scholarly journals Estimating Crop and Grass Productivity over the United States Using Satellite Solar-Induced Chlorophyll Fluorescence, Precipitation and Soil Moisture Data

2020 ◽  
Vol 12 (20) ◽  
pp. 3434
Author(s):  
Maryia Halubok ◽  
Zong-Liang Yang
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Chlorophyll Fluorescence ◽  
Spatial Variability ◽  
Great Plains ◽  
Gross Primary Production ◽  
European Space Agency ◽  
The United States ◽  
Soil Moisture Data ◽  
The Us

This study investigates how gross primary production (GPP) estimates can be improved with the use of solar-induced chlorophyll fluorescence (SIF) based on the interdependence between SIF, precipitation, soil moisture and GPP itself. We have used multi-year datasets from Global Ozone Monitoring Experiment-2 (GOME-2), Tropical Rainfall Measuring Mission (TRMM), European Space Agency Climate Change Initiative Soil Moisture (ESA CCI SM), and FLUXNET observations from ten stations in the continental United States. We have employed a GPP quantification framework that makes use of two factors whose influence on the SIF–GPP relationship was not evaluated previously—namely, differential plant sensitivity to water supply at different stages of its lifecycle and spatial variability patterns in SIF that are in contrast to those of GPP, precipitation, and soil moisture. It was found that over the Great Plains and Texas, fluorescence emission levels lag behind precipitation events from about two weeks for grasses to four weeks for crops. The spatial variability of SIF and GPP is shown to be characterized by different patterns: SIF demonstrates less variation over the same spatial extent as compared to GPP, precipitation and soil moisture. Thus, using newly introduced SIF–precipitation lead–lag relationships, we estimate GPP using SIF, precipitation and soil moisture data for grasses and crops over the US by applying the multiple linear regression technique. Our GPP estimates capture the drought impact over the US better than those from Moderate Resolution Imaging Spectroradiometer (MODIS). During the drought year of 2011 over Texas, our GPP values show a decrease by 50–75 gC/m2/month, as opposed to the normal yielding year of 2007. In 2012, a drought year over the Great Plains, we observe a significant reduction in GPP, as compared to 2007. Hence, estimating GPP using specific SIF–GPP relationships, and information on different plant functional types (PFTs) and their interactions with precipitation and soil moisture over the Great Plains and Texas regions can help produce more reasonable GPP estimates.

scholarly journals Sampling density and date influence spatial representation of tree ring reconstructions

2020 ◽  
Author(s):  
Justin T. Maxwell ◽  
Grant L. Harley ◽  
Trevis J. Matheus ◽  
Brandon M. Strange ◽  
Kayla Van Aken ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Spatial Variability ◽  
Tree Ring ◽  
Tree Rings ◽  
Large Scale ◽  
The United States ◽  
Severe Drought ◽  
Ring Network ◽  
Tree Ring Data ◽  
The Us

Abstract. Our understanding of the natural variability of hydroclimate before the instrumental period (ca. 1900 in the United States; US) is largely dependent on tree-ring-based reconstructions. Large-scale soil moisture reconstructions from a network of tree-ring chronologies have greatly improved our understanding of the spatial and temporal variability in hydroclimate conditions, particularly extremes of both drought and pluvial (wet) events. However, certain regions within these large-scale reconstructions in the US have a sparse network of tree-ring chronologies. Further, several chronologies were collected in the 1980s and 1990s, thus our understanding of the sensitivity of radial growth to soil moisture in the US is based on a period that experienced multiple extremely severe droughts and neglects the impacts of recent, rapid global change. In this study, we expanded the tree-ring network of the Ohio River Valley in the US, a region with sparse coverage. We used a total of 72 chronologies across 15 species to examine how increasing the density of the tree-ring network influences the representation of reconstructing the Palmer Meteorological Drought Index (PMDI). Further, we tested how the sampling date influenced the reconstruction models by creating reconstructions that ended in the year 1980 and compared them to reconstructions ending in 2010 from the same chronologies. We found that increasing the density of the tree-ring network resulted in reconstructed values that better matched the spatial variability of instrumentally recorded droughts and to a lesser extent, pluvials. By sampling tree in 2010 compared to 1980, the sensitivity of tree rings to PMDI decreased in the southern portion of our region where severe drought conditions have been absent over recent decades. We emphasize the need of building a high-density tree-ring network to better represent the spatial variability of past droughts and pluvials. Further, chronologies on the International Tree-Ring Data Bank need updating regularly to better understand how the sensitivity of tree rings to climate may vary through time.

