Agricultural drought monitoring using MODIS-based drought indices over the USA Corn Belt

In 2012, the USA Corn Belt, an intensive agricultural region of the USA, was hit by a widespread severe drought, affecting states such as Illinois, Iowa, Nebraska, and Indiana. In this study, time series (2000–2012) of Moderate Resolution Imaging Spectroradiometer (MODIS) measurements were investigated to assess the 2012 drought conditions during the corn-growing season. Seven MODIS indices generated based on eight day MODIS reflectance and land surface temperature (LST) products were examined with standardized precipitation index (SPI) and Palmer-Z across the Corn Belt to evaluate the relative performance of each MODIS index to detect agricultural drought. The normalized difference infrared index (NDII6) anomaly shows the highest correlation coefficient (r) with SPI at three time scales and correlates best with Palmer-Z, which suggests good sensitivity of the NDII6 anomaly to precipitation and moisture deficiency in agricultural areas. The temporal and spatial features of drought provided by MODIS indices were compared with maps of the USA Drought Monitor (USDM), the current advanced tool for drought monitoring. The rapid intensification of drought across the Corn Belt in 2012 summer captured by MODIS index anomalies agreed with the changes of USDM maps quite well, especially in August and September when extreme drought occurred. Through comparison with the USDM drought map, the NDII6 anomaly demonstrated an advantage in monitoring drought condition over irrigated land and showed the potential to advance fine-scale agricultural drought monitoring by providing more detailed spatial characterization.     

Scaling of impervious surface area and vegetation as indicators to urban land surface temperature using satellite data

Vegetation and impervious surface as indicators of urban land surface temperature (LST) across a spatial resolution from 30 to 960 m were investigated in this study. Enhanced thematic mapper plus (ETM+) data were used to retrieve LST in Nanjing, China. A land cover map was generated using a decision tree method from IKONOS imagery. Taking the normalized difference vegetation index (NDVI) and percent vegetation area (V) to present vegetated cover, and the normalized difference building index (NDBI) and percent impervious surface area (I) to present impervious surface, the correlation coefficients and linear regression models between the LST and the indicators were simulated. Comparison results indicated that vegetation had stronger correlation with the LST than the impervious surface at 30 and 60 m, a similar magnitude of correlation at 120 and 240 m, and a much lower correlation at 480 and 960 m. In total, the impervious surface area was a slightly better indicator to the LST than the vegetation because all of the correlation coefficients were relatively high (>0.5000) across the spatial resolution from 30 to 960 m. The indicators of LST, V and I are slightly better than the NDVI and NDBI, respectively, based on the correlation coefficients between the LST and the four indices. The strongest correlation of the LST and vegetation at the resolution of 120 m, and the strongest correlation between the LST and impervious surface at 120, 480 and 960 m, denoted the operational scales of LST variations.     

Vegetation,water and thermal stress index for study of drought in Nepal and central northeastern India

Drought is the degradation of land in arid, semi-arid and dry sub-humid regions caused primarily by human activity and climatic variations. The present study is the first attempt to identify and monitor drought using a vegetation index, a vegetation-water index and land surface temperature (LST) data for Nepal and central northeastern India. We propose a Vegetation Water Temperature Condition Index (VWTCI) for monitoring drought on a regional scale. The VWTCI includes the Normalized Difference Water Index (NDWI), which measures the water status in vegetation, the Normalized Difference Vegetation Index (NDVI) and LST data. To validate the approach, the VWTCI was compared with the Vegetation Temperature Condition Index (VTCI) and Tropical Rainfall Measuring Mission (TRMM) 3B31 Precipitation Radar (PR) data. The study revealed a gradual increase in the extent of drought in the central part of the study area from 2000 to 2004. Certain constant drought areas were also identified and the results indicate that these areas are spreading slowly towards the northeast into the central part of the study area. Comparison of the drought areas also shows a decrease in rainfall in June and July from 2000 to 2004.     

