A remote sensing approach for evaluating regional-scale topsoil loss in the Midwestern United States

2020 ◽  
Author(s):  
Isaac Larsen ◽  
Evan Thaler ◽  
Qian Yu
Keyword(s):  
United States ◽  
Remote Sensing ◽  
High Resolution ◽  
Soil Erosion ◽  
Carbon Cycle ◽  
Soil Loss ◽  
Agricultural Landscapes ◽  
Economic Losses ◽  
Corn Belt ◽  
Midwestern United States

<p>Soil erosion in agricultural landscapes reduces crop yields and influences the global carbon cycle. However, the magnitude of historical topsoil loss remains poorly quantified at large, regional spatial scales, hindering predictions of economic losses to farmers and quantification of the role soil erosion plays in the carbon cycle. We focus on one of the world’s most productive agricultural regions, the Corn Belt of the Midwestern United States and use a novel spectral remote sensing method to map areas of complete topsoil loss in agricultural fields. Using high-resolution satellite images and the association between topsoil loss and topographic curvature, we use high resolution LiDAR topographic data to scale-up soil loss predictions to 3.7x10<sup>5</sup> km<sup>2</sup> of the Corn Belt. Our results indicate 34±12% of the region has completely lost topsoil as a result of agriculturally-accelerated erosion. Soil loss is most prevalent on convex slopes, and hilltops throughout the region are often completely denuded of topsoil indicating that tillage is a major driver of erosion, yet tillage erosion is not simulated in models used to assess soil loss trends in the U.S. We estimate that soil regenerative farming practices could restore 16±4.4 Pg of carbon to the exposed subsoil in the region. Soil regeneration would offset at least $2.5±0.3 billion in annual economic losses to farmers while generating a carbon sink equivalent to 8±3 years of U.S. CO<sub>2</sub> emissions, or ~14% of the global soil carbon lost since the advent of agriculture.  </p>

scholarly journals The extent of soil loss across the US Corn Belt

2021 ◽  
Vol 118 (8) ◽  
pp. e1922375118
Author(s):  
Evan A. Thaler ◽  
Isaac J. Larsen ◽  
Qian Yu
Keyword(s):  
United States ◽  
Soil Erosion ◽  
Soil Loss ◽  
Crop Yields ◽  
Scale Up ◽  
The United States ◽  
Agricultural Landscapes ◽  
Soil Survey ◽  
Economic Losses ◽  
Corn Belt

Soil erosion in agricultural landscapes reduces crop yields, leads to loss of ecosystem services, and influences the global carbon cycle. Despite decades of soil erosion research, the magnitude of historical soil loss remains poorly quantified across large agricultural regions because preagricultural soil data are rare, and it is challenging to extrapolate local-scale erosion observations across time and space. Here we focus on the Corn Belt of the midwestern United States and use a remote-sensing method to map areas in agricultural fields that have no remaining organic carbon-rich A-horizon. We use satellite and LiDAR data to develop a relationship between A-horizon loss and topographic curvature and then use topographic data to scale-up soil loss predictions across 3.9 × 105 km2 of the Corn Belt. Our results indicate that 35 ± 11% of the cultivated area has lost A-horizon soil and that prior estimates of soil degradation from soil survey-based methods have significantly underestimated A-horizon soil loss. Convex hilltops throughout the region are often completely denuded of A-horizon soil. The association between soil loss and convex topography indicates that tillage-induced erosion is an important driver of soil loss, yet tillage erosion is not simulated in models used to assess nationwide soil loss trends in the United States. We estimate that A-horizon loss decreases crop yields by 6 ± 2%, causing $2.8 ± $0.9 billion in annual economic losses. Regionally, we estimate 1.4 ± 0.5 Pg of carbon have been removed from hillslopes by erosion of the A-horizon, much of which likely remains buried in depositional areas within the fields.

Fungicide efficacy on tar spot and yield of corn in the Midwestern United States

2022 ◽  
Author(s):  
Darcy E. P. Telenko ◽  
Martin I. Chilvers ◽  
Adam Byrne ◽  
Jill Check ◽  
Camila Rocco Da Silva ◽  
...  
Keyword(s):  
United States ◽  
Growth Stage ◽  
Field Trials ◽  
Corn Belt ◽  
Yield Losses ◽  
Single Application ◽  
Midwestern United States ◽  
Tar Spot ◽  
Significant Yield ◽  
Foliar Fungicide

