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1. Extracting Trusted Pixels from Historical Cropland Data Layer Using Crop Rotation Patterns: A Case Study in Nebraska, USA

   https://doi.org/10.1109/Agro-Geoinformatics.2019.8820236  

   Zhang, Chen; Di, Liping; Lin, Li; Guo, Liying (July 2019, 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics))

   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. 

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2. Applying Machine Learning to Cropland Data Layer for Agro-Geoinformation Discovery

   https://doi.org/10.1109/IGARSS47720.2021.9554628  

   Zhang, Chen; Yang, Zhengwei; Di, Liping; Lin, Li; Hao, Pengyu; Guo, Liying (July 2021, 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS)

   The Cropland Data Layer (CDL) is currently the only subfield level high resolution crop-specific land cover data product over the entire conterminous United States (CONUS). It has been widely used in agricultural industry, business decision support, research, and education worldwide. However, CDL data has its limitations. It is an end-of-season land cover map which is not available within growing season. Moreover, CDLs in early years have many misclassified pixels (relatively low accuracy) due to cloud cover and lack of satellite images. This paper will present the studies of using machine learning technique to address these issues in CDL data. Specifically, we will present the design and implementation of a machine learning model for agro-geoinformation discovery from CDL. Several application scenarios of the proposed model, including prediction of crop cover, crop acreage estimation, in-season crop mapping, and refinement of the earlyyear CDL data, are demonstrated and discussed. 

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3. CalCROP21: A Georeferenced multi-spectral dataset of Satellite Imagery and Crop Labels

   https://doi.org/10.1109/BigData52589.2021.9671569  

   Ghosh, Rahul; Ravirathinam, Praveen; Jia, Xiaowei; Khandelwal, Ankush; Mulla, David; Kumar, Vipin (December 2021, 2021 IEEE International Conference on Big Data (Big Data))

   Mapping and monitoring crops is a key step towards the sustainable intensification of agriculture and addressing global food security. A dataset like ImageNet that revolutionized computer vision applications can accelerate the development of novel crop mapping techniques. Currently, the United States Department of Agriculture (USDA) annually releases the Cropland Data Layer (CDL) which contains crop labels at 30m resolution for the entire United States of America. While CDL is state of the art and is widely used for a number of agricultural applications, it has a number of limitations (e.g., pixelated errors, labels carried over from previous years, and errors in the classification of minor crops). In this work, we create a new semantic segmentation benchmark dataset, which we call CalCROP21, for the diverse crops in the Central Valley region of California at 10m spatial resolution using a Google Earth Engine based robust image processing pipeline and a novel attention-based spatio-temporal semantic segmentation algorithm STATT. STATT uses re-sampled (interpolated) CDL labels for training but is able to generate a better prediction than CDL by leveraging spatial and temporal patterns in Sentinel2 multi-spectral image series to effectively capture phenologic differences amongst crops and uses attention to reduce the impact of clouds and other atmospheric disturbances. We also present a comprehensive evaluation to show that STATT has significantly better results when compared to the resampled CDL labels. We have released the dataset and the processing pipeline code for generating the benchmark dataset. 

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4. Crop Field Boundary Delineation using Historical Crop Rotation Pattern

   https://doi.org/10.1109/Agro-Geoinformatics.2019.8820240  

   Rahman, Md. Shahinoor; Di, Liping; Yu, Zhiqi; Yu, Eugene G.; Tang, Junmei; Lin, Li; Zhang, Chen; Gaigalas, Juozas (July 2019, 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics))

   GIS data layer on crop field boundary has many applications in agricultural research, ecosystem study, crop monitoring, and land management. Crop field boundary mapping through field survey is not time and cost effective for vast agriculture areas. Onscreen digitization on fine-resolution satellite image is also labor-intensive and error-prone. The recent development in image segmentation based on their spectral characteristics is promising for cropland boundary detection. However, processing of large volume multi-band satellite images often required high-performance computation systems. This study utilized crop rotation information for the delineation of field boundaries. In this study, crop field boundaries of Iowa in the United States are extracted using multi-year (2007-2018) CDL data. The process is simple compared to boundary extraction from multi-date remote sensing data. Although this process was unable to distinguish some adjacent fields, the overall accuracy is promising. Utilization of advanced geoprocessing algorithms and tools on polygon correction may improve the result significantly. Extracted field boundaries are validated by superimposing on fine resolution Google Earth images. The result shows that crop field boundaries can easily be extracted with reasonable accuracy using crop rotation information. 

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5. Remote Sensing Based Crop Monitoring Techniques: A Case Study for the Navajo Nation

   https://doi.org/10.5194/isprs-archives-XLVIII-M-5-2024-165-2025  

   Thangavel, Varatharajaperumal; Nagarajan, Sudhagar; Arockiasamy, Madasamy; Khan, Md Tarique; Sklivanitis, George (January 2025, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences)

   Abstract. Agriculture plays a major role in eradicating poverty, promoting prosperity, and nourishing a projected 10 billion people by 2050 globally. In a changing climate, achieving optimal agricultural yields requires a deeper understanding of available natural resources and crops. This is especially important for places like the Navajo Nation, which faces significant challenges in food supply chain management due to various factors such as water demand, water quality, and insufficient information about land fertility and crops timings/seasons. Additionally, it is the largest Native American reservation in the U.S. It covers 27,425 square miles across Arizona, Utah, and New Mexico and has a population of 165,158 people, according to the 2020 census. Agriculture has been a key part of life in the Navajo Nation since the late 19th and early 20th centuries, playing a big role in the region’s development and stability. However, the lack of knowledge about decisions and actions during the crop growing season has resulted in lower crop productivity, as evidenced by the USDA statistical report for the Navajo Nation in 2012 and 2017. To support farmers by providing better decision-making and actionable insights, high-resolution, open-source Sentinel-2 satellite images are being used to develop advanced crop mapping techniques for identifying the spatial extent of various agricultural crops in the Navajo Nation. To address this, a collection of research papers was reviewed, leading to the development of a new methodology for analysing Sentinel-2 data from the 2017 and 2023 growing seasons within the Navajo Nation. The collected data was pre-processed by creating monthly median composites of surface reflectance to remove noise and enhance the results more accurately. After preprocessing, spectral indices were calculated from the spectral bands, including NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), GCVI (Green Chlorophyll Vegetation Index), and LSWI (Land Surface Water Index), to differentiate the crops more precisely. The training datasets were obtained from the USDA’s Crop Data Layer (CDL) and split into 80% for training and 20% for validating the Random Forest supervised classification algorithm. The classification resulted in an accuracy of 80%. Finally, the accuracy of the results was compared with independent ground truth data. This research identifies notable discrepancies between the CDL data and the Navajo Nation agricultural census statistical report, particularly in estimating corn acreage for the Chinle and Fort Defiance agencies. Ultimately this approach information is used to provide actionable insights to Navajo Nation farmers. 

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