Remote Sensing Commons^™

Long-Term Variability Of Air Quality And Greenhouse Gas Emissions From Rice Crop Burning In Punjab During 2012–2020, Harsimranjit Kaur Romana, Dericks Praise Shukla, Ramesh P. Singh

Mathematics, Physics, and Computer Science Faculty Articles and Research

Punjab, India's primary rice and wheat production hub, has witnessed rapid expansion of paddy cultivation over the past two decades, driven by minimum support price incentives, changes in government policies, alignment of sowing with the monsoon season and the adoption of high-yielding varieties. This transition has intensified groundwater extraction and shortened the fallow period between rabi and kharif crop seasons, reducing the window between rice harvesting and wheat sowing, leading to widespread open-field burning of rice residue and recurrent post-monsoon air-quality deterioration across the Indo-Gangetic Plain. Despite numerous short-term or single-pollutant assessments, a spatially resolved, multi-pollutant and multi-decadal evaluation linking …

High Spatiotemporal Resolution Monitoring Of Crop Water Stress Across The Contiguous United States Using Harmonized Landsat And Sentinel-2 Data, Na Chen, Yanlei Feng, Na Wang, Jevan Yu, Mohammad Reza Alizadeh, Yifeng Cui, Ning Ye, Wenzhe Jiao, Joshua B. Fisher, César Terrer

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Accurate and timely monitoring of crop water stress is essential for efficient agricultural water management, ultimately maintaining and improving crop productivity. While Landsat has been used for this purpose, its temporal resolution hampers timely detection of crop water stress. The recently released Harmonized Landsat and Sentinel-2 Version 2.0 dataset, which enables a higher-frequency time series of satellite observations (2–3 days, 30 m), offers a promising solution to this challenge. However, its potential for crop stress monitoring remained unexplored. In this study, we utilized 923 HLS satellite tiles to assess crop water stress across the contiguous United States (CONUS). Crop water …

Unraveling Crop Nitrogen-Water Dynamics With Hyperspectral-Thermal Sensing In Northern Central Valley, California, Shahryar Fazli, Surendra Maharjan, Wenzhao Li, Joshua B. Fisher, Rejoice Thomas, Fernando Romero Galvan, Gabriela Shirkey, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Ensuring global food security in the face of climate change requires optimizing crop water use and nutrient management. This study investigates the relationship between canopy nitrogen (N) and evapotranspiration (ET) across sunflower, rice, walnut, alfalfa, and plum crops using advanced remote sensing technologies. High-resolution hyperspectral data from NASAs Earth Surface Mineral Dust Source Investigation (EMIT) and thermal multispectral data from the Landsat-based OpenET system were analyzed over 1,135 km² in California. Regression analysis revealed strong spatial association between canopy N and ET for sunflower (R² = 0.82), rice (R² = 0.71), and walnut (R² = 0.68), …

High Spatial Resolution Crop Type And Land Use Land Cover Classification Without Labels: A Framework Using Multi-Temporal Planetscope Images And Variational Bayesian Gaussian Mixture Model, Minh Tri Le

