Mapping tropical forest aboveground biomass using airborne SAR tomography.

Mapping tropical forest aboveground biomass (AGB) is important for quantifying emissions from land use change and evaluating climate mitigation strategies but remains a challenging problem for remote sensing observations. Here, we evaluate the capability of mapping AGB across a dense tropical forest using tomographic Synthetic Aperture Radar (TomoSAR) measurements at P-band frequency that will be available from the European Space Agency's BIOMASS mission in 2024. To retrieve AGB, we compare three different TomoSAR reconstruction algorithms, back-projection (BP), Capon, and MUltiple SIgnal Classification (MUSIC), and validate AGB estimation from models using TomoSAR variables: backscattered power at 30 m height, forest height (FH), backscatter power metric (Q), and their combination.

Estimation of root zone soil moisture using passive microwave remote sensing: A case study for rice and wheat crops for three states in the Indo-Gangetic basin.

This paper investigates estimation of root zone soil moisture using two passive microwave remote sensing datasets, Advanced Microwave Scattering Radiometer - 2 and Soil Moisture Active Passive satellites sensors. The study is focused on two crops, namely rice and wheat for the Indo-Gangetic basin, India, having a dynamic crop and soil type and land use land cover. A total of 21 rice crop and 23 wheat crop locations are chosen from the states of Uttar Pradesh, Madhya Pradesh and Bihar falling in the basin.

Spectral Contextual Classification of Hyperspectral Imagery With Probabilistic Relaxation Labeling.

In this paper, a spectral-spatial classification framework based on probabilistic relaxation labeling using compatibility coefficients is proposed for hyperspectral images. It is a two-stage classifier that uses maximum a posteriori (MAP) estimation to maximize posterior probabilities of classification map obtained in first stage to incorporate spatial information for better classification accuracy. Two different forms of compatibility coefficients based on correlation and mutual information are used for MAP estimation.

Respiratory disease in relation to outdoor air pollution in Kanpur, India.

This paper examines the effect of outdoor air pollution on respiratory disease in Kanpur, India, based on data from 2006. Exposure to air pollution is represented by annual emissions of sulfur dioxide (SO(2)), particulate matter (PM), and nitrogen oxides (NO(x)) from 11 source categories, established as a geographic information system (GIS)-based emission inventory in 2 km × 2 km grid. Respiratory disease is represented by number of patients who visited specialist pulmonary hospital with symptoms of respiratory disease.