The influence of land-cover type on the relationship between NDVI–LST and LST-Tair

Air temperature (T_2m or T_air) measurements from 20 ground weather stations in Berlin were used to estimate the relationship between air temperature and the remotely sensed land surface temperature (LST) measured by Moderate Resolution Imaging Spectroradiometer over different land-cover types (LCT). Knowing this relationship enables a better understanding of the magnitude and pattern of Urban Heat Island (UHI), by considering the contribution of land cover in the formation of UHI. In order to understand the seasonal behaviour of this relationship, the influence of the normalized difference vegetation index (NDVI) as an indicator of degree of vegetation on LST over different LCT was investigated. In order to evaluate the influence of LCT, a regression analysis between LST and NDVI was made. The results demonstrate that the slope of regression depends on the LCT. It depicts a negative correlation between LST and NDVI over all LCTs. Our analysis indicates that the strength of correlations between LST and NDVI depends on the season, time of day, and land cover. This statistical analysis can also be used to assess the variation of the LST–T_2m relationship during day- and night-time over different land covers. The results show that LST_Day and LST_Night are correlated significantly (p = 0.0001) with T_2mDay (daytime air temperature) and T_2mNight (night-time air temperature). The correlation (r) between LST_Day and T_Day is higher in cold seasons than in warm seasons. Moreover, during cold seasons over every LCT, a higher correlation was observed during daytime than during night-time. In contrast, a reverse relationship was observed during warm seasons. It was found that in most cases, during daytime and in cold seasons, LST is lower than T_2m. In warm seasons, however, a reverse relationship was observed over all land-cover types. In every season, LST_Night was lower than or close to T_2mNight.     

An agricultural drought severity index using quasi-climatological anomalies of remotely sensed data

Obtaining an agricultural drought index using solely remotely sensed products has numerous benefits over their in situ counterparts such as if a country does not have the resources to implement an in situ ground network. One such index, created by Rhee et al. (2010), uses a combination of precipitation data from the Tropical Rainfall Measuring Mission (TRMM), with land-surface temperature (LST) data and vegetation indices (VIs) using data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to assess drought conditions. With TRMM data becoming no longer available (as of mid-2015), this study sought to test precipitation data from the Climate Prediction Center (CPC) Morphing (CMORPH) Technique over the study period of January 2003–September 2014, in order to take the place of the TRMM data set in a drought severity index (DSI). This study also attempted to refine the methodology using the quasi-climatological anomalies (short-term climatological anomalies) of each parameter within the DSI. We validated the results of the DSI against in situ percentage available water (PAW) data from a soil water balance (SWB) model over the country of Uruguay. The results of the DSI correlated well with the PAW over the warmer months (October–March) of the year with average r-values ranging from 0.74 to 0.81, but underperformed during the colder months (April–September) with average r-values ranging from 0.38 to 0.50. This underperformance is due to the fact that precipitation during this season continues to have high variability, whereas PAW stays relatively constant. Spatially the DSI correlates well over the majority of the country with the possible exception of underperformance near the coastal area in the southeastern portion of the country. Ultimately, this research has the ability to aid Uruguay in better drought monitoring and mitigation practices as well as emergency aid resource allocation.     

Modelling light use efficiency using vegetation index and land surface temperature from MODIS in Harvard Forest

Light use efficiency (LUE) is of great importance for carbon cycle and climate change research. This study presents a new LUE model incorporation of vegetation indices (VIs) and land surface temperature (LST) derived from the Moderate-Resolution Imaging Spectroradiometer (MODIS) in Harvard Forest. Three indices, including the normalized difference vegetation index (NDVI), the two-band enhanced vegetation index (EVI2) and the soil-adjusted vegetation index (SAVI), were selected as indicators of forest canopy greenness. A single VI provided moderate estimates of LUE with coefficients of determination (R ²) 0.6219, 0.7094 and 0.7502 for NDVI, EVI2 and SAVI, respectively. Our results demonstrated that canopy LUE was related both to the canopy photosynthesis efficiency and air temperature (R ²?=?0.5634). Therefore, the MODIS LST product was incorporated as a surrogate for monitoring of environmental stresses as the observed relationship between LST and both air temperature (R ²?=?0.8828) and vapour pressure deficit (VPD) (R ²?=?0.6887). The new model in terms of (VI)?×?(Scaled (LST)) provided improved estimates of LUE estimation with R ² of 0.7349, 0.7561 and 0.7879 for NDVI, EVI2 and SAVI, respectively. The results will be useful for the development of future LUE models based entirely on remote-sensing observations.     

