Global synergy cropland map
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'Information on global cropland distribution is critical for agricultural monitoring and food security.  We propose a new self-adapting statistics allocation model (SASAM) that fuses multiple existing maps with national and subnational statistics to develop new global cropland map. This synergy method is based on the agreement between input cropland maps, and is independent of training samples. The statistics of cropland area are used as a standard to rank the input cropland maps and build a score table to indicate the agreement among the input datasets. Statistics of national, first and second subnational levels are allocated to the pixels with higher agreement scores, and the multi-level allocation results are then integrated to obtain the extent of cropland. We applied SASAM to produce a global cropland synergy map with a spatial reso' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["cropland","remote sensing","spatial distribution","statistics"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Information on global cropland distribution is critical for agricultural monitoring and food security.  We propose a new self-adapting statistics allocation model (SASAM) that fuses multiple existing maps with national and subnational statistics to develop new global cropland map. This synergy method is based on the agreement between input cropland maps, and is independent of training samples. The statistics of cropland area are used as a standard to rank the input cropland maps and build a score table to indicate the agreement among the input datasets. Statistics of national, first and second subnational levels are allocated to the pixels with higher agreement scores, and the multi-level allocation results are then integrated to obtain the extent of cropland. We applied SASAM to produce a global cropland synergy map with a spatial reso' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Information on global cropland distribution is critical for agricultural monitoring and food security.  We propose a new self-adapting statistics allocation model (SASAM) that fuses multiple existing maps with national and subnational statistics to develop new global cropland map. This synergy method is based on the agreement between input cropland maps, and is independent of training samples. The statistics of cropland area are used as a standard to rank the input cropland maps and build a score table to indicate the agreement among the input datasets. Statistics of national, first and second subnational levels are allocated to the pixels with higher agreement scores, and the multi-level allocation results are then integrated to obtain the extent of cropland. We applied SASAM to produce a global cropland synergy map with a spatial reso
cropland; remote sensing; spatial distribution; statistics