Background: The coronavirus disease (COVID-19) pandemic has affected more than 200 countries and has infected more than 2,800,000 people as of April 24, 2020. It was first identified in Wuhan City in China in December 2019. Objective: The aim of this study is to identify the top 15 countries with spatial mapping of the confirmed cases. A comparison was done between the identified top 15 countries for confirmed cases, deaths, and recoveries, and an advanced autoregressive integrated moving average (ARIMA) model was used for predicting the COVID-19 disease spread trajectories for the next 2 months. Methods: The comparison of recent cumulative and predicted cases was done for the top 15 countries with confirmed cases, deaths, and recoveries from COVID-19. The spatial map is useful to identify the intensity of COVID-19 infections in the top 15 countries and the continents. The recent reported data for confirmed cases, deaths, and recoveries for the last 3 months was represented and compared between the top 15 infected countries. The advanced ARIMA model was used for predicting future data based on time series data. The ARIMA model provides a weight to past values and error values to correct the model prediction, so it is better than other basic regression and exponential methods. The comparison of recent cumulative and predicted cases was done for the top 15 countries with confirmed cases, deaths, and recoveries from COVID-19. Results: The top 15 countries with a high number of confirmed cases were stratified to include the data in a mathematical model. The identified top 15 countries with cumulative cases, deaths, and recoveries from COVID-19 were compared. The United States, the United Kingdom, Turkey, China, and Russia saw a relatively fast spread of the disease. There was a fast recovery ratio in China, Switzerland, Germany, Iran, and Brazil, and a slow recovery ratio in the United States, the United Kingdom, the Netherlands, Russia, and Italy. There was a high death rate ratio in Italy and the United Kingdom and a lower death rate ratio in Russia, Turkey, China, and the United States. The ARIMA model was used to predict estimated confirmed cases, deaths, and recoveries for the top 15 countries from April 24 to July 7, 2020. Its value is represented with 95%, 80%, and 70% confidence interval values. The validation of the ARIMA model was done using the Akaike information criterion value; its values were about 20, 14, and 16 for cumulative confirmed cases, deaths, and recoveries of COVID-19, respectively, which represents acceptable results. Conclusions: The observed predicted values showed that the confirmed cases, deaths, and recoveries will double in all the observed countries except China, Switzerland, and Germany. It was also observed that the death and recovery rates were rose faster when compared to confirmed cases over the next 2 months. The associated mortality rate will be much higher in the United States, Spain, and Italy followed by France, Germany, and the United Kingdom. The forecast analysis of the COVID-19 dynamics showed a different angle for the whole world, and it looks scarier than imagined, but recovery numbers start looking promising by July 7, 2020.

Soil organic carbon (SOC) is a dynamic soil property that represents the key component of the forest ecosystems. The main objective of the present study is to evaluate SOC using the remote sensing images as well as field methods at Ranthambhore Tiger Reserve Forest area. The soil samples were collected randomly from the region at several field locations, to estimate the surface soil carbon concentrations in the laboratory. The study derived results for bare soil index, NDVI, SOC and relationship of SOC with NDVI using regression analysis, while comparing reference SOC (field measured SOC) and predicted SOC (estimated from satellite image). The remote sensing images were used to predict the precise carbon content associated with organic matter in the soil using NDVI and related equations, to prepare digital soil organic carbon map. The relationship between the NDVI and both reference/predicted SOC is established using the equation to derive the digital SOC for the study area using remote sensing data. The statistical relationship between reference SOC, pH concentrations, and NDVI values were presented against the predicted SOC to provide the variation between each variable

This paper analyzes the site-specific infield fertilizer treatments, its application rate discrepancies and crop yield assessment using rice equivalent productivity in terms of their economic potential using MOD13A1-normalized difference vegetation index (NDVI) (a moderate-resolution imaging spectroradiomete derived 16 day composite normalized difference vegetation index product, with spatial resolution of 500 m). Soil quality and final crop yield response to nitrogen (N), phosphorus (P), and potassium (K) fertilizers were taken from selected experimental Agri-plots in the part of Kuru region in North India, to calculate site-specific rice equivalent yield (REY) in the crop year of 2005-2006. A 3 × 3 spatial window average pixel reflectance of the NDVI layer at the regional level was used to assess its relationship with contemporaneous cropping systems, such as rice-wheat, rice-sugarcane, and rice-onion in the study area. A robust linear regressive relationship of R ² = 0.69, has been found between site-specific vegetation index values and calculated REY. Inverse distance weighted spatial interpolation method was used to analyze the spatial variability of three major fertilizer nutrients (NPK) response in the study area. The potassium nutrient availability showed high levels of spatial autocorrelation, suggesting that proper fertilizer application ratio with genuine irrigation practices may be used for underpinning of the high crop yield variety acreages. In order to strengthen the crop productivity, we have suggested the diversified triple-based cropping systems with satellite mounted sensor derived NDVI products as a holistic and feasible monitoring approach.