Increasing interest in Remote Sensing and advancement technologies such as ArcGIS, QGIS, ENVI, ERDAS, and others have open gateways to various researches—agriculture being one of them. The easy viability of hyperspectral images (thanks to USGS EROS Center) and the enthusiasm of the scientists in using their expertise interpreting and analyzing these images to produce robust, sensible, and understandable products has not only benefited the scientific community but also the local stakeholders, decision-makers, and mankind as a whole.
Various attempts have been made regarding the study of agriculture using remote sensing. The basic idea is to use and interpret these remotely sensed images such as MODIS, Landsat, Sentinel, AVHRR, ASTER, and VIRRS and produce user-friendly datasets that have the capacity to distinguish between agricultural land and others. The National Land Cover Datasets (NLCD) produced by USGS, for example, has few classes that distinguish cropland from others. The Cropland Data Layer (CDL) produced by the U.S. Department of Agriculture goes even further to classify different crop types such as corn, rice, wheat, and others. The NASA funded Global Food Security-Support Analysis Data (GFSAD30 ) provide high-resolution global cropland data and their water use that contributes towards global food security in the twenty-first century. The GFSAD30 products are derived through multi-sensor remote sensing data (e.g., Landsat, MODIS, AVHRR), secondary data, and field-plot data and aims at documenting cropland dynamics from 1990 to 2017. There are various other datasets produced at global, regional, and local scale that focus on providing detailed, comprehensive, and consistent geospatial datasets at different temporal and spatial scale.
Remote Sensing in agriculture
The application of remote sensing in agriculture ranges from simply identifying the patches of cropland to sophisticated applications like precision agriculture. The easy (free) assess to remotely sensed data (via USGS) and the advancement of geo-spatial analysis tools have triggered the studies in a vigorous way. However, due to some technical limitations, budget, and time constraints, the studies are lagging behind. Let’s give a quick look at how remote sensing has helped in agriculture:
1. Land Cover Mapping
Land cover mapping is one of the most popular applications of remote sensing. Land cover mapping focuses on distinguishing different land cover types on the earth’s surface. These land cover include cropland, grassland, forest, water, urban area, and others. While mapping the land cover, the remotely sensed images such as Landsat are processes by integrating with multi-source ancillary datasets such as temperature, elevation, precipitation using some robust classifiers such as decision-tree or support vector machine, then a hierarchical theme-based post-classification is done for generating land cover products at different temporal, spectral, and spatial scales.
Land cover mapping has helped researchers as well as stakeholders in decision/policy making, planning the agro-based economy, management of food supply, water resources management, so on so forth. The identification of crop-types, on the other hand, helps in best management practices, deciding the crop types to cultivate, and forecasting the crop yields. The integration of crop-types with current and historical weather and climate, crop yield models, soil characteristics, and market condition hell build decision support system which in return helps in crop management including selecting crop based on field and soil type, developing the treatment plans to improve crop yields and reduce the risk of diseases or pest damage.
Precision agriculture also called as Precision farming and site-specific crop management refers to a group of techniques, technologies, and management strategies designed to optimize plant growth and farm profitability by adjusting treatments to suit variable biophysical conditions that occur within an agricultural field instead of applying the same treatment uniformly across the entire area. Precision agriculture uses new technologies like remote sensing, GIS, and GPS to increase crop yields and profitability while lowering the levels of traditional inputs needed to grow crops (land, water, fertilizer, herbicides, and insecticides). In other words, a controlled way of farming where farmers can decide what crops to plant, what nutrient (and what amount) to use, and when to farm based on the models and algorithms developed using advanced technologies like GPS and GIS tools.
The use of remote sensing to monitor the crop condition during growing season and GIS technology to analyze the results has made it possible to identify the problems and map their location. In addition, use of GPS to collect field-data of soil samples and integrating these results with current and historical weather and climate data help making the best decisions in terms of choosing the best crop types, supplying the nutrients, and using the proper treatments.
Due to the advancement in remote sensing and added functionalities in GIS, the characterization, modeling, and mapping of almost any crops have been possible—which is to say, the future of precision agriculture heavily relies upon GIS and Remote Sensing.
