Nighttime Moderate Resolution Imaging Spectroradiometer Research Articles

Reconstruction of All-Weather Daytime and Nighttime MODIS Aqua-Terra Land Surface Temperature Products Using an XGBoost Approach

Generating spatiotemporally continuous land surface temperature (LST) data is in great demand for hydrology, meteorology, ecology, environmental studies, etc. However, the thermal infrared (TIR)-based LST measurements are prone to cloud contamination with missing pixels. To repair the missing pixels, a new XGBoost-based linking approach for reconstructing daytime and nighttime Moderate Resolution Imaging Spectroradiometer (MODIS) LST measurements was introduced. The instantaneous solar radiation and two soil-related predictors from China Data Assimilation System (CLDAS) 0.0625°/1-h data were selected as the linking variables to depict the relationship with instantaneous MODIS LST data. Other land surface properties, including two vegetation indices, the water index, the surface albedo, and topographic parameters, were also used as the predictor variables. The XGBoost method was used to fit an LST linking model by the training datasets from clear-sky pixels and was then applied to the MODIS Aqua-Terra LSTs during summer time (June to August) in 2017 and 2018 across China. The recovered LST data was further rectified with the Savitzky–Golay (SG) filtering method. The results showed the distribution of the reconstructed LSTs present a reasonable pattern for different land-cover types and topography. The evaluation results using in situ longwave radiation measurements showed the RMSE varies from 3.91 K to 5.53 K for the cloud-free pixels and from 4.42 K to 4.97 K for the cloud-covered pixels. In addition, the reconstructed LST products correlated well with CLDAS LST data with similar LST spatial patterns. The variable importance analysis revealed that the two soil-related predictors and the elevation variable are key parameters due to their great contribution to the XGBoost model performance.

The summer upwelling of the Eastern Aegean Sea detected from MODIS SST scenes from 2003 to 2015

ABSTRACTThe summer upwelling of the Eastern Aegean Sea is investigated by daily night-time MODerate resolution Imaging Spectroradiometer Sea Surface Temperature scenes in the periods from June to November (extended summer) 2003–2015. The analysis is supported by wind data downloaded from the European Centre for Medium-Range Weather Forecasts Re-Analysis archive. On average over these summers, upwelling and Etesian wind occurred 69% and 78% of time, respectively. The highest correlation between upwelling and wind was found from June to September, with the beginning of the upwelling events shifted between 24 and 48 h after the start of the Etesian wind. The upwelling always begins in the same areas of the Eastern Aegean coast with the only differences that these areas tesian wind pattern. The largest upwelling events occurred in summers 2003–2009 and not in the following years of the study period. The upwelled cold surface water, still pushed by the persistent Etesian wind, fills part of South-Eastern Aegean and eventually outflows the Aegean Sea through Kassos and Karpathos Straits of the eastern Cretan Arc.

Quantifying the spatial and temporal relationship between air and land surface temperatures of different land-cover types in Southeastern China

ABSTRACTNear surface air temperature (Ta) is an important variable utilized for various fields of research such as agriculture, epidemic forecasting, and climate change. It is usually obtained from a limited number of weather stations that are unevenly distributed within a region. Thus, the spatial information of Ta is rarely provided especially in complex topographical and underdeveloped regions. To compensate for this shortcoming, Ta can be estimated by using the land surface temperature (Ts) from satellite data because remote sensing has an advantage of describing the spatial heterogeneity over a large geographical area. In previous research, authors focused on analysing the effect of limited factors on Ta observations by using a small number of weather stations. However, this study explores a thorough sensitivity analysis of several factors on the relationship between Ta (obtained from a dense network of automatic weather stations) and Ts of different land-cover types using statistical analysis. This article discovered the relationships between Ta and Ts. First, comparisons between daytime and night-time Moderate Resolution Imaging Spectroradiometer (MODIS) Ts data with Ta data showed that a better agreement was achieved during the night than the day. Second, when comparing the results of different land-cover types, the correlation coefficient of the vegetated area was higher than that of water and impervious surface. Third, a comparison of statistical results for different seasons indicated that the correlation between Ta and Ts was weakest in a hot season. Fourth, altitude seemed to have no significant effect on the Ta–Ts relationship. Finally, the relationship between Ta and Ts strengthened with increasing window size except for the vegetated area but tended to saturate as the Ts window increased to the optimal size. These results will be helpful for building an appropriate model to derive air temperature directly from the remotely sensed data in the future.

