and impractical for many applications. In situ towers can provide long-term observations useful to study temporal LST evolution, but this is not proper for research involving spatial variability due to their limited spatial area coverage. Field campaigns, on the other hand, can cover a larger domain but usually within a limited time period. With the advancement of the satellite remote sensing technique, LST is measured at regional to global scales with high spatial and temporal resolutions. Each satellite mission commonly lasts from a few years to more than a decade, such as the Aqua and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) (Wan, 2014). Encouraged by the success of a split-window (SW) technique in estimating sea surface temperature (SST) from space, much effort has been spent on its extension to LST retrieval. The SW method utilizes differential atmospheric absorption in the two adjacent thermal infrared channels with wavelengths centered at 11 and 12 μm, respectively (Li et al., 2013). Compared with SST remote sensing, however, LST retrieval faces unique challenges due to larger heterogeneity and anisotropy of the land surface and relies (explicitly or implicitly) on knowledge of land surface emissivity (LSE). With modifications to treat the spatial-temporal and spectral variations of LSE, the difference between LST and the air surface temperature, total precipitable water (TPW) in the atmosphere, and viewing zenith angle (VZA), a variety of SW algorithms for LST retrieval have been developed (Price, 1984; Sobrino et al., 1993; Wan and Dozier, 1996; Yu et al., 2008; Li et al., 2013). Some of the algorithms have been successfully applied to operational satellite LST production for sensors such as the Advanced Very High Resolution Radiometer (AVHRR) (Prata and Platt, 1991), Advanced Along-Track

The GOES-R Series. https://doi.org/10.1016/B978-0-12-814327-8.00012-3

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Copyright © 2020 Published by Elsevier Inc.

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12.  Land Surface Temperature Product from the GOES-R Series

Scanning Radiometer (AATSR) (Prata, 1993), MODIS (Wan and Dozier, 1996), Spinning Enhanced Visible and InfraRed Imager (SEVIRI) (Trigo et al., 2009), and Visible Infrared Imaging Radiometer Suite (VIIRS) (Yu et al., 2005; Liu et al., 2015). By now, satellite LSTs have been routinely produced for more than five decades from a variety of geostationary and polar-orbiting satellites. Like many geostationary and polar-orbiting weather satellites, LST is a required baseline product for the GOES-R mission. In this chapter, we introduce some fundamental information about GOES-R LST products. First, the mission’s requirement for LST production is presented, followed by the description of the developed LST retrieval algorithm suitable for the sensor and for meeting the requirement. The GOES-R LST product details are given in Section 12.3, including the LST computation procedure, derivation of quality flags, LST data availability from different ABI scan modes, and corresponding data resolutions in time and in space. How GOES-R LST data are evaluated and validated is described in Section 12.4. In Section 12.5, future enhancement is addressed for some improvements and monitoring needs. Finally, a summary and concluding remarks are given in Section 12.6.

12.2  GOES-R ABI LST ALGORITHM 12.2.1  Mission Requirement GOES-R LST has multiple spatial coverages, including the full disk (FD), contiguous United States (CONUS), and two mesoscale domains (MESO1 and MESO2) (Schmit et al., 2018). LST is determined as a baseline product for each cloud-free land pixel observed by the ABI sensor. The measurement requirements for the three LST products are shown in Table 12.1. Note that in spite of the sensor’s native scanning rate, the three LST products are currently produced hourly. Furthermore, the spatial resolution of FD LST was defined as 10 km at nadir, which requires aggregation procedures after LST retrieval at the sensor’s 2-km native spatial resolution. Due to its high variability in both time and space, LSTs with the sensor’s native scanning rate and spatial resolution may improve the products’ validation quality and applications.

