Agriculture

What about the effective cropped area and crop production figures? Simply inconsistencies.

Often, during most of the crop season, agricultural areas are principally the soil on which crop will grow later on. From the analysis of optical multi-spectral images, it is a general agreement, that it is not possible to identify on-going field preparations such as ploughing / re-ploughing, sowing, and the earliest stages of plant emergence. Hence, first estimates of the actual cultivated area are not available earlier than crop start developing their plant structure, assuming that weather conditions are favourable to the data acquisition. Nevertheless, the specific sensitivity of Synthetic Aperture Radar (SAR) to important soil properties, such as roughness and moisture content can be exploited. These properties of soil as well as the evolution over time is not casual, as far as agricultural surfaces are concerned. In fact, knowledge of crop calendar, land practices, and precipitation data, multi-temporal SAR data offer valuable information to determine at the earliest stage of the crop season, when and where fields are prepared, and later, the phenological crop status such as flowering, ripening, plant drying and harvesting.

Rice

Rice is the most important food crop in developing countries, which still produce 1.6 times as much rice as wheat, the second most important staple. Projections made by the International Food Policy Research Institute (IFPRI/CGIAR) show that the demand for rice will increase by about 1.8% per year over the 1990-2020 period. This means that over the next 30 years, rice consumption will increase by nearly 70%, and Asian rice production must increase to about 840 million tons by the year 2025, from the present level of about 490 million tons, if rice prices are to be maintained at current levels.

The goal behind Rice Information System is to provide to public institutions such as national agencies (primarily Bureau of Agricultural Statistics and National Early Warning Units) - in collaboration with the private sector (local insurers, re-insurers, farmer consortia) - reliable information on rice, e.g.
    - acreage
    - transplanting/emergence moment
    - growth status
    - harvest time
    - yield

The basic idea behind the generation of rice acreage using SAR is the analysis of changes in the acquired data over time. Measurement of temporal changes of SAR response due to the rice plants phenological status - an increase in the SAR backscatter corresponds to a growth in the rice plants - lead to the identification of the areas subject to transplanting / emergence moment and the rice growth. The rice acreage statistics are stored in map format showing the rice extent and, in form of numerical tables, quantifying the dimension of the area at the smallest administrative level - typically village unit - cultivated by rice. These products are linked to district, region, province and country, so that statistics on any of these administrative units can be produced. The figure shows a typical output.

Rice yield prediction is performed using an Agro Meteorological Model. It is a quantitative deterministic model that simulates rice growth, based on sets of rice parameters, daily meteorological data, and soil characteristics. The daily meteorological data - minimum/maximum/average temperature, sun radiation, relative humidity, wind speed, sun illumination hours, precipitation - is used to predict the rice crop growth. Production (t), finally, is simply calculated by combining yield estimation (t/ha) and the acreage (ha) derived from the SAR data.

Global Monitoring for Food Security

Existing Food Security Information Services based on Earth Observation generally provide following small scale information:

• Qualitative vegetation indices primarily used for trend analysis;
• Water stress;
• Estimated rainfall;
• Sporadic ground observations of agricultural variables.

The Global Monitoring for Food Security service contributes to improved food security in Africa, by providing an Earth Observation service based on the integration of low (1 km) to high (10 m) resolution optical and SAR data. The key activities are:

• Facilitating access to Earth Observation data;
• Providing an operational service;
• Validating Earth Observation products;
• Capacity building.

In Africa, off crop season, crop areas are principally bare soil on which maize will grow later on. After ploughing and planting phase, dependent upon land practices and weather conditions, maize starts developing their plant structure. Months later, after the vegetative and reproduction stage, plants dry before they are harvested. The specific sensitivity of the radar backscatter to soil properties, such as roughness and moisture content, makes possible the detection of these changes already at the earliest stage (e.g. ploughing, sowing, and plant emergence). During the second phase (namely flowering to plant drying stage), the information content of high frequency SAR data and optical data is highly correlated: in the optical case the strong reflectance is due to the high chlorophyll content of the plant, while later to the loss of chlorophyll (drying stage). In the SAR case, the plant humidity and mainly the volume scattering are the key factors determining the high reflectivity. The lower reflectivity during the plant drying is exclusively caused by the loss of wetness in the plant. The figure shows a typical set of output generated using high resolution SAR data.

For further information refer to the publications

• The makings of an internet-based rice information system: piloting in the Philippines, First Symposium on Geoinformatics, Philippines, 2004.
• Synergetic use of multi-temporal ALOS PALSAR and ENVISAT ASAR data for topographic/land cover mapping and monitoring at national scale in Africa, IGARSS Symposium Cape Town, 2009.