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The role of feature-level fusion using the Dempster-Shafer rule and that of the data-level fusion in the Markov random field (MRF) context is studied to obtain an optimally segmented image. Subsequently, segments are validated, and classification accuracy for the test data is evaluated.

To develop an operational methodology for estimating soil moisture and crop biophysical parameters and to generate a crop cover map, backscattering signatures of vegetation canopies are investigated using multitemporal Radarsat synthetic aperture radar (SAR) data over a predominantly cotton-growing area in India during low to peak crop growth stage. A simple parameterization of the water-cloud model with volumetric soil moisture content ([m.sub.v]) and leaf area idex (LAI) is used to simulate the microwave backscattering coefficient ([[sigma].sup.0]), as it is found to be a good candidate for operational purposes as demonstrated by several workers in past. The influence of crop height (H), LAI, and [m.sub.v] on [[sigma].sup.0] is investigated during peak crop growth stage. A linear relationship between LAI and crop height is derived semiempirically, and a linear zone is chosen for analysis during the peak croup-growing stage. Estimation of average volume fraction of leaves ([[bar.V].sub.l]) and attenuation factor (L) by two different approaches is discussed: 1) using linear relationship between LAI versus crop height and 2) from the water-cloud model parameter (K) estimation by iterative minimum least square error approach. It is observed that model-estimated parameters agree well with the measured values within an acceptable error limit. At lower soil moisture, [m.sub.v] [congruent to] 0.02 ([cm.sup.3] x [cm.sup.-3]), the dynamic range of [[sigma.sup.0] is found to be about +5 dB for 0-70 cm of crop height but monotonously decreases to null at a transition point, having [m.sub.v] [approximately equal to] 0.38 ([cm.sup.3] x [cm.sup.-3]). A positive correlation is found between backscattering coefficient and crop height till this transition point but shows a negative correlation beyond that, signifying the predominant attenuation by vegetation over soil. Differential moisture sensitivity (d[[sigma].sup.0]/d[m.sub.v]) of the backscattering coefficient decreases by half from 20.55 dB/([cm.sup.3] x [cm.sup.-3]) for dry and bare-field conditions to 10.68 dB/([cm.sup.3] x [cm.sup.-3]) for wet and crop-covered fields ([m.sub.v] = 0.38 [cm.sup.3] x [cm.sup.-3], H = 70 cm), whereas differential crop height sensitivity (d[[sigma].sup.0]/dH) varies from 0.22-0.03 dB/cm for bare-field conditions to crop-covered fields with crop height 70 cm. It is found that the percentage of relative error is smallest (2.27 %) for LAI and attenuation factor estimation using the value of [[bar.V].sub.l], from LAI models, whereas it is 4.25% when estimating from the attenuation coefficient (K) from the model. Index Terms--Crop attenuation, differential moisture and crop height sensitivity, leaf area index (LAI), synthetic aperture radar (SAR) backscatter, water-cloud model.

Retrieval of transplantation date (TP) using RISAT-1 medium resolution SCANSAR data for operational pre-harvest crop monitoring has been demonstrated. The methodology used 3-date HH-polarized RISAT data capturing the onset of transplantation (minimal backscatter) and rises as crop grows, for the country scale within 2–4 days of planting and was found to closely agree to ground conditions. Transplantation date ranged from June in north to October in Tamil Nadu. This variation significantly reduced to a month in the summer paddy due to irrigation. Transplantation patterns were different in both the seasons' paddy. Validation exercise for TP in four major states confirmed the feasibility of retrieval datasets for transplantation date monitoring as it matched within 2–4 days in north, south India and MP but 6–8 days in Assam. The technique has reached a level of maturity and is routinely used for monitoring the transplantation date by the Department of Agriculture, India. [ABSTRACT FROM AUTHOR]