Tar spot of corn caused by Phyllachora maydis has recently led to significant yield losses in the eastern corn belt of the Midwestern United States. Foliar fungicides containing quinone outside inhibitors(QoI), demethylation inhibitors(DMI), and succinate dehydrogenase inhibitors(SDHI) are commonly used to manage foliar diseases in corn. To mitigate the losses from tar spot thirteen foliar fungicides containing single or multiple modes of action (MOA/FRAC groups) were applied at their recommended rates in a single application at the standard tassel/silk growth stage timing to evaluate their efficacy against tar spot in a total of eight field trials in Illinois, Indiana, Michigan, and Wisconsin during 2019 and 2020. The single MOA fungicides included either a QoI or DMI. The dual MOA fungicides included a DMI with either a QoI or SDHI, and fungicides containing three MOAs included a QoI, DMI, and SDHI. Tar spot severity estimated as the percentage of leaf area covered by P. maydis stroma of the non-treated control at dent growth stage ranged from 1.6 to 23.3% on the ear leaf. Averaged across eight field trials all foliar fungicide treatments reduced tar spot severity, but only prothioconazole+trifloxystrobin, mefentrifluconazole+pyraclostrobin+fluxapyroxad, and mefentrifluconazole+pyraclostrobin significantly increased yield over the non-treated control. When comparing fungicide treatments by the number of MOAs foliar fungicide products that had two or three MOAs decreased tar spot severity over not treating and products with one MOA. The fungicide group that contained all three MOAs significantly increased yield over not treating with a fungicide or using a single MOA.

scholarly journals Estimation of soil loss using remote sensing and GIS-based universal soil loss equation in northern catchment of Lake Tana Sub-basin, Upper Blue Nile Basin, Northwest Ethiopia

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Veera Narayana Balabathina ◽  
R. P. Raju ◽  
Wuletaw Mulualem ◽  
Gedefaw Tadele
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Soil Erosion ◽  
Soil Loss ◽  
Erosion Rate ◽  
Soil Erodibility ◽  
Lake Tana ◽  
Erosion Rates ◽  
Soil Erosion Rate ◽  
Soil Loss Equation

Abstract Background Soil erosion is one of the major environmental challenges and has a significant impact on potential land productivity and food security in many highland regions of Ethiopia. Quantifying and identifying the spatial patterns of soil erosion is important for management. The present study aims to estimate soil erosion by water in the Northern catchment of Lake Tana basin in the NW highlands of Ethiopia. The estimations are based on available data through the application of the Universal Soil Loss Equation integrated with Geographic Information System and remote sensing technologies. The study further explored the effects of land use and land cover, topography, soil erodibility, and drainage density on soil erosion rate in the catchment. Results The total estimated soil loss in the catchment was 1,705,370 tons per year and the mean erosion rate was 37.89 t ha−1 year−1, with a standard deviation of 59.2 t ha−1 year−1. The average annual soil erosion rare for the sub-catchments Derma, Megech, Gumara, Garno, and Gabi Kura were estimated at 46.8, 40.9, 30.9, 30.0, and 29.7 t ha−1 year−1, respectively. Based on estimated erosion rates in the catchment, the grid cells were divided into five different erosion severity classes: very low, low, moderate, high and extreme. The soil erosion severity map showed about 58.9% of the area was in very low erosion potential (0–1 t ha−1 year−1) that contributes only 1.1% of the total soil loss, while 12.4% of the areas (36,617 ha) were in high and extreme erosion potential with erosion rates of 10 t ha−1 year−1 or more that contributed about 82.1% of the total soil loss in the catchment which should be a high priority. Areas with high to extreme erosion severity classes were mostly found in Megech, Gumero and Garno sub-catchments. Results of Multiple linear regression analysis showed a relationship between soil erosion rate (A) and USLE factors that soil erosion rate was most sensitive to the topographic factor (LS) followed by the support practice (P), soil erodibility (K), crop management (C) and rainfall erosivity factor (R). Barenland showed the most severe erosion, followed by croplands and plantation forests in the catchment. Conclusions Use of the erosion severity classes coupled with various individual factors can help to understand the primary processes affecting erosion and spatial patterns in the catchment. This could be used for the site-specific implementation of effective soil conservation practices and land use plans targeted in erosion-prone locations to control soil erosion.

scholarly journals A Remote Sensing Based Method to Detect Soil Erosion in Forests

2019 ◽  
Vol 11 (5) ◽  
pp. 513 ◽  
Author(s):  
Hanqiu Xu ◽  
Xiujuan Hu ◽  
Huade Guan ◽  
Bobo Zhang ◽  
Meiya Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Erosion ◽  
Soil Loss ◽  
Southern China ◽  
Red Soil ◽  
Forest Area ◽  
Landsat 8 ◽  
Large Spatial Scale ◽  
Five Factors