12.2.2  ABI LST Algorithm LST retrieval is based on the physics of radiative transfer process from Earth’s surface to the satellite sensor. Infrared sensors or microwave sensors can be used to measure surface temperature. Microwave channels are able to provide all-weather LST retrieval but with a very coarse spatial resolution, typically at tens of kilometers; infrared channels, on the other hand, offer very high spatial resolution measurements. Therefore, LST is more commonly retrieved using infrared channels between 10 and 12 μm, where both the emission from Earth’s surface and atmospheric transmittance reach their maximum. Given its characteristics, LST retrieval via infrared channels can only be available under cloud-free sky conditions. In the infrared part of spectrum, satellite received radiance can be expressed as B ( λ ,Tb ) = ε ( λ )τ ( λ ) B ( λ ,Ts ) + I atm ( λ ) + I atm ( λ ) ↑

↓

(12.1)

TABLE 12.1  GOES-R Mission Requirements for Land Surface Temperature Observational requirement

Geographic Horiz. Mapping Msmnt. Msmnt. Msmnt. Refresh LEVELa coverageb Res. accuracy range (K) accuracyc (K) precision (K) rate VAGLd

Long-term Extent stability qualifiere

LST (Skin): CONUS

T

C

2 km

1 km

213–330

2.5

2.3

60 min

3236 s

TBD

LST (Skin): Hemispheric

T

FD

10 km 5 km

213–330

2.5

2.3

60 min

806 s

TBD

LZA <70

LST (Skin): Mesoscale

T

M

2 km

213–330

2.5

2.3

60 min

159 s

TBD

LZA <70

1 km

R T = target, G = goal. R C = CONUS, FD = full disk, H = hemisphere, M = mesoscale. c R The measurement accuracy 2.5 K is conditional with (1) known emissivity, (2) known atmospheric correction and (3) 80% channel correction; 5 K otherwise. d R VAGL = Vender Allocated Ground Latency. e R LZA = local zenith angle. a

b

LZA <70



12.3 ABI LST Product

135

where B(λ, Tb) is the radiance received by the satellite sensor at wavelength λ, Tb is its brightness temperature; ε(λ) and τ(λ) are the corresponding surface emissivity and the atmospheric transmittance at the same wavelength, respectively; Iatm(λ)↑)↑ and Iatm(λ)↓ are sensor received radiances from atmospheric emission and from surface reflected solar emission, respectively; B(λ, Ts) is the surface emitted radiance at surface temperature Ts. This surface emitted radiance can be described by Planck’s law: B ( λ ,T ) =

2 hc 2 λ5

1 e

hc λ kT

(12.2) −1

where B is the radiance at wavelength λ and temperature T, h is the Planck constant, c is the speed of light, and k is the Boltzmann constant (k = 1.38064852 × 10−23 m2 kg s−2 K−1). The problem of LST retrieval is to estimate the surface temperature from satellite-observed brightness temperatures. Difficulties arise because of multiple factors, including the nonlinearity of the Planck function, coupling of surface emissivity and temperature in the radiative transfer function, and impact from the atmospheric radiance and absorption. Most commonly used approaches linearize the radiative transfer equation, Eq. (12.1), by Taylor expansion and combine the multichannel regression algorithm for optimal atmospheric correction. Due to its simplicity, robustness, and computation efficiency, the SW algorithm with the following formula was selected for operational GOES-R LST retrieval among nine different candidate algorithms (Yu et al., 2009). Ts = C + A1T11 + A2 ( T11 − T12 ) + A3ε + D ( T11 − T12 ) ( sec θ − 1)

(12.3)

where Ts is the LST to be estimated; T11 and T12 represent the sensed brightness temperatures at around 11 and 12 μm, that is, ABI bands 14 and 15, respectively; ε is the average of the surface emissivity at ABI channels 14 and 15; θ is the satellite VZA; and C, A1, A2, A3, and D are the corresponding algorithm coefficients, which are stratified by day/night and dry/moist atmospheric conditions. Theoretically, the term with T11 presents as the first guess of the LST estimation, the terms with (T11 − T12) can be considered for atmospheric correction, and the term with (secθ − 1) for radiative transfer path correction.

