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Utilising cosmic-ray neutron probes is a relatively new approach in obtaining larger area soil moisture and various operational monitoring networks have been established worldwide utilising this technology to measure operationally this parameter. One such network located in the United Kingdom (UK) is the Cosmic-ray Soil Moisture Observing System, so-called COSMOS-UK, established in 2013. The present study aims at investigating the true footprint and the variations within the footprint detectable area at the COSMOS-UK sites using as a case study one such site located in Riseholme, UK. At the selected experimental site extensive fieldwork was conducted in July 2017 that allowed examining the agreement among the soil moisture data retrieved by the Time Domain Transmissometer (TDT) sensors and the corresponding estimates from the COSMOS-UK network station probe. The COSMOS-UK site footprint was compared using GPS-aided information from ground instrumentation, assisted by drone imagery acquisition and the implementation of geospatial interpolation methods in a Geographical Information System (GIS) environment. Altogether, this information was used for assessing the soil moisture footprint extent from the COSMOS-UK site. The COSMOS-UK station footprint was representative for an area shorter in size than the alleged footprint of 600 m diameter, as generally proposed in various relevant investigations. The COSMOS network slightly overestimated soil moisture content measured by the TDT sensor probes installed in the area. Our study findings although concern specifically the studied experimental site contribute towards efforts aiming at assessing the COSMOS-UK soil moisture measurement footprint showcasing the added value of geospatial analysis in this direction.

Water vapour is the most variable constituent in the atmosphere which is responsible for serious noise in the optical satellite images. This research is focused on the vertical distribution of water vapour and deducing its possible effects on the atmospheric correction process. The vertical distribution of precipitable water vapour, water vapour mixing ratio with geopotential height and pressure were estimated through the weather research and forecasting (WRF) model by downscaling the National Center for Environmental Prediction (NCEP) global reanalysis product. In addition, the most widely used LANDSAT TM satellite image has been used for this assessment. The WRF model was applied with three domains centred on a LANDSAT captured image over the area. The 6S atmospheric correction code was utilised for viewing the effect of precipitable water vapour on satellite image correction. The analysis was conducted on two pressure levels (1,000 and 100 hPa) representing the troposphere and stratosphere, respectively. The validation of the atmospheric correction has been performed by estimating the land surface temperature (LST) over the Walnut Creek region and its comparison with the Soil Moisture Experiments in 2002 (SMEX02) LST field validation datasets. The overall analyses indicate a higher accuracy of LST repossession with 100 hPa corrected image.

The Tropical Rainfall Measuring Mission (TRMM) is a joint space mission between NASA and the Japan Aerospace Exploration Agency (JAXA) designed to monitor and study tropical rainfall. In this study, the daily rainfall from TRMM has been utilized to simulate the soil moisture content up to 30 cm vertical soil profile of at an interval depth of 15 cm by using the HYDRUS 1D numerical model for the three plots. The simulated soil moisture content using ground-based rainfall and TRMM-derived rainfall measurements indicate an agreeable goodness of fit between the both. The Nash–Sutcliffe efficiency using ground-based and TRMM-derived rainfall was found in the range of 0.90–0.68 and 0.70–0.40, respectively. The input data sensitivity analysis of precipitation combined with different irrigation treatment indicates a high dependency of soil moisture content with rainfall input. The overall analysis reveals that TRMM rainfall is promising for soil moisture prediction in absence of ground-based measurements of soil moisture.

