Use of Remote Sensing Technology in Agriculture

Introduction 

Agriculture provides raw materials, fuel, fibers, and food (of course!) to humanity. This role needs to be fulfilled within climate change and environmental sustainability, combined with the expanding population while maintaining agricultural activities’ viability to sustain livelihoods. The application of remote sensing in agriculture can help the evolution of agricultural practices face different types of challenges by providing information related to crop status at different scales all through the season.

Working of Remote Sensing in Agriculture

Remote sensing and agriculture go hand-in-hand. The basic working of this technology with UAVs, satellites, and other platforms is almost the same. Energy, in the form of light, will travel from the sun to the Earth. Light waves travel virtually like ocean waves, the distance between the peak of one wave to the peak of the next is known as wavelength. The energy emitted from the sun is known as electromagnetic energy and is part of the electromagnetic spectrum. The wavelengths that are used for agricultural applications cover a small amount of the electromagnetic spectrum.

Key tools used for data capture, analysis and interpretaion of remotely sensed data is as follows.

• Ground bases: Infrared thermometer, spectral radiometer, Pilot-Balloons and radars.
• Air Bases: Aircraft air based remote sensing tools.
• Satellite based: The digital image processing, using powerful computers

Role of Remote Sensing in Agriculture

Agriculture resources are important renewable dynamic natural resources. In India, agriculture sector alone sustain the livelihood of most of the population and contributes significantly to the net national product too. Increasing agriculture productivity has been the main concern since scope for increasing area under agriculture is rather limited. This demands judicious and optimal management of both land and water resources.

Currently, agriculture faces problems of Anthropogenic decreases in soil fertility, soil sickness, environmental pollution, wide yield gap, GHG emission, frequeny unpredictable weather due to climate change, increased intensity of pest and diseases, Water use inefficiency, etc. Using remote sensing and spatial data analysis it is possible to create recommendation of strategies for narrowing yield gap, climate smart agricultural practices, cropping calendar, and increase more grain per drop.

During the last two decades, remote sensing techniques are applied to explore agriculture applications such as crop growth monitoring (plant populations, nutrient deficiencies, diseases, water deficiency or surplus, weed infestations, insect & herbicide damage), comprehensive and reliable information on land use\cover, forest area, soils, geological information, extent of wastelands, agriculture crops, water resources (both surface and underground) and hazard\natural calamities like drought and flood, wind and hail damage etc. 

Benefits

• remote sensing has been the most useful tool to acquire spatial and temporal information therefore it has several advantages for agronomical research.
  • Remote sensing plays a significant role in crop classification, crop monitoring, and yield assessment.
  • The monitoring of the agricultural production system follows strong seasonal patterns in relation to the biological life cycle of crops. All these factors are highly variable in space and time dimensions. Moreover, agricultural productivity can change within short time periods, due to unfavourable growing conditions. Agricultural systems should be monitored periodically. For sustainable agricultural management, all influenced factorS need to be analyzed based on a spatial- temporal. Remote sensing is an important tool for time series monitoring and giving an accurate picture of agricultural condition. It has high revisit frequency and high accuracy. 
• Season-wise information on crops, their acreage, vigor and production enables the country to adopt suitable measures to meet shortages, if any, and implement proper support and procurement policies.
• Information from remote sensing can be used as base maps in variable rate applications of fertilizers and pesticides. Information from remotely sensed images allows farmers to treat only affected areas of a field.
• Problems within a field may be identified remotely before they can be visually identified.
• The use of remote sensing to identify prime grazing areas, overgrazed areas or areas of weed infestations for suitable action. 
• Lending institutions use remote sensing data to evaluate the relative values of land by comparing archived images with those of surrounding fields.

Specific use cases of Remote sensing

1. Crop Production : During early days, the data of remote sensing focused on land covers and crop types but now its focus is on biophysical characterization of plant. Remote sensing technology has potential to estimate crop productivity on the basis of crop and soil biophysical attributes. The data obtained from remote sensing may be used for estimating crop production. This technique reduces the labor cost and improves precision agriculture.
2. Assessment of Field Condition : Remote sensing plays a significant role in assessing the plant heath by using bio- physical indicators. Many physiological changes occur in crops due to various stresses and the same can be detected and recorded by remote sensing. Monitoring of drought by using remote sensing is used and accepted. Moreover, VCI (Vegetation Condition Index) and NDVI (Normalized Difference Vegetation Index) is also utilized to identify the drought conditions in field.
3. Optimizing Agricultural Inputs : The most important role of remote sensing is precision agriculture which helps to optimize the water and nutrient in field. Identifying the need of particular nutrient and need of water at critical crop growth period helps to reduce production cost and improve water and fertilizer use efficiency. In areas where drought occurs, drip irrigation along with remote sensing improves the crop production and reduces the inputs. Under wet environment, nitrogen fertilizer leaches more due to variation in water content, SOM content and yield. These conditions cause TSF (traditional single-rate N fertilization) failures.
4. Pest Identification and Control : Remote sensing has a great potential to detect the weed infestation in an area and can be used towards site specific management of weeds. It not only identifies the weed species but also helps to develop the appropriate amount of herbicide to control. Furthermore, it is also a good approach for assessing and monitoring infected leaves in field by spectral response to yellowing and chlorophyll of leaves. Its application detects the pattern disturbance and helps to manage pests in the field.
5. Estimating Crop Production : Remote sensing is an innovative way to forecast the crop yield by finding a relationship among vegetation indices and yield. Basically the crop yield is dependent on many factors such as variety, soil type, weather, pest and diseases. The spectral response of remote sensing is dependent on all these factors.
6.  Soil and Land Agricultural Mapping : Agricultural land resource maps are very diverse, ranging from land maps as basic data to thematic maps derived from them. In the 1980s, for the purposes of surveying and compiling land maps, aerial photographs were interpreted manually using a three- dimensional stereoscope against overlapping aerial photographs (mosaic). Land use and cover in aerial photographs can be used as a marker of soil formation and soil type. Technological advances have produced a variety of images with varying detail and accuracy, ranging from Landsat images suitable for review scale map preparation (1:250,000 scale) or SPOT images for more detailed scale (1:50,000 scale or greater). various maps can be made from soil maps, such as land suitability map, commodity recommendation map, agro-ecological zone (AEZ) map, commodity zoning map, land management recommendation map and so on. In addition, satellite images are used to create maps of paddy fields and other land uses, such as oil palm, coconut, sugar cane, cocoa plantations. It can also identify land availability map, swamp land maps types and agriculture land conversion.

Contributors : Akanksha Sikarwar, Rahul Mishra, Seema Bhardwaj, Vimal Shukla, Mayank Vyas, Rahul Prajapati and Yogesh Sikaniya
                   Indian Institute of Soil Science ,Bhopal (Madhya Pradesh); R.V.S.K.V.V, Gwalior (Madhya Pradesh)