Mathematics, Physics, and Computer Science Faculty Articles and Research

Previous studies often combined high spatial resolution data (e.g., PlanetScope) with wider spectral range data (e.g., Sentinel-2) and relied on supervised classification methods to produce land use and land cover (LULC) maps. This study proposed a new unsupervised framework to generate crop type and LULC maps at high spatial resolution (< 5 m) using available PlanetScope data solely without requiring ground truths. We used PlanetScope surface reflectance images and their derived spectral indices during growing seasons to create multi-temporal input features, which were fed into an unsupervised Variational Bayesian Gaussian Mixture Model (VBGMM). The VBGMM, unlike the traditional unsupervised classification methods, (1) first estimated optimal parameters that are most suitable based on the input features and then (2) assigned pixels to the cluster with maximum posteriori probability of a mixture of several Gaussian distributions. The crop type and LULC maps were then generated by labeling the derived clusters using the best possible assignment method, referring to the existing crop type or LULC products. We evaluated the produced PlanetScope-based crop type and LULC maps using true labels, corresponding reference maps, and other unsupervised classification methods. The results demonstrated the robustness and effectiveness of the proposed framework in mapping crop types and LULC at 3–5 m pixels across various ecosystems, climate zones, and human-managed landscapes. The spatial patterns of PlanetScope-based maps were (1) highly comparable with all the reference datasets at 10–30 m spatial resolution and (2) better than the traditional GMM and K-means clustering methods. The VBGMM produced classification maps with high confidence, yielding class probabilities above 0.9 for over 90 % of all study areas. The area percentage for all crop type and LULC classes agreed well with their reference maps, with R2 of 0.95 and RMSE of 1.04 %. The confusion matrices using true labels indicated that PlanetScope-based maps achieved a higher overall accuracy of 84 % than the supervised referenced maps of 81 %. Besides, the entropy comparison showed that our framework-based maps were better at capturing fine-scale features such as developed areas within cities that commonly mix with open space and vegetation, deforestation and cropland conversion in South America, smallholder croplands in Africa and Asia, and generating homogeneous crop fields in North America. This study further highlighted the potential for future research to implement our proposed framework to generate timely and extensive annotated datasets, which can be used for operationally training machine learning models to map crop types and LULC, track deforestation, detect wildfires, and delineate flooded areas at larger scales using medium/coarse Earth observations.

Remote Sensing - Based Mapping And Analysis Of Winter Cover Crop Adoption For Sustainable Agriculture, Minnehaha County, Belinda Buechler

Electronic Theses and Dissertations

Winter cover crops planted by farmers such as cereal rye, crimson clover, radishes, hairy vetch, and winter wheat are used to conserve and protect the soil during winter. These crops offer numerous benefits such as improving soil health, reducing erosion, fixing nitrogen, increasing carbon sequestration, and weed suppression. Over time, winter cover cropping has been recognized as a sustainable agricultural practice and has gained attention from Federal and State conservation programs, farmers, and non-governmental organizations. Due to their vital benefits, agencies such as the United States Department of Agriculture (USDA), Natural Resources Conversation Services (NRCS) have partnered with cost-share programs …

A Comparative Analysis Of Openet For Evaluating Evapotranspiration In California Almond Orchards, Kyle Knipper, Martha Anderson, Nicholas Bambach, Forrest Melton, Zac Ellis, Yun Yang, John Volk, Andrew J. Mcelrone, William Kustas, Matthew Roby, Will Carrara, Sebastian Castro, Ayse Kilic, Joshua B. Fisher, Anderson Ruhoff, Gabriel B. Senay, Charles Morton, Sebastian Saa, Richard G. Allen

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

The almond industry in California faces water management challenges that are being exacerbated by droughts, climate change, and groundwater sustainability legislation. The Tree-crop Remote sensing of Evapotranspiration eXperiment (T-REX) aims to explore opportunities to improve precision irrigation management for woody perennial cropping systems. Almond orchards in the California Central Valley were equipped with eddy covariance flux measurements to evaluate satellite remote sensing-based evapotranspiration (RSET) models. OpenET provides high-resolution (30-m spatial and daily temporal) RSET data, synthesizing decades of research for practical water management. This study provides an evaluation of OpenET performance at six almond sites covering a large range in …

Agricultural Groundcover Update April 2024, Justin Laycock

Natural resources published reports

• In April, over 12% (1,876,000 ha) of the arable farmland in the south-west of Western Australia had less than 50% vegetative groundcover, which is inadequate to prevent wind erosion.
• Northern grainbelt had the highest risk of wind erosion and over 26% of this farmland had inadequate groundcover, predominantly found on landscapes known for sandy soils.
• About 1.5% (238,900 ha) of arable land had a high to very high risk of wind erosion because groundcover was less than 30%.

