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Showing posts from April, 2021

C-band SAR for Crop Types Classification

Crop information provided should be timely, geographically representative over the larger area, robust while improving itself with the ever changing needs, developments and the new research. Current research in SAR, which measurers intensity and scattered energy from the target surface, has recevied attention in the areas of agricultural monitoring. Its ability to work even in the presence of the clouds is the additional advantage, while the crop structures and the water presence in the vegetable canopy influence how the wavelengths are scattered. C-band along with optical imagery has resulted higher accuracy in determing crop types. The cropping systems to be classified; however, are complex due to different crop growing period, time and the types of the grown in the specific geography. It suggests the research to be focused on the *sensors*, *number of optical images required*, and *timing of receving the image*.  A research was conducted to understand *how the C-band data can map cr

Learn R - Programming (YouTube Video Links)

Following is the youtube video links (or bookmark) for lea R ning R listed as the learning resources. Each link has been provided with the video length (T-) .  The naming contains name of the video - the channel's / creator's name .  The list will be continuously updated.  R_Intro   <-  baseR 🏁 Simulation in R - Roger Peng  T- 14:51 1. Tidyverse✂️🧹 1.1 ggplot2 Intro. ggplot - Hefin Rhys  T- 1:17:24 1.2 dplyr dplyr - Roger Peng  T- 15:30 Manipulating and exploring data with dplyr - Heifn Rhys  T- 26:06 Dplyr Essentials - dataslice  T- 11:20 2. Statistics  📊 3. Shiny ✨ R Shiny Basics - Statistical Learning Group  T- 30:11 4. R markdown 📁 5. Specifics 🏹 Machine Learning *  Into. mlr package- Hefin Rhys  T- 24:06 6. Packages 📦 #TidyTuesday🎮 ~More / Tips-Tricks  💡 See Also 👀 Free-resources-for-learning-r learn-r-programming-quick-g uide

Remote Sensing for Estimating Crop Area

Crop Area and Crop Yield are the two main required parameters for the crop information. Crop area estimation are assumed to be simply easier when the two parameters are compared, while this is usually not true. Different factors significantly impact the crop area estimation and making the process relatively arduous; such as field size, different cropping system, differences in phenology, and sometimes due to damage from weather and pests, while crop area estimation are to be taken at multiple growth stages of the crops.  The notable review of the crop area estimation comes from Crag and Atkinson (2013): crop area estimated is done by complete inventory of all the farms or by the samples. The sampling can be Area Frame Sampling (AFS) or farm list sampling or combination of both. In all cases, experts opinions are usually seeked.  The traditional estimation measures, as mentioned above, are time-taking, involves high-costs, difficults in its ways, and human errors are likely. For overcom

SAR: Basics

RADAR or RAdio Dectection and Ranging  is believed to have started at the beginning of the 20th century whose credits go to German inventor Christian Huelsmeyer who had developed the system to develop the system to detect distant metallic objects, OR British enginner Robert Watson Watt who had object detecting system far up to 30km. The technology emerged during World War II and the developement of which has then had developed further to be kept on the airplanes by 1940s. Its applications grew even in the areas of Earth Observation. The advantage of the RADAR system always remained in its utilizations with disregards of weather or any time of the day or night. The surface was interacted differently according to radar signals with more information about the surface. Side Looking Airborne Radar (SLAR) systems developed around in 1950s, where the radar sensor mounted on the platform moved in the straight line at altitude H and the radar system points to the nadir at the look angle a. The

High Resolution Imagery for Crop Yields Approximation

Crop yield forecasting is an essentially the most important aspect in the agriculture which can benefit in understanding the yield gaps, aiding in suggesting better practices. Current availiablities of different geoinfomational products have extended the ways in the crop monitoring. While the crop monitoring process is the continuous process, both the high temporal resolution as well as finer spatial resolution might not be possible at many cases. For instance, Planet/Dove is equipped with daily revist cycle and WorldView-3 provides finer spatial resolution. Incorporation of both has the possibilites for understanding the crops at the field levels. Satellite data, if properly available in growing periods can be helpful in precision farming and can be implemented in guiding the samping based estimation of yields. Satellite imageries (VHR) themselves are recieved untimely in unperiodic manner. For this, sensors with high spatial and temporal can expand the applications in the crop monito

GNSS (Basics)

Brief History Different Navigation System has been developed throughout the centuries. Location and navigation is an old age concept. The magnetic compass was developed around 13th century. Only in the 20th century the major developments can be related to. The high frequency Radio-based was developed around 1950s to 70s, and Radio-based and transit in 1960s. Develpment of GPS was developed in 1980s and development of GLONASS was in 1993 being similar to GPS. International GPS service was developed in 1994 which is now known as GNSS. The major concepts of GNSS was in 2006. Similar developments of eLoran are seen after 2000. After 2010s, conceptualizaton of integrated receivers with GNSS, eLoran and eChayka. NAVSTAR Global Positioning System: previous name of GPS, initially developed for military purposes around 1973 by US DOD; abbreviated from NAVigation Satellite Timing and Ranging Global Positioning System Other System includes GLONASS (3 orbit) , Galileo (3 orbit) and Beidou (35 sate

GIS (Basics)

GIS or Geographic Information System: Collectes information of the location or earth for the decision making Computer based system to for geo reference data Capturing, storing, retrieving, analyzing and displaying data positional referenced to earth   Raw Data  > Data Input > Database Managment > "Manipulation" > Analysis > Output and Visualization Geographic Data Spatial Data : shape, size and structure of the location; contains polygonal information; data may be vector or raster Non-spatial data (Attribute or tabular data): information about the attributes such as population, presented in tabular information with different attributes Both spatial and non spatial data gives the knowledge on the earth surface and what it contains; further helping in understanding of the temporal and spatial relationships. The components of GIS include hardware, software, data and people . The input data can me multiple for GIS. Data Models: ~ Helps in the representation of th

TERMS: Remote Sensing

Data collection and measurements near the object is not alway possible. For this certain sensors should be applied for this process. The science and art of obtaining the data of different features or phenomenon in certain area or time, not being able to collect from close contact, and also their analysis is called remote sensing . Our eyes are the best example of such remote sensors. However, field measurements may be required for the correct accuracy.  How is remote sensing done? A. Source: illuminates the target; sun is the prime example of the source - the source can be artifical B. Target: object which is/are illuminated C. Platform or Sensor: collects the reflectance D: Receiving (GRS - Ground Receivng Station) E: Preprocessing: Data freed from errors F. Distribution: received by users Atmosphere can interact with the target while with the source or while reaching before the sensor. The part of energy is scattered. Sensors are developed as to choose certain spectral regions where


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