12.3  ABI LST PRODUCT Upstream ABI data as well as ancillary data sets are required to generate the LST product. The former includes the so-called Level 1b sensor data record (SDR), that is, ABI sensed radiances at channels 11.2 and 12.3 μm, respectively, and their corresponding quality flags, geolocation data, and viewing geometry information, and so-called Level 2 derived sensor data, including cloud mask, estimated TPW EDR, and the snow/ice mask. The latter consists of the land/sea mask, emissivity, and estimated water vapor from National Centers for Environmental Prediction (NCEP) analysis and model forecast data as a fallback source to TPW EDR. LST retrieval for each pixel (Fig. 12.1) starts from extracting ABI sensor data sets and derived sensor data, followed by the processing of ancillary data sets. The ancillary data are mapped to the ABI pixel location and a land masking process is performed to label each pixel with land/sea, inland water, and snow/ice properties, recorded in the LST quality control flags. ABI sensor data are then filtered by the cloud mask to ensure that only cloud clear and probably clear pixels are retained for LST retrieval. Prior to calculating LST for each cloudless land pixel, a day/night flag is set using the solar zenith angle of the sensor geometric data and a dry/moist atmospheric condition is determined using TPW EDR. Note that the TPW EDR resolution is coarser than that of LST, so a remapping of the TPW data is needed. LST of each pixel is calculated with a specific coefficient set stratified by the day/night and dry/moist atmospheric conditions. Meanwhile, the most important information related to retrieval quality and conditions is recorded in the product quality information (PQI) flag and the quality control (QC) flags (Yu et al., 2012a), including input data availability, surface type, cloud index, atmospheric condition, day/night, view angle, LST quality, and emissivity quality. An additional aggregation step is performed in generating FD LST EDR, since the FD data resolution was determined at 10 km. The FD LST EDR quality flags’ definition is slightly different from that of the CONUS and MESO LST EDRs, and details of their definitions can be found in Yu et al. (2012a). An example of the different LST products and their spatial coverage is presented in Fig. 12.2 (Animation 12.1 in the online version at https://doi.org/10.1016/B978-0-12-814327-8.00012-3).

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12.  Land Surface Temperature Product from the GOES-R Series Land/sea mask

Emissivity

NCEP WV*

ABI snow/ice mask ABI cloud mask

Data maping

Extract ancillary data

ABI goelocation Extract ABI inputs

ABI brightness temperature

Land check

Criterion values

Cloud filtering

Coeffs

ABI solar-view geometry

QC control

Dry/moist

ABI sensor QC flags ABI ancillary ABI sensor input Non-ABI ancillary

LST EDR

LST wrap up

SW LST algorithm

Day/night

Other Input

FIG. 12.1  High-level flowchart of LST production for illustrating the main processing steps.

FIG. 12.2  Example LST images from FD (left) and CONUS (right). Animation 12.1 of this figure is available in the online version at https:// doi.org/10.1016/B978-0-12-814327-8.00012-3.

12.4  VALIDATION AND EVALUATION 12.4.1  In Situ LST Observations Though challenging, remotely sensed LST must be validated before its applications are declared operational. Traditionally, LST validation is carried out via its direct comparison to in situ LST measurements (Yu et al., 2012b). Multiple factors can have significant impacts on validation. Compared to the 2-km resolution of CONUS and MESO LSTs and 10-km resolution of FD LST, the ground station usually observes an area within a few tens of meters. The mismatch of the field of view requires that the in situ station site is homogeneous enough to well represent a satellite pixel. This is rarely the case since LST usually has very large spatial variability. Moreover, a good estimate of the broadband surface emissivity is needed to evaluate the in situ LST measurement. Due to rapid temporal variation, in situ LST measurements must be well matched in time to the satellite counterpart. A comprehensive LST validation is impossible unless these problems can be solved.