Agricultural Groundcover Update May 2024, Justin Laycock

Natural resources published reports

• In May, over 9% (1,410,000 ha) of the arable farmland in the south-west of Western Australia had less than 50% vegetative groundcover, which is inadequate to prevent wind erosion.
• Northern grainbelt had the highest risk of wind erosion and over 26% of this farmland had inadequate groundcover, predominantly found on landscapes known for sandy soils.
• About 1.3% (208,900 ha) of arable land had a high to very high risk of wind erosion because groundcover was less than 30%. Half of this land was in the West Midlands Ag Soil Zone.

Agricultural Groundcover Update March 2024, Justin Laycock

Natural resources published reports

• In March, over 10% (1,577,000 ha) of the arable farmland in the south-west of Western Australia had less than 50% vegetative groundcover, which is inadequate to prevent wind erosion.

• The northern grainbelt had the highest risk of wind erosion and over 20% of this farmland had inadequate groundcover.

• About 1.3% (191,000 ha) of arable land had a high to very high risk of wind erosion because groundcover was less than 30%.

Microwave Emission Model Parameter Tuning For Surface Soil Moisture Retrieval Using Uav-Mounted Dual Polarization L-Band Radiometer, Santiago Hoyos Echeverri

Open Access Theses & Dissertations

Surface soil moisture retrieval from L-band brightness temperature has been developed for the past decades due to multiple beneficial characteristics of 1-2 GHz frequency bands for remote sensing of the environment. Numerous microwave emission models have been proposed for tower and satellite-based operations with successful retrieval of surface soil moisture and vegetation water content. As a result of the development of cost-effective and low-mass microwave L-band radiometers such as the Portable L-band Radiometer (PoLRa), surface soil moisture surveying traditionally developed by satellite missions SMOS and SMAP can now be developed at local scales, bringing these operations to commercial small unmanned …

Agricultural Groundcover Update February 2024, Justin Laycock

Natural resources published reports

• About 92% of the grainbelt had adequate (more than 50%) vegetative groundcover to prevent wind erosion in February 2024.

• Nearly 8% of the grainbelt (1,193,400 ha) had less than 50% groundcover, which is inadequate to prevent wind erosion.

• The northern grainbelt had the highest risk of wind erosion and 16.5% of this farmland had inadequate groundcover.

• Less than 0.7% of the grainbelt had a high to very high risk of wind erosion because groundcover was less than 30%.

Agricultural Groundcover Update January 2024, Justin Laycock

Natural resources published reports

Summary

• About 94% of the grainbelt had adequate (more than 50%) vegetative groundcover to prevent wind erosion in January 2024.
• In the northern half of the grainbelt, a larger-than-average area has 51–60% groundcover, which is expected to decrease to below 50% over the coming months.
• Just under 6% of the grainbelt (855,000 ha) had less than 50% groundcover, which is inadequate to prevent wind erosion. West Midlands Ag Soil Zone had the highest risk of wind erosion and 14.5% of this farmland had inadequate groundcover.
• Less than 0.5% of the grainbelt had a high to very high risk of wind …

Agricultural Groundcover Update December 2023, Justin Laycock

Natural resources published reports

Summary

• About 96% of the grainbelt had adequate vegetative groundcover (more than 50%) to prevent wind erosion in December 2023.
• In the northern half of the grainbelt, a larger-than-average area has 51–60% groundcover, which is expected to decrease to below 50% over the summer.
• Just under 4% of the grainbelt (553,000 ha) had less than 50% groundcover, which is inadequate to prevent wind erosion. West Midlands Ag Soil Zone had the highest risk of wind erosion and 11.4% of this farmland had inadequate groundcover.
• Less than 0.5% of the grainbelt had a high to very high risk of wind erosion …

A Tale Of Two Working Landscapes, Sage C. Sutcliffe

Graduate Student Theses, Dissertations, & Professional Papers

No abstract provided.