This study presents a methodology to classify rice cultural types based on water regimes using multi-temporal synthetic aperture radar (SAR) data. The methodology was developed based on the theoretical understanding of radar scattering mechanisms with rice crop canopy, considering crop phenology and variation in water depth in the rice field, emphasizing the sensitivity of SAR to crop geometry and water. The logic used was the characteristic decrease in SAR backscatter that is associated with the puddled or transplanted field due to specular reflection for little exposure of crop, with increase in backscatter as the crop growth progresses due to volume scattering. Besides, the multiple interactions between SAR and vegetation/water also lead to an increase in backscatter as the crop growth progresses. Classification thresholds were established based on the information provided by each pixel in each image, the pixel's typical temporal behaviour due to crop phenology and changing water depth in rice field and their corresponding SAR signature. Based on this logic, the study site (i.e. South 24 Paraganas district, West Bengal) was classified into three major rice cultural types, namely shallow water rice (SWR; 5 cm ≤ water depth ≤ 30 cm), intermediate water rice (IWR; 30 cm ≤ water depth ≤ 50 cm) and deep water rice (DWR; water depth > 50 cm) during the kharif season. These three types represent most of the traditional rice-growing areas of India. The methodology was validated with the field data collected synchronously with the satellite passes. Classification results showed an overall accuracy of 98.5% (95.5% kappa coefficient) compared with a maximum-likelihood classifier (MLC) with an overall accuracy of 95.5% (84.2% of kappa coefficient) with 95% confidence interval. The relationship between field parameters, especially exposed plant height and water depth with SAR backscatter, was explored to design empirical models for each of the three rice classes. Significant relationships were observed in all the rice classes (coefficient of determination, R 2, value more than 0.85) even though they had similar growth profiles but varied with water depth. The two main conclusions drawn from this study are (i) the importance of multi-temporal SAR data for the classification of rice culture types based on water regimes and (ii) the advantages and flexibility of the knowledge-based classifier for classification of RADARSAT-1 data. However, being empirical, the approach needs modification according to the current rainfall pattern and rice-growing practice. [ABSTRACT FROM AUTHOR]

This study contributes to the local responses to address the impact of climate change in agriculture sector in the context of Khyber Pakhtunkhwa and Punjab provinces of Pakistan. Pakistan's agriculture is vulnerable to the cataclysmic transformations in the climate system; causing productivity loss, soil degradation, water scarcity, resource depletion etc. Climate change awareness empowers the farming communities to take appropriate measures to mitigate the negative consequences of this phenomenon but the responses are not necessarily identical in scale and forms. In developed countries, climate change awareness is much advanced therefore, their climate change adaptations are also far better than those employed by farmers in the developing countries like Pakistan. The climate change awareness obviously determines the nature, quality and strength of climate change adaptations. The current study measures the impact of climate change awareness on climate change adaptation and climate change adaptation issues (constraints) and their interactions to help in addressing the native agricultural issues emerging from global climate change. The statistical analysis of the field data supports the assumptions about positive association between climate change awareness and climate change adaptations (R2=44.6, B=.875 and P-value=.000) and negative interaction between climate change awareness and climate change adaptation issues (R²=.318, B=-.707 and P-value=.000). Further, the role of geographic-zones and respondents' educational level has also been found significant. Therefore, more efforts, if directed, to enhance climate change awareness through dissemination of authentic and need based information, would significantly help farmers to undertake more relevant, effective and efficient adaptation measures, thereby, contributing in increasing the agriculture yield and reduce losses. However, imprudent projection of climate change adaptation issues without proper awareness about the associated dynamics, complexities and implications may petrify the farmers thus, yielding negative ramifications with regards to the outcomes. [ABSTRACT FROM AUTHOR]

Rice is the most important food crop of India. Majority of Rice is sown in kharif season in the country. This is monsoon season for the country where cloud cover poses a major problem for optical remote sensing. Therefore, for these states rice acreage estimation is being done using Synthetic Aperture Radar (SAR) data operationally in India since 1998. A case study is presented in this paper for analysis of past 6 years' (2012–13 to 2017–18) estimations. Multi temporal Radarsat-2 (HH), RISAT-1 ScanSAR (HH) and Sentinel-1 (VV) data was used in years 2012, 2013–2016, and 2017, respectively for paddy identification. Hierarchal Decision Rule based classification (HDRC) approach was used to identify rice areas under sample segments. Extensive ground truth collected by state remote sensing departments and agriculture departments was utilized in setting the limits of HDRC models and accuracy assessment. Yield was estimated using weather based and remote sensing-based models. Area, production and yield estimates were made and compared with those given by DES. RMSE and R2 were used as statistical measures to assess the accuracy of results. The RMSE % ranged from 2.3 to 4.3; 0.84 to 1.35; 0.24 to 0.27 for area, production and yield respectively. The coefficient of determination (R2) ranged from 0.62 to 0.92; 0.75 to 0.91; 0.5 to 0.83 for area, production and yield respectively. The study showed that use of multi temporal SAR data (both HH and VV) is quite useful for paddy acreage estimation, especially during monsoon. [ABSTRACT FROM AUTHOR]