Rainwater-induced soil erosion occurring in the forest is a special phenomenon of soil erosion in many red soil areas. Detection of such soil erosion is essential for developing land management to reduce soil loss in areas including southern China and other red soil regions of the world. Remotely sensed canopy cover is often used to determine the potential of soil erosion over a large spatial scale, which, however, becomes less useful in forest areas. This study proposes a new remote sensing method to detect soil erosion under forest canopy and presents a case study in a forest area in southern China. Five factors that are closely related to soil erosion in forest were used as discriminators to develop the model. These factors include fractional vegetation coverage, nitrogen reflectance index, yellow leaf index, bare soil index and slope. They quantitatively represent vegetation density, vegetation health status, soil exposure intensity and terrain steepness that are considered relevant to forest soil erosion. These five factors can all be derived from remote sensing imagery based on related thematic indices or algorithms. The five factors were integrated to create the soil erosion under forest model (SEUFM) through Principal Components Analysis (PCA) or a multiplication method. The case study in the forest area in Changting County of southern China with a Landsat 8 image shows that the first principal component-based SEUFM achieves an overall accuracy close to 90%, while the multiplication-based model reaches 81%. The detected locations of soil erosion in forest provide the target areas to be managed from further soil loss. The proposed method provides a tool to understand more about soil erosion in forested areas where soil erosion is usually not considered an issue. Therefore, the method is useful for soil conservation in forest.

scholarly journals Effects of high spatial and temporal resolution Earth observations on simulated hydrometeorological variables in a cropland (southwestern France)

2017 ◽  
Vol 21 (11) ◽  
pp. 5693-5708 ◽  
Author(s):  
Jordi Etchanchu ◽  
Vincent Rivalland ◽  
Simon Gascoin ◽  
Jérôme Cros ◽  
Tiphaine Tallec ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Land Surface ◽  
Agricultural Landscapes ◽  
Surface Model ◽  
Spatial And Temporal Variability ◽  
Area Index ◽  
The Impact ◽  
Sentinel 2

Abstract. Agricultural landscapes are often constituted by a patchwork of crop fields whose seasonal evolution is dependent on specific crop rotation patterns and phenologies. This temporal and spatial heterogeneity affects surface hydrometeorological processes and must be taken into account in simulations of land surface and distributed hydrological models. The Sentinel-2 mission allows for the monitoring of land cover and vegetation dynamics at unprecedented spatial resolutions and revisit frequencies (20 m and 5 days, respectively) that are fully compatible with such heterogeneous agricultural landscapes. Here, we evaluate the impact of Sentinel-2-like remote sensing data on the simulation of surface water and energy fluxes via the Interactions between the Surface Biosphere Atmosphere (ISBA) land surface model included in the EXternalized SURface (SURFEX) modeling platform. The study focuses on the effect of the leaf area index (LAI) spatial and temporal variability on these fluxes. We compare the use of the LAI climatology from ECOCLIMAP-II, used by default in SURFEX-ISBA, and time series of LAI derived from the high-resolution Formosat-2 satellite data (8 m). The study area is an agricultural zone in southwestern France covering 576 km2 (24 km  ×  24 km). An innovative plot-scale approach is used, in which each computational unit has a homogeneous vegetation type. Evaluation of the simulations quality is done by comparing model outputs with in situ eddy covariance measurements of latent heat flux (LE). Our results show that the use of LAI derived from high-resolution remote sensing significantly improves simulated evapotranspiration with respect to ECOCLIMAP-II, especially when the surface is covered with summer crops. The comparison with in situ measurements shows an improvement of roughly 0.3 in the correlation coefficient and a decrease of around 30 % of the root mean square error (RMSE) in the simulated evapotranspiration. This finding is attributable to a better description of LAI evolution processes with Formosat-2 data, which further modify soil water content and drainage of soil reservoirs. Effects on annual drainage patterns remain small but significant, i.e., an increase roughly equivalent to 4 % of annual precipitation levels with simulations using Formosat-2 data in comparison to the reference simulation values. This study illustrates the potential for the Sentinel-2 mission to better represent effects of crop management on water budgeting for large, anthropized river basins.

scholarly journals An Objective High-Resolution Hail Climatology of the Contiguous United States

2012 ◽  
Vol 27 (5) ◽  
pp. 1235-1248 ◽  
Author(s):  
John L. Cintineo ◽  
Travis M. Smith ◽  
Valliappa Lakshmanan ◽  
Harold E. Brooks ◽  
Kiel L. Ortega
Keyword(s):  
United States ◽  
High Resolution ◽  
Spatial Resolution ◽  
Great Plains ◽  
Economic Losses ◽  
Climatic Data ◽  
Southeast United States ◽  
Severe Storms ◽  
Severe Thunderstorms ◽  
Spatial And Temporal Characteristics

