In the second half of the twentieth century, following demands for more accurate environmental monitoring of the Earth’s two satellite systems, geostationary (GOES) and polar-orbiting (POES) were developed to observe the ocean atmosphere and land for more accurate monitoring, modeling and prediction of environmental impacts on economy and human life. The measurements from the GOES satellite were used for weather monitoring and predictions. POES data were used intensively to estimate land cover change, vegetation health and soil moisture, to detect and monitor drought and vegetation stress, to predict fire and malaria risk, to model crop and pasture production and for other purposes affecting the humanity. Most of these POES products are used successfully for prediction of food shortages and the related food security problems. This chapter describes the POES data collection and processing (especially noise removal) from the two NOAA operational polar-orbiting satellite systems (initial – NOAA/AVHRR and new – SNPP/VIIRS) and data preparation for continued applications and predictions in agriculture, human health, food security, climate and land changes, disasters detection (drought, crop losses etc.). Since the late 20th, these data have been used for malaria monitoring.

The longest Normalized Difference Vegetation Index (NDVI) time series produced from the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) has ended in 2017. At some point in the near future, all AVHRR sensors will be retired. To maintain continuity and consistency of this global data set, it is imperative to extend NDVI from other sensors, especially the operational Visible Infrared Imaging Radiometer Suite (VIIRS), which planned to maintain continuity at least through 2038. NDVI could be de-composited into two components: (1) the multi-year climatology and (2) the vegetation condition index (VCI). The former contains climate information and a majority of sensor noise, and the latter contains weather information and residual sensor noise. With the assumption that VCI from different sensors are similar, we re-composited the cross-sensor/cross-production NDVI with original VCI and the cross-sensor/cross-production climatology, and compared various cross-converted datasets with the three base NDVI datasets: two NDVI productions derived from AVHRR observation and another from VIIRS observation. As a result, the re-composited NDVI agrees well with the target base NDVI spatially and temporally, with an accuracy of 0.02 NDVI unit at a global scale. The comparison with several regression approaches distinguish the superiority of the new re-compositing approach.

Malaria is a huge global burden, endemic to nearly 110 world countries with more than 200 million clinical cases and nearly a million deaths each year. Annually, the number of malaria cases fluctuate from low to high, depending on weather conditions. Moist and warm weather stimulates mosquitoes’ activity in spreading malaria, while drought suppresses vector activity, reducing malaria transmission. Many attempts to use weather parameters, mostly precipitation and temperature, for global and regional malaria monitoring have not been successful since the weather station network is very limited and stations are spread far apart from each other. Therefore, the recent two-decade efforts to monitor malaria were focused on high spatial resolution satellite data. Very successful malaria modeling results were obtained with the introduction of satellite-based Vegetation Health (VH) method. The VH assesses vegetation health in response to seasonal weather conditions. Since vegetation is the place of mosquitoes and parasite habitat, VH-based vegetation conditions showed to sbe good indicators of annual mosquitoes’ activity in spreading malaria. This chapter provides modeling results of malaria area and intensity from VH-based estimates of moisture and thermal vegetation conditions.

Remotely observing global vegetation from space has endured for nearly 50 years. Many datasets have been developed to monitor vegetation status. Tailored to specifically monitor global food security concerning drought and crop yield, a suite of datasets based on vegetation health concepts and Advanced Very High Resolution Radiometer (AVHRR) observation was developed in the 1980s and utilized throughout the world. Nowadays, satellites based imaging radiometers have evolved into the Visible Infrared Imaging Radiometer Suite (VIIRS) era. With proper algorithm development, the blended version of the data suite, composed of the AVHRR dataset from 1981 to 2012 and VIIRS dataset from 2013 and afterwards, has bridged the long-term AVHRR observation and high-quality VIIRS data. This paper explains the blended version of the data suite.

Malaria is a mosquito-borne infectious disease , which ranks among the major world health challenges affecting people in the poorest countries of sub-Sahara Africa, Southeast Asia, Western Pacific and Latin America. Among 3.2 billion people living in these regions, 10–15% are at risk of malaria with up to one million deaths annually, mostly children under age five. Following WHO, between years 2010 and 2017, the number of world malaria-infected cases and death were over 200 million annually, from which over 400 thousand people died. This Chapter classify malaria as a very important health, economic and social burden, providing general information about malaria's impact on human, causes and symptoms, explain interactions between parasite-vector and human and how these processes are regulated by the control and eradication measures, economy, social, politics and what challenges we are still facing. The Chapter show that malaria extremely active in tropical and subtropical areas, is reaching into some temperate zones. The African region carries a disproportionately high share of the global malaria burden. Among extrinsic factors, economic and social conditions, poverty, environment (ecosystem, climate and weather), political commitment, control and prevention efforts and even behavioral customs are the most important determinants of malaria's burden. Malaria control includes indoor and outdoor residual spraying, insecticide-treated nets, medicine, vaccination. Challenges facing malaria impacts on human include intensive population growth, lack of funding, increasing mosquitos' resistance to insecticides and parasite resistance to drugs, insufficient surveillance, economic and social problem, climate and weather changes.

Understanding the environmental contribution to malaria’s distribution and intensity it is necessary to know how environment impacts on malaria parasite, vector and the corresponding number of affected people. This chapter discusses environmental features important for an appropriate development of vector and parasite and an intensity of malaria transfer to people. Generally, both mosquitoes and plasmodium need a warm and moist weather for excessive development. The environment in this cycle is represented by two parameters: long-term (multi-year) climate (principally moisture and temperature) and short-term (inside one year) moisture and thermal conditions. Climate-based malaria distribution and int4ensity have been well studied and described. However, the climate does not explain why one-year malaria affects a large number of people and the other year affects much less. Such a situation is controlled by weather. Moist and warm weather creates more malaria cases, while drought suppresses vector activity and malaria transmission. Many investigations across tropical regions used weather data to predict malaria area, intensity and the number of affected people. Unfortunately, the weather station network, which provides moisture and thermal data is too sparse, especially in tropical zones, to be used effectively. Available weather stations, controlled by the World Meteorological Organization (WMO), provide weather information in malaria endemic rea (if to assume that stations are equally distributed) for each 3000–36,000 km2 area. Therefore, we focus on operational satellite measurements, providing moisture and thermal conditions at the level of vegetation, which is the place of vector and parasite habitat. In addition, successful satellite application to malaria monitoring was achieved following development and application of new, theoretically grounded Vegetation Health (VH) method and VH products through estimation of moisture and thermal conditions of vegetation cover.