Agriculture provides for the most basic needs of humankind: food and fiber. The introduction of new farming techniques in the past century (e.g., during the Green Revolution) has helped agriculture keep pace with growing demands for food and other agricultural products. However, further increases in food demand, a growing population, and rising income levels are likely to put additional strain on natural resources. With growing recognition of the negative impacts of agriculture on the environment, new techniques and approaches should be able to meet future food demands while maintaining or reducing the environmental footprint of agriculture. Emerging technologies, such as geospatial technologies, Internet of Things (IoT), Big Data analysis, and artificial intelligence (AI), could be utilized to make informed management decisions aimed to increase crop production. Precision agriculture (PA) entails the application of a suite of such technologies to optimize agricultural inputs to increase agricultural production and reduce input losses. Use of remote sensing technologies for PA has increased rapidly during the past few decades. The unprecedented availability of high resolution (spatial, spectral and temporal) satellite images has promoted the use of remote sensing in many PA applications, including crop monitoring, irrigation management, nutrient application, disease and pest management, and yield prediction. In this paper, we provide an overview of remote sensing systems, techniques, and vegetation indices along with their recent (2015–2020) applications in PA. Remote-sensing-based PA technologies such as variable fertilizer rate application technology in Green Seeker and Crop Circle have already been incorporated in commercial agriculture. Use of unmanned aerial vehicles (UAVs) has increased tremendously during the last decade due to their cost-effectiveness and flexibility in obtaining the high-resolution (cm-scale) images needed for PA applications. At the same time, the availability of a large amount of satellite data has prompted researchers to explore advanced data storage and processing techniques such as cloud computing and machine learning. Given the complexity of image processing and the amount of technical knowledge and expertise needed, it is critical to explore and develop a simple yet reliable workflow for the real-time application of remote sensing in PA. Development of accurate yet easy to use, user-friendly systems is likely to result in broader adoption of remote sensing technologies in commercial and non-commercial PA applications.

Remote sensing and GIS are important tools for studying land use/land cover (LULC) change and integrating the associated driving factors for deriving useful outputs. This study is based on utilization of Earth observation datasets over the highly urbanized Allahabad district in India. Allahabad district has experienced intense change in LULC in the last few decades. To monitor the changes, advanced techniques in remote sensing and GIS, such as Cellular Automata (CA)-Markov Chain Model (CAMCM) were used to identify the spatial and temporal changes that have occurred in LULC in this area. Two images, 1990 and 2000, were used for calibration and optimization of the Markovian algorithm, while 2010 was used for validating the predictions of CA-Markov using the ground based land cover image. After validating the model, plausible future LULC changes for 2020 were predicted using the CAMCM. Analysis of the LULC pattern maps, achieved through classification of multitemporal satellite datasets, indicated that the socio-economic and biophysical factors have greatly influenced the growth of agricultural lands and settlements in the area. The two urbanization indicators calculated in this study viz. Land Consumption Ratio (LCR) and Land Absorption Coefficient (LAC) were also used, which indicated a drastic change in the area in terms of urbanization. The predicted LULC scenario for year 2020 provides useful inputs to the LULC planners for effective and pragmatic management of the district and a direction for an effective land use policy making. Further suggestions for an effective policy making are also provided which can be used by government officials to protect this important land resource.

In Nepal, arsenic (As) contamination is a major issue of current drinking water supply systems using groundwater and has recently been one of the major environmental health management issues especially in the plain region, i.e., in the Terai districts, where the population density is very high. The Terai inhabitants still use hand tube and dug wells (with hand held pumps that are bored at shallow to medium depth) for their daily water requirements, including drinking water. The National Sanitation Steering Committee (NSSC), with the help of many other organizations, has completed arsenic blanket test in 25 districts of Nepal by analysing 737,009 groundwater samples. Several organizations, including academic institutions, made an effort to determine the levels of arsenic concentrations in groundwater and their consequences in Nepal. The results of the analyses on 25,058 samples tested in 20 districts, published in the status report of arsenic in Nepal (2003), demonstrated that the 23% of the samples were containing 10-50 µg/L of As, and the 8% of the samples were containing more than 50 µg/L of As. Recent status of over 737,009 samples tested, the 7.9% and 2.3% were contaminated by 10-50 µg/L and OPEN ACCESSWater 2011, 3 2 >50 µg/L, respectively of As. The present paper examines the various techniques available for the reduction of arsenic concentrations in Nepal in combination with the main results achieved, the socio-economic status and the strategies. This paper aims to comprehensively compile all existing data sets and analyze them scientifically, by trying to suggest a common sustainable approach for identifying the As contamination in the nation, that can be easily adopted by local communities for developing a sustainable society. The paper aims also to find probable solutions to quantify and mitigate As problem without any external support. The outcome of this paper will ultimately help to identify various ways for: identify risk areas; develop awareness; adopt the World Health Organization (WHO) guideline; identify alternative safe water sources and assess their sustainability; give priorities to water supply and simple eco-friendly treatment techniques; investigate impacts of arsenic on health and agriculture; strengthen the capability of government, public, Non-governmental Organization (NGO) and research institutions.

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The Kunwari River Basin (KRB) needs effective management of water resources for sustainable agriculture and flood hazard mitigation. The Soil and Water Assessment Tool (SWAT), a semi distributed physically based model, was chosen and set up in the KRB for hydrologic modeling. SWAT-CUP (SWAT-Calibration and Uncertainty Programs) was used for model calibration, sensitivity and uncertainty analysis, following the Sequential Uncertainty Fitting (SUFI-2) technique. The model calibration was performed for the period (1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999), with initial 3 years of warm up ; then, the model was validated for the subsequent 6 years of data (2000)(2001)(2002)(2003)(2004)(2005). To assess the competence of model calibration and uncertainty, two indices, the p-factor (observations bracketed by the prediction uncertainty) and the r-factor (achievement of small uncertainty band), were taken into account. The results Environ. Process. of SWAT simulations indicated that during the calibration the p-factor and the r-factor were reported as 0.82 and 0.76, respectively, while during the validation the p-factor and the r-factor were obtained as 0.71 and 0.72, respectively. After a rigorous calibration and validation, the goodness of fit was further assessed through the use of the coefficient of determination (R 2 ) and the Nash-Sutcliffe efficiency (NS) between the observed and the final simulated values. The results indicated that R 2 and NS were 0.77 and 0.74, respectively, during the calibration. The validation also indicated a satisfactory performance with R 2 of 0.71 and NS of 0.69. The results would be useful to the hydrological community, water resources managers involved in agricultural water management and soil conservation, as well as to those involved in mitigating natural hazards such as droughts and floods.