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Change detections are  -based on spectral classification of the input data such as post-classification comparison and direct two-date classification and  -based on radiometric change between different acquisition dates, ----image algebra methods such as band differencing, ratioing and vegetation indices;  ----regression analysis ----principal component analysis and  ----change-vector analysis Lu et al, (2004) generalized the change detection methods into seven types, namely, arithmetic operation, transformation, classification comparison, advanced models, GIS integration, visual analysis and some other methods. ~Pixel Level Change Detection Techniques: Image Differencing Image Ratio Image Regression Post level Comparison Multidata direct comparison Artificial Neural Network Support Vector Machine Decision Type GIS based Multi temporal spectral mixture analysis Fuzzy change detection Multi sensor data fusion ~Feature Level Change Detection Vegetation Index differencing PCA K

R ADAR stands for Radio Detection and Ranging which uses longer wavelengths compared to optical remote sensing and they can be active; producing their own source of energy.   Terms: Slat Range: radar antenna and target’s distance Ground Range: satellite ground target and target Azimuth: along track direction or distance Incidence angle: angle between the line of sight of radar and vertical to the terrain Range resolution: depends on the length of the pulse Azimuth resolution: determined by antenna beam width and distance to the target Wavelength and frequency: between 0.5 cm to 100cm frequencies Radar polarization: orientation of the electric field of the electromagnetic wave, the polarization can be like polarized (HH, VV), cross polarized (HV, VH), and compact polarized  (transmitting right circular and receive H and V coherently) SARs respond to two characteristics in the agriculture land: structure and moisture:  Roughness: caused by tillage, soil erosion and weathering in the land

F looding means the water presence in the ground surface below or above vegetation, or the presence of water in the surface which would not be in normal cases. SAR imagery has been used in studying, understanding and interpreting the study of the floods. The benefits of the SAR include the ability to collect images both day and night, and in any weather conditions and the appearance of the water surface appearing black compared to other surfaces. SAR signals can scatter in different ways based on whether the surface is smooth or rough, and can be affected based on the size of the object on the surface, their position in the surface and the numbers (or presence) of such structural surfaces.  The practicality in SAR is the type of wavelength’s ability to penetrate the target (L-band having more penetration capability than C-band). L-band or P-band, in flooded areas with dense forest can be more interpretative compared to C-band (bands getting mixed between surfaces). The advantage of SAR

T he needs for forecasts are in the warning system. It requires crop simulation models CSMs, equational representation of the crop growth, and yields which plays important roles in the understandings of the agronomic results, and they are only the approximating the real world. CSMs:  statistical models functional models mechanistic models simple models: across large land areas based on statistics of climate and historical yields, lesser details of soil plant system CSMs relies on the meteorological data, agrometeorological data, soil data, remotely sensed data and agricultural statistics; different indices have been developed based on them. Standard regression techniques are applied which is called model calibration which results yield functions, which estimates the yield. The accuracies heavily relies on the input statistics. Agrometeorological model: Weather can have huge impact on all the process of crop growth and yields. Yield variability can be considered due to large impacts of

The fermented kodo [finger millet] (first, cooked like rice and then mixed with some kind of locally prepared yeast - and then stored in a semi-dark corner of the room) is prepared. The kodo is then boiled with water to generate streams of vapours to eventually collect the local alcoholic drink, rakshi. Inside the big cylinder-like structure, there is a small dish placed to collect the vapour turned into the liquid due to the cold area at the top. And how well the yeast is prepared also depends on the maker, and this is a whole new skill. All the preparing structures (utensils) are usually of copper. But, they may be of the clay or of the other types too.  Careful attention is given so as to not let the vapours escape from any holes. For this, the formed gas (vapours) is not allowed to escape.  The taste of the alcohol drink depends on how well the kodo was fermented earlier. And how long the steam is collected from the same sets of boiled kodo . Often popularly called Tin Pane (तिन प

 SOIL: Greater reflectivity due to higher dielectric constant of water; presence of water in soil means the easy detection from the longer wavelength. VEGETATION: Vegetation will have volume scattering. Similar the scattering from the soil may result in further scattering; thus wavelength of 2 to 6 cm is better as volume scattering is more and the scattering from the surface is reduced. Longer wavelengths of 10 to 30 cm can be suitable for trees. HH or VV (like polarized are able to penetrate more in vegetation compared to HV or VH (cross-polarized waves). If the crops are aligned in azimuthal direction, more energy is received compared to range direction.  MOUNTAINS: The SAR while observes, all the mountains seems to be located at the same distance or the same point from the spacecraft; this is called foreshortening or layover. It happens because all the backscattered signals return to the spacecraft at the same time.  WATER / ICE: Smooth water has no results to the antenna.  For sea

The following texts are the extracts from the research article. All the texts have been copied and very little edits have been done and have been presented for the study purposes. An Overview of Platforms for Big Earth Observation Data Management and Analysis https://www.mdpi.com/692396    Google Earth Engine: It is the cloud-based platform for large-scale scientific analysis and visualization of geospatial datasets as a free service based  on Google’s infrastructure GEE provides a JavaScript API and a Python API for data management and analysis. There are f our object types to represent data that can be manipulated by its API.  The Image type is the raster data (one or more bands, with name, data type, scale, and projection). The time series of Images is by the ImageCollection type, vector data by the Feature type. This type is represented by a geometry (point, line, or polygon) and a list of attributes. The FeatureCollection type represents groups of related Features and provides fun