12.4  Validation and Evaluation

137

FIG. 12.3  Distribution of SURFRAD stations in CONUS. Based on VIIRS surface-type data, the Bondville and Boulder sites cover cropland and mixed forest; Desert Rock covers closed-shrubland, open-shrubland, barren/desert, evergreen-broadleaf forest, and deciduous-broadleaf forest; Fort Peck covers crop-mosaic, grassland, and mixed forest; Goodwin Creek covers crop-mosaic, woody-savanna, and mixed forest; Penn State covers crop-mosaic, cropland, and deciduous-broadleaf forest; and Sioux Falls covers cropland and mixed forest. Surface types represented by each site may vary from season to season.

Based on the above factors, the National Oceanic and Atmospheric Administration’s (NOAA) Surface Radiation Budget (SURFRAD) network (Yu et  al., 2012b; Augustine and Dutton, 2013) is selected for validation of GOES-R LST. It is the first US national-scale network to continuously measure land surface radiation budget since 1995. It includes seven long-term observation stations (Fig. 12.3) and covers a variety of different land surface types, including evergreen-broadleaf-forest, deciduous-broadleaf-forest, mixed-forest, closed-shrubland, open-shrubland (desert), woody-savanna, grassland, cropland, crop-mosaic, snow, and barren/desert. SURFRAD provides in situ measurements of downwelling and upwelling infrared radiation, along with other meteorological parameters (Augustine et al., 2005). Due to its high quality, existence of long-term time series, and systematic reliability, the data have been extensively used to support satellite system validation, numerical model verification, and modern climate, weather, and hydrology research applications. In this study, one year of SURFRAD data from June 1, 2017 to May 31, 2018 were used to evaluate the GOES-16 LST product, in addition to the prelaunch GOES-R LST algorithm evaluation studies (Yu et al., 2009). To eliminate the impact of seasonality, only whole years of in situ measurements are included. The start date of the validation period was when the cloud mask EDR was declared provisionally mature. SURFRAD LST is derived from upwelling and downwelling radiation. The observed upwelling (F↑) and downwelling (F↓) radiative fluxes are converted to temperature as follows: F ↑ = εσ Ts4 + ( 1 − ε ) F ↓

(12.4)

where ε is the broadband surface emissivity and σ is the Stefan-Boltzmann constant (σ = 5.67051 × 10−8 W m−2 k−4). The station LST is then calculated using the following equation: 1

 F ↑ − (1 − ε ) F ↓  4 Ts =   εσ  

(12.5)

A daily emissivity product has been developed by the GOES-R LST Algorithm Working Group (AWG) to evaluate the in situ LST.

12.4.2  Data Matchup and Quality Control Procedures The satellite LST is matched to in situ observations in both time and space. The maximum differences are 1 min in time and 0.02° for CONUS and MESO LSTs and 0.1° for FD LST in space, respectively. Since LST variability in time and space can be very large, the satellite pixel size and the in situ observation temporal output period are used as the maximum spatial difference and temporal difference, respectively, in order to obtain the closest matchup between the satellite product and its in situ counterpart. To ensure the best comparison quality, an effective cloud filtering is needed. In the ground system (GS), the four-level cloud mask information, clear, probably clear, probably cloudy,

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12.  Land Surface Temperature Product from the GOES-R Series

and cloudy, were included in the intermediate data (IP). Due to technical difficulties of the GS, however, IPs were not available for a significant amount of time. Only two-level cloud mask information, clear (including clear and probably clear) and cloudy (including probably cloudy and cloudy), can be used in filtering out the cloudy matchups. To reduce potential cloud contamination and to obtain more objective validation results, a simplified multistep additional cloud filtering procedure was used after the application of the two-level cloud mask EDR. The matchup is determined as being potentially impacted by cloud if any of the following three criteria satisfies: (1) If any neighboring pixel (3 × 3 box centered at the matchup pixel) is flagged as cloudy; (2) If the standard deviation (STD) of the band 14 brightness temperature in the neighboring 3 × 3 pixel box is larger than a threshold value; and (3) If the STD of the 30-min (centered at the matchup time) downwelling radiation from in situ observations is higher than a predetermined threshold value.