scholarly journals The Impact of SMOS Soil Moisture Data Assimilation within the Operational Global Flood Awareness System (GloFAS)

2020 ◽  
Vol 12 (9) ◽  
pp. 1490 ◽  
Author(s):  
Calum Baugh ◽  
Patricia de Rosnay ◽  
Heather Lawrence ◽  
Toni Jurlina ◽  
Matthias Drusch ◽  
...  
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Data Assimilation ◽  
The United States ◽  
Streamflow Prediction ◽  
Soil Moisture Data ◽  
Skill Scores ◽  
High Flows ◽  
The Impact ◽  
Awareness System

In this study the impacts of Soil Moisture and Ocean Salinity (SMOS) soil moisture data assimilation upon the streamflow prediction of the operational Global Flood Awareness System (GloFAS) were investigated. Two GloFAS experiments were performed, one which used hydro-meteorological forcings produced with the assimilation of the SMOS data, the other using forcings which excluded the assimilation of the SMOS data. Both sets of experiment results were verified against streamflow observations in the United States and Australia. Skill scores were computed for each experiment against the observation datasets, the differences in the skill scores were used to identify where GloFAS skill may be affected by the assimilation of SMOS soil moisture data. In addition, a global assessment was made of the impact upon the 5th and 95th GloFAS flow percentiles to see how SMOS data assimilation affected low and high flows respectively. Results against in-situ observations found that GloFAS skill score was only affected by a small amount. At a global scale, the results showed a large impact on high flows in areas such as the Hudson Bay, central United States, the Sahel and Australia. There was no clear spatial trend to these differences as opposing signs occurred within close proximity to each other. Investigating the differences between the simulations at individual gauging stations showed that they often only occurred during a single flood event; for the remainder of the simulation period the experiments were almost identical. This suggests that SMOS data assimilation may affect the generation of surface runoff during high flow events, but may have less impact on baseflow generation during the remainder of the hydrograph. To further understand this, future work could assess the impact of SMOS data assimilation upon specific hydrological components such as surface and subsurface runoff.

Investigating the Relationship between the Evaporative Stress Index and Land Surface Conditions in the Contiguous United States

2020 ◽  
Vol 21 (7) ◽  
pp. 1469-1484
Author(s):  
Yafang Zhong ◽  
Jason A. Otkin ◽  
Martha C. Anderson ◽  
Christopher Hain
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Great Plains ◽  
Land Surface ◽  
The United States ◽  
Stress Index ◽  
Northern Great Plains ◽  
Reference Network ◽  
North Central ◽  
Central United States

AbstractDespite the key importance of soil moisture–evapotranspiration (ET) coupling in the climate system, limited availability of soil moisture and ET observations poses a major impediment for investigation of this coupling regarding spatiotemporal characteristics and potential modifications under climate change. To better understand and quantify soil moisture–ET coupling and relevant processes, this study takes advantage of in situ soil moisture observations from the U.S. Climate Reference Network (USCRN) for the time period of 2010–17 and a satellite-derived version of the evapotranspiration stress index (ESI), which represents anomalies in a normalized ratio of actual to reference ET. The analyses reveal strong seasonality and regional characteristics of the ESI–land surface interactions across the United States, with the strongest control of soil moisture on the ESI found in the southern Great Plains during spring, and in the north-central United States, the northern Great Plains, and the Pacific Northwest during summer. In drier climate regions such as the northern Great Plains and north-central United States, soil moisture control on the ESI is confined to surface soil layers, with subsurface soil moisture passively responding to changes in the ESI. The soil moisture–ESI interaction is more uniform between surface and subsurface soils in wetter regions with higher vegetation cover. These results provide a benchmark for simulation of soil moisture–ET coupling and are useful for projection of associated climate processes in the future.