Agricultural Groundcover Update November 2023, Justin Laycock

Natural resources published reports

Summary

• About 98% of the grainbelt had adequate (more than 50%) vegetative groundcover to prevent wind erosion in November 2023. This amount of groundcover is normal for the middle of harvest.
• In the northern half of the grainbelt, a larger-than-average area had 51–60% groundcover, which is expected to decrease to below 50% over summer.
• Just over 2% of the grainbelt (324,000 ha) had less than 50% groundcover, which is inadequate to prevent wind erosion. Mullewa to Morawa Ag Soil Zone had the highest risk of wind erosion and 9.7% of this farmland had inadequate groundcover.
• Less than 0.5% of the …

Agricultural Groundcover Update October 2023, Justin Laycock

Natural resources published reports

Summary

• About 98% of the grainbelt had adequate vegetative groundcover (more than 50%) to prevent wind erosion in October 2023. This amount of groundcover is normal at the end of spring and pre-harvest in most areas.
• There was a larger than average area with 51–60% groundcover, and groundcover in these areas is expected to reduce over summer to below 50%.
• About 2% of the grainbelt (293,000 ha) had less than 50% groundcover, which is inadequate to prevent wind erosion. Mullewa to Morawa Ag Soil Zone had the highest risk of wind erosion and 8% of this farmland had inadequate groundcover. …

Analyzing The Adoption, Cropping Rotation, And Impact Of Winter Cover Crops In The Mississippi Alluvial Plain (Map) Region Through Remote Sensing Technologies, Zobaer Ahmed

Graduate Theses and Dissertations

This dissertation explores the application of remote sensing technologies in conservation agriculture, specifically focusing on identifying and mapping winter cover crops and assessing voluntary cover crop adoption and cropping patterns in the Arkansas portion of the Mississippi Alluvial Plain (MAP). In the first chapter, a systematic review using the PRISMA methodology examines the last 30 years of thematic research, development, and trends in remote sensing applied to conservation agriculture from a global perspective. The review uncovers a growing interest in remote sensing-based research in conservation agriculture and emphasizes the necessity for further studies dedicated to conservation practices. Among the 68 …

Mapping California Rice Using Optical And Sar Data Fusion With Phenological Features In Google Earth Engine, Li Wenzhao, Hesham El-Askary, Daniele C. Struppa

Mathematics, Physics, and Computer Science Faculty Articles and Research

California, known for its diverse agriculture, is also a major producer of rice, especially in its northern regions in Sacramento River Valley. Traditional methods, predominantly reliant on optical-based satellite imagery, encounter limitations due to atmospheric interference and sensor resolution. The ability of Synthetic Aperture Radar (SAR) to penetrate atmospheric distortions and exhibit high sensitivity to vegetation structure presents a distinct advantage over optical-based methods. Utilizing Optical and SAR data fusion, this study advances the enhanced pixel-based phenological feature composite (Eppf) method using SVM classification algorithm, which can track phenological changes and patterns, providing valuable insights for agricultural planning and management. …

Land And Water Resources For Irrigated Agriculture In The Pilbara, Paul Galloway, John A. Simons, Karen Holmes, Dennis Van Gool

Resource management technical reports

This report documents the procedures used to identify suitable locations for irrigation development in the Pilbara region. It is the first study to investigate the potential for irrigated agriculture across the Pilbara. We used a desktop analysis to ascertain water availability and spatial data modelling to determine the potential of the land and soil resource to support irrigated agriculture. This study was part of the Pilbara Hinterland Agricultural Development Initiative (PHADI).

We used existing rangeland land inventory information augmented with digital spatial environmental data, in a process known as map disaggregation, to create soil and landform maps that had a …

Extreme Development Of Dragon Fruit Agriculture With Nighttime Lighting In Southern Vietnam, Shenyue Jia, Son V. Nghiem, Seung-Hee Kim, Laura Krauser, Andrea E. Gaughan, Forest R. Stevens, Menas Kafatos, Khanh D. Ngo

