Chen Zhang

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Verified

Chen verified their affiliation via an institutional email.

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• Ph.D. in Earth Systems and Geoinformation Sciences
• Professor (Assistant) at George Mason University

An accurate crop planting map can provide essential information for decision support in agriculture. The method of post-season and in-season crop mapping has been widely studied in the land use and land cover community. However, it remains a challenge to predict the spatial distribution of crop planting before the growing season. This paper is the...

Google Earth Engine (GEE) is an ideal platform for large-scale geospatial agricultural and environmental modeling based on its diverse geospatial datasets, easy-to-use APIs, rich reusable library, and high-performance computational capacity. However, using GEE to prepare agricultural land use data for geospatial agricultural and environment modelin...

A timely and detailed crop-specific land cover map can support many agricultural applications and decision makings. However, in-season crop mapping over a large area is still challenging due to the insufficiency of ground truth in the early stage of a growing season. To address this issue, this paper presents an efficient machine-learning workflow...

Soil moisture is an essential parameter to understand crop conditions throughout the growing season. Collecting soil moisture data by field observation is labor-intensive, especially when attempting to obtain Conterminous United States (CONUS) geographic coverage. In addition, using soil moisture for assessing current and future crop conditions is...

CONTEXT Mapping crop types from satellite images is a promising application in agricultural systems. However, it is a challenge to automate in-season crop type mapping over a large area because of the insufficiency of ground truth and issues of scalability, reusability, and accessibility of the classification model. This study introduces a framewor...

Mapping nationwide in-season crop-type data is a significant and challenging task in agriculture remote sensing. The existing data product for U.S. crop-type planting, such as the Cropland Data Layer (CDL), falls short in facilitating near-real-time applications. This paper designed a workflow aimed at automating the generation of in-season CDL-lik...

This study presents a streamlined, automated classification method to map land-cover type Local Climate Zones (LCZs). Using a two-phase hybrid approach, we first generated training samples through universal decision rules and subsequently, a Machine Learning (ML) algorithm was trained on the generated samples to classify LCZs. The proposed model ha...

Sugarcane is a significant crop in terms of annual biomass in the world. Timely and accurate mapping of sugarcane planting is important for food security and sustainability. However, accurately remote-sensing-based mapping sugarcane remains challenging due to two reasons: (1) the scarcity of sugarcane training samples, and (2) the diverse sugarcane...

Given the increasing prevalence of droughts, unpredictable rainfall patterns, and limited access to dependable water sources in the United States and worldwide, it has become crucial to implement effective irrigation scheduling strategies. Irrigation is triggered when some variables, such as soil moisture or accumulated water deficit, exceed a give...

Mapping rice area is a critical resource planning task in many South Asia countries where rice is the primary crop. Remote sensing-based methods typically rely on domain knowledge, such as crop calendar and crop phenology, and supervised classification with ground truth samples. Applying such methods on Google Earth Engine (GEE) has been proven eff...

Changes in land surface temperature (LST) affect human society and the natural environment, especially for agricultural activities. In recent decades, satellite remote sensing has been used as an alternative approach to ground observation sites for monitoring LST. Prior research has offered broad insights into global and continental-level Land Surf...

This study develops a general method to evaluate the contributions of localized urbanization and global climate change to long-term urban land surface temperature (ULST) change. The method is based on the understanding that long-term annual ULST is controlled by three factors: (1) localized urbanization, (2) global climate change, and (3) interannu...

This paper describes a set of Near-Real-Time (NRT) Vegetation Index (VI) data products for the Conterminous United States (CONUS) based on Moderate Resolution Imaging Spectroradiometer (MODIS) data from Land, Atmosphere Near-real-time Capability for EOS (LANCE), an openly accessible NASA NRT Earth observation data repository. The data set offers a...

Large–area crop type identification and mapping for cropland are intensively crucial for agriculture research, yield forecast, and disaster management. The United States Department of Agriculture (USDA) produces the Cropland Data Layer (CDL) for Contiguous United States cropland that involves crop type spatial distribution with 30m resolution. Howe...

Lagos, Nigeria, is considered a rapidly growing urban hub. This study focuses on an urban development characterization with remote sensing-based variables for Lagos as well as understanding spatio-temporal precipitation responses to the changing intensity of urban development. Initially, a harmonic analysis showed an increase in yearly precipitatio...

Currently Lyme disease (LD) is the most common vector-borne disease in the United States. Understanding the potential effects of urban expansion on LD risk is an emerging global health concern. The U.S. Northeastern corridor has experienced a spatio-temporal increase in Lyme disease (LD) and rapid urban expansion over the past decades. The effects...

This study aims to develop a general method and evaluate the contributions of localized urbanization and global climate change to long-term urban land surface temperature (ULST) change. Combined daytime and nighttime daily MODIS products were used to fill data biases from satellite-observed data and applied to tropical regions during dry season fro...

Space-based crop identification and acreage estimation have played a significant role in agricultural studies in recent years, due to the development of Remote Sensing technology. The Cropland Data Layer (CDL), which was developed by the U.S. Department of Agriculture (USDA), has been widely used in agricultural studies and achieved massive success...

In the past few decades, most urban areas in the world have been facing the pressure of an increasing population living in poverty. A recent study has shown that up to 80% of the population of some cities in Africa fall under the poverty line. Other studies have shown that poverty is one of the main contributors to residents’ poor health and social...

Cyberinformatics tools have been extensively applied to aid decision support in agriculture. This paper presents an overview of cyberinformatics tools to support decision making for the National Agricultural Statistics Service (NASS) of the U.S. Department of Agriculture (USDA). We review three web-based applications: CropScape, VegScape, and Crop-...

