Chen Zhang


Computer Scientist

GIS/Remote Sensing Analyst

Full-Stack Developer




* Full publication list and citations can be found on my Google Scholar Profile.

Zhang, C., Di, L., Hao, P., Yang, Z., Lin, L., Zhao, H., and Guo, L., 2021. Rapid in-season mapping of corn and soybeans using machine-learned trusted pixels from Cropland Data Layer. International Journal of Applied Earth Observation and Geoinformation, 102, 102374. doi: 10.1016/j.jag.2021.102374.

Abstract: 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 for the rapid in-season mapping of corn and soybeans fields without ground truth data for the current year. We use trusted pixels, a set of pixels that are predicted from the historical Cropland Data Layer (CDL) data with high confidence in the current year’s crop type, to label training samples on multi-temporal satellite images for crop type classification. The entire mapping process only involves a limited number of satellite images acquired within the growing season (normally 3–4 images per scene) and no field data needs to be collected. According to the investigation on 12 states of the U.S. Corn Belt, it is found that a considerable number of trusted pixels can be identified from the historical CDL data by the trusted pixel prediction model based on artificial neural network. According to the experiment on 49 Landsat-8 scenes and 31 Sentinel-2 tiles, the in-season maps of corn and soybeans are expected to reach 85%–95% agreement with CDL as well as field data by mid-July. Once the in-season satellite imagery becomes available, the crop cover map can be rapidly created even with limited computational resources. This study provides a new perspective and detailed guidance for rapid in-season mapping of corn and soybeans, which can be potentially applied to identify more diverse crop types and scaled up to the entire United States.

Full-text access: ScienceDirect

Zhang, C., and Lin, L., 2021. Image Processing Methods in Agricultural Observation Systems. In: Di L., Üstündağ B. (eds) Agro-geoinformatics: Theory and Practice, Springer Remote Sensing/Photogrammetry. Springer, Cham. doi: 10.1007/978-3-030-66387-2_6.

Abstract: 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 traditional image processing hardware and software, the state-of-the-art technologies for agricultural image processing, such as mobile device-based image processing and cloud computing-based image processing, are covered. Image data could be acquired by different sensors in different ways. We discuss three common approaches to collect agricultural image data, in situ, airborne-based, and space-borne-based data collection, as well as the big data challenge in agro-geoinformatics. As the core image processing operation in the agricultural observation system, information extraction aims to understand agro-geoinformation from the raw image data. This chapter also illustrates several image information extraction methods that are widely employed in agro-geoinformatics, such as knowledge-based expert system, machine learning-based decision tree, and artificial neural network. Furthermore, a case study of the production of Cropland Data Layer (CDL) data, a comprehensive, raster-formatted, geo-referenced, annual crop-specific land cover map produced by the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS), is demonstrated.

Full-text access: Springer

Zhang, C., Di, L., Yang, Z., Lin, L., and Hao, P., 2020. AgKit4EE: A toolkit for agricultural land use modeling of the conterminous United States based on Google Earth Engine. Environmental Modelling & Software, 129, 104694. doi: 10.1016/j.envsoft.2020.104694.

Abstract: 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 application programming interface (API), rich reusable library, and high-performance computational capacity. However, using GEE to prepare geospatial data requires not only the skills of programming languages like JavaScript and Python, but also the knowledge of GEE APIs and data catalog. This paper presents the AgKit4EE toolkit to facilitate the use of the Cropland Data Layer (CDL) product over the GEE platform. This toolkit contains a variety of frequently used functions for use of CDL including crop sequence modeling, crop frequency modeling, confidence layer modeling, and land use change analysis. The experimental results suggest that the proposed software can significantly reduce the workload for modelers who conduct geospatial agricultural and environmental modeling with CDL data as well as developers who build the GEE-enabled geospatial cyberinfrastructure for agricultural land use modeling of the conterminous United States. AgKit4EE is an open source and it is free to use, modify, and distribute. The latest release of AgKit4EE can be imported to any modeling workflow developed using GEE Code Editor ( The source code, examples, documentation, user community, and wiki pages are available on GitHub (

Full-text access: ResearchGate, ScienceDirect

Zhang, C., Yang, Z., Di, L., Lin, L., and Hao, P., 2020. Refinement of Cropland Data Layer Using Machine Learning. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, pp. 161-164. doi: 10.5194/isprs-archives-XLII-3-W11-161-2020.

Abstract: 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 ones. There are many erroneous pixels in the early-year CDL product due to the cloud cover of the original Landsat images, which affect many follow-on researches and applications. To address this issue, we explore the feasibility of using machine learning technology to refine and correct misclassified pixels in the historical CDLs in this study. An end-to-end deep learning-based framework for restoration of misclassified pixels in CDL image is developed and tested. By feeding the CDL time series into the artificial neural network, a crop sequence model is trained and the misclassified pixels in an original CDL map can be restored. In the experiment with the 2005 CDL data of the State of Illinois, the misclassified pixels over Agricultural Statistics Districts (ASD) #1760 were corrected with a reasonable accuracy (> 85%). The findings suggest that the proposed method provides a low-cost and reliable way to refine the historical CDL data, which can be potentially scaled up to the entire CONUS.

Full-text access: ResearchGate, ISPRS

Zhang, C., Di, L., Lin, L., and Guo, L., 2019. Machine-learned prediction of annual crop planting in the U.S. Corn Belt based on historical crop planting maps. Computers and Electronics in Agriculture, 166, 104989. doi: 10.1016/j.compag.2019.104989.