Institute for ECHO Faculty Books and Book Chapters

Dragon fruit is widely grown in Southeast Asia and other tropical or subtropical regions. As a high-value cash crop ideal for exportation, dragon fruit cultivation has boomed during the past decade in southern Vietnam. Light supplementing during the winter months using artificial lighting sources is a widely adopted cultivation technique to boost productivity in the major dragon fruit planting regions of Vietnam. The application of electric lighting at night leads to a significant increase of nighttime light (NTL) observable by satellite sensors. The strong seasonality signal of NTL in dragon fruit cultivation enables identifying dragon fruit plantations using NTL images. …

Spatial-Temporal Modeling Of Agricultural Water Surface Features In Northeastern Arkansas., Daniel Dewayne Shults

Student Theses and Dissertations

The Mississippi River Valley Alluvial Aquifer (MRVA) has been the primary source of irrigation water in Northeastern Arkansas since the early 1900s. Over time the MRVA water level has declined due to over-pumping of farm wells and water restrictive geology. The aquifer is expected to continue declining but with water conservation strategies in place, this vital resource could be preserved for future farmers. This thesis investigated using remotely sensed data to assess landforms associated with agricultural water conservation across Northeastern Arkansas. The remotely sensed data proved to be useful at creating temporal assessments of pre-existing irrigation reservoirs, identifying new irrigation …

Economically Optimal Nitrogen Side-Dressing Based On Vegetation Indices From Satellite Images Through On-Farm Experiments, Qianqian Du

Department of Agricultural Economics: Dissertations, Theses, and Student Research

Optimal N fertilizer rates for corn (Zea mays L.) vary substantially within and among fields, and by corn growth stages. Improving N side-dressing management can improve fertilizer use efficiency, farmers’ profitability, and the sustainability of crop production. The objective of this study is to introduce a framework along with a methodology that can find the site-specific economically optimal N rates (EONRs) within one field for a particular growing season. An on-farm experiment was conducted in the 2019 corn growing season. A base N rate was applied uniformly on the field. NDRE images from the Sentinel-2 satellite were observed during …

Estimation Of Spatial Change In Cropland Area And Evaluation Of Irrigation Performance In Imperial Valley Using Remotely Sensed Data, Usha Poudel

UNLV Theses, Dissertations, Professional Papers, and Capstones

The Imperial Valley (IV) in the US is an extensively irrigated agricultural region, which includes multiple crops changing on an annual and semiannual basis. The valley is facing grave concerns about water management due to its semi-arid environment, water intensive crops, and limited water supply. A simple, inexpensive, and repeatable method to detect changes in cropping patterns may assist irrigation managers to understand crop diversification and associated consumptive use. In addition, a spatial assessment of existing water irrigation system performance and productivity is crucial to benchmark and improve current water management strategies. This thesis estimates the spatial pattern of change …

Spatiotemporal Observations Of Water Stress In Kansas Winter Wheat And Corn From Remotely Sensed Evapotranspiration And Ndwi, Lindi Diane Oyler

Masters Theses

"Optimizing water use is a growing concern, especially in agricultural communities where water use is high. An important challenge in agricultural water optimization is knowing when and where crop water stress is occurring, particularly on large scales where in-situ measurements are no longer practical to obtain. In an effort to combat this challenge, this study utilizes remotely sensed evapotranspiration (ET) and Normalized Difference Water Index (NDWI) to evaluate the responses of integrated satellite datasets to water-stressed conditions over fields of irrigated corn, irrigated winter wheat, and rainfed winter wheat from 2007 to 2017 in southwestern Kansas. Using two different ET …

Augmenting Land Cover/Land Use Classification By Incorporating Information From Land Surface Phenology: An Application To Quantify Recent Cropland Expansion In South Dakota, Lan Hoang Nguyen

Electronic Theses and Dissertations

Understanding rapid land change in the U.S. NGP region is not only critical for management and conservation of prairie habitats and ecosystem services, but also for projecting production of crops and biofuels and the impacts of land conversion on water quality and rural transportation infrastructure. Hence, it raises the need for an LCLU dataset with good spatiotemporal coverage as well as consistent accuracy through time to enable change analysis. This dissertation aims (1) to develop a novel classification method, which utilizes time series images from comparable sensors, from the perspective of land surface phenology, and (2) to apply the land …