Improving snow process modeling with satellite‐based estimation of near‐surface‐air‐temperature lapse rate

AbstractIn distributed hydrological modeling, surface air temperature (Tair) is of great importance in simulating cold region processes, while the near‐surface‐air‐temperature lapse rate (NLR) is crucial to prepare Tair (when interpolating Tair from site observations to model grids). In this study, a distributed biosphere hydrological model with improved snow physics (WEB‐DHM‐S) was rigorously evaluated in a typical cold, large river basin (e.g., the upper Yellow River basin), given a mean monthly NLRs. Based on the validated model, we have examined the influence of the NLR on the simulated snow processes and streamflows. We found that the NLR has a large effect on the simulated streamflows, with a maximum difference of greater than 24% among the various scenarios for NLRs considered. To supplement the insufficient number of monitoring sites for near‐surface‐air‐temperature at developing/undeveloped mountain regions, the nighttime Moderate Resolution Imaging Spectroradiometer land surface temperature is used as an alternative to derive the approximate NLR at a finer spatial scale (e.g., at different elevation bands, different land covers, different aspects, and different snow conditions). Using satellite‐based estimation of NLR, the modeling of snow processes has been greatly refined. Results show that both the determination of rainfall/snowfall and the snowpack process were significantly improved, contributing to a reduced summer evapotranspiration and thus an improved streamflow simulation.

Time series decomposition of remotely sensed land surface temperature and investigation of trends and seasonal variations in surface urban heat islands

AbstractPrevious time series methods have difficulties in simultaneous characterization of seasonal, gradual, and abrupt changes of remotely sensed land surface temperature (LST). This study proposed a model to decompose LST time series into trend, seasonal, and noise components. The trend component indicates long‐term climate change and land development and is described as a piecewise linear function with iterative breakpoint detection. The seasonal component illustrates annual insolation variations and is modeled as a sinusoidal function on the detrended data. This model is able to separate the seasonal variation in LST from the long‐term (including gradual and abrupt) change. Model application to nighttime Moderate Resolution Imaging Spectroradiometer (MODIS)/LST time series during 2000–2012 over Beijing yielded an overall root‐mean‐square error of 1.62 K between the combination of the decomposed trend and seasonal components and the actual MODIS/LSTs. LST decreased (~ −0.086 K/yr, p < 0.1) in 53% of the study area, whereas it increased with breakpoints in 2009 (~0.084 K/yr before and ~0.245 K/yr after 2009) between the fifth and sixth ring roads. The decreasing trend was stronger over croplands than over urban lands (p < 0.05), resulting in an increasing trend in surface urban heat island intensity (SUHII, 0.022 ± 0.006 K/yr). This was mainly attributed to the trends in urban‐rural differences in rainfall and albedo. The SUHII demonstrated a concave seasonal variation primarily due to the seasonal variations of urban‐rural differences in temperature cooling rate (related to canyon structure, vegetation, and soil moisture) and surface heat dissipation (affected by humidity and wind).

Daily mean air temperature estimation from MODIS land surface temperature products based on M5 model tree

ABSTRACTThe use of satellite data to estimate air temperature (Ta) near the Earth's surface has become an effective way for a large area with high spatial and temporal resolution. In this study, M5 model tree was used to estimate Ta in Khuzestan province (in the southwest of Iran) using satellite remotely sensed land surface temperature (Ts) data acquired by the MODerate Resolution Imaging Spectroradiometer (MODIS)‐Terra sensor. The input variables for the M5 model tree were the daytime and nighttime MODIS Ts, extraterrestrial solar radiation and Julian day. A total of 365 images of MOD11A1 Ts product for the year 2007, covering the area of this study were collected from the Land Processes Distributed Active Archive Center (LP DAAC). The results of this study showed that mean daily Ta can be estimated with acceptable levels of the statistical indicators from MODIS data and from the two geographic parameters using the M5 model. Root mean square error and R2 for the comparison between observed and estimated Ta for the tested data are 2.3 and 0.96 °C, respectively.

On the accuracy of thin-ice thickness retrieval using MODIS thermal imagery over Arctic first-year ice

AbstractWe have studied the accuracy of ice thickness (hi) retrieval based on night-time MODIS (Moderate Resolution Imaging Spectroradiometer) ice surface temperature (Ts) images and HIRLAM (High Resolution Limited Area Model) weather forcing data from the Arctic. The study area is the Kara Sea and eastern part of the Barents Sea, and the study period spans November-April 2008–11 with 199 hi charts. For cloud masking of the MODIS data we had to use manual methods in order to improve detection of thin clouds and ice fog. The accuracy analysis of the retrieved hi was conducted with different methods, taking into account the inaccuracy of the HIRLAM weather forcing data. Maximum reliable hi under different air-temperature and wind-speed ranges was 35–50 cm under typical weather conditions (air temperature <–20cC, wind speed <5ms–1) present in the MODIS data. The accuracy is best for the 15–30 cm thickness range, ∼38%. The largest hi uncertainty comes from air temperature data. Our ice-thickness limits are more conservative than those in previous studies where numerical weather prediction model data were not used in the hi retrieval. Our study gives new detailed insight into the capability of Ts-based hi retrieval in the Arctic marginal seas during freeze-up and wintertime, and should also benefit work where MODIS hi charts are used.