Human activities in many parts of the world have greatly changed the natural land cover. This study has been conducted on Pichavaram forest, south east coast of India, famous for its unique mangrove bio-diversity. The main objectives of this study were focused on monitoring land cover changes particularly for the mangrove forest in the Pichavaram area using multi-temporal Landsat images captured in the 1991, 2000, and 2009. The land use/land cover (LULC) estimation was done by a unique hybrid classification approach consisting of unsupervised and support vector machine (SVM)-based supervised classification. Once the vegetation and non-vegetation classes were separated, training site-based classification technology i.e., SVM-based supervised classification technique was used. The agricultural area, forest/plantation, degraded mangrove and mangrove forest layers were separated from the vegetation layer. Mud flat, sand/beach, swamp, sea water/sea, aquaculture pond, and fallow land were separated from non-vegetation layer. Water logged areas were delineated from the area initially considered under swamp and sea water-drowned areas. In this study, the object-based post-classification comparison method was employed for detecting changes. In order to evaluate the performance, an accuracy assessment was carried out using the randomly stratified sampling method, assuring distribution in a rational pattern so that a specific number of observations were assigned to each category on the classified image. The Kappa accuracy of SVM classified image was highest (94.53 %) for the 2000 image and about 94.14 and 89.45 % for the 2009 and 1991 images, respectively. The results indicated that the increased anthropogenic activities in Pichavaram have caused an irreversible loss of forest vegetation. These findings can be used both as a strategic planning tool to address the broad-scale mangrove ecosystem conservation projects and also as a tactical guide to help managers in designing effective restoration measures.

The work presented here showed a comprehensive evaluation of dual-polarimetric RISAT-1 data for land use/land cover (LULC) classification. The textural images were extracted with the help of gray-level co-occurrence matrix approach. Analysis of inter-class separability using transformed divergence method was performed to recognize the potential textural images. The best combination of textural images was also identified on the basis of standard deviation of preferred textural images and correlation coefficients. The maximum likelihood classifier-based classification results for different scenarios were compared. Furthermore, various classification algorithms, maximum likelihood classifier (MLC), artificial neural network (ANN), random forest (RF) and support vector machine (SVM), were performed on the best identified scenario in order to observe the most suitable algorithm for LULC classification. The combination of radiometric and their related textural images was found improving the overall classification accuracy than individual datasets. The highest overall classification accuracy was found using SVM (88.97%) followed by RF (88.45%), ANN (83.65%) and MLC (78.18%).

Geographical information system and remote sensing are proven to be an efficient tool for locating water harvesting structures by prioritization of mini-watersheds through morphometric analysis. In this study, the morphometric analysis and prioritization of ten mini-watersheds of Malesari watershed, situated in Bhavnagar district of Saurashtra region of Gujarat state, India, are studied. For prioritization of mini-watersheds, morphometric analysis is utilized by using the linear parameters such as bifurcation ratio, drainage density, stream frequency, texture ratio, and length of overland flow and shape parameters such as form factor, shape factor, elongation ratio, compactness constant, and circularity ratio. The different prioritization ranks are assigned after evaluation of the compound factor. Digital elevation model from Shuttle Radar Topography Mission, digitized contour, and other thematic layers like drainage order, drainage density, and geology are created and analyzed over ArcGIS 9.1 platform. Combining all thematic layers with soil and slope map, the best feasibility of positioning check dams in mini-watershed has been proposed, after validating the sites through the field surveys.

In natural ecosystems, the linkages between inputs of carbon from plants, soil moisture (SM) and microbial activity are central to our understanding of nutrient cycling. Predictions of microbial activities in soil are important as they indicate the potential of the soil to support biochemical processes that are essential for the maintenance of soil fertility as well as productivity. The dehydrogenase activity (DHA) in soil provides information on microbial activities of the soil. However, estimation of DHA activity over complex terrain such as soils of the central Himalaya is not always possible due to very harsh environment and climatic conditions. In this study, the attempts were made to estimate the DHA in the soil of mid altitude central Himalaya using computational intelligence techniques. The linear and non-linear correlation results indicate that the fluctuations in SM and organic carbon (OC) in the root zone affect DHA and can be used as predictors for DHA. Therefore, the performances of support vector machines (SVMs) and generalized linear models (GLMs) were attempted for the prediction of DHA over mid altitude central Himalaya using information of SM and OC. The results showed that the SVM was giving a much better performance than GLM using SM and OC and could be promising and cost effective approach for soil DHA prediction over complex ecosystem. Our results are also of considerable scientific and practical value to the wider scientific community, given the number of practical applications and research studies in which SM and OC datasets are used.