Validating MODIS-derived land surface evapotranspiration with in situ measurements at two AmeriFlux sites in a semiarid region

Reducing uncertainties in the estimation of land surface evapotranspiration (ET) from remote-sensing data is essential to better understand earth-atmosphere interactions. This paper demonstrates the applicability of temperature-vegetation index triangle (T-s-VI) method in estimating regional ET and evaporative fraction (EF, defined as the ratio of latent heat flux to surface available energy) from MODIS/Terra and MODIS/Aqua products in a semiarid region. We have compared the satellite-based estimates of ET and EF with eddy covariance measurements made over 4 years at two semiarid grassland sites: Audubon Ranch (AR) and Kendall Grassland (KG). The lack of closure in the eddy covariance measured surface energy components is shown to be more serious at MODIS/Aqua overpass time than that at MODIS/Terra overpass time for both AR and KG sites. The T-s-VI-derived EF could reproduce in situ EF reasonably well with BIAS and root-mean-square difference (RMSD) of less than 0.07 and 0.13, respectively. Surface net radiation has been shown to be systematically overestimated by as large as about 60 W/m(2). Satisfactory validation results of the T-s-VI-derived sensible and latent heat fluxes have been obtained with RMSD within 54 W/m(2). The simplicity and yet easy use of the T-s-VI triangle method show a great potential in estimating regional ET with highly acceptable accuracy that is of critical significance in better understanding water and energy budgets on the Earth. Nevertheless, more validation work should be carried out over various climatic regions and under other different land use/land cover conditions in the future.

Sublimation of snow from confierous forests in a climate model

Essery, R L H, Pomeroy, J W, Parvianen, J & Storck, P, Sublimation of snow from confierous forests in a climate model. Journal of Climate 16, pp 1855-1864 (2003).

Inclusive development? State-led land development model in New Town, Kolkata

Urban land development in India is changing under the auspices of economic liberalisation. Kolkata has been in the forefront of this transformation through development of new townships in the urban peripheries based on a distinctive state-led land development model. Within this context New Town, Kolkata (also known as Rajarhat) provides a highly illuminative case to articulate the ways in which the state is implementing its neoliberal agenda in land development. It rides on political and ideological high ground by seeking to create a ‘model development’ of state–market partnership for dual goals of fostering capitalist interest while fulfilling welfarist principles. Interesting insights have emerged that point to a policy paradox. On one hand, the process follows market principles of efficacy and efficiency; on the other hand, state’s keenness to extend control persists, thereby creating a highly uneven terrain for state–market interaction. New Town reflects a typical quasi-market condition shaped by the monopolistic state, the poorly structured role of the private sector, an absence of civic bodies, and minimal land and housing provision for the poor. In India, as internationally, the economic liberalisation market ideology is increasingly construed as good governance. In this context New Town is a step in the right direction, but the progress is patchy, uneven, and still evolving.

Assessment of NDVI, land surface temperature and precipitation anomalies for drought monitoring in Bayankhongor province, Mongolia

During the last decade Mongolia’s region was characterized by a rapid increase of both severity and frequency of drought events, leading to pasture reduction. Drought monitoring and assessment plays an important role in the region’s early warning systems as a way to mitigate the negative impacts in social, economic and environmental sectors. Nowadays it is possible to access information related to the hydrologic cycle through remote sensing, which provides a continuous monitoring of variables over very large areas where the weather stations are sparse. The present thesis aimed to explore the possibility of using NDVI as a potential drought indicator by studying anomaly patterns and correlations with other two climate variables, LST and precipitation. The study covered the growing season (March to September) of a fifteen year period, between 2000 and 2014, for Bayankhongor province in southwest Mongolia. The datasets used were MODIS NDVI, LST and TRMM Precipitation, which processing and analysis was supported by QGIS software and Python programming language. Monthly anomaly correlations between NDVI-LST and NDVI-Precipitation were generated as well as temporal correlations for the growing season for known drought years (2001, 2002 and 2009). The results show that the three variables follow a seasonal pattern expected for a northern hemisphere region, with occurrence of the rainy season in the summer months. The values of both NDVI and precipitation are remarkably low while LST values are high, which is explained by the region’s climate and ecosystems. The NDVI average, generally, reached higher values with high precipitation values and low LST values. The year of 2001 was the driest year of the time-series, while 2003 was the wet year with healthier vegetation. Monthly correlations registered weak results with low significance, with exception of NDVI-LST and NDVI-Precipitation correlations for June, July and August of 2002. The temporal correlations for the growing season also revealed weak results. The overall relationship between the variables anomalies showed weak correlation results with low significance, which suggests that an accurate answer for predicting drought using the relation between NDVI, LST and Precipitation cannot be given. Additional research should take place in order to achieve more conclusive results. However the NDVI anomaly images show that NDVI is a suitable drought index for Bayankhongor province.