12.4.3  Validation Results and Analysis Fig.  12.4 shows the validation results of CONUS LST with respect to SURFRAD observations over a period of one year. Though the results vary among different sites, overall the CONUS LST met the mission requirement (Table 12.1) with a bias of −0.84 K and a precision of 1.93 K (Fig. 12.4). The LST bias and precision at five of the sites are under 1.0 K and within 2 K, respectively.

FIG. 12.4  CONUS LST validation results with respect to SURFRAD observations.



12.4  Validation and Evaluation

139

FIG.  12.5  (A) The surface elevation sampled at an area of 15 × 15 CONUS pixels centered at the center of the FD pixel matched to the Desert Rock site; (B) A sample CONUS LST at the same location (March 16, 2018) at 04:02 UTC; and (C) The aggregated LST from (B) as the FD LST proxy.

Among the seven SURFRAD stations, the most significant bias is observed at the Desert Rock site. The overall measurement bias and precision are mainly attributed to error of the retrieval algorithm and the input data and from the heterogeneity at the site. In the GS LST retrieval algorithm, for instance, the input data set TPW is utilized to determine the correct algorithm coefficient set. Inaccurate TPW information may result in using an incorrect algorithm coefficient set, which may degrade the production performance. Besides, the surface topography near some SURFRAD sites, e.g., Desert Rock site, can be complicated. Though surface emission characteristics are relatively homogeneous over the area, it is not so for surface temperature due to very high surface elevation variability. In an area with the size of 15 × 15 CONUS pixels centered at the Desert Rock site, for instance, the surface elevation changes from 723 to 2391 m (Fig. 12.5A). Such large elevation variability has a significant impact on the homogeneity of LST and leads to a high uncertainty of LST validation results (Fig. 12.5B). Similar underestimates at this site can also be observed in MODIS-derived LSTs (Li et al., 2014). Given a larger pixel size from the ABI sensor, this impact can potentially be even higher. It is worth mentioning that the CONUS LST pattern is highly consistent with the surface elevation near this site, while the aggregated one (FD proxy) may fail to reveal detailed variability (Fig. 12.5). Results from most sites show outliers (mostly underestimates by the satellite) in the matchups, even after the additional cloud-filtering procedure. This is possibly due to cloud contamination, that is, satellite cloud mask and additional cloud filtering fail to identify some of the cloud-impacted retrievals. At the Bondville site, it was observed that the daytime discrepancy between the satellite LST and its in situ counterpart in late spring and part of fall is significantly higher than the rest of a year. Similar peculiarity was found in other satellite LST products, including those from MODIS and VIIRS of Suomi National Polar-orbiting Partnership (S-NPP) and the Joint Polar Satellite System (JPSS). Detailed examination of this site indicates that the area consists mostly of cropland, which can be highly impacted by human activities. While the in situ station views a small area of grassland nearby, the much larger matchup satellite pixel also incorporates a farm field, which is highly impacted by human activities. Especially during the growing and harvesting season, the satellite and in situ station view areas have very different surface emission characteristics. This results in a high LST difference of the matchup data pairs, especially during daytime. As a result, daytime matchups during such a period of time have been removed from the validation. Validation results of MESO and FD LSTs are shown in Table 12.2. Similarly, both products meet the mission requirement in general. One challenge to validate the FD LST consists in its very coarse resolution, 5 times the CONUS and MESO LSTs. The large difference in the field of view of the satellite sensor and of the in situ station makes a stricter requirement of the site homogeneity. At the Desert Rock site, the underestimate by satellite LST is even higher than from CONUS and MESO LSTs. Another example is the Goodwin Creek site. While it is relatively homogeneous within the 2-km resolution pixel, it is not the case for the 10-km FD LST pixel (Fig. 12.6). The validation results at this site show an overestimate of 3.35 K and a precision of 2.67 K.

