Inferring Vegetation Water Content From C- and L-Band SAR Images

This paper addresses the capability of synthetic aperture radar and optical images in combination with theoretical models to detect the vegetation water content (VWC) at field level. In this paper, a retrieval algorithm for the estimation of VWC from AirSAR acquired on vegetated fields during the SMEX'02 experiment is addressed. The aforementioned campaign has been chosen because, along with sensor observations, extensive ground truth measurements were acquired. The retrieval procedure, which is based on a Bayesian approach, has been initially developed for soil moisture extraction. It consists of two modules: one is pertinent to bare soils and the other one has been modified for vegetated fields. The last one uses the synergy with optical images to correct for the contribution of VWC. The VWC, a variable in the inversion procedure, as well as soil moisture can be estimated. The results indicate a good correlation with both ground measurements and VWC calculated from Landsat images through the use of normalized difference water index (NDWI). Furthermore, in the inversion procedure, the introduction of the dependence on roughness improves the estimates. This indicates that, even for dense vegetation, the contribution from bare soil greatly influences the radar signal. Three main levels of VWC are discriminated in the inversion procedure: values below 1 kg/m², values between 1 and 3 kg/m², and values greater than 3 kg/m².     

Variations in energy use by Indian households: An analysis of micro level data

In this paper an attempt has been made to determine the factors that influence individual's decision to choose a particular energy carrier, viz., firewood, LPG, kerosene, and electricity. From the economic perspective, an individual's propensity to choose a particular energy carrier is determined by his/her economic status, carrier availability and prices, household size and other regional and demographic variables. For this reason, the choice of selecting a carrier for a service (e.g. cooking) is expected to vary among individuals. To assess this, the study uses the 1999–2000 National Sample Survey (NSS) data covering over 118,000 households and analyses the rural and urban areas separately. A multinomial logit selection model has been applied for estimating the energy carrier choice decision. The results show that individuals are influenced by per capita income, household size, educational status of the head of the household, occupation of the household members, in addition to other household location characteristics. It is found that the monthly household income and household size have nonlinear relationship on the probability of choosing a fuel.