Detection of natural oil seeps signature from SST and ATI in South Yellow Sea combining ASTER and MODIS data

Daytime Advanced Spaceborne Thermal Emission and reflection Radiometer (ASTER) data and the consequent nighttime Moderate Resolution Imaging Spectroradiometer (MODIS) data have been utilized to retrieve the sea surface temperature (SST) and apparent thermal inertia (ATI). They were used to detect the signature of natural oil seepage in Yingge Sea Basin (YSB) and South Yellow Sea (SYS). In this paper, ATI was used for the first time to detect the signature of natural oil seepage. ATI is an approximation of the real thermal inertia and can be expressed as a function of surface albedo and temperature difference between day and night. Surface albedo can be calculated by weighting spectral reflectivities in visible and near-infrared bands from ASTER data. The spectral reflectivities can be obtained after performing atmospheric correction using the 6S model. An iterative self-consistent split-window algorithm was employed to retrieve the daytime SSTs from ASTER data and nighttime SSTs from MODIS data. Because of the spatial resolution difference between ASTER and MODIS data, the nighttime SST derived from MODIS data was downscaled from 1 km to 90 m using the pixel block intensity modulation (PBIM) method. Two study areas, YSB and SYS, were selected to detect the possible signature of the natural oil seepage. The approximate SST and ATI values in the YSB where natural oil seepage actually developed were derived in advance. A look-up table of albedo, temperature difference and ATI for different sea waters with or without natural oil seepage was constructed. These were then used as prior knowledge to enclose the possible signatures of natural oil seepage in SYS. Our results show that there are some signatures of natural oil seepage in the study area in SYS. Compared with SST, ATI was more sensitive to the signature of natural oil seepage. The reason for this might be that the temperature difference was used in the calculation of the ATI, which decreased the influence of the SST accuracy on ATI values.

Real-Time, High-Resolution, Space–Time Analysis of Sea Surface Temperatures from Multiple Platforms

Abstract A sea surface temperature (SST) analysis system designed to initialize short-term atmospheric model forecasts is evaluated for a month-long, relatively clear period in May 2004. System inputs include retrieved SSTs from the Geostationary Operational Environmental Satellite (GOES)-East and the Moderate Resolution Imaging Spectroradiometer (MODIS). The GOES SSTs are processed via a sequence of quality control and bias correction steps and are then composited. The MODIS SSTs are bias corrected and checked against the background field (GOES composites) prior to assimilation. Buoy data, withheld from the analyses, are used to bias correct the MODIS and GOES SSTs and to evaluate both the composites and analyses. The bias correction improves the identification of residual cloud-contaminated MODIS SSTs. The largest analysis system improvements are obtained from the adjustments associated with the creation of the GOES composites (i.e., a reduction in buoy/GOES composite rmse on the order of 0.3°–0.5°C). A total of 120 analyses (80 night and 40 day) are repeated for different experimental configurations designed to test the impact of the GOES composites, MODIS cloud mask, spatially varying background error covariance and decorrelation length scales, data reduction, and anisotropy. For the May 2004 period, the nighttime MODIS cloud mask is too conservative, at times removing good SST data and degrading the analyses. Nocturnal error variance estimates are approximately half that of the daytime and are relatively spatially homogeneous, indicating that the nighttime composites are, in general, superior. A 30-day climatological SST gradient is used to create anisotropic weights and a spatially varying length scale. The former improve the analyses in regions with significant SST gradients and sufficient data while the latter reduces the analysis rmse in regions where the innovations tend to be well correlated with distinct and persistent SST gradients (e.g., Loop Current). Data thinning reduces the rmse by expediting analysis convergence while simultaneously enhancing the computational efficiency of the analysis system. Based on these findings, an operational analysis configuration is proposed.

Nighttime Multilayered Cloud Detection Using MODIS and ARM Data

This study reports on recent progress toward the discrimination between pixels containing multilayered clouds, specifically optically thin cirrus overlying lower-level water clouds, and those containing single-layered clouds in nighttime Moderate Resolution Imaging Spectroradiometer (MODIS) data. Cloud heights are determined from analysis of the 15-m mC O 2 band data (i.e., the CO 2-slicing method). Cloud phase is inferred from the MODIS operational bispectral technique using the 8.5- and 11-mm IR bands. Clear-sky pixels are identified from application of the MODIS operational cloud-clearing algorithm. The primary assumption invoked is that over a relatively small spatial area, it is likely that two cloud layers exist with some areas that overlap in height. The multilayered cloud pixels are identified through a process of elimination, where pixels from single-layered upper and lower cloud layers are eliminated from the data samples. For two case studies (22 April 2001 and 28 March 2001), ground-based lidar and radar observations are provided by the Atmospheric Radiation Measurement (ARM) Program’s Southern Great Plains (SGP) Clouds and Radiation Test Bed (CART) site in Oklahoma. The surface-based cloud observations provide independent information regarding the cloud layering and cloud height statistics in the time period surrounding the MODIS overpass.