Background and objectives The HIV-associated renal diseases represent a spectrum. Indian data on this is sparse. This study was undertaken to find out the prevalence and clinicopathological spectrum of renal involvement in HIV among antiretroviral therapy (ART) naïve patients (Group 1) and among those on ART (Group 2). Methods Systematic random sampling was undertaken to select 109 patients each from virology outpatient department (VOPD) and ART centre of a tertiary care hospital. They were screened and further investigated if renal involvement was found. Results Renal involvement was present in 25/109 (22.94%) and 15/109 (13.76%) patients of Groups 1 and 2, respectively. Among patients of Groups 1 and 2, 9/24 (37.5%) and 2/13 (15.4%), respectively, had clinically significant proteinuria, but none in the nephrotic range. Statistically significant relationships of renal involvement were observed with CD4 count <100/μl and with low BMI. Of the patients of Group 2, 20% of those on a tenofovir-based regimen had renal involvement with tubular changes, while only 4.6% of those on other regimens had renal involvement. This difference was statistically significant (p<0.05; OR=5.25). Conclusion Renal involvement was less common among those on ART. Low CD4 count and body mass index (BMI) were associated with renal dysfunction. Patients on a tenofovir-based regimen had more renal involvement compared with not on a tenofovir-based regimen. [ABSTRACT FROM AUTHOR]

The article reviews a burgeoning literature on the environment from two major groups of authors; on the one hand, International Relations scholars and, on the other, a host of 'green writers' comprising mainly biologists, environmental activists and philosophers. Both groups share a tendency to focus on the same four or five environmental problems introduced by a 'state of the environment reporting'. They perceive solutions mainly as top-down institutional arrangements above or between governments, and conclude with prescriptions for practitioners. Careful reading nevertheless reveals major contradictions between the theoretical assumptions of International Relations scholars and the empirical observations of green writers. While the former tend to place the nation-state at the centre of analysis, the latter group emphasizes the importance of a wide range of non-state actors who increasingly define local and transnational politics on the environment. Against this background, and with a brief introduction to recently arrived reflectivist approaches, a dialogue is suggested with the ambition of arriving at a study of global environmental change that consists not only of international, but also global politics on the environment. The article concludes with some suggestions for improvement. [ABSTRACT FROM AUTHOR]

The study brings out the potential of SAR polarimetric time series data for the study of crop biophysical parameter assessment of major winter field crops in India. We examined the entropy-alpha-anisotropy approach in radar polarimetric C-band data and related the polarimetric parameters to the biomass to understand the interaction of microwave signal with the major winter crops across dominant biomass ranges in India. The crop phenology response in terms of the changes in polarimetric parameters like entropy (H), anisotropy (A), and alpha (α) angle and their derived parameters has been studied for dominant winter field crops like wheat, mustard, and gram with variation in their variables like biomass. A trend is established emerging from this study which shows that entropy, alpha, and anisotropy for agricultural areas are closely related to the growth rate of the crops and their phenological stages. This paper outlines a methodology for using RADARSAT-2 full-polarimetric data for vegetation (winter crops) monitoring using the abovementioned standard polarimetric parameters for understanding crop vigor and phenology. The polarimetric parameters were observed to be more dynamic in wheat (H 0.45 to 0.8) than in mustard (H 0.6 to 0.85) in December to February data though late sown crop of mustard showed a higher range with respect to their biomass. [ABSTRACT FROM AUTHOR]