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Remote Sensing and Geographic Information System: A Tool for Precision Farming

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Geospatial Technologies for Crops and Soils

Abstract

The right time application of the right amount of input is a prerequisite to optimizing profitability and sustainability with a lesser impact on environmental degradation. Such can be achieved through precision farming (PF). It can offer a great potential to minimize the yield gap by optimizing food production using best management practices. It can also help to maintain the consumption of natural resources at an ecologically benign and environmentally sustainable level. PF is a holistic approach to enhance crop productivity with the aid of satellite-based technology and information technology (IT) to assess and manage the spatial and temporal variability of resources and inputs such as seeds, fertilizers, chemicals, etc. within the field. Application of remote sensing (RS) and geographic information system (GIS) shows a great promise to precision agriculture (PA) because of its role in monitoring spatial variability overtime at high resolution. This chapter highlights various applications of RS and GIS techniques in PA or smart agriculture.

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Abbreviations

AIEM:

Advanced Integral Equation Model

ALI:

Advanced Land Imager

ARVI:

Atmospherically Resistant Vegetation Index

ASTER:

Advance Spaceborne Thermal Emission and Reflection Radiometer

AVHRR:

Advance Very High Resolution Radiometer

AVIRIS:

Airborne Visible Infrared Imaging Spectrometer

AWS:

Amazon Web Services

CASI:

Compact Airborne Spectrographic Imager

DCNI:

Double-peak Canopy Nitrogen Index

DEM:

Digital Elevation Model

DGPS:

Differential Global Positioning System

DSSAT:

Decision Support System for Agrotechnology Transfer

DVI:

Difference Vegetation Index

EO :

Earth Observing

EOS :

Earth Observing System

EROS:

Earth Resources Observation and Science

ERS:

European Remote Sensing satellite

FASAL:

Forecasting Agricultural Output Using Space, Agrometeorology and Land Based Observations

FIS:

Farm Information Systems

GDVI:

Green Difference Vegetation Index

GI:

Greenness Index

GIS:

Geographic Information System

GNDVI:

Green Normalized Difference Vegetation Index

GNSS:

Global Navigation Satellite System

GOSAVI:

Green Optimized Soil-Adjusted Vegetation Index

GPS:

Global Positioning System

GRVI:

Green–Red Vegetation Index

GSAVI:

Green Soil–Adjusted Vegetation index

GWR:

Geographically Weighted Regression

HH:

Horizontal Transmit and Horizontal Receive

HNDVI:

Hyperspectral Normalized Difference Vegetation Index

HV:

Horizontal Transmit and Vertical Receive

HVI:

Hyperspectral Vegetation Index

IEM:

Integral Equation Model

IRS:

Indian Remote Sensing

IRSS:

Indian Remote Sensing Satellite

IT:

Information Technology

JERS:

Japanese Earth Resource Satellite

LAI:

Leaf Area Index

LANDSAT:

Land Satellite

LASSIE:

Low-Altitude Stationary Surveillance Instrumental Equipment

LIDAR:

Light Detection and Ranging

LORIS:

Local Resources Information System

MCAR:

Modified Chlorophyll Absorption Ratio

MCARI:

Modified Chlorophyll Absorption Ratio Index

MODIS:

Moderate-resolution Imaging Spectrometer

MSAVI :

Modified Soil-Adjusted Vegetation Index

MSR:

Modified Simple Ratio

MSS:

Multispectral Sensor

MTVI:

Modified Triangular Vegetation Index

MZ:

Management Zone

NAOC:

Normalized Area Over Reflectance Curve

NASA:

National Aeronautics and Space Administration

NDI:

Normalized Difference Index

NDNI:

Normalized Difference Nitrogen Index

NDRE:

Normalized Difference Red Edge

NDVI:

Normalized Difference Vegetation Index

NDWI:

Normalized Difference Water Index

NG:

Normalized Green

NGNDVI:

Normalized Green Normalized Difference Vegetation Index

NR:

Normalized Red

NUE:

Nitrogen Use Efficiency

OLS:

Ordinary Least Square

OMNBR:

Optimal Multiple Narrow Band Reflectance Indexes

OSAVI:

Optimized Soil-Adjusted Vegetation Index

PA:

Precision Agriculture

PCA:

Principal Component Analysis

PF:

Precision Farming

PFDC:

Precision Farming Development Center

PLS:

Partial Least Squares

PSSR:

Pigment Specific Simple Ratio

PVI:

Perpendicular Vegetation Index

RDVI:

Renormalized Difference Vegetation Index

RGRI:

Red–Green Ratio Index

RIICE:

Remote Sensing–based information and Insurance for Crops in Emerging Economics

RISAT:

Radar Imaging Satellite

RS:

Remote Sensing

RVI:

Ratio Vegetation Index

RVSI:

Red-Edge Vegetation Stress Index

SAR:

Synthetic Aperture Radar

SAVI:

Soil-Adjusted Vegetation Index

SNR:

Signal-to-Noise Ratio

SOC:

Soil Organic Carbon

SOM:

Soil Organic Matter

SPAD:

Soil Plant Analysis Development

SPOT:

Système Pour l’Observation de la Terre

SR:

Simple Ratio

SRM:

Satellite-based Rice Monitoring

SRTM:

Shuttle Radar Topography Mission

SWIR:

Shortwave Infrared Region

TCARI:

Transformed Chlorophyll Absorption Reflectance Index

TKK:

Tata Kisan Kendra

TM:

Thematic Mapper

TVI:

Triangular Vegetation Index

TVIMSR:

Triangular Vegetation Index Modified Simple Ratio

UAV:

Unmanned Aerial Vehicles

USDA:

United States Department of Agriculture

VH:

Vertical Transmit and Horizontal Receive

VNIR:

Visible Near Infrared

VRT:

Variable Rate Technology

VV:

Vertical Transmit and Vertical Receive

WDVI:

Weighted Difference Vegetation Index

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Authors and Affiliations

  1. Department of Agricultural Chemistry and Soil Science, Bidhan Chandra Agricultural University, Mohanpur, India

    Pabitra Kumar Mani, Agniva Mandal & Buddhadev Sarkar

  2. Department of Agronomy, Bidhan Chandra Agricultural University, Mohanpur, India

    Saikat Biswas

  3. Soil and Land Resources Assessment Division, National Remote Sensing Centre, ISRO, Hyderabad, Telangana, India

    Tarik Mitran

  4. Department of Agronomy, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi, India

    Ram Swaroop Meena

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Editors and Affiliations

  1. Soil and Land Resources Assessment Division, National Remote Sensing Centre Department of Space, ISRO, Hyderabad, Telangana, India

    Tarik Mitran

  2. Department of Agronomy, Institute of Agricultural Science, Banaras Hindu University, Varanasi, Uttar Pradesh, India

    Ram Swaroop Meena

  3. Agroecosystem and Modeling Division Agricultural Sciences and Applications Group, National Remote Sensing Centre Department of Space, ISRO, Hyderabad, Telangana, India

    Abhishek Chakraborty

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Mani, P.K., Mandal, A., Biswas, S., Sarkar, B., Mitran, T., Meena, R.S. (2021). Remote Sensing and Geographic Information System: A Tool for Precision Farming. In: Mitran, T., Meena, R.S., Chakraborty, A. (eds) Geospatial Technologies for Crops and Soils. Springer, Singapore. https://doi.org/10.1007/978-981-15-6864-0_2

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