College of Behavioral & Social Sciences
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The collections in this community comprise faculty research works, as well as graduate theses and dissertations..
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Item Intercomparison of Machine-Learning Methods for Estimating Surface Shortwave and Photosynthetically Active Radiation(MDPI, 2020-01-23) Brown, Meredith G. L.; Skakun, Sergii; He, Tao; Liang, ShunlinSatellite-derived estimates of downward surface shortwave radiation (SSR) and photosynthetically active radiation (PAR) are a part of the surface radiation budget, an essential climate variable (ECV) required by climate and vegetation models. Ground measurements are insufficient for generating long-term, global measurements of surface radiation, primarily due to spatial limitations; however, remotely sensed Earth observations offer freely available, multi-day, global coverage of radiance that can be used to derive SSR and PAR estimates. Satellite-derived SSR and PAR estimates are generated by computing the radiative transfer inversion of top-of-atmosphere (TOA) measurements, and require ancillary data on the atmospheric condition. To reduce computational costs, often the radiative transfer calculations are done offline and large look-up tables (LUTs) are generated to derive estimates more quickly. Recently studies have begun exploring the use of machine-learning techniques, such as neural networks, to try to improve computational efficiency. Here, nine machine-learning methods were tested to model SSR and PAR using minimal input data from the Moderate Resolution Imaging Spectrometer (MODIS) observations at 1 km spatial resolution. The aim was to reduce the input data requirements to create the most robust model possible. The bootstrap aggregated decision tree (Bagged Tree), Gaussian Process Regression, and Neural Network yielded the best results with minimal training data requirements: an 𝑅2 of 0.77, 0.78, and 0.78 respectively, a bias of 0 ± 6, 0 ± 6, and 0 ± 5 W/m2, and an RMSE of 140 ± 7, 135 ± 8, and 138 ± 7 W/m2, respectively, for all-sky condition total surface shortwave radiation and viewing angles less than 55°. Viewing angles above 55° were excluded because the residual analysis showed exponential error growth above 55°. A simple, robust model for estimating SSR and PAR using machine-learning methods is useful for a variety of climate system studies. Future studies may focus on developing high temporal resolution direct and diffuse estimates of SSR and PAR as most current models estimate only total SSR or PAR.Item Remote Sensing of Coconut Trees in Tonga Using Very High Spatial Resolution WorldView-3 Data(MDPI, 2020-09-23) Vermote, Eric F.; Skakun, Sergii; Becker-Reshef, Inbal; Saito, KeikoThis paper presents a simple and efficient image processing method for estimating the number of coconut trees in the Tonga region using very high spatial resolution data (30 cm) in the blue, green, red and near infrared spectral bands acquired by the WorldView-3 sensor. The method is based on the detection of tree shadows and the further analysis to reject false detection using geometrical properties of the derived segments. The algorithm is evaluated by comparing coconut tree counts derived by an expert through photo-interpretation over 57 randomly distributed (4% sampling rate) segments of 200 m × 200 m over the Vaini region of the Tongatapu island. The number of detected trees agreed within 5% versus validation data. The proposed method was also evaluated over the whole Tonga archipelago by comparing satellite-derived estimates to the 2015 agricultural census data—the total tree counts for both Tonga and Tongatapu agreed within 3%.Item Assessing within-Field Corn and Soybean Yield Variability from WorldView-3, Planet, Sentinel-2, and Landsat 8 Satellite Imagery(MDPI, 2021-02-26) Skakun, Sergii; Kalecinski, Natacha I.; Brown, Meredith G. L.; Johnson, David M.; Vermote, Eric F.; Roger, Jean-Claude; Franch, BelenCrop yield monitoring is an important component in agricultural assessment. Multi-spectral remote sensing instruments onboard space-borne platforms such as Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) have shown to be useful for efficiently generating timely and synoptic information on the yield status of crops across regional levels. However, the coarse spatial resolution data inherent to these sensors provides little utility at the management level. Recent satellite imagery collection advances toward finer spatial resolution (down to 1 m) alongside increased observational cadence (near daily) implies information on crops obtainable at field and within-field scales to support farming needs is now possible. To test this