Utilizing Large Scale Datasets To Evaluate Aspects Of A Sustainable Bioeconomy, Gwanseon Kim

Theses and Dissertations--Agricultural Economics

This dissertation combines large scale datasets to evaluate crop prediction, land values, and consumption of a crop being considered to advance a sustainable bioeconomy. In chapter 2, we propose a novel application of the multinomial logit (MNL) model to estimate the conditional transition probabilities of crop choice for the state of Kentucky. Utilizing the recovered transition probabilities the forecast distributions of total acreages for alfalfa, corn, soybeans, tobacco, and wheat produced in the state from 2010 to 2015 can be recovered. The Cropland Data Layer is merged with the Common Land Unit dataset to allow for the identification of crop …

Utilization Of Various Methods And A Landsat Ndvi/Google Earth Engine Product For Classifying Irrigated Land Cover, Andrew Nemecek

Graduate Student Theses, Dissertations, & Professional Papers

Methods for classifying irrigated land cover are often complex and not quickly reproducible. Further, moderate resolution time-series datasets have been consistently utilized to produce irrigated land cover products over the past decade, and the body of irrigation classification literature contains no examples of subclassification of irrigated land cover by irrigation method. Creation of geospatial irrigated land cover products with higher resolution datasets could improve reliability, and subclassification of irrigation by method could provide better information for hydrologists and climatologists attempting to model the role of irrigation in the surface-ground water cycle and the water-energy balance. This study summarizes a simple, …

Uas-Based Remote Sensing For Weed Identification And Cover Crop Termination Determination, Shailaja Vemula

Student Theses and Dissertations

This project aimed at demonstrating the utility of using unmanned aerial system (UAS) based remote sensing to assess percentage weed coverage and cash crop vegetative coverage development corresponding to cover crop (cereal rye) termination at different growth stages. A UAS equipped with an RGB (visible bands) camera was used to acquire aerial imagery of cover crop integrated soybean plots. The specific objectives were: 1) to discriminate crop and weed vegetation based on (a) spectral information and (b) location relative to the crop rows; (2) to verify optimum timing for cover crop termination using vegetative cover development based on visible spectroscopy …

Land Surface Phenology And Seasonality Using Cool Earthlight In Croplands Of Eastern Africa And The Linkages To Crop Production, Woubet G. Alemu, Geoffrey M. Henebry

GSCE Faculty Publications

Across Eastern Africa, croplands cover 45 million ha. The regional economy is heavily dependent on small holder traditional rain-fed peasant agriculture (up to 90%), which is vulnerable to extreme weather events such as drought and floods that leads to food insecurity. Agricultural production in the region is moisture limited. Weather station data are scarce and access is limited, while optical satellite data are obscured by heavy clouds limiting their value to study cropland dynamics. Here, we characterized cropland dynamics in Eastern Africa for 2003–2015 using precipitation data from Tropical Rainfall Measuring Mission (TRMM) and a passive microwave dataset of land …

A Global Analysis Of Sentinel-2a, Sentinel-2b And Landsat-8 Data Revisit Intervals And Implications For Terrestrial Monitoring, Jian Li, David P. Roy

GSCE Faculty Publications

Combination of different satellite data will provide increased opportunities for more frequent cloud-free surface observations due to variable cloud cover at the different satellite overpass times and dates. Satellite data from the polar-orbiting Landsat-8 (launched 2013), Sentinel-2A (launched 2015) and Sentinel-2B (launched 2017) sensors offer 10 m to 30 m multi-spectral global coverage. Together, they advance the virtual constellation paradigm for mid-resolution land imaging. In this study, a global analysis of Landsat-8, Sentinel-2A and Sentinel-2B metadata obtained from the committee on Earth Observation Satellite (CEOS) Visualization Environment (COVE) tool for 2016 is presented. A global equal area projection grid defined …