Lingjun Kang

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Data provenance, also called data lineage, records the derivation history of a data product. In the earth science domain, geospatial data provenance is important because it plays a significant role in data quality and usability evaluation, data trail audition, workflow replication, and product reproducibility. The generation of the geospatial provenance(More)
Crop growth stages are important factors for segmenting the crop growing seasons and analyzing their growth conditions against normal conditions by periods. Time series of high temporal resolution, up to daily, satellite remotely sensed data are used in establishing crop growth estimation model and estimate the growth stages. The daily surface reflectance(More)
Geospatial Web Services (GWS) make geospatial information and computing resources discoverable and accessible over the Web. Among them, Open Geospatial Consortium (OGC) standards-compliant data, catalog and processing services are most popular, and have been widely adopted and leveraged in geospatial research and applications. The GWS metrics, such as visit(More)
Vegetation condition assessment is very useful and helpful for researchers and decision makers to evaluate crop loss and value, and identify and manage risks in the flood hazard areas. Crop responses to flooding vary with crop types, crop growing stages, soil characteristics, weather condition, flood duration and depth, etc. How to measure and understand(More)
Flooding introduces significant changes to crop condition profiles that can be derived from remote sensing. These changes correlate to crop damage caused by flood events. Crop condition profiles can be directly or indirectly constructed using different vegetation indices if specific crop are pre-determined. Crop condition profiles may be resulted from(More)
Traditional method of visiting field and surveying farmers to estimate crop yield has been considered inefficient and impractical especially in cases when fields are not easily accessible. Remote sensing techniques, therefore, has been utilize to overcome these obstacles with good success. Normalize Difference Vegetation Index (NDVI) based models are(More)
Remote sensing derived NDVI data is fundamental to crop monitoring and crop yield estimation research. The usefulness of NDVI relies on reducing noise caused by varying atmospheric conditions such as cloud, haze, and dust as well as by sensor viewing geometry. Different techniques have been applied for noise reduction of composite NDVI products. However,(More)
Precipitation has significant impacts on crop growing, which is indicated by NDVI. NDVI-precipitation correlation accompanied with different NDVI temporal responses to precipitation have been widely investigated. However, various factors (e.g. crop type, soil profile) contribute to variances of NDVI-precipitation correlation. Few studies have quantitatively(More)
WOFOST (WOrld FOod STudies) is a well-known, widely applied simulation model to analyze quantitatively the growth and production annual field crops that was originally developed for crops in European countries. It is the base model for Monitoring Agricultural ResourceS (MARS) Crop Growth Monitoring System (CGMS) in operational use for yield estimation in(More)
Normalized Difference Vegetation Index (NDVI)-precipitation correlation has long been studied. In previous studies, the correlation was usually based on global regression model, which assumed such correlation be constant across the space. However, NDVI-precipitation correlation is spatially dependent and affected by local factors (e.g., soil background). In(More)