To meet the demand for increasing global food production while using limited water resources, crop water stress must be improved in agriculture. Remote-sensing-based plant stress indicators have the benefits of high spatial resolutions, a cheap cost, and short turnaround times. This study discusses the current advancements in agricultural water stress monitoring and irrigation scheduling, some of the challenges that have been met, and the upcoming research needs. Remote sensing systems are prepared to handle the intricate and technical evaluations of agricultural productivity, security, and crop water stress quickly and effectively. We explore the use of remote-sensing systems in the evaluation of crop water stress by looking at the existing research, technologies, and data. This study examines the connection between relative water content (RWC), equivalent water thickness (EWT), and agricultural water stress. Using remote sensing, evapotranspiration, and sun-induced chlorophyll content are examined in connection to crop drought. Spectral indices, remote sensing satellites, and multi-spectral sensing systems, as well as systems that measure land surface temperature, are examined. This critical study focuses on cutting-edge techniques for assessing crop water stress.

A flood is a natural disaster. Heavy rainfall and overflow frequently cause enclosed land areas to fill with water, resulting in considerable loss of human life and property, including damage to buildings, bridges, electric supply networks, and transportation, and economic concern. This work was carried out in the Upper Indus Basin (UIB). We developed an artificial intelligence model for forecasting the flood events in this study. Long-short term memory (LSTM) and seasonal auto-regressive integrated moving average (SARIMA) were used in this study to forecast flood events. This study used a dataset from 1971–2009 and divided it into training, testing, and forecasting from 1971–2004, 2005–2009, and 2010–2014, respectively. The best statistical analysis result was observed with the LSTM model, which documented the value of root mean squared error (RMSE) at 22.79 and 35.05 for training and testing, respectively. Hence, the results of the study highlight that the LSTM model was the most suitable among the artificial neural networks for flood event forecasts. This current study will help in the forecasting of high storms for effective water resources management.

Water purification is crucial for ensuring access to safe and clean drinking water. As a sustainable and effective solution, green adsorbents have gained significant attention in recent times. These adsorbents are composed of natural or waste-based materials that are biodegradable, renewable, and abundant, making them an eco-friendly substitute for traditional adsorbents. This chapter offers a comprehensive outline of the present and prospects of green adsorbents in water purification. It encompasses a comprehensive overview of the many forms of green adsorption agents comprising natural, agriculture waste-based, & industrial waste-based adsorbents, as well as their synthesis and modification methods. Furthermore, it also explores the potential applications of green adsorbents in removing heavy metals, organic pollutants, and inorganic contaminants from water. The challenges and future directions of green adsorbent research are also discussed, including the limitations of these adsorbents and the opportunities for enhancing their performance. Overall, this chapter offers valuable insights into the potential of green adsorbents as a sustainable and eco-friendly solution for water purification.

In Pakistan, energy production from nuclear, hydro, and gas sources is about 99%, and only 1% is produced from renewable energy resources. Biomass is an emerging renewable resource for Pakistan obtained by converting loose biomass into pellets of high density. In this regard, biomass is receiving more and more attention day by day due to its abundant availability. Due to improper management of biomass in terms of transportation, handling, storing, and lack of awareness, its use for energy production is very low. The environmental and health effects of dumping and incineration techniques are becoming more dangerous for developing countries and rural communities day by day. The densification technique is a currently emerging technology for developing countries. Pellet development using yard waste is a new concept that can meet energy demands in addition to reducing environmental pollution and sources of waste management. For this purpose, the biomass of yard waste was shredded and then dried under sunlight or open drying to reduce the moisture content in the biomass material to less than 16% before being carbonized. Five samples with different moisture content levels were made and pellets were prepared from them. The results achieved by the characterization of pellets show that yard waste pellets at 5% moisture content had the highest calorific value of about 17.76 MJ/kg. Flue gas analysis was carried out and emissions from all were determined at all five moisture contents.

Freshwater scarcity is a global concern and is caused by the overuse of water, exceeding the sustainable level. In Pakistan, overpopulation, climate change alleviation, and economic development influence the asset of water. For water management, development, and conservation, policymakers need to formulate the right plans and projects. For this purpose, they need to evaluate the impact of different projects and plans and evaluate considerable data and information. Different tools are being adopted for integrated water resource management. Among them, WEAP is user-friendly, reliable, and efficient, and is used widely across the globe. It will aid the policymakers to develop suitable projects and plans. This study was carried out by using WEAP, in the Lower Bari Doab Canal command area (LBDC), Punjab, Pakistan. This study sheds light on the current water allocation situation and simulated scenarios of population growth rates and economic growth to evaluate the future water demand situation. The results of the current account year (2015) show that the domestic water demand of Okara (101.51 MCM) and the agricultural water demand (1713 MCM) of Sahiwal was seen to be the maximum among the four districts. Unmet demand was found to be maximum for the districts laying in the tail end (Sahiwal and Khanewal). The results of the scenarios show that, if the current water consumption, population growth, and economic development continue, unmet demand will increase in the future, and if the population grows at a higher rate and economic development increases, it will result in higher unmet water demand in the coming years. WEAP was found to be a user-friendly and efficient model to better understand water demand.

Although technological advancements have sparked the beginning of the fourth agricultural revolution, human beings are still facing severe problems such as shrinking croplands, dwindling water supplies, negative consequences of climate change, and so on in achieving agricultural resilience to meet the demands of the growing population over the globe. Geospatial techniques involving the integrated use of geographic information system (GIS), remote sensing (RS), and artificial intelligence (AI) provide a strong basis for sustainable management of agricultural resources aimed at increased agricultural production. In recent times, these advanced tools have been increasingly used in agricultural production at local, regional, and global levels. This chapter focuses on the widespread application of geospatial techniques for agricultural resource management by monitoring crop growth and yield forecasting, crop disease and pest infestation, land use and land cover mapping, flood monitoring, and water resource management. Moreover, we also discuss various methodologies involved in monitoring and mapping abovementioned agricultural resources. This chapter will provide deep insight into the available literature on the use of geospatial techniques in the monitoring and management of agricultural resources. Moreover, it will be helpful for scientists to develop integrated methodologies focused on exploring satellite data for sustainable management of agricultural resources.