Progress 02/25/23 to 02/24/24
Outputs
Target Audience:This project produced the in-season crop map of CONUS at 30 meter spatial resolution and place the product on line at iCrop for users to download and conduct online analysis. The target audiences include USDA agencies, state/local agricultural officers, farmers, agri-business operators, crop advisors, and researchers. Changes/Problems:We requested an one-year no-cost extension of the project to completely validate the in-season crop map product and make the production of the map operational. The requestwas approved by NIFA. What opportunities for training and professional development has the project provided?The project has provided 1 PhD student and 1 young research faculty opportunities to work on cutting-edge digital agriculture/agro-informatics research. The in-season crop map support a large number of online users, andmany of them are undergraduate and graduate students. How have the results been disseminated to communities of interest?We disseminated the project results to the communties through journal publications and conference presentations. We advertised the in-season crop maps at CropScape web service system that have over 50,000 users. We incorporated the in-season crop map into the CropSmart digital twin so that the digital twin users can directly use the in-season crop maps. What do you plan to do during the next reporting period to accomplish the goals?Rigorous validation of the algorithm and products is one of important activities of this project. Only after rigorous validation, the algorithm developed by this project can be used for operational production. There are two methods for validation proposed by this project: 1) comparing with ground truth, 2) comparing with CDL products. Original plan was to validate with both ground truths and CDLs of 2021, 2022, and 2023 to demonstrate the effectiveness and long-term temporal stability of the algorithm to ensure it is operational ready. Due to the Covid-19, we were not able to collect ground truths for 2021. Because the current-year USDA CDL product is available to public only in February of the next year, we were only able to compare our products with the CDL products for year 2021 and 2022. Therefore, during theone-year no-cost extension period, we will conduct the following activities: Comparing 2023 in-season crop map with the 2023 USDA CDL product which will be available in February 2024. Finishing the work item by 3/31/2024. Producing the monthly in-season crop maps of CONUS for May, June, and July of 2024. Finishing each monthly in-season products by the end of the month or early next month. Collecting ground truth of 2024 seasons in multiple states. Finishing the ground truth collection by September 2024. Evaluating the pre-season and in-season crop map products against the ground truths and refining the algorithm if necessary by end of November 2024. Preparing the operational production of the in-season crop map products and the final project report by Feb 24, 2025.
Impacts
What was accomplished under these goals?
We have successfully developed the algorithms to automatically produce in-season crop map of the CONUS at the accuracy comparable with USDA NASS CDL product, and make the map available to public in July of current year instead of CDL's availability inFeb/March of next year. With the agorithm, we have successfully generated and validated the in-season crop map for 2022 and 2023. We will continue to produce and validate the in-season crop map for 2024. The availability of the in-season crop map of CONUS at 30-meter spatial resolution in July of current year has greatly facilitated the in-season cropping decision making.
Publications
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2023
Citation:
Zhang, C., Marfatia, P., Farhan, H., Di, L., Lin, L., Zhao, H., Li, H., Islam, M.D. and Yang, Z., 2023, July. Enhancing USDA NASS cropland data layer with segment anything model. In 2023 11th International Conference on Agro-Geoinformatics (Agro-Geoinformatics) (pp. 1-5). IEEE.
- Type:
Journal Articles
Status:
Published
Year Published:
2023
Citation:
Zhang, C., Di, L., Lin, L., Zhao, H., Li, H., Yang, A., Guo, L. and Yang, Z., 2023. Cyberinformatics tool for in-season crop-specific land cover monitoring: Design, implementation, and applications of iCrop. Computers and Electronics in Agriculture, 213, p.108199.
- Type:
Journal Articles
Status:
Published
Year Published:
2024
Citation:
Li, H., Di, L., Zhang, C., Lin, L., Guo, L., Eugene, G.Y. and Yang, Z., 2024. Automated In-season Crop-type Data Layer Mapping without Ground Truth for the Conterminous United States based on Multisource Satellite Imagery. IEEE Transactions on Geoscience and Remote Sensing.
