Agricultural production is an undertaking that human beings create agricultural products by making use of organismic growth and development rules, a process tends to be restricted and influenced by the natural environment. To make better use of agricultural resources, investigation and monitoring of soil properties, geology, climate change, layout of crops planted and their growth and development in various ways are necessary.
Traditional monitoring of agricultural planting structures relies mainly on labor surveys which are comparatively disadvantageous in conduction frequency, human costs, timeliness and accuracy and no longer meets the demands of the modern agriculture.
Satellite RS technology featuring broad coverage, short revisiting period and cost-efficient acquisition plays an important role in investigation, evaluation, monitoring and management of large-scale outdoor agricultural production. It has already been used in a wide range of fields including crop identification, area reckoning, crop classification, yield estimation, growth and diseases & pests monitoring, etc.
The Nong’an County of the Changchun city in northeast China’s Jilin Province, as a demonstration area, is taken to analyze the local crops distribution.
RS image data of the county is segmented after the segmenting scales are confirmed.
RS information is classified and extracted by virtue of the selected and extracted sample objects or pixel features of the training area, along with the experience and knowledge.
The Normalized Vegetation Index (NDVI), also known as the Standardized Vegetation Index, is applied to detect vegetation in large areas. The reflectance spectrum of green vegetation features high absorptivity of the red spectral band (b3) and high reflectivity of the near-infrared spectral band (b4). So NDVI stands for the specific value between the difference ratio between the near-infrared spectral band (b4) & red visible spectral band (b3) and the sum value of both bands, calculated by: NDVI=(b4-b3)/(b4+b3), and a higher value leads to a higher vegetation coverage.
（1）The farmland in the Nong’an County is mostlydry land used for growing corns, which accounts for over 90% of the total crop area, while 10% or less for paddy fields.
Agricultural Planting Structure Distribution Map of Nong’an County in 2016.
(2) The Nong’an County is enclothed with heavy vegetation. The white-color spots in the following map stands for water bodies and buildings; while the greener areas represents the heavier vegetation coverage.
Vegetation Coverage of Nong’an County in August, 2016
We Can Do More
RS monitors the planting area, growth, yield estimation, soil moisture, diseases and pests and other crops information.
(1) Planting area monitoring: different crop differs in color, texture and shape on the RS images, and the data of crop planting area can be extracted to obtain crop planting area and scale.
(2) Growth monitoring: referring to the macroscopic monitoring of seedling, growing and changing of crops, namely to monitor the growing conditions and trends by LAI (Leaf Area Index) and NDVI (Normalized Differential Vegetation Index).
(3) Yield estimation: RS imagery is also able to monitor and forecast crop yield based on its unique spectral reflectance, which can be used to reversely deduct the crop growth (such as LAl and biomass). The output information could be acquired finally through the correlation model of growth data and yield.
(4) Soil moisture monitoring: the spectral features of the same soil may vary according to different soil moisture content. RS monitoring of the content is via visible lights at near-infrared, thermal infrared and microwave bands. A relationship model is established with soil moisture content parameters to carry out reverse deduction of the moisture content.
(5) Plant diseases and pests monitoring and forecast: the vegetation’s response to diseases and insect pests, lack of fertilizer and other threats varies with threat types and degrees including biochemical (cellulose, blade, etc.) and biophysical (canopy structure, coverage, LAI, etc.) changes, along with subsequent changes in spectral pattern of plants absorptivity. Thus it is possible to detect the threats at an early stage. Periodic extraction of the affected area and spatial distribution can be made via RS imagery
With a superiority of fast macroscopic data acquisition, the RS technique makes it possible to evaluate and monitor the volume, quality and spatial distribution of agricultural natural resources including arable lands, grasslands and waters and thereby provides scientific support for the exploitation & protection of agricultural resources, agri-planning, eco-environmental protection and sustainable development of agriculture.
The RS technique is an important technical means of disaster relief monitoring and evaluation. It is capable of dynamically monitoring major agricultural and natural disasters like droughts and floods, including their occurrence, impacted spheres, affected area & degree, contributing to the pre-disaster warning, post-disaster relief and alleviate the damages to the agricultural production.