Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: MODIS LAI product is very important because it has potential for the monitoring of global scale ecosystem, global change of carbon and water. This study aims to analyze the temporal variability of annual MODIS LAI product using field measured LAI dataset for four vegetation types. The MODIS annual LAI pattern shows the high variety or inaccuracy of LAI during rainy season over South Korea. This phenomenon is caused by high cloud coverage and fails of processing by a radiative transfer model in this rainy season. Annual LAI pattern estimated by a radiative transfer model has least variance of LAI values during this time. In coniferous forest, MODIS annual LAI pattern shows the abnormal decreasing of LAI during non- growing season comparing with field-measured LAI. This decrease of LAI being an inaccurate phenomenon is caused by rather higher cloud coverage and decreasing of understory plants. MODIS LAI product in deciduous and grass coverage is underestimated by comparing with field-measured LAI. In rice paddy, MODIS LAI shows longer growing season than field measured LAI. For the accurate analysis of temporal MODIS LAI product, it needs considering of vegetation coverage types, cloud coverage, and algorithm types of MODIS LAI.
['Sun-Hwa Kim', 'Ji-Hoon Park', 'Choong-Sik Woo', 'Kyu-Sung Lee']
I am writing a research paper on a idea Analysis of temporal variability of MODIS leaf area index (LAI) product over temperate forest in Korea
[{"from": "human", "value": "I am writing a research paper on a idea Analysis of temporal variability of MODIS leaf area index (LAI) product over temperate forest in Korea"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: MODIS LAI product is very important because it has potential for the monitoring of global scale ecosystem, global change of carbon and water. This study aims to analyze the temporal variability of annual MODIS LAI product using field measured LAI dataset for four vegetation types. The MODIS annual LAI pattern shows the high variety or inaccuracy of LAI during rainy season over South Korea. This phenomenon is caused by high cloud coverage and fails of processing by a radiative transfer model in this rainy season. Annual LAI pattern estimated by a radiative transfer model has least variance of LAI values during this time. In coniferous forest, MODIS annual LAI pattern shows the abnormal decreasing of LAI during non- growing season comparing with field-measured LAI. This decrease of LAI being an inaccurate phenomenon is caused by rather higher cloud coverage and decreasing of understory plants. MODIS LAI product in deciduous and grass coverage is underestimated by comparing with field-measured LAI. In rice paddy, MODIS LAI shows longer growing season than field measured LAI. For the accurate analysis of temporal MODIS LAI product, it needs considering of vegetation coverage types, cloud coverage, and algorithm types of MODIS LAI."}]