Spatio-temporal sensitivity of MODIS land surface temperature anomalies indicates high potential for large-scale land cover change detection in Arctic permafrost landscapes

Abstract

The accelerated warming of the Arctic climate may alter the local and regional surface energy balances, for which changing land surface temperatures (LSTs) are a key indicator. Modeling current and anticipated changes in the surface energy balance requires an understanding of the spatio-temporal interactions between LSTs and land cover, both of which can be monitored globally by measurements from space. This paper investigates the accuracy of the MODIS LST/Emissivity Daily L3 Global 1 km V005 product and its spatio-temporal sensitivity to land surface properties in a Canadian High Arctic permafrost landscape. The land cover ranged from fully vegetated wet sedge tundra to barren rock. MODIS LSTs were compared with in situ radiometer measurements from wet tundra areas collected over a 2-year period from July 2008 to July 2010 including both summer and winter conditions. The accuracy of the MODIS LSTs was −1.1°C with a root mean square error of 3.9°C over the entire observation period. Agreement was lowest during the freeze-back periods where MODIS LST showed a cold bias likely due to the overrepresentation of clear-sky conditions. A multi-year analysis of LST spatial anomalies, i.e., the difference between MODIS LSTs and the MODIS LST regional mean, revealed a robust spatiotemporal pattern. Highest variability in LST anomalies was found during freeze-up and thaw periods as well as for open water surface in early summer due to the presence or absence of snow or ice. The summer anomaly pattern was similar for all three years despite strong differences in precipitation, air temperature and net radiation. Summer periods with regional mean LSTs above 5.0°C showed the greatest spatial diversity with four distinct 2.0°C classes. Summer anomalies ranged from −4.5°C to 2.6°C with an average standard deviation of 1.8°C. Dry ridge areas heated up the most, while wetland areas and dry areas of sparsely vegetated bedrock with a high albedo remained coolest. The observed summer LST anomalies can be used as a baseline against which to evaluate both past and future changes in land surface properties that relate to the surface energy balance. Summer anomaly classes mainly reflected a combination of albedo and surface wetness. The potential to use this tool to monitor surface drying and wetting in the Arctic should therefore be further explored. A multi-sensor approach combining thermal satellite measurements with optical and radar imagery promises to be an effective tool for a dynamic, process-based ecosystem monitoring scheme.

Rapid degradation of permafrost underneath waterbodies in tundra landscapes—Toward a representation of thermokarst in land surface models

“…The impact of waterbodies on the broad‐scale land surface temperatures has been investigated in different Arctic landscapes using satellite data [ Muster et al , ; Boike et al , ]. These investigations have revealed clear warming trends for regions in which the water coverage increased.…”

Application of multisensoral remote sensing data in the mapping of alkaline fens Natura 2000 habitat

“…It has been determined that LST data from the night collection (NT) ( Table 2) have the greatest indicator potential among different types of source data. LST with much lower resolution (derived from the MODIS satellite) was previously used to analyse the vegetation, and its importance as an indicator was proven in the vegetation identification (Nemani and Running, 1997;Roberts et al, 2015), analysis of land cover change detection in the Arctic permafrost landscape (Muster et al, 2015) and analysis of spatial changes in urban areas (Nguyen et al, 2015). So far, the LST has not been used for the identification of Natura 2000 habitats, including mires.…”

Assessing Spatiotemporal Variations of Landsat Land Surface Temperature and Multispectral Indices in the Arctic Mackenzie Delta Region between 1985 and 2018

“…However, calculating long-term means of the LST compensates for these issues and creates spatially extensive information on the mean summer LST. This is reasoned by the fact that the surface thermal regime is, on average, determined by the physical surface characteristics and not by atmospheric conditions [13]. Accordingly, this allows for characterizing the surface thermal regime of the Arctic MDR by revealing patterns of cold-and hotspots and influencing factors that may serve as a proxy for permafrost distribution and development.…”

“…The environmental change of the Arctic occurs on multiple scales and in remote regions, hence remote sensing provides a unique opportunity to detect and to monitor these landscape dynamics in a continuous manner. Satellite-derived Land Surface Temperature (LST) characterizes the land-atmosphere exchange of energy and depends on a variety of surficial properties, such as vegetation type, soil, and plant moisture, or surface roughness [13]. LST may change with the alteration of the surficial properties, allowing environmental change to be characterized by means of time series remote sensing.…”

“…AVHRR has been used to detect LST warming trends of approximately +0.72 • C per decade for the pan-Arctic regions between 1981 and 2005 [14,15]. MODIS derived LST has proven to be in accordance with in situ observed air temperatures and has been successfully applied to detect temperature trends, anomalies, and surficial changes [13,[16][17][18]. Furthermore, by analyzing the relationship between the Normalized Difference Vegetation Index (NDVI) and LSTs on a pan-Arctic scale, the influence of the land surface thermal properties on arctic vegetation types and abundance has been revealed [19].…”