Land surface temperature variation in relation to vegetation type using MODIS satellite data in Gujarat state of India

The Moderate Resolution Imaging Spectroradiometer (MODIS) has provided an improved capability for moderate resolution land surface monitoring and for studying surface temperature variations. Surface temperature is a key variable in the surface energy balance. To investigate the temporal variation of surface temperature in relation to different vegetation types, MODIS data from 2000–04 were used, especially in the reproductive phase of crops (September–October). The vegetation types used for this study were agriculture in desert areas, rainfed agriculture, irrigated agriculture, and forest. We found that among the different vegetation types, the desert‐based agriculture showed the highest surface temperature followed by rainfed agriculture, irrigated agriculture, and forest. The variation in surface temperature indicates that the climatic variation is mostly determined by the different types of vegetation cover on the Earth's surface rather than rapid climate change attributable to climatic sources. The mean land surface temperature (LST) and air temperature (T _a) were plotted for each vegetation type from September to October during 2000 and 2004. Higher temperatures were observed for each vegetation type in 2000 as compared to 2004 and lower total rainfall was observed in 2000. The relationship between MODIS LST and T _a measurements from meteorological stations was established and illustrated that years 2000 and 2004 had a distinct climatic variability within the time‐frame in the study area. In all test sites, the study found that there was a high correlation (r = 0.80–0.98) between LST and T _a.     

Comparison of remotely sensed and meteorological data-derived drought indices in mid-eastern China

Numerous drought indices have been developed and applied to monitor the severity of drought. It has been demonstrated that the evaluation of the indices is very important for further utilization of remotely sensed and meteorological information. The objective of this article is to investigate and compare the different methods derived from satellite/meteorological data for drought monitoring during the typical dry year (2006) in mid-eastern China. The compared six drought indices include the vegetation condition index (VCI), percent of average seasonal greenness (PASG), temperature condition index (TCI), vegetation supply water index (VSWI), percentage of precipitation anomalies (PPA) and standardized precipitation index (SPI). These indices are calculated based on different data sources including reflective data, thermal data, the combination of reflective and thermal data and meteorological data. The correlation matrix and regression relationships among the integrals under all drought indices, the integral under the relative air humidity (RAH) curve and cumulative rainfall at the location of 11 agro-meteorological stations for 2006 were calculated. Spatial comparison analysis among the drought indices reveals that all the indices have certain coincidence in the detected regional-scale distribution of drought especially those derived from the same data set, while obviously local-scale distribution differences were found among the different groups of indices. Compared to curves of the reflective and thermal indices, the overall trend of VSWI series has better consistence with the PPA curve. Based on correlation and regression analysis, it is demonstrated that VSWI can better reflect both the amount of precipitation and the severity of drought due to lack of rainfall. Furthermore, land surface temperature (LST) contributes more to the result of hybrid index (VSWI) than reflective information. There is logarithmic relationship between integral of VSWI and cumulative precipitation, while obvious linear correlations were found between integral under VSWI curve and integral under the RAH/TCI/PASG curves. According to the filed observation of droughts from agro-meteorological stations in the study area, it can be concluded that any single index is not sufficient to precisely depicting drought characteristics. The combined use of different indices at the same time or indices which integrate various sources of information may obtain more consistent results with the actual situation.     

TVDI在冬小麦春季干旱监测中的应用

应用冬小麦春季生长期的NOAA/AVHRR资料,反演归一化植被指数（NDVI）、土壤调整植被指数（SAVI）和下垫面温度（T_s）,分析了植被指数和下垫面温度空间特征,采用温度植被旱情指数（TVDI）,研究了河北省2005年3~5月的冬小麦旱情状况。结果表明：基于SAVI的温度植被旱情指数与土壤表层相对湿度的相关性好于基于NDVI的温度植被旱情指数。通过与气象站土壤水分观测资料进行相关性分析,表明温度植被旱情指数与10 cm土壤相对湿度关系最好,20 cm次之,50 cm较差。因此,基于SAVI的温度植被旱情指数更适于监测冬小麦春季的旱情。     

An integrated PC environment for assessment of drought

Abstract

The design of a versatile software environment to support routine activities of a satellite based operational drought monitoring system is presented in this article. In addition, software provides the assistance to analyse and interpret drought conditions based on satellite derived normalised difference vegetation index (NDVI) statistics and ground information pertaining to a given area. The environment currently under development deals with district-wise drought assessment for nine states, taluk-wise for Karnataka State and operates on a standard PC with EGA graphics.     

