Most farmers think that improving the production of crops and increase in income. Therefore, factors such as optimization of fertilizers, soil moisture, and irrigation techniques, prevention of damage by harmful insects, weather conditions, and management costs have become issues of concern. The distribution of plants, and plant life itself, is dependent on various environmental conditions. A change in environment is often described as “environmental stress.” Damage by harmful insects, under nutrition, and environmental pollution often cause results of environmental stress. If changes in the physiological functions of farm products caused by environmental changes can be measured without any physical contact, then monitoring of the environment, forecast of yield, growth diagnosis, and management of crops will become possible. Remote sensing is a nondestructive technique to observe or measure objects and phenomena remotely, that is without any physical contact, using observation equipment. Remote sensing enables an object to be measured on spatial, temporal, and spectral scales. In particular, satellite remote sensing enables the observation of the entire earth by a consistent measuring method. Remote sensing was expected to have the potential to change the ways of agriculture; however, such utility did not advance as expected. There is a new discovery discovered because of the long temporal observation, for example change of CO2. There are the possibilities of “new discoveries” in agriculture as well as system science because remote sensing enables observation on various scales: spatial, temporal, and spectral.
Utilization of remote sensing technology is expanding in agriculture and environmental fields in accordance with the improvement of satellite sensors on board, hardware- software development, and diffusion of GIS in recent years. 23 subjects (research themes) on agricultural census and crop growth management are included in Chapter 1 of Part 2 in the Handbook. In the first phases, basic researches like acquiring ground truth data and hyperspectral measurements of crops on ground and air-borne sensors are introduced. In the second phases, methodology development for supporting to arrange agricultural census by governments, autonomies, and agricultural communities, are highlighted which include estimation of cultivation acreage, and cropping maps. In the final phases of Chapter 1, researchers reported on high quality low cost crop production applying satellite information on crop growth management. For instance, emphasis were given on technology to estimate protein content of rice grain before harvest for high nutrient quality, or determination of maturity of wheat grain for deciding harvest timing. It was also reported that in some root crops, chemical component such as starch value is detectable, or possible to give effective information for grassland renovation using satellite data. These technologies have been enrolled in a regional agricultural system, and committed for practical use in Hokkaido. In overseas, especially in monsoon Asia, satellite images are being used as useful source of information on paddy distribution, and regional cropping system.
In order to survey agriculture information, such as infrastructure and field crops, there were some examples using satellite images. In these cases, they are categorized three; first one is detailed and high accuracy analysis in Japan, second is Landsat and SPOT image analysis in developing countries, third is judging from high resolution satellite images. High accuracy is demanded in Japan under the complex of land use and many kinds crops. As a result, analysis of a series of satellite images and detailed analysis used by high resolution satellite images (IKONOS) were performed. By the reason of no detailed maps or agricultural statistics in developing country, shifting cultivation, land use analysis and land use change are reported using Landsat and SPOT images. High resolution images (IKONOS or QuickBird images) are useful to judge the condition of detailed land use, small scale ponds, agricultural facilities and so on. These information is useful for planning the usage of water and land resources.
A potential change in climate will increase the number of extreme events. Such events may cause natural disasters and severely affect human lives and agricultural production worldwide. Remote sensing technologies have enabled rapid collection of data where contemporaneous field observations are unavailable or incomplete. The author focuses on satellite remote sensing-based approaches to monitoring and prediction, and outlines methods to estimate environmental parameters and detect and predict natural disasters. This article describes an evaluation of environment, disaster detection and protection against disasters. The scope of evaluation of environment covers from ethology to continental desertification. The scope of disaster detection covers fire, volcanic activity, soil erosion, flood, drought and damage from salty breezes. Many problems still remain as to how to effectively predict a disaster and avert its damage before the disaster actually occurs, using a combination of remote sensing techniques, geographical information system (GIS) and administrative frames.
The interpretations of agricultural characteristics were performed in Japan and the world using satellite data. For the interpretation, we use color image of SWIR. Paddy has the characteristics of flooding, and this phenomenon was easily detecting the color image. Upland-farming fields are determined by the characteristics for mixing vegetated and non-vegetated fields because crop rotation systems are developed at upland farming fields. Grasslands have the color of light green and yellow green at the color image of SWIR, and broadleaf forests have almost same color. For the reason, it is difficult to divide broadleaf forest and upland farming field. L band Synthetic Aperture Radar (SAR) has the ability to divide the forests and the fields. The forests have high back scattering coefficient and the fields have low coefficient. Grasslands are easily extracted using Color image of SWIR and L-band SAR image.