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12.  Land Surface Temperature Product from the GOES-R Series

TABLE 12.2  Validation Results of MESO and FD LSTs With SURFRAD MESO Site

Number Accuracy (K)

Bondville_IL

1012

Boulder_CO Desert_Rock_NV

Full Disk Precision (K)

Number Accuracy (K)

0.04

2.27

1100

−0.17

2.08

108

−0.98

1.95

767

−1.54

2.01

112

−4.82

1.36

358

−6.00

1.79

29

−1.09

1.65

784

−1.04

1.88

1169

0.68

2.37

1180

3.35

2.67

Penn_State_PA

602

0.65

2.39

701

1.52

2.16

Sioux_Falls_SD

217

−0.57

1.95

1104

−0.24

1.60

3249

0.13

2.27

5994

0.07

2.09

Fort_Peck_MT Goodwin_Creek_MS

Overall

Precision (K)

FIG. 12.6  Satellite view of the 10-km × 10-km box near the Goodwin Creek site. The white dot, smaller box, and larger box are the approximate locations of the in situ station, and its corresponding matchup pixel from CONUS and FD LSTs, respectively.

12.4.4  LST Inter-Sensor Comparison Inter-sensor LST comparison between the GOES-16 ABI and other sensors, including MODIS (Terra and Aqua) and VIIRS, was carried out to further evaluate product quality and its consistency with the other sensors. A data set of one day per week from May 2017 to October 2017 was collected for the comparison study. The LSTs were matched with ABI LST in space and time, in which the observation maximum temporal difference of the compared sensor to ABI is set to be 7.5 min and the satellite VZA difference is limited to be equal or <5°. Due to a relatively smaller pixel size of MODIS and VIIRS, their LSTs have been spatially aggregated to the ABI pixel for comparison. For each data pair, the following cloud filtering procedures are applied: 1. Cloud mask from ABI indicates clear; and 2. >75% pixels of MODIS or VIIRS falling into the same ABI pixel are classified (or labeled) as confidently clear. ABI LST agrees well with LST estimates from MODIS and VIIRS (Fig. 12.7). The correlation of all three data sets reaches as high as 0.98. The best agreement can be found between GOES-16 and Aqua, with a bias of 0.15 K and a



141

12.5 Future Enhancements

LST inter-sensor comparison: GOES-16 v.s. other sensors 340

GOES-16 LST (k)

320 300 280 260 240 220

All: Bias=0.48;STD=2.01;R2=0.98; N=1279132 Night: Bias=–0.42;STD=1.55;R2=0.96; N=588795 Day: Bias=1.24;STD=2.04;R2=0.97; N=6990337

All: Bias=0.15;STD=1.42;R2=0.98; N=633243 Night: Bias=–0.01;STD=1.26;R2=0.98; N=468785 Day: Bias=0.60;STD=1.72;R2=0.96; N=164458

All: Bias=–1.50;STD=1.73;R2=0.98; N=633161 Night: Bias=–1.82;STD=1.42;R2=0.96; N=487337

220 240 260 280 300 320 340 TERRA LST (k)

220 240 260 280 300 320 340 AQUA LST (k)

220

Day:

Bias=–0.43;STD=2.19;R2=0.97; N=145824

240 260 280 300 320 340 SNPP LST (k)

FIG. 12.7  GOES-16 LST compared with LSTs from Terra (left), Aqua (center), and S-NPP (right). Colors denote the matchup density.

FIG. 12.8  Four-day GOES-16 LST time series (blue) compared to the combined S-NPP (red) and NOAA-20 (green) LST at the SURFRAD Fort Peck site. GOES-16 LST characterizes the diurnal cycle observed by the in situ station (black) much better.