premise, we focus on assessing the efficiency of multiple satellite sensors, namely WorldView-3, Planet/Dove-Classic, Sentinel-2, and Landsat 8 (through Harmonized Landsat Sentinel-2 (HLS)), and investigate their spatial, spectral (surface reflectance (SR) and vegetation indices (VIs)), and temporal characteristics to estimate corn and soybean yields at sub-field scales within study sites in the US state of Iowa. Precision yield data as referenced to combine harvesters’ GPS systems were used for validation. We show that imagery spatial resolution of 3 m is critical to explaining 100% of the within-field yield variability for corn and soybean. Our simulation results show that moving to coarser resolution data of 10 m, 20 m, and 30 m reduced the explained variability to 86%, 72%, and 59%, respectively. We show that the most important spectral bands explaining yield variability were green (0.560 μm), red-edge (0.726 μm), and near-infrared (NIR − 0.865 μm). Furthermore, the high temporal frequency of Planet and a combination of Sentinel-2/Landsat 8 (HLS) data allowed for optimal date selection for yield map generation. Overall, we observed mixed performance of satellite-derived models with the coefficient of determination (R2) varying from 0.21 to 0.88 (averaging 0.56) for the 30 m HLS and from 0.09 to 0.77 (averaging 0.30) for 3 m Planet. R2 was lower for fields with higher yields, suggesting saturation of the satellite-collected reflectance features in those cases. Therefore, other biophysical variables, such as soil moisture and evapotranspiration, at similar fine spatial resolutions are likely needed alongside the optical imagery to fully explain the yields.Item Is Soil Bonitet an Adequate Indicator for Agricultural Land Appraisal in Ukraine?(MDPI, 2021-11-02) Shumilo, Leonid; Lavreniuk, Mykola; Skakun, Sergii; Kussul, NataliiaAgriculture land appraisal analysis is an important component of the land market. This task is especially essential for Ukraine, which plans to lift the moratorium on land transactions and legalize farmland sales in 2021. Most post-Soviet countries adopted the notion of a soil bonitet—a quantitative score representing natural soil fertility. This score is also proposed in Ukraine to perform agricultural land appraisals. However, this is a static parameter and does not account for the dynamics of actual crop production on the agricultural lands. Moreover, the bonitet score is not crop-specific. Therefore, in this study, we use maps of bonitet based on the soil map and natural-agricultural districts of Ukraine and crop yields at the village scale to explore the relationships between bonitet values and actual crop production in Ukraine. We found that land appraisal is not correlated with the actual soil bonitet.Item Remote sensing evaluation of winter cover crop springtime performance and the impact of delayed termination(Wiley, 2022-09-16) Thieme, Alison; Hively, W. Dean; Gao, Feng; Jennewein, Jyoti; Mirsky, Steven; Soroka, Alexander; Keppler, Jason; Bradley, Dawn; Skakun, Sergii; McCarty, Gregory W.In 2019, the Maryland Department of Agriculture's Winter Cover Crop Program introduced a delayed termination incentive (after May 1) to promote springtime biomass accumulation. We used satellite imagery calibrated with springtime in situ measurements collected from 2006–2021 (n = 722) to derive biomass estimates for Maryland fields planted to cereal cover crop species (286,200 ha total over two seasons). Cover crop C content remained steady throughout the cover crop growing season (42.6% of biomass), whereas N concentration had an inverse relationship with biomass and ranged from 1.7 to 2.9%. Throughout Maryland, delayed termination fields (n = 19,120; average termination of May 18) were, on average, estimated to accumulate an additional 789 kg of biomass, 15 kg of N, and 336 kg of C per hectare when compared to fields associated with standard termination dates (n = 28,811; average termination of April 16). Over two cover crop seasons (2019–2021), the delayed termination incentive yielded an extra 75,660,000 kg biomass, 1,526,000 kg N, and 32,230,000 kg C across 96,040 hectares. Regularly terminated field incentives cost an average of US$0.10 per kg of biomass and $4.09 per kg of N, with variability associated with agronomic management (species, planting method). Delayed termination fields cost of $0.08 per kg of biomass and $3.51 per kg of N. Late-planted cover crops that were terminated early had minimal environmental benefit, and wheat, which comprised 68% of cover crop area, performed poorly compared with other cereal species. Our findings demonstrate that substantial additional springtime biomass, C, and N accumulation were achieved through the delayed termination incentive.