Page 268 - Special Topic Session (STS) - Volume 4

STS583 Yakob M. S.
500m) or Landsat 8 (15-30m) are rectified or classified products (with
resolutions in the range of 250 m to 500 m, and 15 m to 30 m, respectively)
freely downloadable in near-real-time. In addition, Sentinel 1 (SAR-GRD, 9-m
resolution), Sentinel 2 (10-m resolution) and Sentinel 3 (300-m resolution) are
also now available from the ESA hub or Google Earth Engine.

3.3 Crop monitoring and yield forecast
Timely and reliable crop production forecasts are crucial for making
informed food policy decisions and enabling rapid responses to emerging
food shortfalls. In light of increasing inter-seasonal crop production variability,
occasioned by the highly unpredictable climate, increasing food consumption
and limited financial resources, decision-makers continue to need reliable crop
monitoring system or crop production forecasts, which can provide them with
adequate lead time for resource allocation and thus facilitate appropriate
response and contingency planning.
Acquiring the crop condition information at early stages of crop growth is
even more important than acquiring the exact production after harvest time,
especially when large-scale production shortage or surplus happens.
Acquiring crop condition as early as possible has great influence on the policy
making on the price, circulation and storage of production (Chen Shupeng,
1990, Lin Pei, 1992, Sun Jiulin, 1996).
Regional or national crop growth estimates based on field reports are
often expensive, prone to large errors, and cannot provide real-time spatially
disaggregated estimates or forecasting crop condition. Moreover, obtaining
data through field data collection requires quite a reasonable amount of time
while real time information is needed for earlier intervention and early warning
systems. In this regard, with the development of remote sensing applications
and satellite along with some modelling techniques has become the
uppermost approach to monitor crop condition. USDA of the U.S. and EU, as
well as FAO, all have built their own crop monitoring systems using different
models (Liu Haiqi, 1999, Rassmussen, 1997).
These models require different approaches, skills and data sources. The
evaluation criteria for selecting the model to use should be based on the
forecasting system’s capacity to induce changes in the relevant agents’
behaviour, resulting from their perception of risk reduction. Wilson et al.
(1981) identified the ideal properties of models: reliability, objectivity,
consistency with scientific knowledge, adequacy to scales, minimum cost and
simplicity.
Most of the space products for yield monitoring are available free of
charge, the costs mainly derive from the running costs of the monitoring
system itself.
The Indian’s Mahalanobis National Crop Forecast Centre has an annual
budget of US$ 1.7 million to issue periodical forecasts for eight crops, to meet

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