Note on the NDVI‐LST relationship and the use of temperature‐related drought indices over North America

Sun, Donglian; Kafatos, Menas

doi:10.1029/2007gl031485

Cited by 214 publications

(132 citation statements)

References 23 publications

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“…However, the use of TsHARP in this study may prove precarious. This is because the correlation between NDVI and LST weakens during the night [24] and that can compromise the performance of TsHARP for downscaling nighttime LST data. Hence, to prevent this issue, altitude, ε 12µm , and WSA are also added as LST predictors.…”

Section: Research Objective and Experimental Setupmentioning

confidence: 99%

“…Hence, to make this study insusceptible to short-term weather effects, summer 2014 (DOYs: 152-243) was selected as the study period. The selection of this study period is based on the following four reasons: (i) it can provide a representative dataset for the purpose of this study; (ii) using LST data from the same year will ensure that the impact of phenological changes on the LST patterns will be reduced; (iii) the negative correlation of LST and NDVI, which is vital for the successful downscaling of LST data, is stronger during summer months [24]; and (iv) using summer data will ensure the availability of the most cloud-free daytime and nighttime LST data. This is especially important since for the estimation of the diurnal range maps cloud-free conditions during daytime and nighttime are required.…”

Section: Research Objective and Experimental Setupmentioning

confidence: 99%

“…The composition and selection of appropriate LST predictors should refer to the understood relations of LST to other biophysical variables (e.g., vegetation cover, land surface albedo, topography and soil moisture status) [20], which is a key for meeting the connectivity assumption [11]. The choice of LST predictors should also consider: (i) the spatial scale, since the suitability of the kernel is partly determined by this factor (a key assumption for statistical downscaling studies is that the relationship between the LST data and the predictors is scale invariant [15,23]); (ii) the local particularities of the study area; and (iii) the temporal cycle (diurnal or annual) [11,15] because it might render some LST predictors less effective or even ineffective (e.g., the correlation between the Normalized Difference Vegetation Index (NDVI), which is a widely used LST predictor, and LST is not persistent in time due to seasonal variations in the vegetation cover [24,25]). …”

Section: Introductionmentioning

confidence: 99%

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Improving the Downscaling of Diurnal Land Surface Temperatures Using the Annual Cycle Parameters as Disaggregation Kernels

et al. 2016

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Abstract:The downscaling of geostationary diurnal thermal data can ease the lack of land surface temperature (LST) datasets that combine high spatial and temporal resolution. However, the downscaling of diurnal LST data is more demanding than single scenes. This is because the spatiotemporal interrelationships of the original LST data have to be preserved and accurately reproduced by the downscaled LST (DLST) data. To that end, LST disaggregation kernels/predictors that provide information about the spatial distribution of LST during different times of a day can prove especially useful. Such LST predictors are the LST Annual Cycle Parameters (ACPs). In this work, multitemporal ACPs are employed for downscaling daytime and nighttime~4 km geostationary LST from SEVIRI (Spinning Enhanced Visible and Infrared Imager) down to 1 km. The overall goal is to assess if the use of the ACPs can improve the estimation of the diurnal range of DLST (daytime DLST minus nighttime DLST). The evaluation is performed by comparing the DLST diurnal range maps with reference data from MODIS (Moderate Imaging Spectroradiometer) and also with data retrieved from a modified version of the TsHARP (Thermal Sharpening) algorithm. The results suggest that the ACPs increase the downscaling performance, improve the estimation of diurnal DLST range and produce more accurate spatial patterns.

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Section: Research Objective and Experimental Setupmentioning

confidence: 99%

Section: Research Objective and Experimental Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improving the Downscaling of Diurnal Land Surface Temperatures Using the Annual Cycle Parameters as Disaggregation Kernels

et al. 2016

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“…Also, NDVI-LST correlation was dependent on both the season and the time of day rather than negatively associated. Strong negative correlations were found only in the warm seasons (i.e., during May-October), indicating that caution should be taken when relying on this well-known negative relationship during cold seasons [11].…”

Section: Introductionmentioning

confidence: 99%

Anomalous Variations of NDVI for a Non-Vegetated Urban Industrial Area of Gumi, Korea