STD of 1.42 K, followed by Terra, with 0.48 and 2.01 K in bias and STD, respectively, and S-NPP, with −1.50 and 1.73 K in bias and STD. It is believed that such comparison differences are mostly due to satellite-view geometric difference between the satellites. In general, the nighttime comparison results are closer than those of daytime. Due to its hourly output frequency, GOES-R LST usually characterizes the diurnal cycle much better than its polar-orbiting counterpart. Fig.  12.8 shows a comparison of the time series by the GOES-R ABI and VIIRS from combined S-NPP and NOAA-20 at the Fort Peck site. While the GOES-16 LST gets most of the detailed temporal variability observed by the in situ station, the combined VIIRS LST catches the daily maximum time and only close to the daily minimum. The ABI LST product was declared provisionally mature on March 19, 2018. By definition of the GOES-R Program, this means the product performance has been demonstrated through a large, but still (seasonally or otherwise) limited number of independent measurements; the analysis is sufficient for limited qualitative determinations of product fitness-for-purpose; and the product is potentially ready for testing for operational use.

12.5  FUTURE ENHANCEMENTS After reaching provisional maturity status of the GOES-16 LST product, the LST AWG has been working toward fully validated maturity, i.e., the product performance will be assessed over a larger and wider range of representative conditions, with comprehensive documentation of performance, including known anomalies and their remediation strategies.

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12.  Land Surface Temperature Product from the GOES-R Series

To facilitate monitoring and improvement of the LST product and to support operational users, a convenient yet robust monitoring and validation system will be established. It will enable visual inspection of the data and of the metadata consistency, provide anomaly alert, and will facilitate the near real-time monitoring and validation of the retrievals. Specifically, the system will generate LST images and inspect data production on a daily basis, and validate the product with in situ observations on a weekly basis. Anomaly alerts and a weekly validation/monitoring summary will be sent to the team through email and validation results will be posted to an FTP server for reporting. Moving forward, the LST AWG is working on an enterprise LST algorithm that will be applicable to multiple sensors, including VIIRS, ABI, and other sensors with similar atmospheric spectral window bands. Such an algorithm will improve the product consistency among sensors and is expected to reduce the maintenance cost. The enterprise LST algorithm shall be optimized from a list of candidate SW LST algorithms after comprehensive sensitivity analysis and performance evaluation with proxy data from different sensors. It is expected that emissivity difference between the two SW channels will be applied in the algorithm, which is different from the current baseline LST algorithm where only the mean emissivity is applied. Further, the retrieval will be stratified by satellite view angle ranges, as well as by day/night and humidity conditions.

12.6 SUMMARY GOES-16 has been in operation since late 2017. To evaluate the algorithm performance, validation using in situ observations and comparison with LST products from other sensors has been conducted. The two evaluation methods are complementary to each other. They both are useful to quantify and characterize the accuracy of the satellite LST product and help refine the LST retrieval algorithms. We have collected a one-year ABI LST data set since the cloud mask EDR claimed provisional maturity and validated the retrievals with respect to the SURFRAD in situ observations. The CONUS LST validation yielded a bias of −0.84 K and a precision of 1.93 K. For the MESO LST, the results are 0.13 and 2.27 K, respectively, and for the FD LST, they are 0.07 and 2.09 K. All three LST products meet the mission requirement in accuracy and precision. As a result, the ABI LST product was declared provisionally mature on March 19, 2018. Given the difference in sensor field of view between satellite and in situ stations, homogeneity is a key requirement when selecting in situ ground stations for LST validation. Though the SURFRAD network has been known for its long-term high-quality data, some of the sites still have heterogeneity issues. Given that LST has very large spatial variability and ABI’s relatively large pixel size compared to most polar-orbiting satellites, this becomes even more challenging for ABI LST validation. From the validation results, the agreement of satellite LST to its in situ counterpart varies among different sites. This is especially the case for the FD LST product, whose pixel size is approximately 10 km at nadir. For example, while the validation results at the Goodwin Creek site meet or marginally meet the mission requirement for CONUS and MESO LSTs, a very large discrepancy is observed for FD LST with an accuracy of 3.35 K and a precision at 2.67 K. Some issues have been found at the Bondville site. In late spring and fall, a large overestimate of LST is often observed in satellite daytime retrievals. This is inherent not only for ABI LST but also for LST derived from other sensors. The site is located within a farm field and can be highly affected by human activities. To improve validation reliability, daytime matchups during these periods are disregarded. Similar situations at the Penn State site have not been clearly identified but can potentially have some negative impacts. The site belongs to the Pennsylvania State University, which has different planned agriculture-related experiments among different years. While the surface emission characteristics surrounding the Desert Rock site are pretty homogeneous, large elevation variation is seen, leading to a high LST heterogeneity around the site. This along with the complicated surface topography can potentially result in large validation uncertainty. The bias at this site is −4.6 and − 4.82 K for CONUS and MESO LSTs, respectively, and reaches as high as −6 K for FD LST. Such errors in enterprise LST represent significant improvement, though its accuracy is still almost 1 K larger than the mission requirement. Effective cloud screening is another challenge to LST validation. Even with more stringent cloud screening procedures, a significant amount of outliers can still be found in validation results from most sites. Most outliers indicate large underestimate by the satellite and are likely caused by cloud contamination. The inter-sensor LST comparison between ABI and other sensors yielded satisfactory results in general, with a better agreement during nighttime compared to daytime. The best agreement is found between GOES-16 LST and Aqua LST. While the LST AWG is working on improvement, validation, and maintenance of the baseline algorithm, an enterprise LST retrieval algorithm has been developed as well. The enterprise algorithm can be applied for both the geostationary and polar-orbiting satellite missions as long as SW channels are available. The algorithm has been accepted by the JPSS mission and will be in operation soon. Preliminary evaluation results with the ABI sensor slightly outperform the baseline LST.

