New Generation Hyperspectral Data From DESIS Compared to High Spatial Resolution PlanetScope Data for Crop Type Classification

Aneece, Itiya; Foley, Daniel J.; Thenkabail, Prasad S.; Teluguntla, Pardhasaradhi

doi:10.1109/jstars.2022.3204223

Cited by 13 publications

(2 citation statements)

References 105 publications

(134 reference statements)

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“…The spatial resolution is approximately 30 m at the pixel scale. A study by Aneece et al [ 250 ] compared agricultural image classification data from the Central Valley, California in 2020, between DESIS 30 m (hyperspectral) and PlanetScope’s Dove-R (4-band) 3–4 m high-spatial-resolution data. This one-on-one comparison between hyperspectral DESIS vs. high-spatial-resolution Dove data revealed that the hyperspectral images outperformed the high-spatial-resolution data.…”

Section: The International Space Station (Iss) Hosts Experimental Ins...mentioning

confidence: 99%

Current and Near-Term Earth-Observing Environmental Satellites, Their Missions, Characteristics, Instruments, and Applications

Ustin,

Middleton

2024

Sensors

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Among the essential tools to address global environmental information requirements are the Earth-Observing (EO) satellites with free and open data access. This paper reviews those EO satellites from international space programs that already, or will in the next decade or so, provide essential data of importance to the environmental sciences that describe Earth’s status. We summarize factors distinguishing those pioneering satellites placed in space over the past half century, and their links to modern ones, and the changing priorities for spaceborne instruments and platforms. We illustrate the broad sweep of instrument technologies useful for observing different aspects of the physio-biological aspects of the Earth’s surface, spanning wavelengths from the UV-A at 380 nanometers to microwave and radar out to 1 m. We provide a background on the technical specifications of each mission and its primary instrument(s), the types of data collected, and examples of applications that illustrate these observations. We provide websites for additional mission details of each instrument, the history or context behind their measurements, and additional details about their instrument design, specifications, and measurements.

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Section: The International Space Station (Iss) Hosts Experimental Ins...mentioning

confidence: 99%

Current and Near-Term Earth-Observing Environmental Satellites, Their Missions, Characteristics, Instruments, and Applications

Ustin,

Middleton

2024

Sensors

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“…Using satellite imagery [35,36,37,38], aerial photography [39,40,41,42], and drones, remote sensing and image-based models collect spatial and temporal information about crop growth levels. These models [43,44,45] estimate crop yield using techniques such as vegetation indices, image classification, and object detection process.…”

Section: Models Based On Processmentioning

confidence: 99%

Devising of an Efficient Multi-parametric System for Prediction of Crop Yield using Augmented Incremental Machine & Deep Learning Operations

2024

Journal of Electrical Systems

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Agricultural research and management have become increasingly dependent on accurate crop yield forecasting. This paper introduces a comprehensive model that incorporates multiple parameters to predict crop yield with high levels of precision and accuracy. The applications of this research are extensive and consequential. Accurate crop yield forecasts can assist farmers, agronomists, and policymakers with resource allocation, crop selection, and land management decisions. Our model provides valuable insights for optimizing agricultural practices and increasing overall productivity by taking into account key factors such as weather, soil type and fertility, crop variety, farming practices, genetics, satellite imagery & samples. The proposed model contains several internal components, each of which serves an efficient set of distinct functions. Deep Q Learning is used to analyze the impact of weather on crop variety, allowing the model to account for the impact of precipitation, temperature, humidity, Sunlight, and resistance to pests and diseases. Deep Dyna Q Classifier is used to evaluate the impact of soil type, fertility, and farming practices, thus accounting for variations in nutrient availability, irrigation, fertilization, and pest pressure. The crop's genetics are evaluated using Auto Encoders and a VARMA Model, which take into account the impact of inherent traits on productivity. Moreover, relevant spatial information sets are extracted from satellite images using a ResNet101 Model process. The rationale for integrating these internal components is based on their individual strengths and their capacity to capture complex interactions between various parameters. Our model achieves exceptional performance through the utilization of deep learning, reinforcement learning, and statistical modelling. For predicting the yield of Mango, Cotton, Wheat, Bajra, and Rice Paddy crops, the experimental results demonstrate a remarkable AUC of 99.2%, precision exceeding 99.5%, accuracy of 99.8%, recall of 99.4%, and an impressive AUC of 99.2%. In conclusion, our multi-parametric engine provides a robust and effective method for predicting crop yield. Its superior performance is a result of its ability to seamlessly integrate multiple data sources and employ advanced deep learning techniques. This research paves the way for informed agricultural decisionmaking, allowing stakeholders to optimize resource allocation, boost productivity, and ultimately contribute to food security and sustainability levels.

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Precision agriculture in the United States: A comprehensive meta-review inspiring further research, innovation, and adoption

Barbosa Júnior,

Moreira,

Carreira

et al. 2024

Computers and Electronics in Agriculture

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New Generation Hyperspectral Data From DESIS Compared to High Spatial Resolution PlanetScope Data for Crop Type Classification

Cited by 13 publications

References 105 publications

Current and Near-Term Earth-Observing Environmental Satellites, Their Missions, Characteristics, Instruments, and Applications

Current and Near-Term Earth-Observing Environmental Satellites, Their Missions, Characteristics, Instruments, and Applications

Devising of an Efficient Multi-parametric System for Prediction of Crop Yield using Augmented Incremental Machine & Deep Learning Operations

Precision agriculture in the United States: A comprehensive meta-review inspiring further research, innovation, and adoption

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