|
Progress 02/25/22 to 02/24/23
Outputs
Target Audience:The project's target audiences are the agricultural sectors, governments, universities, companies, and public and private research teams who demand in-season and pre-season crop information for the United States. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?The project has provided training and professional development in agro-geoinformatics, GIS, remote sensing, crop mapping, land use land cover change, software development, and geospatial cyberinfrastructure for postdoctoral research fellows and Ph.D. students. Specifically, the following opportunities are provided in this reporting period: Biweekly group meetings with mentors, researchers, and students to discuss the progress, methods, data, and tools for the project. Internships for undergraduate students to involve in the project. A seminar entitled "Exploring agro-geoinformation for the United States using the FAIR cyberinformatics tools" was organized at IEEE Geoscience & Remote Sensing Society (GRSS) Washington, DC & Northern VA Chapter Virtual Seminar. Presentations about the latest progress and results of the project were given at International Conference on Agro-geoinformatics. The research findings and insights obtained from the project were incorporated into several graduate and undergraduate courses, such as GGS 773 Interoperability of Geographic Information Systems and GGS 366 Spatial Computing at George Mason University. How have the results been disseminated to communities of interest?The results of in-season and pre-season crop cover maps have been disseminated through the iCrop data service system as well as ArcGIS Server. The iCrop system is a project version of CropScape, the award-winning data service system delivering CDL data products of USDA NASS. According to Google Analytics as of 2022, the CDL and its derivative data products (e.g., crop frequency layers, crop mask layer) have been distributed to over 290,000 users worldwide via CropScape as well as iCrop. With a similar user interface and functionalities, the current user community can smoothly transfer from CropScape to iCrop. Meanwhile, this easy-to-use system will disseminate the project results to new users who demand in-season and crop information for CONUS. The geospatial data on iCrop are disseminated through the OGC web service standard interfaces. When implementing the service layer of iCrop, we used MapServer as the back-end server to power the web mapping capabilities via standardized OGC specifications, such as WMS and WCS. Meanwhile, we used ArcGIS as an alternative back-end WMS/WCS server to host the new geospatial data products described in this study through ArcGIS REST APIs. These standard interfaces enabled interoperability with the front-end client as well as other WMS/WCS-compliant GIS software and applications. What do you plan to do during the next reporting period to accomplish the goals?In the next reporting period, the project will accomplish the following specific objectives: Optimize the in-season mapping algorithms using the upcoming 2022 CDL data. Implement the result distribution service system and integrate them into iCrop. Testing the integrated system. Operational applying the system in producing and servicing pre-/in-season cropland classification data. Release documentation of the system and open source code. Produce the 2023 pre-season crop cover map for CONUS before the growing season (by April 2023) and the in-season crop cover map for CONUS within the growing season (May - July 2023). Write and publish papers about the algorithm, method, data, and system from the project in international journals.
Impacts
What was accomplished under these goals?
The goal of the project is to facilitate timely and informed agricultural decision making by developing the capability of generating and providing in-season CDL-like crop maps for CONUS through easy-to-use cyberinfrastructure. The following objectives under these goals have been accomplished in this reporting period: Improve the crop type classification algorithm. The automatic crop type classification algorithm for in-season and pre-season crop cover mapping has been optimized and used for the production of 2022 crop cover maps. The machine learning algorithms, selections of parameters, and prediction models are comparatively studied. The refined algorithm was applied to classify not only dominant crop types (e.g., corn, soybeans, wheat, cotton) but also other minor crop types across the entire United States. Enhance the cyberinformatics tool and system. The web application and services of the cyberinformatics tool designed for this project, iCrop (https://cloud.csiss.gmu.edu/icrop/), were enhanced. The Findable, Interoperable, Accessible, Reusable (FAIR) capability for mapping and geoprocessing has been tested using common GIS clients and software. Specifically, we tested the interoperability of iCrop on the GeoPlatform (https://www.geoplatform.gov/) developed by Federal Geographic Data Committee (FGDC). Survey ground truth for result validation. A set of ground truth data across the United States during the growing season of 2022 was collected and processed. The crop types of over 4,000 land units were surveyed in the DC-Maryland area, Corn Belt area, and Central Valley of California. This ground truth data set was used to validate the 2022 in-season crop mapping results and improve the classification model. Produce and release in-season crop-specific land cover data for 2022. The in-season crop-specific land cover data for 2022 has been produced. The data was created using Landsat 8/9 and Sentinel-2 images acquired within the growing season of 2022. The major crop types of the result can reach 85% - 95% agreement with the ground truth data. The data has been published through the FAIR web services, such as Web Map Service (WMS) and Web Coverage Service (WCS), and released through iCrop as early as August 2022.