Remote sensing the orographic effects of dry-hot valley on vegetation distribution in the southeast Tibetan Plateau

ABSTRACT

Mountains in the southeast Tibetan Plateau (TP) often intercept and precipitate abundant monsoon-transported vapours, but some deep valleys of this region are likely subjected to heavy water stress possibly related to orographic effects. Understanding the orographic effects of these dry-hot valleys (DHV) on vegetation distribution is crucial to project local ecological response to global warming. In the study, we used multiple satellite observations with limited in-situ records to investigate the links between vegetation cover and geomorphology in the southeast TP. We designed two types of transects to distinguish altitudinal properties of heat and vegetation between the DHV and non-DHV areas with satellite-retrieved enhanced vegetation index and land surface temperature (LST). Our results showed that the DHVs are characterized by the seemingly ‘abnormal’ decreasing of vegetation density from intermediate elevation simultaneously towards both ridge and valley. The significant increase in LST lapse rate with valley depth (1.8 × 10^?3°C km^?1 m^?1, p < 0.01) suggested the positive role of local valley wind system in the DHV development. Satellite observations revealed that there are, respectively, about 530, 420, and 300 km of DHVs developed in the Nujiang, Lancangjiang, and upper Yangtze rivers, and the DHVs are mostly deeper than 1600 m. Current global warming may lead to the altitudinal expansion of DHV dry and hot effects on local ecosystems, which should be carefully accounted in local ecosystem conservation and management.     

A detection problem: Sensitivity and uncertainty analysis of a land surface temperature approach to detecting dynamics of water use by groundwater-dependent vegetation

Sustainable management of groundwater-dependent vegetation (GDV) requires the accurate identification of GDVs, characterisation of their water use dynamics and an understanding of associated errors. This paper presents sensitivity and uncertainty analyses of one GDV mapping method which uses temperature differences between time-series of modelled and observed land surface temperature (LST) to detect groundwater use by vegetation in a subtropical woodland. Uncertainty in modelled LST was quantified using the Jacobian method with error variances obtained from literature. Groundwater use was inferred where modelled and observed LST were significantly different using a Student's t-test. Modelled LST was most sensitive to low-range wind speeds (<1.5 m s⁻¹), low-range vegetation height (<=0.5 m), and low-range leaf area index (<=0.5 m² m⁻²), limiting the detectability of groundwater use by vegetation under such conditions. The model-data approach was well-suited to detection of GDV because model-data errors were lowest for climatic conditions conducive to groundwater use.     

On the correlation of indices of vegetation and surface temperature over south-eastern Australia

Abstract

Relationships between radiant surface temperature (T _R) and vegetation indices for scenes with equal areas of forest and agricultural land use were studied using a Landsat Thematic Mapper (TM) scene during spring and a NOAA-Advanced Very High Resolution Radiometer (AVHRR) scene during summer. The relationships between TR and the Normalized Difference (ND) index of vegetation for agricultural land use were different from those for forests. At the same T _R, the forests had lower near infrared reflectance. This caused the ND of forests to fall below the T _R/ND relationships formed by agricultural land use. This difference between forest and agricultural land use did not exist when visible reflectance (VIS) was used as the index of vegetation. When the two land use systems were combined VIS accounted for about 86 per cent of the variance in T _R. The slope of the relationships between VIS and T _R differed for TM and AVHRR scenes. This was explained by differences in the T _R and VIS reflectance of surfaces with near-zero evaporation. These surfaces were predominantly bare soil in the TM scene and senesced vegetation in the AVHRR scene.     

The assessment of different vegetation indices for spatial disaggregating of thermal imagery over the humid agricultural region

ABSTRACT

Land surface temperature (LST) plays a significant role in surface water circulation and energy balance at both global and regional scales. Thermal disaggregation technique, which relies on vegetation indices, has been widely used due to its advantage in producing relatively high resolution LST data. However, the spatial enhancement of satellite LST using soil moisture delineated vegetation indices has not gained enough attention. Here we compared the performances of temperature vegetation dryness index (TVDI), normalized difference vegetation index (NDVI), and fractional vegetation coverage (FVC), in disaggregating LST over the humid agriculture region. The random forest (RF) regression was used to depict the relationship between LST and vegetation indices in implementing thermal disaggregating. To improve the model performance, we used the thin plate spline (TPS) approach to calibrate the RF residual estimation. Results suggested that the models based on TVDI performed better than those based on NDVI and FVC, with a reduced average root mean square error and mean absolute error of 0.20 K and 0.16 K, respectively. Moreover, based on the surface energy balance model, we found the surface evapotranspiration (ET) derived with the TVDI disaggregated LST as inputs achieved higher accuracy than those derived with NDVI and FVC disaggregated LST. It is indicated that TVDI, a soil moisture delineated vegetation indices, can improve the performance of LST enhancement and ET estimation over the humid agriculture region, when combining random forest regression and TPS calibration. This work is valuable for terrestrial hydrology related research.     

Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data

While existing remote sensing-based drought indices have characterized drought conditions in arid regions successfully, their use in humid regions is limited. We propose a new remote sensing-based drought index, the Scaled Drought Condition Index (SDCI), for agricultural drought monitoring in both arid and humid regions using multi-sensor data. This index combines the land surface temperature (LST) data and the Normalized Difference Vegetation Index (NDVI) data from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, and precipitation data from Tropical Rainfall Measuring Mission (TRMM) satellite. Each variable was scaled from 0 to 1 to discriminate the effect of drought from normal conditions, and then combined with the selected weights. When tested against in-situ Palmer Drought Severity Index (PDSI), Palmer's Z-Index (Z-Index), 3-month Standardized Precipitation Index (SPI), and 6-month SPI data during a ten-year (2000-2009) period, SDCI performed better than existing indices such as NDVI and Vegetation Health Index (VHI) in the arid region of Arizona and New Mexico as well as in the humid region of North Carolina and South Carolina. The year-to-year changes and spatial distributions of SDCI over both arid and humid regions generally agreed to the changes documented by the United States Drought Monitor (USDM) maps.     

Developing the vegetation drought response index for South Korea (VegDRI-SKorea) to assess the vegetation condition during drought events

South Korea has experienced severe droughts and water scarcity problems that have influenced agriculture, food prices, and crop production in recent years. Traditionally, climate-based drought indices using point-based meteorological observations have been used to help quantify drought impacts on the vegetation in South Korea. However, these approaches have a limited spatial precision when mapping detailed vegetation stress caused by drought. For these reasons, the development of a drought index that provides detailed spatial-resolution information on drought-affected vegetation conditions is essential to improve the country’s drought monitoring capabilities, which are needed to help develop more effective adaptation and mitigation strategies. The objective of this study was to develop a satellite-based hybrid drought index called the vegetation drought response index for South Korea (VegDRI-SKorea) that could improve the spatial resolution of agricultural drought monitoring on a national scale. The VegDRI-SKorea was developed for South Korea, modifying the original VegDRI methodology (developed for the USA) by tailoring it to the available local data resources. The VegDRI-SKorea utilizes a classification and regression tree (CART) modelling approach that collectively analyses remote-sensing data (e.g. normalized difference vegetation index (NDVI)), climate-based drought indices (e.g. self-calibrated Palmer drought severity index (PDSI) and standardized precipitation index (SPI)), and biophysical variables (e.g. elevation and land cover) that influence the drought-related vegetation stress. This study evaluates the performance of the recently developed VegDRI-SKorea for severe and extreme drought events that occurred in South Korea in 2001, 2008, and 2012. The results demonstrated that the hybrid drought index improved the more spatially detailed drought patterns compared to the station-based drought indices and resulted in a better understanding of drought impacts on the vegetation conditions. The VegDRI-SKorea model is expected to contribute to the monitoring of drought conditions nationally. In addition, it will provide the necessary information on the spatial variations of those conditions to evaluate local and regional drought risk assessment across South Korea and assist local decision-makers in drought risk management.     

Impacts of Urban land surface temperature on tract landscape pattern,physical and social variables

ABSTRACT

This paper first focuses on the study of the relationship between the urban heat island (UHI) and the selected physical variables (percentage of urban surface covers, Normalized Difference Vegetation Index (NDVI)) and social variables (population density (PDEN)), and then concentrates on the study of the relationship between UHI and the landscape spatial geometric patterns. The researched results discover that urban Land Surface Temperature (LST) is not only impacted by land cover composition, i.e. land use/cover, which is expressed in this paper as the PURB (commerce/industry/transportation), but also its spatial geometric configuration, i.e. various landscape geometric pattern metrics, which in this paper are expressed by compositional percentage of landscape area (PLAND), configurational edge density (ED), patch density (PD), landscape shape index (LSI), clumpiness index (CI), and Shannon’s diversity index (SHDI). The results show that the proportion of vegetation coverage out of a tract impacts its contribution to an entire UHI in Washington District of Columbia (DC), in particular, interspersing vegetation within a tract is capable of making a stronger mitigation effect to UHI than its concentrated form. Thus, a scatter spatial arrangement and distribution of vegetation is proposed to mitigate UHI effect.     

