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Soil moisture plays an important role in agricultural monitoring, drought and flood prediction, forest fire prediction, water supply management, and other natural resource-related activities. Soil moisture observations can warn of imminent drought and flood conditions before other  standard indicators are triggered.   As defined in AMS Glossary of Meteorology, soil moisture is "the total amount of  water vapor-containing water in  unsaturated soil." Soil moisture (sometimes called soil water) refers to surface water  in the pores of the soil, not rivers, lakes, or groundwater. Soil moisture levels are determined by a variety of factors other than weather conditions, such as soil type and associated vegetation. Second, soil moisture affects the various dynamics of soil and plants. Surface soil moisture is  water located 10 cm above the soil, and root zone soil moisture is  water  available to plants-generally considered to be located 200 cm ab...

ALOS-1 (also known as Daichi) was launched on January 24, 2006 and completed its operational phase on  May 12, 2011, after a successful five-year acquisition worldwide. ALOS was one of the world's largest earth observation satellites tasked with collecting high-resolution land observation data on a global scale. The satellite consists of three instruments: an altitude visible and near infrared radiation meter (AVNIR-2) for accurate observation of land cover, and an L-band synthetic aperture radar (PALSAR) phased array type for day and night and all weather conditions. Was installed. Panchromatic remote sensing device (PRISM) for stereo mapping.   The purpose of its mission was to provide the user community with enough data to create a map at a scale of 1: 25,000. Development of digital elevation models and related geographic data products. Conduct community observations to support the Sustainable Development Goals. Natural resource research; development of sensor and sate...

There are many government agencies or NGO have been published a wide range of landcover data. Those data ranging from national level to global level. Commercial vendors also provides wide range of landcover mapping services and products from medium resolution to very high resolution, using wide range of techniques like image classification, segmentation, or "the old but reliable" method visual interpretation. With the widely usage of latest AI technology like Machine Learning and Deep Learning, Landcover mapping even reaching the stage of combination the benefits of Image Classification and Visual Interpretation. These Deep Learning Algorithm could give mapping results in polygon format from raster domain, something that not straightforwardly possible in the previous decades.  One cool thing about these landcover data is most of them are available for free. One of them is European Globcover Dataset. It provides landcover data at medium resolution in global scale. I don't ...