2. Regional Crop Condition Monitoring
The remotely sensed data used in conjunction with historical and current crop data, weather data, and field reports provide an overall assessment of the crop and food supply situation–and integration of these data with digital maps of administrative boundaries, recent price and market conditions on food stocks and consumption rates can be used to predict the prospects of current crops. Various interactive Web-based tools are developed to maintain these information.
Some of the sources for regional crop condition monitoring to look at are:
- Global Information and Early Warning System (GIEWS) of the United Nations Food and Agriculture Organization: It provides a detailed analysis and frequent reports on crop conditions and food supply situation for all countries.
- Crop Condition Assessment Program of Statistics Canada: It provides maps, statistical data, and NDVI curves updated weekly for the entire region. The users can interactively choose regions of interest by name or by selecting them from maps at several scales.
3. Land use land cover change
Land cover indicates the physical land type such as forest or open water whereas land use documents how people are using the land. By comparing land cover data and maps over a period of time, coastal managers can document land use trends and changes. Land use land cover change simply refers to the conversion of a piece of land’s use by humans, from one purpose to another. For example, land may be converted from cropland to grassland, or from wild land (e.g. tropical forests) to human-specific land uses (e.g. palm oil plantations).
Remote sensing has an important contribution to make in documenting the actual change in land use/land cover on regional and global scales. Using the time-series remotely sensed images, it helps to detect the change in land cover, for an instance, grassland conversion to cropland and/or cropland conversion to grassland. Remote sensing allows an easy and quick way to look at different land-cover types (grass, shrubs, trees, barren, water, and man-made features) at different time intervals–and this snapshot of land cover at different time interval give a broader picture of how the land cover has changed over a period of time. The rate of change can be abrupt, such as the changes caused by logging, hurricanes, and fire, or subtle and gradual, such as regeneration of forests and damage caused by insects. Using remote sensing techniques, we can keep track of the long-term natural changes in climate conditions, geomorphological and ecological processes, human-induced alterations of vegetation cover and landscapes, interannual climate variability, and the human-induced greenhouse effect and make right decisions at time.
Knowing the land use and land cover trend, one can have a better understanding of how and where to plan the agricultural practices and get benefited likewise.
4. Irrigated Land Cover Mapping
Another important application of remote sensing in agriculture is Irrigated Land Cover Mapping. Satellite observations provide reliable, economical, and synoptic data of the Earth’s surface. These data contribute to mapping land cover, including agricultural lands. Existing methods for agricultural land cover characterization have often been derived through image classification techniques. However, the variety of irrigated crops and the spatial patterns of their phenology require multi-temporal, consistent, composite vegetation growth information with sufficient spatial detail, along with a rich library of field reference training and ancillary data (e.g., climate and topography), to classify irrigated lands using satellite observations. Obtaining these parameters consistently at the national scale is a major challenge.
Remotely sensed images with various spatial and temporal characteristics have been extensively used to map cropland extent and its
dynamics. Specifically, the Moderate Resolution Imaging Spectroradiometer (MODIS) imagery provides a unique capability to map cropland extent at resolutions of 250–1000m. Landsat data provide less frequent coverage but enable cropland mapping at the much finer spatial resolution of 30 m, as exemplified by the crop-specific classes of the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) cropland data layer (CDL), the planted/cultivated classes of the U.S. Geological Survey (USGS) National Land Cover Dataset (NLCD), and Global Food Security-Support Analysis Data (GFSAD30).
Detailed and current information about where irrigated agriculture is now located and how its distribution and scope change over space and time can contribute to solutions to this challenge. Such information is critical if we want to fully understand the impact of agriculture on water use and formulate effective management policies for this limited resource.
There are various application of remote sensing. Some of the applications include:
- Remote Sensing in forestry.
- Remote Sensing in geospatial intelligence.
- Remote Sensing in urban mapping
- Remote Sensing in geology
- Remote Sensing in archaeology
- Remote Sensing in the military
- Remote Sensing in meteorology
- Remote Sensing in researches, and more.
I will be covering these topics in upcoming blogs. Stay tuned!