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The physical basis, requirements specification, and planned validation of individual geophysical algorithms are described in the Algorithm Theoretical Basis Documents for each product and may supplement the material in the individual chapters. These documents can be found at the NOAA Center for Satellite Applications and Research website at https://www.star.nesdis.noaa.gov/goesr/documentation_ATBDs.php. Additional documents and other user resources are found at the GOES-R Series website https://www.goes-r.gov/.

Acknowledgments This study was supported by the National Oceanic and Atmospheric Administration GOES-R AWG grant NA14NES4320003. We thank the GOES-R Program for its support and all members of the GOES-R Land Science Team for their tremendous contributions to the LST effort. The views, opinions, and findings contained in this report are those of the authors and should not be construed as an official National Oceanic and Atmospheric Administration or US Government position, policy, or decision.

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Further Reading Becker, F., Li, Z.L., 1990. Towards a local split window method over land surfaces. Int. J. Remote Sens. 11, 369–393. https://doi. org/10.1080/01431169008955028. Caselles, V., Coll, C., Valor, E., 1997. Land surface emissivity and temperature determination in the whole Hapex Sahel area from AVHRR data. Int. J. Remote Sens. 18 (5), 1009–1027. https://doi.org/10.1080/014311697218548. Coll, C., Valor, E., Schmugge, T., Caselles, V., 1997. A procedure for estimating the land surface emissivity difference in the AVHRR channels 4 and 5. In: Remote Sensing Application to the Valencian Area, Spain. Sobrino, J.A., Li, Z.L., Stoll, M.P., Becker, F., 1994. Improvements in the split-window technique for land surface temperature determination. IEEE Trans. Geosci. Remote Sens. 32 (2), 243–253. https://doi.org/10.1109/36.295038. Ulivieri, C., Cannizzaro, G., 1985. Land surface temperature retrievals from satellite measurements. Acta Astronaut. 12 (12), 985–997. https://doi. org/10.1016/0094-5765(85)90026-8. Ulivieri, C., Castronouvo, M.M., Francioni, R., Cardillo, A., 1992. A SW algorithm for estimating land surface temperature from satellites. Adv. Space Res. 14 (3), 59–65. https://doi.org/10.1016/0273-1177(94)90193-7. Vidal, A., 1991. Atmospheric and emissivity correction of land surface temperature measured from satellite using ground measurements or satellite data. Int. J. Remote Sens. 12 (12), 2449–2460. https://doi.org/10.1080/01431169108955279.