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2022
Citation:
Zhang, C., Di, L., Lin, L., Li, H., Guo, L., Yang, Z., Eugene, G.Y., Di, Y. and Yang, A., 2022. Towards automation of in-season crop type mapping using spatiotemporal crop information and remote sensing data. Agricultural Systems, 201, 103462.
- Type:
Journal Articles
Status:
Published
Year Published:
2022
Citation:
Lin, L., Di, L., Zhang, C., Guo, L., Di, Y., Li, H. and Yang, A., 2022. Validation and refinement of cropland data layer using a spatial-temporal decision tree algorithm. Scientific Data, 9(1), 63.
- Type:
Journal Articles
Status:
Under Review
Year Published:
2023
Citation:
Zhang, C., Di, L., Lin, L., Li, H., Yang, A., Guo, L., Eugene, G.Y., and Yang, Z. iCrop: a FAIR cyberinformatics tool for machine-learning-based U.S. crop-specific land cover data sharing and analysis. Computer and Electronics in Agriculture(Under review)
- Type:
Journal Articles
Status:
Under Review
Year Published:
2023
Citation:
Li, H., Di, L., Zhang, C., Lin, L., Guo, L., Eugene, G.Y., Yang, Z. Automated in-season crop-type data layer mapping without ground truth for the Conterminous United States based on multisource satellite imagery. IEEE JSTARS (Under review)
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2022
Citation:
Zhang, C. Exploring agro-geoinformation for the United States using the FAIR cyberinformatics tools. In IEEE Geoscience & Remote Sensing Society (GRSS) Washington, DC & Northern VA Chapter Virtual Seminar, Online, December 9, 2022.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2022
Citation:
Zhang, C. Agriculture 4.0 Technologies for In-season and Early-season Crop Cover Mapping. In 10th International Conference on Agro-Geoinformatics and 43rd Canadian Symposium on Remote Sensing, Session: Agriculture, Online, July 13, 2022.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2022
Citation:
Lin, L. Validation and Evaluation of Refined Cropland Data Layer: A Case Study. In 10th International Conference on Agro-Geoinformatics and 43rd Canadian Symposium on Remote Sensing, Session: Agriculture, Online, July 13, 2022.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2022
Citation:
Li, H., Di, L., Zhang, C., Lin, L. and Guo, L., 2022, July. Improvement Of In-season Crop Mapping For Illinois Cropland Using Multiple Machine Learning Classifiers. In 2022 10th International Conference on Agro-geoinformatics (Agro-Geoinformatics). IEEE.
|
Progress 02/25/21 to 02/24/22
Outputs
Target Audience:The project's target audiences are the agricultural sectors, governments, universities, companies, and public and private research teams who demand in-season and pre-season crop information of the United States. Changes/Problems:There were no major changes and problems during this report period. What opportunities for training and professional development has the project provided?The project has provided graduate research assistants, undergraduate interns, and jounir researchers with various training and professional development opportunities, such as one-on-one work with a mentor, internship, study group, and individual study. Specifically, the project initiated a biweekly group discussion to discuss the state-of-the-art methods, data, technologies, and tools for crop mapping. In addition, students and jounir researchers have been given opportunities to present the preliminary result and findings of the project in international conferences and workshops, such as IGARSS, International Conference on Agro-geoinformatics, and AGU Meeting. How have the results been disseminated to communities of interest?The results of in-season and pre-season crop cover maps have been disseminated through the iCrop agro-geoinformatic web service system. The iCrop system is a project version of CropScape, the award-winning web service system delivering CDL data products of USDA NASS. According to Google Analytics as of 2021, the CDL and its derivative data products (e.g., crop frequency layers, crop mask layer) have been distributed to over 250,000 users worldwide via CropScape. With the similar user interface and functionalities, the current user community can smoothly transfer from CropScape to iCrop. Meanwhile, this easy-to-use system will disseminate the project results to new users who demand in-season and pre-season crop information for CONUS What do you plan to do during the next reporting period to accomplish the goals? Validate the CONUS in-season mapping algorithms and results using the 2021 CDL data. Comparatively study alternative learning algorithms, selections of parameters, optimizing the pre-season prediction model. Comparatively study alternative deep learning algorithms, revise the cropland classification algorithm, develop the ensemble algorithm. Produce the 2022 pre-season crop cover map for CONUS before the 2022 growing season (by April) and validate the product with field data and forthcoming 2022 USDA CDL to further validate the algorithm and automatic workflow for pre-season prediction Produce in-season crop cover map for CONUS within the 2022 growing season (May - July), and validate the product with field data and forthcoming 2022 USDA CDL to further validate the algorithm and automatic workflow for in-season mapping Enhance the iCrop system by deploying more web services and data products into it. Write and publish papers about the algorithm, method, data, and system from the project in international journals.
