Fan Huang

Disaggregation of Remotely Sensed Land Surface Temperature: A New Dynamic Methodology

The trade-off between the spatial and temporal resolutions of satellite-derived land surface temperature (LST) gives birth to disaggregation of LST (DLST). However, the concurrent enhancement of the spatiotemporal resolutions of LST remains difficult, and many studies disregard the conservation of thermal radiance between predisaggregated and postdisaggregated LSTs. Here we propose a new dynamic methodology to enhance concurrently the spatiotemporal resolutions of satellite-derived LSTs. This methodology conducts DLST by the controlling parameters of the temperature cycle models, i.e., the diurnal temperature cycle (DTC) model and annual temperature cycle (ATC) model, rather than directly by the LST. To achieve the conservation of thermal radiance between predisaggregated and postdisaggregated LSTs, herein we incorporate a modulation procedure that adds temporal thermal details to coarse resolution LSTs rather than straightforwardly transforms fine-resolution scaling factors into LSTs. Indirect validations at the same resolution show that the mean absolute error (MAE) between the predicted and reference LSTs is around 1.0u2009K during a DTC; the associated MAE is around 2.0u2009K during an ATC, but this relatively lower accuracy is due more to the uncertainty of the ATC model. The upscaling validations indicate that the MAE is around 1.0u2009K and the normalized mean absolute error is around 0.3. Comparisons between the DTC- and ATC-based DLST illustrate that the former retains a higher accuracy, but the latter holds a higher flexibility on days when background low-resolution LSTs are unavailable. This methodology alters the static DLST into a dynamic way, and it is able to provide temporally continuous fine-resolution LSTs; it will also promote the design of DLST methods for the generation of high-quality LSTs.

Localization or Globalization? Determination of the Optimal Regression Window for Disaggregation of Land Surface Temperature

The past decade has witnessed the disaggregation of remotely sensed land surface temperature (DLST), which aims for the generation of high temporal and spatial resolution land surface temperature (LST) and which has steadily evolved into a relatively independent subfield of thermal remote sensing. Limited by Toblers first law of geography, DLST methods require a regression between LSTs and scaling factors using image pixels within a globalized or a localized regression window. Recommendations regarding the selection of the regression window have been provided, but they are mainly subjective and based on highly specific examples. In this context, 100 DLST samples with diversified land cover types and climates were employed to assess the global window strategy (GWS) and the local window strategy (LWS). To optimize disaggregation accuracy and computational complexity, the assessments show that the optimal moving-window size (MWS) for the LWS can be estimated by the resolution ratio between pre- and postdisaggregated LSTs. To identify the better strategy between the GWS and the LWS, an indirect criterion based on aggregation-disaggregation (ICAD) was formulated, which determines the better strategy from medium to high resolution according to the associated performances from low to medium resolution. Validations demonstrate that the accuracy predicted by the ICAD achieves 72%, and in cases in which predictions are incorrect, the performances of the GWS and the LWS are similar. Further evidences indicate that the use of historical high-resolution LSTs improves the LWS by using a locally varying MWS. These findings are able to guide researchers in choosing the most suitable regression window for any particular DLST.

Enhanced Modeling of Annual Temperature Cycles with Temporally Discrete Remotely Sensed Thermal Observations

Satellite thermal remote sensing provides land surface temperatures (LST) over extensive areas that are vital in various applications, but this technique suffers from its sampling style and the impenetrability of clouds, which frequently generates data gaps. Annual temperature cycle (ATC) models can fill these gaps and estimate continuous daily LST dynamics from a number of thermal observations. However, the standard ATC model (termed ATCS) remains incapable of quantifying the short-term LST variations caused by synoptic conditions. By incorporating in-situ surface air temperatures (SATs) and satellite-derived normalized difference vegetation indexes (NDVIs), here we proposed an enhanced ATC model (ATCE) to describe the daily LST fluctuations. With Aqua/MODIS LST products as validation data, we implemented and tested the ATCE over the Yangtze River Delta region of China. The results demonstrate that, when compared with the ATCS, the overall root mean square errors of the ATCE decrease by 1.0 and 0.8 K for the day and night, respectively. The accuracy improvements vary with land cover types with greater improvements over the forest, grassland, and built-up areas than over cropland and wetland. The assessments at different time scales further confirm that LST fluctuations can be better described by the ATCE. Though with limitations, we consider this new model and its associated parameters hold great potentials in various applications.

Positive or Negative? Urbanization-Induced Variations in Diurnal Skin-Surface Temperature Range Detected Using Satellite Data: Urbanization-induced variations in DTR

Diurnal temperature range (DTR) is an important indicator for assessing the local climate change due to urbanization. Studies that focused on surface air temperature (SAT) have reported decreased DTRSAT in urban areas. However, this urbanization-induced effect becomes more complex with regard to land skin-surface temperature (LST), which is highly localized and extremely sensitive to land surface properties. We thus investigated the urban-rural DTRLST difference (ΔDTRLST) over 354 cities across China using satellite-retrieved LSTs within 2008 2013. Our major findings include the following: (1) urban areas experience increased (decreased) DTRLST compared with rural areas on the annual average for the majority of cities located in southern (northern) China; (2) the ΔDTRLST is mostly positive in warmmonths but negative in cold months. It generally becomes more positive from January to August and becomes more negative afterward; and (3) the ΔDTRLST is positively related to the daytime surface urban heat island intensity; it is yet negatively correlated with the urban-rural difference in vegetation abundance. We consider these insights valuable for in-depth understanding urban thermal environment and will likely help improve urban planning.