Bhang

2014

AJRS

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NDVI is often used to investigate how vegetation contributes to the surface urban heat islands (SUHI) effect.Generally, NDVIs obtained for partially vegetated or built-up areas have been found to be less than 0.4. However, anomalous NDVIs that are inconsistent with established concepts and relationships are frequently obtained for non-vegetated urban land cover. It is possible that these anomalous NDVIs have been distorted by the surface colors and patterns of rooftops. Therefore, we obtained NDVIs for an urban industrial area of Gumi, South Korea, and investigated how these NDVIs were affected by such surface colors and patterns. By assessing 148 factory rooftops, obtained NDVIs indeed showed a tendency to be affected by the surface colors and patterns of rooftops. A color classification of white, blue, purple, and red revealed that blue-and purple-colored rooftops resulted in higher NDVIs than other rooftops. Moreover, these rooftops were sometimes misidentified as forest and vegetation. The color tone, affected by brightness intensity, also contributed to the NDVIs obtained for blue-and purple-colored rooftops. Extreme cases showed that NDVIs obtained for rooftop surfaces could attain values indicative of dense vegetation (i.e., NDVIs ≥ 0.6), when blue-colored rooftops were combined with complex surface patterns that generated more shadow. From these results, we concluded that the established relationship between LST and NDVI is likely to be invalid for non-vegetated urban industrial areas, and that NDVIs obtained for such areas should be used with caution, particularly in studies of the SUHI effect.

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“…Estimation of dTsa is generally performed using LST and Ta. To obtain high-resolution spatial temperature parameters for energy fluxes, previous studies have developed many methods to retrieve LST and Ta from remote sensing data [Price, 1984;Vogt et al, 1997;Wan et al, 2002;Mostovoy et al, 2006;Sun and Kafatos, 2007;Vancutsem et al, 2010].…”

Section: Introductionmentioning

confidence: 99%

Retrieval of the land surface‐air temperature difference from high spatial resolution satellite observations over complex surfaces in the Tibetan Plateau

et al. 2015

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The temperature difference between the surface and the air (dTsa) directly indicates the intensity and heat fluxes of land-atmosphere interaction. Considering the effects of surface characteristics and air condition on the surface temperature, using 1 km data from the MOD02 thermal infrared bands of the EOS/moderate-resolution imaging spectroradiometer (MODIS) on satellite Aqua, other MODIS products and temperatures observed from weather stations at 14:00 China standard time (CST), the study analyzes the relationships between dTsa and brightness temperature in the infrared atmospheric window band (Bt31 and Bt32), the water vapor band (Bt28), the atmospheric temperature band (Bt25), and the CO 2 band (Bt34). A model estimating dTsa is built. The model coefficients are estimated for 96 stations representing 96 sets of surface and atmospheric conditions, and 71 sets of coefficients among them passing 90% confidence levels of estimating dTsa are selected as references. Combined with the probabilistic neural network (PNN) method and nine parameters reflecting surface characteristics in one season and month, the Tibetan Plateau surface is classified as 71 types with 71 sets of coefficients. PNN is certified an efficient classification method for multiple parameters and mass data. Based on PNN and estimated model, estimated dTsa shows 1.36°C root-mean-square error and a standard deviation of 0.74°C, and dTsa distribution exhibits all centers with peak value and valley value of European Centre for Medium-Range Weather Forecasts, MYD07, and simple regression model results, showing its superiority. The model is worthy of further exploration and application in an effort to advance the retrieval of surface energy fluxes from remote sensing.

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Note on the NDVI‐LST relationship and the use of temperature‐related drought indices over North America

Cited by 214 publications

References 23 publications

Improving the Downscaling of Diurnal Land Surface Temperatures Using the Annual Cycle Parameters as Disaggregation Kernels

Improving the Downscaling of Diurnal Land Surface Temperatures Using the Annual Cycle Parameters as Disaggregation Kernels

Anomalous Variations of NDVI for a Non-Vegetated Urban Industrial Area of Gumi, Korea

Retrieval of the land surface‐air temperature difference from high spatial resolution satellite observations over complex surfaces in the Tibetan Plateau

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