Impacts
What was accomplished under these goals?
Automatic crop type classification algorithm prototypes for both in-season crop mapping and pre-season crop cover prediction have been developed and are being evaluated. The algorithms used the "trusted pixels" extracted from the past spatio-temporal crop information to label training samples on satellite images (e.g., Sentinel-2 data) for supervised crop type classification. According to the experiment, it is found that the "trusted pixels" are widely distributed across the CONUS. The prototypical implementation of the algorithm is currently available through Google Earth Engine: https://czhang11.users.earthengine.app/view/agkit4ee-inseason. A CDL-like CONUS in-season crop cover map of 2021 has been produced with the automatic mapping algorithm prototype. The map was created in early August 2021 for the growing season of 2021 by using Sentinel-2 images acquired from April to July. The in-season map is being evaluated with 2021 USDA CDL data, which was released at the end of Febuary 2022. The preliminary evaluation results indicated that the in-season crop map created by this project has reached 85%-95% agreement with the 2021 USDA CDL. The mapping result has been published at iCrop web service portal (described at item 4) through the Web Map Service and Web Coverage Service, which can be used in common GIS clients and software, such as ArcGIS and QGIS. It has been found that the USDA CDL datasets contain some classification errors mainly at the edge of classes, along the road, and at the middle of fields. An algorithm have been developed to correct those errors based on historical CDL time series. Currently 2017-2021 CDL datasets have been refined and published at iCrop. A paper about the algorithm and the refined CDL was published at a prestigious journal, Nature - Scientific Data. An agro-geoinformatic web service portal, iCrop - in-season crop mapping explorer (https://cloud.csiss.gmu.edu/icrop/), has been prototyped and deployed for testing and evaluation. This portal distributes all new geospatial data products (e.g., in-season crop cover, refined historical CDL) generated from this project. Like its award-winning precedent, CropScape, the iCrop web service system provides a suite of OGC web services and geoprocessing capabilities, including WMS and WCS data services, zonal statistics, PDF map production, change analysis, and on-demand data download.
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2022
Citation:
Lin, L., Di, L., Zhang, C., Guo, L., Di, Y., Li, H. and Yang, A., 2022. Validation and refinement of cropland data layer using a spatial-temporal decision tree algorithm. Scientific Data, 9(1), pp.1-9.
- Type:
Book Chapters
Status:
Published
Year Published:
2021
Citation:
Lin, L. and Zhang, C., 2021. Land Parcel Identification. Agro-geoinformatics: Theory and Practice, p.163.
- Type:
Book Chapters
Status:
Published
Year Published:
2021
Citation:
Zhang, C. and Lin, L., 2021. Image Processing Methods in Agricultural Observation Systems. Agro-geoinformatics: Theory and Practice, p.81.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2021
Citation:
Zhang, C., Yang, Z., Di, L., Lin, L., Hao, P. and Guo, L., 2021, July. Applying Machine Learning to Cropland Data Layer for Agro-Geoinformation Discovery. In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS. IEEE.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2021
Citation:
Zhang, C., Di, L., Yang, Z., Lin, L., Zhao, H. and Eugene, G.Y., 2021, July. An Overview of Agriculture Cyberinformatics Tools to Support USDA NASS Decision Making. In 2021 9th International Conference on Agro-Geoinformatics. IEEE.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2021
Citation:
Lin, L., Di, L., Guo, L., Zhang, C., Di, Y. and Yang, A., 2021, Evaluating the Spatial and Temporal Accuracy of Crop Acreage Estimation from Different Agencies. In AGU Fall Meeting 2021. AGU.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2021
Citation:
Zhang, C., Di, L., Guo, L., Lin, L., Di, Y. and Yang, Z., 2021, December. Introducing Cyberinformatics Tool for Exploring Pre-and In-season US Crop Cover. In AGU Fall Meeting 2021. AGU.
|
|