## 4 Included Application Cases  
In this Special Issue, we have collected ten papers representing the diversity of GIS and remote sensing research in environmental monitoring. The papers included in the Special Issue cover a wide range of GIS and remote sensing applications in different areas, such as onshore and offshore applications. They use the remote sensing information in different ways, such as including time series analyses, combining bands to create indexes, or detecting variations and merging information from different sources to generate new information. [PERSON] et al. [16] presented the use of Google Earth Engine (GEE) to explore the changes in land cover classification and its spatial pattern from 1990 to 2019 on Haitan Island (China). The authors selected an island to evaluate this option due to its high susceptibility to climate change and human activities. They used the Landsat missions as the information sources, including images from Landsat 5, Landsat 7, and Landsat 8. The included images contained the bands, vegetation indexes, and the tasseled cap transformation. Six land cover classes were defined by the authors. Their results were characterized by user and producer accuracies higher than 80%, and the kappa coefficient values ranged from 0.86 to 0.90. In the studied area, forest and built-up land were the two land cover classes that increased by 30.94% and 16.20% in the area, respectively. These changes were mainly explained by the modifications in the reforestation strategy of China and China's policy for boosting the economy and social development, which accelerated the island's development. [PERSON] et al. [17] described the effect of declining precipitation on the grasslands in the Inner Mongolian plateau. As a metric, the authors used the normalized difference vegetation index (NDVI) from 1982 to 2010. The authors used the MODIS satellite as the image source. The data period selected was one image every two weeks. Precipitation and temperature were the climatologic variables included. Their results indicate that the NDVI was more greatly affected by the precipitation than the temperature. The precipitation can explain 35.47% of the variability of NDVI data, whereas the temperature only explains 0.56%. Regarding the spatial differences of this effect, in the dry areas, the climate has a higher impact on the NDVI than in the wet regions. Another option available, thanks to having large datasets of data from different years, is the possibility of forecasting the future values of a studied variable. In this sense, [PERSON] et al. [18] modeled the vegetation based on vegetation indexes and climatic data. In their paper, the authors included data from February 2013 to December 2017 of different crops (maize and olive) in a region of Comunidad de Madrid, Spain. As the image source, the authors selected the MODIS satellite. Meanwhile, the SiAR network weather station was selected for obtaining the agroclimatic data. Their forecast model predicted the NDVI values for the different crops. Their results indicate the positive influence of some of the studied weather parameters, such as temperature, precipitation, humidity, and solar radiation. Another opportunity offered by remote sensing is the possibility of analyzing large portions of the earth's surface. In some cases, measuring a parameter over large areas might be challenging due to the extension of the area or the high spatial variability of the studied parameter. Soil moisture is a parameter with high spatial variability in complex environments where irrigated and rainfed crops coexist with urban and natural areas. In [19], [PERSON] et al. studied the possibility of using in situ sensors to obtain a model that can be applied to large areas to estimate soil moisture. They used a total of nine sensors located in areas characterized by different soil and different vegetation. The authors selected the Sentinel 2 satellites as an image source due to the need for high spatial resolution.