Cropland Data Layer Data Research Articles

Towards automation of in-season crop type mapping using spatiotemporal crop information and remote sensing data

CONTEXTMapping 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 framework for automatic crop type mapping using spatiotemporal crop information and Sentinel-2 data based on Google Earth Engine (GEE). The main advantage of the framework is using the trusted pixels extracted from the historical Cropland Data Layer (CDL) to replace ground truth and label training samples in satellite images. OBJECTIVEThis paper will achieve three objectives: (1) assessing spatiotemporal crop information derived from the historical crop cover maps; (2) mapping crop cover, mainly crop fields without regular historical crop rotation patterns, from remote sensing data using supervised learning classification and validating mapping results; and (3) automating in-season crop mapping and exploring the scalability of the framework. METHODSThe proposed crop mapping workflow consists of four stages. The data preparation stage preprocesses CDL and Sentinel-2 data into the required structure. The spatiotemporal crop information sampling stage extracts trusted pixels from the historical CDL time series and labels Sentinel-2 data. Then a crop type classification model can be trained using the supervised learning classifier in the model training stage. In the mapping/validation stage, an in-season crop cover map over the full Sentinel-2 tile will be produced using the trained model and the classification performance will be validated using CDL or other ground truth data. RESULTS AND CONCLUSIONSWe systematically perform a group of experiments for in-season mapping of five major crop types (corn, cotton, rice, soybeans, and soybeans-wheat double cropping) over the Mississippi Delta region. The result indicates that the crop cover map of the study area is expected to reach 80%–90% agreement with CDL within the growing season. To further facilitate the use of the framework, we also develop a GEE-enabled online prototype, In-season Crop Mapping Kit, and explore its scalability over agricultural fields in various ecoregions including California, Idaho, Kansas, and Illinois. SIGNIFICANCEThe mapping-without-ground-truth approach described in this paper can significantly reduce ground truthing process and save substantial resource needs and labor costs, which is applicable to the production of in-season CDL-like data for the entire United States. The findings and outputs will benefit the agriculture community and other agricultural sectors ranging from government, academia, and companies.

Rapid in-season mapping of corn and soybeans using machine-learned trusted pixels from Cropland Data Layer

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.

Pre-season crop type mapping using deep neural networks

Reliable crop type maps are needed early in the growing season to retrieve crop type information from satellite imagery in order to produce in-season crop condition and yield outlooks. However, the inability to have early crop maps due to limited earth observations available during the beginning of the season is a major challenge for developing reliable satellite-based early warning systems.This paper introduces a novel crop type prediction modeling system based on deep Neural Networks (NN) to produce preseason crop type maps at the field scale resolution using historical crop maps. The architecture of this modeling system comprises of two end-to-end NN based modules that form an autoencoder configuration: a spatio-temporal Encoder, built off of the Bidirectional ConvLSTM network, and a Decoder, which can learn both spatial and temporal patterns necessary to accurately predict the crop sequences.To build this system, we used USDA-NASS historical Cropland Data Layer (CDL) data of Nebraska and trained nine different neural network models using a combination of three rotation cycles (3-, 4- and 5-year rotations) and three sets of historical CDL data with varying durations (2010–2016; 2006–2016; and 2002–2016). All trained models performed well when applied to predict 2017 land cover, and achieved maximum accuracy of 88%. However, the amount of training time taken to reach 88% accuracy varies for each model. Models with more temporal information were found to learn faster and achieve maximum accuracy quickly.Further, we compared the NN model with a Markov Chain (MC) based approach, a common approach used for crop pattern recognition, by applying them to predict 2018 and 2019 land cover maps using 2006–2016 CDL data. Results showed that the NN model outperformed the MC model with higher overall accuracy (0.77) and kappa coefficient (0.57) compared to that of the MC model (overall accuracy = 0.67 and kappa coefficient = 0.44). In summary, our novel system based on deep neural networks showed that it can learn complex spatial and temporal patterns from the historical land cover data and produce reasonable early crop maps, which can be used in satellite-based early warning systems.