Abstract: 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 first attempt to use machine learning approach on the prediction of field-level annual crop planting from historical crop planting maps. We present an end-to-end machine learning framework for crop planting prediction using Cropland Data Layer (CDL) time series as reference data and multi-layer artificial neural network as prediction model. The proposed framework was first tested at Lancaster County of Nebraska State, then scaled up to the U.S. Corn Belt. According to the experiment results from 53 Agricultural Statistics Districts, we found the machine-learned crop planting map was expected to reach 88% agreement with the future CDL. Meanwhile, the crop acreage estimates derived from the machine-learned prediction were highly correlated (R2 > 0.9) with the crop acreage estimates of CDL and official statistics by the U.S. Department of Agriculture National Agricultural Statistics Service. This study provides a low-cost and efficient way to predict annual crop planting map, which can be used to support many agricultural applications and decision makings before the beginning of a growing season.

Full-text access: ResearchGate, ScienceDirect

Zhang, C., Di, L., Yang, Z., Lin, L., Eugene, G.Y., Yu, Z., Rahman, M.S. and Zhao, H., 2019. Cloud Environment for Disseminating NASS Cropland Data Layer. The 8th International Conference on Agro-Geoinformatics. doi: 10.1109/Agro-Geoinformatics.2019.8820465.

Abstract: 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 geospatial Web services. This study utilizes cloud computing technology to improve the performance of CropScape application and Web services. A cloud-based prototype of CropScape is implemented and tested. The experiment results show the performance of CropScape is significantly improved in the cloud environment. Comparing with the original system architecture of CropScape, the cloud-based architecture provides a more flexible and effective environment for the dissemination of CDL data.

Full-text access: ResearchGate, IEEE

Zhang, C., Di, L., Lin, L. and Guo, L., 2019. Extracting Trusted Pixels from Historical Cropland Data Layer Using Crop Rotation Patterns: A Case Study in Nebraska, USA. The 8th International Conference on Agro-Geoinformatics. doi: 10.1109/Agro-Geoinformatics.2019.8820236.

Abstract: 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 ground truth in the crop mapping and other agricultural applications. A case study in the Nebraska state of USA is demonstrated. The common crop rotation patterns of four major crop types, corn, soybeans, winter wheat, and alfalfa, are compared and analyzed. The experiment results show a considerable number of pixels in CDL following the certain crop sequence during the past decade. Each observed crop type has at least one reliable crop rotation pattern. Based on the reliable crop rotation patterns, a great proportion of pixels can be correctly mapped a year ahead of the release of current-year CDL product. These trusted pixels can be potentially used to label training samples for crop type classification at early growing season.

Full-text access: ResearchGate, IEEE

Zhang, C., Di, L., Sun, Z., Lin, L., Eugene, G.Y. and Gaigalas, J., 2019. Exploring cloud-based Web Processing Service: A case study on the implementation of CMAQ as a Service. Environmental Modelling & Software, 113, pp. 29-41. doi: 10.1016/j.envsoft.2018.11.019.

Abstract: 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 data. In this study, we explore the cloud-based Web Processing Service (WPS) and present the Cloud WPS framework to support implementing Earth science model as WPS. Specifically, to make CMAQ easier to use for scientists through the latest standard-based Web service technology, CMAQ-WS, a prototype of CMAQ as a Service, is developed and tested. The result of the experiment shows the framework significantly improves not only the performance of but also ease of use of the CMAQ model thus providing great benefits to the environmental modeling community. Meanwhile, the proposed framework provides a general solution to integrate Earth science model, WPS, and cloud infrastructure, which can greatly reduce the workload of Earth scientists.

Full-text access: ResearchGate, ScienceDirect

Zhang, C., Di, L., Sun, Z., Eugene, G.Y., Hu, L., Lin, L., Tang, J. and Rahman, M.S., 2017. Integrating OGC Web Processing Service with cloud computing environment for Earth Observation data. The 6th International Conference on Agro-Geoinformatics. doi: 10.1109/Agro-Geoinformatics.2017.8047065.

Abstract: 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 WPS architecture integrating the cloud computing environment and OGC Web Services. Based on the architecture, we implement the architecture using GeoBrain Cloud, an Apache Cloudstack based private cloud computing platform, and a series of state-of-the-art open-source libraries and software. The result suggests that Web Processing Services and cloud computing environment could be successfully integrated by applying the proposed architecture.

Full-text access: ResearchGate, IEEE

Zhang, C., Sun, Z., Heo, G., Di, L. and Lin, L., 2016. Developing a GeoPackage mobile app to support field operations in agriculture. The Fifth International Conference on Agro-Geoinformatics. doi: 10.1109/Agro-Geoinformatics.2016.7577656.

Abstract: 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, GeoPackage files can be easily accessed, managed, and visualized in field operation. Based on Cordova framework’s powerful extensibility, the application can be run on multiple mobile platforms such as iOS, Android, and Windows Phone to meet requirement of clients using different types of mobile operating system.

Full-text access: ResearchGate, IEEE

Zhang, C., Sun, Z., Heo, G., Di, L. and Lin, L., 2016. A GeoPackage implementation of common map API on Google Maps and OpenLayers to manipulate agricultural data on mobile devices. The Fifth International Conference on Agro-Geoinformatics. doi: 10.1109/Agro-Geoinformatics.2016.7577654.

Abstract: 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 implementing Common Map API on both Google Maps TM and OpenLayers to assist in the manipulation of agricultural data on mobile devices. The app provides geospatial operations to access, manage, analyze, and visualize agricultural data on Google Maps TM and OpenLayers at the same time. Besides, by integrating with Apache Cordova architecture, users are able to run the app on multiple mobile platforms such as iOS and Android with little effort.

Full-text access: ResearchGate, IEEE