Disaggregation of MODIS surface temperature over an agricultural area using a time series of Formosat-2 images

Merlin, Olivier; Duchemin, B.; Hagolle, Olivier; Jacob, Frédéric; Coudert, Bruno; Chehbouni, Abdelghani; Dedieu, Gérard; Garatuza, J.; Kerr, Yann H.

doi:10.1016/j.rse.2010.05.025

Cited by 156 publications

(114 citation statements)

References 52 publications

Supporting

Mentioning

107

Contrasting

Unclassified

Order By: Relevance

“…1 is around 0.2, which is a relatively high albedo for an agricultural soil (due to the relatively high loam/low organic matter content in this soil). Previously reported in situ (11)(12)(13) and satellite (14,15) measurements suggest that the albedo of brown agricultural soils can be lower than 0.1, whereas soils covered with stubble can have albedos largely above 0.3, implying albedo differences…”

Section: Resultsmentioning

confidence: 97%

Preferential cooling of hot extremes from cropland albedo management

Davin

Seneviratne

Ciais

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

164

158

View full text Add to dashboard Cite

Changes in agricultural practices are considered a possible option to mitigate climate change. In particular, reducing or suppressing tillage (no-till) may have the potential to sequester carbon in soils, which could help slow global warming. On the other hand, such practices also have a direct effect on regional climate by altering the physical properties of the land surface. These biogeophysical effects, however, are still poorly known. Here we show that no-till management increases the surface albedo of croplands in summer and that the resulting cooling effect is amplified during hot extremes, thus attenuating peak temperatures reached during heat waves. Using a regional climate model accounting for the observed effects of no-till farming on surface albedo, as well as possible reductions in soil evaporation, we investigate the potential consequences of a full conversion to no-till agriculture in Europe. We find that the summer cooling from cropland albedo increase is strongly amplified during hot summer days, when surface albedo has more impact on the Earth's radiative balance due to clear-sky conditions. The reduced evaporation associated with the crop residue cover tends to counteract the albedo-induced cooling, but during hot days the albedo effect is the dominating factor. For heatwave summer days the local cooling effect gained from no-till practice is of the order of 2°C. The identified asymmetric impact of surface albedo change on summer temperature opens new avenues for climateengineering measures targeting high-impact events rather than mean climate properties.

show abstract

Section: Resultsmentioning

confidence: 97%

Preferential cooling of hot extremes from cropland albedo management

Davin

Seneviratne

Ciais

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

164

158

View full text Add to dashboard Cite

show abstract

“…Due to their very high spatial resolution, the thermal band data of the Landsat series, such as the Landsat 5 Thematic Mapper (TM) and the Landsat 7 Enhanced Thematic Mapper (ETM), have been widely applied for LST retrieval for such studies as surface energy budget estimation [3,4], surface moisture and evapotranspiration monitoring [5,6], urban heat island monitoring [7,8] and environmental biogeochemistry process simulation [9], requiring LST as a basic input.…”

Section: Introductionmentioning

confidence: 99%

An Improved Mono-Window Algorithm for Land Surface Temperature Retrieval from Landsat 8 Thermal Infrared Sensor Data

et al. 2015

View full text Add to dashboard Cite

Abstract:The successful launch of the Landsat 8 satellite with two thermal infrared bands on February 11, 2013, for continuous Earth observation provided another opportunity for remote sensing of land surface temperature (LST). However, calibration notices issued by the United States Geological Survey (USGS) indicated that data from the Landsat 8 Thermal Infrared Sensor (TIRS) Band 11 have large uncertainty and suggested using TIRS Band 10 data as a single spectral band for LST estimation. In this study, we presented an improved mono-window (IMW) algorithm for LST retrieval from the Landsat 8 TIRS Band 10 data. Three essential parameters (ground emissivity, atmospheric transmittance and effective mean atmospheric temperature) were required for the IMW algorithm to retrieve LST. A new method was proposed to estimate the parameter of effective mean atmospheric temperature from local meteorological data. The other two essential parameters could be both estimated through the so-called land cover approach. Sensitivity analysis conducted for OPEN ACCESSRemote Sens. 2015, 7 4269 the IMW algorithm revealed that the possible error in estimating the required atmospheric water vapor content has the most significant impact on the probable LST estimation error. Under moderate errors in both water vapor content and ground emissivity, the algorithm had an accuracy of ~1.4 K for LST retrieval. Validation of the IMW algorithm using the simulated datasets for various situations indicated that the LST difference between the retrieved and the simulated ones was 0.67 K on average, with an RMSE of 0.43 K. Comparison of our IMW algorithm with the single-channel (SC) algorithm for three main atmosphere profiles indicated that the average error and RMSE of the IMW algorithm were −0.05 K and 0.84 K, respectively, which were less than the −2.86 K and 1.05 K of the SC algorithm. Application of the IMW algorithm to Nanjing and its vicinity in east China resulted in a reasonable LST estimation for the region. Spatial variation of the extremely hot weather, a frequently-occurring phenomenon of an abnormal heat flux process in summer along the Yangtze River Basin, had been thoroughly analyzed. This successful application suggested that the IMW algorithm presented in the study could be used as an efficient method for LST retrieval from the Landsat 8 TIRS Band 10 data.

show abstract

“…Therefore, studies have been recently conducted to address these issues. For example, other parameters derived from VNIR bands, such as non-photosynthetically vegetation cover, solar albedo, and Normalized Difference Wetness Index (NDWI), were introduced to estimate LST for thermal sharpening [17][18][19]. In addition, more complex models, such as regression tree, artificial neural net, and physical models were applied in the sharpening procedure [5,20,21].…”

Section: Introductionmentioning

confidence: 99%

A Combination of TsHARP and Thin Plate Spline Interpolation for Spatial Sharpening of Thermal Imagery

Chen

et al. 2014

Remote Sensing

View full text Add to dashboard Cite

There have been many studies and much attention paid to spatial sharpening for thermal imagery. Among them, TsHARP, based on the good correlation between vegetation index and land surface temperature (LST), is regarded as a standard technique because of its operational simplicity and effectiveness. However, as LST is affected by other factors (e.g., soil moisture) in the areas with low vegetation cover, these areas cannot be well sharpened by TsHARP. Thin plate spline (TPS) is another popular downscaling technique for surface data. It has been shown to be accurate and robust for different datasets; however, it has not yet been attempted in thermal sharpening. This paper proposes to combine the TsHARP and TPS methods to enhance the advantages of each. The spatially explicit errors of these two methods were firstly estimated in theory, and then the results of TPS and TsHARP were combined with the estimation of their errors. The experiments performed across various landscapes and data showed that the proposed combined method performs more robustly and accurately than TsHARP.

show abstract

Disaggregation of MODIS surface temperature over an agricultural area using a time series of Formosat-2 images

Cited by 156 publications

References 52 publications

Preferential cooling of hot extremes from cropland albedo management

Preferential cooling of hot extremes from cropland albedo management

An Improved Mono-Window Algorithm for Land Surface Temperature Retrieval from Landsat 8 Thermal Infrared Sensor Data

A Combination of TsHARP and Thin Plate Spline Interpolation for Spatial Sharpening of Thermal Imagery

Contact Info

Product

Resources

About