Summarize the basic principles of remote sensing and how it is utilized in crop discrimination ?

Dear Himanshu Tiwari , For me:

Remote sensing is a technology that involves collecting information about an object or area without direct physical contact, not just collecting information alone the principle involves the detection and monitoring of the physical characteristics of an area by assessing its emitted and reflected radiation from a distance. This process utilizes the electromagnetic spectrum to extract information about phenomena . So In the context of agriculture and crop discrimination, remote sensing relies on the principles of capturing and analyzing electromagnetic radiation reflected or emitted by the Earth's surface.

The key principles include:

Electromagnetic Spectrum: Remote sensing utilizes the entire electromagnetic spectrum, ranging from visible light to microwave and beyond. Different wavelengths interact uniquely with various features on the Earth's surface.

Reflection and Absorption: Objects reflect, absorb, and emit radiation differently based on their composition. These interactions are distinctive for different crops and can be detected by remote sensing sensors.

Spectral Signatures: Each material exhibits a unique spectral signature, which is the pattern of reflectance or emission across different wavelengths, so all crops do not have or exhibit as spectral signature like they have different water contents, etc. Spectral signatures serve as fingerprints for crop discrimination.

Multispectral and Hyperspectral Imaging: Remote sensing often involves capturing information in multiple bands (multispectral) or even hundreds of contiguous bands (hyperspectral). This allows for detailed analysis and discrimination of crops based on their spectral characteristics.

Utilization in Crop Discrimination:

Vegetation Indices: Indices like NDVI (Normalized Difference Vegetation Index) are widely used for assessing vegetation health. They leverage the contrast in reflectance between visible and near-infrared bands, highlighting differences in crop vigor.

Crop Classification Algorithms: Various algorithms, including supervised and unsupervised classification, use spectral information to categorize crops. These algorithms learn from training samples to identify and differentiate between different crop types.

Temporal Analysis: Monitoring crops over time using time-series remote sensing data helps track phenological changes, growth stages, and crop health. This temporal information aids in discriminating between different crops.

Spatial Resolution: The spatial resolution of remote sensing imagery influences the ability to discriminate crops. Higher resolution allows for the identification of individual fields and smaller crop features like the LandSat 7 ETM and Sentinel 2 Imagery don't have the same spatial resolution, sentinel 2 resolution is higher so so it allows for better identification of individual field and small crops features.

Key Spectral Features:

Chlorophyll Absorption: Visible bands capture the absorption of light by chlorophyll, contributing to the green color of healthy vegetation.

Red Edge: This spectral region (around 700-750 nm) is sensitive to changes in chlorophyll content and can be indicative of crop stress or specific growth stages.

Near-Infrared (NIR): Healthy vegetation reflects near-infrared radiation strongly. NIR bands are crucial for vegetation indices and discriminating between different crop types.

Shortwave Infrared (SWIR): SWIR bands can reveal information about crop water content and structure, aiding in discrimination, especially in arid regions.

Remote sensing in crop discrimination relies on capturing and analyzing the unique spectral features of crops. Leveraging these principles facilitates accurate and efficient monitoring of agricultural landscapes, contributing to improved crop management and yield predictions.

This a little short answer I can give based on my knowledge and Experience

Frank Veroustraete

Remote sensing is a technology that involves gathering information about an object, area, or phenomenon without physical contact. In the context of agriculture and crop discrimination, remote sensing is often used to collect data about crops and crop health. Here are some of the basic principles of remote sensing and how it is applied in crop discrimination:

Electromagnetic Radiation: Remote sensing relies on the detection and measurement of electromagnetic radiation. Sensors onboard satellites, aircraft or drones, capture reflected or emitted radiation (mostly fluorescence) from the Earth's surface.

Spectral Bands: Different materials reflect or emit radiation at specific wavelengths. Remote sensing sensors often have multiple spectral bands to capture a range of wavelengths. This allows for the identification of specific features of plants and crops like chlorophyll concentrations in leaves.

Resolution: Remote sensing instruments sense in three dimensions. Spatially, spectrally, and temporal with dedicated resolutions. Spatial resolution refers to the level of detail in the imagery, spectral resolution is about the range of wavelengths captured, and temporal resolution is the frequency of data acquisition over a specified time lapse .

Active and Passive Sensors: Remote sensing is based on active sensors emitting microwaves (e.g., radar) or passive sensors that detect naturally occurring energy (e.g., optical, thermal as well as microwave sensors). Passive sensors are commonly used in crop discrimination.

Multispectral and Hyperspectral Imaging: Multispectral sensors capture data in a few specific spectral bands, while hyperspectral sensors capture data in numerous narrow and contiguous bands. Hyperspectral imagery provides more detailed information about the nature of the cover types on the Earth's surface.

Specific appraoches for crop discrimination:

Vegetation Indices: Remote sensing helps in calculating vegetation indices (e.g., NDVI - Normalized Difference Vegetation Index), which allows for a quantitative approach to estimate health and vigor of crops based on their reflectance in specific spectral bands.

Crop Classification: By analyzing the spectral signatures of different crops, remote sensing can be used to classify and discriminate between different types of crops. This is particularly valuable for monitoring large agricultural areas to estimate acreage and productivity of crops in agricultural zones.

Disease and Stress Detection: Changes in crop health, caused by diseases, pests, or environmental stress, can be detected with remote sensing. Plants under stress very frequently exhibit distinct spectral signatures identified with satellite, aerial or drone imagery.

Yield Estimation: Remote sensing data can contribute to estimating crop yield by assessing the vegetation's vigor and health and especially water stress throughout the growing season.

Precision Agriculture: Remote sensing technologies are integral to precision agriculture practices, helping farmers optimize resource use fertilization), monitor crop conditions (water stress), and make informed decisions to enhance productivity.

In summary, remote sensing when applied for crop discrimination, involves capturing and analyzing electromagnetic (EM) radiation to extract valuable information about crop health, type, and conditions, enabling optimal and (artificial) intelligent agricultural management.

Babajide Adegboye

The basic principle of Remote Sensing is the detection or acquisition of data/information about object on earth without physical contact. This happens through active or passive sensor which detects electromagnetic spectrum (energy) reflectance on objects. The electromagnetic spectrum is made up of energy with different wavelengths and frequencies for different applications. One of the important energies in electromagnetic spectrum is the infrared wavelength which is use in studying of plants/crops’ health and other related studies. And, one of the determinant factors in studying the health of plant is the amount of chlorophyll in the leaves. Remote sensing could also be used to study different crops as chlorophyll level differs from one plant to the other. In addition, every crop has its own spectral reflectance. Hence, hyper spectral remote sensing technique can be utilized for crop discrimination.

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