Search

An introduction is presented to the issue of the periodical on topics including agriculture, Crop yield estimation using synthetic aperture radar and modeling and Inversion modeling for retrieving crop biophysical parameters.

Soil organic carbon (SOC) is an important parameter to study the carbon cycle as soil carbon stock inventory as well as to serve as prime indicator in assessing soil health and soil quality. The present study was attempted to investigate C-equivalent correction factor for SOC by Walkley–Black (wet oxidation) and loss on ignition (LOI) methods in relation to TOC analyzer (dry combustion) method. TOC analyzer method supposed to be the best method of total soil organic carbon estimation. Soil sample from 77 sites representing dominant land use/land cover types of crop land, forest and scrub cover were collected in Himalayan region of Uttarakhand state, India. Surface (0–15 cm) and sub-surface (15–30 cm) soil samples were used for estimation of SOC by these three methods. C-equivalent correction factor ranged from 1.10 to 1.17 for SOC determination by Walkley and Black method to TOC analyzer method, whereas it varied from 0.257 to 0.417 for soil organic matter (SOM) by LOI method to TOC analyzer for soils under various land use/land cover types in the Himalayan region. The recovery of SOC by Walkley–Black method varied from 86.84 to 91.04% in the soils of various land use/land cover in the Himalayan landscape. Thus, there is need to develop specific correction factor for soils under various land use/land cover types for improved estimation of soil carbon stock. The regression models developed in the study can be directly used to obtain TOC analyzer equivalent total carbon contents in the soils (surface and sub-surface) for computation of soil carbon stock in Himalayan region. [ABSTRACT FROM AUTHOR]

In India, human population has increased six-fold from 200 million to 1200 million that coupled with economic growth has resulted in significant land use and land cover (LULC) changes during 1880-2010. However, large discrepancies in the existing LULC datasets have hindered our efforts to better understand interactions among human activities, climate systems, and ecosystem in India. In this study, we incorporated high-resolution remote sensing datasets from Resourcesat-1 and historical archives at district (N = 590) and state (N = 30) levels to generate LULC datasets at 5 arc minute resolution during 1880-2010 in India. Results have shown that a significant loss of forests (from 89 million ha to 63 million ha) has occurred during the study period. Interestingly, the deforestation rate was relatively greater under the British rule (1880-1950s) and early decades after independence, and then decreased after the 1980s due to government policies to protect the forests. In contrast to forests, cropland area has increased from 92 million ha to 140.1 million ha during 1880-2010. Greater cropland expansion has occurred during the 1950-1980s that coincided with the period of farm mechanization, electrification, and introduction of high yielding crop varieties as a result of government policies to achieve self-sufficiency in food production. The rate of urbanization was slower during 1880-1940 but significantly increased after the 1950s probably due to rapid increase in population and economic growth in India. Our study provides the most reliable estimations of historical LULC at regional scale in India. This is the first attempt to incorporate newly developed high-resolution remote sensing datasets and inventory archives to reconstruct the time series of LULC records for such a long period in India. The spatial and temporal information on LULC derived from this study could be used by ecosystem, hydrological, and climate modeling as well as by policy makers for assessing the impacts of LULC on regional climate, water resources, and biogeochemical cycles in terrestrial ecosystems. [ABSTRACT FROM AUTHOR]

Abstract: Landslide susceptibility mapping (LSM) along road corridors in the Indian Himalayas is an essential exercise that helps planners and decision makers in determining the severity of probable slope failure areas. Logistic regression is commonly applied for this purpose, as it is a robust and straightforward technique that is relatively easy to handle. Ordinary logistic regression as a data-driven technique, however, does not allow inclusion of prior information. This study presents Bayesian logistic regression (BLR) for landslide susceptibility assessment along road corridors. The methodology is tested in a landslide-prone area in the Bhagirathi river valley in the Indian Himalayas. Parameter estimates from BLR are compared with those obtained from ordinary logistic regression. By means of iterative Markov Chain Monte Carlo simulation, BLR provides a rich set of results on parameter estimation. We assessed model performance by the receiver operator characteristics curve analysis, and validated the model using 50% of the landslide cells kept apart for testing and validation. The study concludes that BLR performs better in posterior parameter estimation in general and the uncertainty estimation in particular. [Copyright &y& Elsevier]

This paper reports results on the LAI Retrieval and Validation Experiment (LRVE) that was conducted for two agricultural areas in Central India during the winter season of 2001–2002. The study aimed at relating field measurements of leaf area index (LAI) to spaceborne Indian Remote Sensing Satellite (IRS) Linear Imaging Self Scanning Sensor-III (LISS-III) data, preparation of site-level LAI maps, and validation of Moderate Resolution Imaging Spectroradiometer (MODIS) 1-km LAI global fields. Measurements of field-level LAI, aerosol optical thickness and water vapor were carried out on the day of LISS-III overpasses. Empirical models based on the site-specific LAI-vegetation index relation were developed and used to generate 23-m resolution LAI maps for two sites (Indore and Bhopal) covering 30 km × 30 km. These LAI images were degraded to 1-km spatial resolution and used for validation of the version 3 and 4 MODIS LAI products (MOD15A2). The results indicate a positive correlation (r = 0.78) between LAI derived from LISS-III data and MODIS data. However an overestimate by a factor of 1.6 to 2.5 in the version 3 MODIS product is observed with root mean square error (RMSE) ranging from 0.20 to 1.26. The factor of overestimation reduces significantly by 50% and RMSE by 40% when version 4 MODIS LAI was analyzed. The improvement in accuracy was observed to be associated with the change in algorithm path adopted for retrieving version 3 and 4 MODIS LAI. Analysis of the MODIS land cover product that is an input in the MODIS LAI retrieval algorithm indicated errors in assigning land cover classes for the study sites, which could be one of the sources of error in MODIS LAI product. [ABSTRACT FROM AUTHOR]

Presents information on the bandpass solar exoatmospheric irradiance and Rayleigh optical thickness for several categories of sensors. Categories of sensors; Characteristics of ground surface that are being quantified through the use of remote sensing data; Constituents of the atmosphere which affect radiance in the visible and near infrared wavebands; Information on the relative spectral measurement.