Abstract The threat of damaging hail from severe thunderstorms affects many communities and industries on a yearly basis, with annual economic losses in excess of $1 billion (U.S. dollars). Past hail climatology has typically relied on the National Oceanic and Atmospheric Administration/National Climatic Data Center’s (NOAA/NCDC) Storm Data publication, which has numerous reporting biases and nonmeteorological artifacts. This research seeks to quantify the spatial and temporal characteristics of contiguous United States (CONUS) hail fall, derived from multiradar multisensor (MRMS) algorithms for several years during the Next-Generation Weather Radar (NEXRAD) era, leveraging the Multiyear Reanalysis of Remotely Sensed Storms (MYRORSS) dataset at NOAA’s National Severe Storms Laboratory (NSSL). The primary MRMS product used in this study is the maximum expected size of hail (MESH). The preliminary climatology includes 42 months of quality controlled and reprocessed MESH grids, which spans the warm seasons for four years (2007–10), covering 98% of all Storm Data hail reports during that time. The dataset has 0.01° latitude × 0.01° longitude × 31 vertical levels spatial resolution, and 5-min temporal resolution. Radar-based and reports-based methods of hail climatology are compared. MRMS MESH demonstrates superior coverage and resolution over Storm Data hail reports, and is largely unbiased. The results reveal a broad maximum of annual hail fall in the Great Plains and a diminished secondary maximum in the Southeast United States. Potential explanations for the differences in the two methods of hail climatology are also discussed.

scholarly journals Effects of multi-temporal high-resolution remote sensing products on simulated hydrometeorological variables in a cultivated area (southwestern France)

2017 ◽  
Author(s):  
Jordi Etchanchu ◽  
Vincent Rivalland ◽  
Simon Gascoin ◽  
Jérôme Cros ◽  
Aurore Brut ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Land Cover ◽  
Land Surface ◽  
Agricultural Landscapes ◽  
Annual Precipitation ◽  
Surface Model ◽  
Area Index ◽  
The Impact ◽  
Sentinel 2

Abstract. Agricultural landscapes often include a patchwork of crop fields whose seasonal evolution is dependent on specific crop rotation patterns and phenologies. This temporal and spatial heterogeneity affects surface hydrometeorological processes as simulated by land surface and distributed hydrological models. Sentinel-2 mission satellite remote sensing products allow for the monitoring of land cover and vegetation dynamics at unprecedented spatial resolutions and revisit frequencies (20 m and 5 days, respectively) that are fully compatible with such heterogeneous agricultural landscapes. Here, we evaluate the impact of Sentinel-2-like remote sensing data on the simulation of surface water and energy flux via the ISBA-SURFEX land surface model. The study area is a 24 km by 24 km agricultural zone in southwestern France. An initial reference simulation was conducted from 2006–2010 using the ECOCLIMAP-II database. This global numerical land ecosystem database was created at a 1 km resolution and includes an ecosystem classification with a consistent set of land surface parameters required for the model, such as the Leaf Area Index (LAI) and albedo measures. The LAI of ECOCLIMAP is climatologic and derived from a 2000–2005 analysis of MODIS satellite products. This low resolution induces that several vegetation covers can be mixed in a model cell. The climatic construction of LAI dynamics also suggests that there is no interannual variability in the vegetation cycle. A second simulation was performed by forcing the same model with annual land cover maps and monthly LAI values derived from a series of 105 8 m-resolution Formosat-2 images for the same period. Both simulations were conducted at the parcel scale, i.e., a computation unit covers an area of connected pixels of the same vegetation type (a crop field, forest patch, etc.). To evaluate our simulations, we used in situ measurements of evapotranspiration and latent and sensible heat flux from two eddy covariance stations in the study area. Our results show that the use of Formosat-2 high-resolution products significantly improves simulated evapotranspiration results with respect to ECOCLIMAP-II, especially when a surface is covered with summer crops (the correlation coefficient with monthly measurements is increased by roughly 0.3 and the root mean square error is decreased by roughly 31 %). This finding is attributable to a better description of LAI evolution processes reflected by Formosat-2 data, which further modify soil water content and drainage levels of deep soil reservoirs. Effects on annual drainage patterns remain small but significant, i.e., an increase roughly equivalent to 4 % of annual precipitation levels from Formosat-2 data in comparison to reference values. In smaller proportions, runoff is also increased by roughly 1 % of annual precipitation when using Formosat-2 data. This study illustrates the potential for the Sentinel-2 mission to better represent effects of crop management on water budgeting for large, anthropized river basins.