Land parcel is the finest unit to describe the location, boundary, and ownership in land management. Land survey is the most popular way to identify land parcel in the history of land management. However, land parcel survey come with huge financial cost while the accuracy of the survey is not acceptable for many applications such as agricultural ma...

Image processing is an essential part of the agricultural observation system. This chapter is the first attempt to provide an overview of the image processing methods, technologies, and tools from the perspective of agro-geoinformatics. First, we introduce the origins, definitions, and basic steps of digital image processing. Along with the traditi...

Training samples is fundamental for crop mapping from remotely sensed images, but difficult to acquire in many regions through ground survey, causing significant challenge for crop mapping in these regions. In this paper, a transfer learning (TL) workflow is proposed to use the classification model trained in contiguous U.S.A. (CONUS) to identify c...

As the most widely used crop-specific land use data, the Cropland Data Layer (CDL) product covers the entire Contiguous United States (CONUS) at 30-meter spatial resolution with very high accuracy up to 95% for major crop types (i.e., Corn, Soybean) in major crop area. However, the quality of early-year CDL products were not as good as the recent o...

Cropland Data Layer (CDL) is an annual crop-specific land use map produced by the U.S. Department of Agricultural (USDA) National Agricultural Statistics Service (NASS). The CDL products are officially hosted on CropScape website which provides capabilities of geospatial data visualization, retrieval, processing, and statistics based on the open ge...

It is still a challenge to generate the timely crop cover map at large geographic area due to the lack of reliable ground truths at early growing season. This paper introduces an efficient method to extract “trusted pixels” from the historical Cropland Data Layer (CDL) data using crop rotation patterns, which can be used to replace the actual groun...

As an important tool for air quality simulation, the Community Multiscale Air Quality (CMAQ) model is widely used in the environmental modeling community. However, setting up and running the CMAQ model could be challenging for many scientists, especially when they have limited computing resources and little experience in handling large-scale input...

The remote-sensing based Flood Crop Loss Assessment Service System (RF-CLASS) is a web service based system developed and managed by the Center for Spatial Information Science and Systems (CSISS). The system uses Moderate Resolution Imaging Spectroradiometer (MODIS)-based flood data, which was implemented by the Dartmouth Flood Observatory (DFO), t...

Research in different agricultural sectors, including in crop loss estimation during flood and yield estimation, substantially rely on inundation information. Spaceborne remote sensing has widely been used in the mapping and monitoring of floods. However, the inability of optical remote sensing to cloud penetration and the scarcity of fine temporal...

Crop type information at the field level is vital for many types of research and applications. The United States Department of Agriculture (USDA) provides information on crop types for US cropland as a Cropland Data Layer (CDL). However, CDL is only available at the end of the year after the crop growing season. Therefore, CDL is unable to support...

Bangladesh is one of the most vulnerable countries to sea level rise due to the climate change. Soil salinity is one of the potential threat to the coastal ecosystem and agriculture which might hinder the country’s future food security. Conventional field-based soil salinity monitoring over vast region may not be cost and time efficient. Satellite...

Pollution is one of the main negative outcomes for rapid economic growth without sustainable development in China. Different types of pollutions are harming people's health and the impacts of pollution on environment and people's health could last for decades. Fine particulate matter(PM2.5), which is one of most common types of air pollutions in Ch...

Feeding multisource Earth observation (EO) data into Earth science models (ESM) remains a daunting challenge. This paper presents a service-oriented approach as an alternative solution. It uses geospatial web services to process the EO data and geoprocessing workflow for automation. Different from existing approaches, it takes advantage of virtual...

Statistics show the volume of Earth Observation (EO) data increases in the exponential level during the past decade. As the new generation computing platform to meet the big data challenge, cloud computing significantly facilitates the large-scale EO data processing depending on its powerful computing capability. In this paper, we propose a Cloud W...

Abstract: Agriculture is one of the most affected sectors by the flood. Spaceborn remote sensing is widely used for flood mapping and monitoring in recent decades. Some applications such as flood crop loss assessment require data with fine temporal resolution to monitor short-lived flood. MODIS is providing remote sensing data with 1-2 days tempora...

It is still a great challenge to efficiently deliver dynamic and heterogeneous Earth observation (EO) information to users based on their real time locations. However, the rapidly evolving techniques create a chance to meet the challenge. This paper proposes a framework to realize a one-stop and location based service (LBS) for geospatial informati...

The Pub/Sub, short for Publish-Subscribe, is a flexible mechanism perferred by many users who'd like to passively know the changes of situation. Once a new message is published by a provider, all the subscribers to the specific kind of messages will receive the message and make corresponding responses. In agricultural crop monitoring, such mechanis...

The timely retrieval of remote sensing imagery by farmers and decision makers is very important for current agricultural activities. Through the various kinds of imageries of agricultural fields, people can conclude the status of the fields and figure out what kind of crops are suitable and how to cultivate and irrigate the fields. This paper demon...

Flood is defined as water that temporarily submerges land. In the United States, flood is being considered as an event only if it last over 72 hours, but the time frame is much longer in the Europe. The information of water bodies is an important source when examining floods. However, this data is not always available or accurate by traditional sur...

GeoPackage, an open format for geospatial information, provides a gateway to bridge agricultural geographic information and mobile devices such as smartphones and tablets. In this paper, we present a Cordova framework based GeoPackage mobile application to support field operations in agriculture. By implementing GeoPackage SDK on mobile application...

Characterized by features of standards-based, platform-independent, portable, self-describing, and compact, GeoPackage, a new open format for geospatial information container, makes it much easier to manipulate geospatial data on mobile devices such as smartphones and tablets. In this paper, we present a GeoPackage based mobile application implemen...