Sustainable Agriculture and Water Resources
Remote sensing community has entered into a new era with the huge volume of satellite images at around 10 to 30 meter resolution fully and open available, including the sentinel series satellite in Europe and GF series satellite in China. These satellites brought more data options for the application in agricultural monitoring. The capability of agricultural monitoring in general is expected to be enhanced and improved with these satellite data in term of the monitoring spatial extent and the quality of the retrieved crop growth information. However, the agricultural cultivation is diverse in the world. There are existing large fields with mono crop and small fields with multiple strips of various crop types. This fact is impacting on the application of satellite data for agricultural monitoring. In order to support sustainable agriculture practices, it is important to find the best trade-off between the field size which can be assessed and the quality of the EO-derived information. Indeed EO-derived information for small fields should not be misleading when monitoring cropping practices towards a more sustainable agriculture.In general, the field size is quite small in many parts of farm land in China in comparison with that in Europe. The fine resolution satellite data are always expected to be used in the agricultural monitoring in China. Taking advantage of the operational availability of Sentinel and GF, how or to what fine extent can the remote sensing information support the framer in the agricultural management? In this project, 8 study sites are selected representing the major cropping systems, 3 sites for winter wheat and maize and another 3 for rice. These sites also will be representing the agricultural systems in the flat area or in hilly area, irrigated or rainfed, in the north or south. The Sentinal1/2 and GF1/3/5/6, CBERS data will be mainly data sources to support this study. The remote sensing parameters, like LAI/FPAR/FCOVER/NDVI will be retrieved with the adapted algorithm. The crop classification algorithm will be applied to make crop type maps. Crop specific N retrieval algorithm will be developed. Finally, the retrieved information in the field level will be communicated with the farmers and jointly come up with the management suggestions for the nutritional application, irrigation and other practices. Through this joint project and the heavy involvement of young scientists from Europe and China, the satellite data finely processing and information retrieval algorithm will be exchanged and the objective of this project will be fulfilled as the task team brings a step forwards to support agricultural monitoring at fine scale.
