The extraction and classification of crops is the core issue of agricultural remote sensing.
The precise classification of crop types is of great significance to the monitoring and …

The upper Yellow River basin over the Tibetan Plateau (TP) is an important ecological
barrier in northwestern China. Effective LULC products that enable the monitoring of …

Climate change and the COVID-19 pandemic have disrupted the food supply chain across
the globe and adversely affected food security. Early estimation of staple crops can assist …

Currently, remote sensing crop identification is mostly based on all available images
acquired throughout crop growth. However, the available image and data resources in the …

Land use/land cover (LULC) data are essential for Earth science research. Due to the high
fragmentation and heterogeneity of landscapes, machine learning-based LULC …

An accurate and efficient estimation of eucalyptus plantation areas is of paramount
significance for forestry resource management and ecological environment monitoring …

Spectrotemporal features that capture changes in reflectance over time are useful for
characterizing the land cover of highly dynamic crops. Currently, temporal statistical metrics …

Field boundaries are at the core of many agricultural applications and are a key enabler for
the operational monitoring of agricultural production to support food security. Recent …

This study focuses on an ad hoc machine-learning method for locating archaeological sites
in arid environments. Pleiades (P1B) were uploaded to the cloud asset of the Google Earth …

Early crop identification can provide timely and valuable information for agricultural planting
management departments to make reasonable and correct decisions. At present, there is …