AgKit4EE: A toolkit for agricultural land use modeling of the conterminous United States based on Google Earth Engine

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 (https://code.earthengine.google.com/?accept_repo=users/czhang11/agkit4ee). The source code, examples, documentation, user community, and wiki pages are available on GitHub (https://github.com/czhang11/agkit4ee).

REFINEMENT OF CROPLAND DATA LAYER USING MACHINE LEARNING

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.

A New Automatic Stratification Method for U.S. Agricultural Area Sampling Frame Construction Based on the Cropland Data Layer

This paper, for the first time, proposes to apply USDA National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) geospatial data for stratifying U.S. agricultural land. A new automated method is proposed to stratify the NASS state level area sampling frames (ASFs) by automatically calculating percent cultivation at the primary sampling unit (PSU) level based on the CDL data. The NASS CDLs are 30–56.0 m raster-formatted, georeferenced, cropland cover classifications derived from satellite data. The CDL stratification experiment was successfully conducted for Oklahoma, Ohio, Virginia, Georgia, and Arizona. The stratification accuracies of the traditional (visual interpretation) and new automated CDL stratification methods were compared based on 2010 June Area Survey data. Experimental results indicated that the CDL stratification method achieved higher accuracies in the intensively cropped areas, while the traditional method achieved higher accuracies in low or nonagricultural areas. The differences in the accuracies were statistically significant at a 95% confidence level. It was found that using multiyear composite, CDL-based cultivated layers did not improve stratification accuracies as compared to the results of single-year CDL data. Two applications of the CDL-automated stratification method in official USDA NASS operations are described. The novelty of the proposed method was using geospatial CDL data to objectively and automatically compute percent cultivation of the ASF PSUs as compared to the traditional method that subjectively determines percent cultivation using visual interpretation of satellite data. This proposed new CDL-based process improved efficiency, objectivity, and accuracy as compared to the traditional stratification method.

A Geospatial Web Service Approach for Creating On-Demand Cropland Data Layer Thematic Maps

<abstract><title><italic>Abstract.</italic></title> A thematic map displays the spatial patterns of one or more data themes for a specified geographic area. Thematic maps are used, for example, in geospatial data distribution and exchange, multi-source geospatial data aggregation, visual communication, and decision support. The latest web technologies and open geospatial standards make it feasible to produce on-demand thematic maps in an internet environment. This article describes a geospatial web service approach for creating customized thematic maps of cropland data layer (CDL) data. The design and implementation of the geospatial web service using open-source software are described in detail. The service meets the requirements of producing, sharing, and printing CDL thematic maps for the areas of interest without requiring additional mapping software. Examples of service invocation are given to demonstrate the applicability of the proposed approach. This service offers a new dissemination channel to distribute CDL data to the general public. Feedback from users in the agricultural community shows that the implemented web service has greatly improved the availability, customization, and timeliness of CDL thematic maps, reduced operational resource requirements and cost, facilitated geospatial data distribution and usability, and broadened the reach of geospatial cropland data products. In the first 20 months of availability, users have produced over 10,000 thematic maps.

CropScape: A Web service based application for exploring and disseminating US conterminous geospatial cropland data products for decision support

The Cropland Data Layer (CDL) contains crop and other specific land cover classifications obtained using remote sensing for the conterminous United States. This raster-formatted and geo-referenced product has been widely used in such applications as disaster assessments, land cover and land use research, agricultural sustainability studies, and agricultural production decision-making. The traditional CDL data distribution channels include paper thematic maps, email data requests, CD/DVD media, and ftp bulk downloading. To effectively and efficiently meet the growing needs of our customers, CropScape (the name invented for a new interactive Web CDL exploring system) was developed to query, visualize, disseminate, and analyze CDL data geospatially through standard geospatial Web services in a publicly accessible online environment. CropScape not only offers the online functionalities of interactive map operations, data customization and downloading, crop acreage statistics, charting and graphing, and multitemporal change analysis as well in an interoperable manner, but also provides Web geoprocessing services such as automatic data delivery and on-demand crop statistics for uses in other applications. This system delivers dynamic user experiences with its comprehensive capabilities in an open geospatial context, and facilitates delivery and analysis of geospatial cropland information for decision support and various research endeavors. More than 17,000 users around the world have visited CropScape in its first 5months of availability.