scholarly journals Irrigation Effects on Land–Atmosphere Coupling Strength in the United States

2017 ◽  
Vol 30 (10) ◽  
pp. 3671-3685 ◽  
Author(s):  
Yaqiong Lu ◽  
Keith Harding ◽  
Lara Kueppers
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Water Vapor ◽  
Great Plains ◽  
Coupling Strength ◽  
Climate Models ◽  
The United States ◽  
Lower Sensitivity ◽  
Local Soil ◽  
Irrigation Effects

Abstract Land–atmosphere coupling strength describes the degree to which the atmosphere responds (e.g., via changes in precipitation) to changes in the land surface state (e.g., soil moisture). The Midwest and Great Plains of the United States have been shown to be “hot spots” of coupling by many climate models and some observations. However, very few of the modeling studies have reported whether the climate models applied irrigation in the Midwest and Great Plains, where 24%–27% of farmland is irrigated, leaving open the question of whether irrigation affects current estimates of coupling strength. This study used a regional climate model that incorporated dynamic crop growth and precision irrigation (WRF3.3–CLM4crop) to investigate irrigation effects on land–atmosphere coupling strength. Coupling strength was quantified using multiple indices and the irrigated land-induced precipitation was tracked using a back trajectory method. The indices showed a consistent and significant decline in local coupling strength with irrigation in the Midwest and northern Great Plains. These reductions were due to increased soil moisture but decreased local precipitation and lower sensitivity of latent heat flux to soil moisture over irrigated regions. The back trajectories of water vapor transport confirmed that irrigation largely did not contribute to local precipitation. Water vapor from irrigated land was transported to the Midwest and U.S. Northeast where it fell as precipitation, suggesting that irrigation has a broader spatial impact on soil moisture–precipitation coupling than simply through local soil moisture–evapotranspiration coupling. The present study suggests that climate models without irrigation schemes may overestimate the land–atmosphere coupling strength over irrigated agricultural regions but underestimate coupling strength over neighboring nonirrigated regions.

scholarly journals Northwestward cropland expansion and growing urea-based fertilizer use enhanced NH<sub>3</sub> emission loss in the contiguous United States

2020 ◽  
Vol 20 (20) ◽  
pp. 11907-11922
Author(s):  
Peiyu Cao ◽  
Chaoqun Lu ◽  
Jien Zhang ◽  
Avani Khadilkar
Keyword(s):  
United States ◽  
Great Plains ◽  
Spring Wheat ◽  
The United States ◽  
N Fertilizer ◽  
Northern Great Plains ◽  
Fertilizer Use ◽  
Nh3 Emission ◽  
The Us ◽  
The 1960S

Abstract. The increasing demands of food and biofuel have promoted cropland expansion and nitrogen (N) fertilizer enrichment in the United States over the past century. However, the role of such long-term human activities in influencing the spatiotemporal patterns of ammonia (NH3) emission remains poorly understood. Based on an empirical model and time-series gridded datasets including temperature, soil properties, N fertilizer management, and cropland distribution history, we have quantified monthly fertilizer-induced NH3 emission across the contiguous US from 1900 to 2015. Our results show that N-fertilizer-induced NH3 emission in the US has increased from <50 Gg N yr−1 before the 1960s to 641 Gg N yr−1 in 2015, for which corn and spring wheat are the dominant contributors. Meanwhile, urea-based fertilizers gradually grew to the largest NH3 emitter and accounted for 78 % of the total increase during 1960–2015. The factorial contribution analysis indicates that the rising N fertilizer use rate dominated the NH3 emission increase since 1960, whereas the impacts of temperature, cropland distribution and rotation, and N fertilizer type varied among regions and over periods. Geospatial analysis reveals that the hot spots of NH3 emissions have shifted from the central US to the Northern Great Plains from 1960 to 2015. The increasing NH3 emissions in the Northern Great Plains have been found to closely correlate to the elevated NH4+ deposition in this region over the last 3 decades. This study shows that April, May, and June account for the majority of NH3 emission in a year. Interestingly, the peak emission month has shifted from May to April since the 1960s. Our results imply that the northwestward corn and spring wheat expansion and growing urea-based fertilizer uses have dramatically altered the spatial pattern and temporal dynamics of NH3 emission, impacting air pollution and public health in the US.