Assessment of two temporal-information-based methods for estimating evaporative fraction over the Southern Great Plains

Evaporative fraction (EF) is an important index for partitioning surface available energy. Temporal information derived from multi-times observations of remote sensing can reduce the uncertainty of EF estimates caused by the retrieval error of land surface parameters. Two temporal-information-based EF methods were assessed using in situ measurements from the Energy Balance Bowen Ratio system and the outputs from the second phase of the North American Land Data Assimilation System (NLDAS-2) over the Southern Great Plains (SGP). One is a newly developed method for estimating daily EF using a semi-empirical parameterization based on the day–night differences of land surface temperature (LST), air temperature, and incoming solar radiation; the other is the triangular feature space method constructed by the day–night difference of LST and fractional vegetation cover. The results showed that the two methods could reasonably estimate EF over the SGP region in spatial distribution. The EF estimated from the newly developed method was closer to the in situ measurements with a bias of ?0.028 and a root mean square error (RMSE) of 0.194, and it was also closer to the outputs of the NLDAS-2 with an RMSE of ~0.13 compared with the results from the feature space method. A strong relationship between the results from the two methods was found with a coefficient of determination up to 0.824 and an RMSE of 0.143. The RMSE for the EF estimates caused by different vegetation indexes in the feature space could reach 0.15, and larger bias mainly occurred on surfaces with low evapotranspiration surface.     

An investigation of drought prediction using various remote-sensing vegetation indices for different time spans

Iran is a country in a dry part of the world and extensively suffers from drought. Drought is a natural and repeatable phenomenon definable at specified time and area. In addition, social and economic issues can be affected by drought. Information such as intensity, duration, and spatial coverage of drought can help decision makers to reduce the vulnerability of the drought-affected areas, therefore lessen the risks associated with drought episodes. Lack of long-term meteorological data for many parts of the country is one of the most important problems for drought monitoring in Iran. One of the useful ways for gathering information about soil and vegetation conditions is using satellite-based imagery. In this study, remotely sensed image data were applied in order to forecast and model the drought. To this end, SPI (standardized precipitation index) drought indicator was used to represent the drought and its intensity in different time spans (1, 3, 6, 9, 12, and 24 months). Some vegetation indices (VIs) including normalized difference vegetation index, temperature condition index, vegetation condition index, and normalized difference vegetation index deviation were extracted using Advanced Very High Resolution Radiometer sensor imagery. These indices were plugged into the model to calculate the SPI. A unique Support Vector Machine classifier improved for all types of the SPI by applying various remotely sensed VIs. The best vegetation index for each kind of SPI was determined. In this framework, meteorological stations were clustered based on their land cover extracted from satellite-based indices before insertion to the model.     

A global regression method for thermal sharpening of urban land surface temperatures from MODIS and Landsat

ABSTRACT

Land surface temperatures (LST) in urban landscapes are typically more heterogeneous than can be monitored by the spatial resolution of satellite-based thermal infrared sensors. Thermal sharpening (TS) methods permit the disaggregation of LST based on finer-grained multispectral information, but there is continued debate over which spectral indices are most appropriate for urban TS, and how they should be configured in a predictive regression framework. In this study, we evaluate the stability of various TS kernels with respect to LST at different spatial (Landsat 8) and diurnal (MODIS) scales, and present a new TS method, global regression for urban thermal sharpening (SGRUTS), based on these findings. Of the spectral indices examined, the normalized difference built-up index (NDBI) and the normalized multi-band drought index (NMDI) were the most spatially stable for Landsat 8 and MODIS overall. Kernel performance varied diurnally, with the index-based impervious surface index (IBI) and broadband α selected for 1030 h, NDBI and NMDI selected for 1330 h, and IBI and NMDI selected for 2230 h and 130 h, respectively. Over a range of field-validated metrics, the SGRUTS scheme comprising a two-factor interaction between NDBI and NMDI was competitive with the best alternative TS models compared. This SGRUTS model is essentially a refinement of the Enhanced Physical Method for urban applications in terms of kernel selection and configuration, and has interpretative advantages over more complex statistical schemes.