A Bayesian approach to estimate sensible and latent heat over vegetated land surface

Sensible and latent heat fluxes are often calculated from bulk transfer equations combined with the energy balance. For spatial estimates of these fluxes, a combination of remotely sensed and standard meteorological data from weather stations is used. The success of this approach depends on the accuracy of the input data and on the accuracy of two variables in particular: aerodynamic and surface conductance. This paper presents a Bayesian approach to improve estimates of sensible and latent heat fluxes by using a priori estimates of aerodynamic and surface conductance alongside remote measurements of surface temperature. The method is validated for time series of half-hourly measurements in a fully grown maize field, a vineyard and a forest. It is shown that the Bayesian approach yields more accurate estimates of sensible and latent heat flux than traditional methods.

A geostatistical analysis of soil, vegetation, and image data characterizing land surface variation

The elucidation of spatial variation in the landscape can indicate potential wildlife habitats or breeding sites for vectors, such as ticks or mosquitoes, which cause a range of diseases. Information from remotely sensed data could aid the delineation of vegetation distribution on the ground in areas where local knowledge is limited. The data from digital images are often difficult to interpret because of pixel-to-pixel variation, that is, noise, and complex variation at more than one spatial scale. Landsat Thematic Mapper Plus (ETM+) and Satellite Pour l'Observation de La Terre (SPOT) image data were analyzed for an area close to Douna in Mali, West Africa. The variograms of the normalized difference vegetation index (NDVI) from both types of image data were nested. The parameters of the nested variogram function from the Landsat ETM+ data were used to design the sampling for a ground survey of soil and vegetation data. Variograms of the soil and vegetation data showed that their variation was anisotropic and their scales of variation were similar to those of NDVI from the SPOT data. The short- and long-range components of variation in the SPOT data were filtered out separately by factorial kriging. The map of the short-range component appears to represent the patterns of vegetation and associated shallow slopes and drainage channels of the tiger bush system. The map of the long-range component also appeared to relate to broader patterns in the tiger bush and to gentle undulations in the topography. The results suggest that the types of image data analyzed in this study could be used to identify areas with more moisture in semiarid regions that could support wildlife and also be potential vector breeding sites.

Diurnal cycle of land surface temperature in a desert encroachment zone as observed from satellites

Climate variability in the African Soudano-Sahel savanna zone has attracted much attention because of the persistence of anomalously low rainfall. Past efforts to monitor the climate of this region have focused on rainfall and vegetation conditions, while land surface temperature (LST) has received less attention. Remote sensing of LST is feasible and possible at global scale. Most remotely sensed estimates of LST are based on the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) that are limited in their ability to capture the full diurnal cycle. Although more frequent observations are available from past geostationary satellites, their spatial resolution is coarser than that of polar orbiting satellites. In this study, the improved capabilities of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on the METEOSAT Second Generation (MSG) instrument are used to remotely sense the LST in the African Soudano-Sahel savanna zone at a resolution of 3 km and 15 minutes. In support of the Radiative Atmospheric Divergence using the ARM Mobile Facility (AMF), GERB and AMMA Stations (RADAGAST) project, African Monsoon Multidisciplinary Analyses (AMMA) project and the Department of Energy's Atmospheric Radiation Measurement (ARM) program, the ARM Mobile Facility was deployed during 2006 in this climatically sensitive region, thereby providing a unique opportunity to evaluate remotely sensed algorithms for deriving LST.