scholarly journals MODELLING AND MAPPING OF SOIL EROSION ON THE OUED EL MALLEH CATCHMENT USING REMOTE SENSING AND GIS

2013 ◽  
pp. 302-307
Author(s):  
Omar El Aroussi
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Soil Erosion ◽  
Land Degradation ◽  
Soil Loss ◽  
Land Use Changes ◽  
Field Studies ◽  
Water Volume ◽  
Water Storage Capacity ◽  
Rusle Model

In Morocco, the spectacular expansion of erosive processes shows increasingly alarming aspects. Due to the considerable costs of detailed ground surveys for studying this phenomenon, remote sensing is an appropriate alternative for analyzing and evaluating the risks of the expansion of soil degradation. According to an FAO study (2001), Erosion threatens 13 million ha of cropland and rangeland in northern Morocco and induces an estimated average water storage capacity loss of 50 million m3 each year through dam silting. The lost water volume could potentially be used to irrigate 5000 to 6000 ha / year. This study analyses soil erosion on the Oued El Malleh catchment, a 34 km2 catchment located in the north of Fez (Morocco). This contribution aims at mapping the spatio-temporal evolution of land use and modelling the erosion and sedimentation processes using the well known RUSLE model. Land use changes were assessed using Landsat-5 TM and Landsat-7 ETM+ images, from the 1987-2011 periods which were validated by field studies. The images were first georeferenced and projected into the Moroccan coordinate system (Merchich North) then processed to evaluate soil loss through a GIS package (Idrisi Andes Software). These static assessments of soil loss were then used in a deposition/sedimentation algorithm to model soil loss propagation to the downstream. The soil loss averages determined by the model vary between 1.09 t/ha/yr as a minimum value for the reforested lands and 169.4 t/ha/yr as a maximum value for the uncultivated lands (badlands). The latter generally correspond to Regosols or low protected soils located on steep slopes. In comparison with RUSLE, the sedimentation model yields lower values of soil losses; only 97.3 t/h/year for the uncultivated lands, and -0.34 t/ha/year in the reforested land, indicating an on-going sedimentation process. By taking into account the temporal variability of erosion and deposition jointly lower values of soil erosion are calculated by the RUSLE model. However, despite this decline, land degradation problems are still important due to the combination of land use and local lithology. The results of this study were used to indentify areas where interventions are needed to limit land degradation processes.

scholarly journals Assessing Post-Fire Effects on Soil Loss Combining Burn Severity and Advanced Erosion Modeling in Malesina, Central Greece

2021 ◽  
Vol 13 (24) ◽  
pp. 5160
Author(s):  
Ioanna Tselka ◽  
Pavlos Krassakis ◽  
Alkiviadis Rentzelos ◽  
Nikolaos Koukouzas ◽  
Issaak Parcharidis
Keyword(s):  
Remote Sensing ◽  
Soil Erosion ◽  
Soil Loss ◽  
Prediction Models ◽  
Fire Effects ◽  
Burned Area ◽  
Strong Impact ◽  
Central Greece ◽  
High Soil ◽  
Post Fire Effects

Earth’s ecosystems are extremely valuable to humanity, playing a key role ecologically, economically, and socially. Wildfires constitute a significant threat to the environment, especially in vulnerable ecosystems, such as those that are commonly found in the Mediterranean. Due to their strong impact on the environment, they provide a crucial factor in managing ecosystems behavior, causing dramatic modifications to land surface processes dynamics leading to land degradation. The soil erosion phenomenon downgrades soil quality in ecosystems and reduces land productivity. Thus, it is imperative to implement advanced erosion prediction models to assess fire effects on soil characteristics. This study focuses on examining the wildfire case that burned 30 km2 in Malesina of Central Greece in 2014. The added value of remote sensing today, such as the high accuracy of satellite data, has contributed to visualizing the burned area concerning the severity of the event. Additional data from local weather stations were used to quantify soil loss on a seasonal basis using RUSLE modeling before and after the wildfire. Results of this study revealed that there is a remarkable variety of high soil loss values, especially in winter periods. More particularly, there was a 30% soil loss rise one year after the wildfire, while five years after the event, an almost double reduction was observed. In specific areas with high soil erosion values, infrastructure works were carried out validating the applied methodology. The approach adopted in this study underlines the significance of using remote sensing and geoinformation techniques to assess the post-fire effects of identifying vulnerable areas based on soil erosion parameters on a local scale.

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