scholarly journals Estimating root zone soil moisture using near-surface observations from SMOS

2014 ◽  
Vol 18 (1) ◽  
pp. 139-154 ◽  
Author(s):  
T. W. Ford ◽  
E. Harris ◽  
S. M. Quiring
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Great Plains ◽  
Surface Soil ◽  
Root Zone ◽  
The United States ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
Cross Correlation Analysis

Abstract. Satellite-derived soil moisture provides more spatially and temporally extensive data than in situ observations. However, satellites can only measure water in the top few centimeters of the soil. Root zone soil moisture is more important, particularly in vegetated regions. Therefore estimates of root zone soil moisture must be inferred from near-surface soil moisture retrievals. The accuracy of this inference is contingent on the relationship between soil moisture in the near-surface and the soil moisture at greater depths. This study uses cross correlation analysis to quantify the association between near-surface and root zone soil moisture using in situ data from the United States Great Plains. Our analysis demonstrates that there is generally a strong relationship between near-surface (5–10 cm) and root zone (25–60 cm) soil moisture. An exponential decay filter is used to estimate root zone soil moisture using near-surface soil moisture derived from the Soil Moisture and Ocean Salinity (SMOS) satellite. Root zone soil moisture derived from SMOS surface retrievals is compared to in situ soil moisture observations in the United States Great Plains. The SMOS-based root zone soil moisture had a mean R2 of 0.57 and a mean Nash–Sutcliffe score of 0.61 based on 33 stations in Oklahoma. In Nebraska, the SMOS-based root zone soil moisture had a mean R2 of 0.24 and a mean Nash–Sutcliffe score of 0.22 based on 22 stations. Although the performance of the exponential filter method varies over space and time, we conclude that it is a useful approach for estimating root zone soil moisture from SMOS surface retrievals.

Biennial Wormwood (Artemisia biennis) Early-Season Control with Herbicides

2004 ◽  
Vol 18 (3) ◽  
pp. 611-618 ◽  
Author(s):  
Bradley E. Fronning ◽  
George O. Kegode
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Great Plains ◽  
The United States ◽  
Northern Great Plains ◽  
Early Season ◽  
Herbicide Treatments ◽  
Moisture Conditions

Biennial wormwood has become a problem for soybean producers in the northern Great Plains of the United States. Research was conducted to evaluate control of biennial wormwood with preemergence (PRE) herbicides alone or followed by postemergence (POST) herbicides in 2000 and 2001 at Fargo, Leonard, and Wyndmere, ND. Favorable soil moisture conditions at Leonard resulted in continual emergence and greater densities of biennial wormwood, whereas the soil at Fargo and Wyndmere was dry and few biennial wormwood seedlings emerged at these locations. Biennial wormwood control with PRE herbicides was greater than 89% at Fargo and Wyndmere but was 80% or lower at Leonard. PRE biennial wormwood control was higher with flumetsulam than with sulfentrazone. When POST treatments were applied after PRE herbicides, biennial wormwood control 4 wk after treatment was 92% or better at Fargo and Wyndmere but was 76% or less at Leonard. The combination of PRE and POST herbicide treatments did not improve control greatly at Fargo or Wyndmere but at Leonard reduced the number of biennial wormwood plants.

scholarly journals Spatiotemporal Changes of Winter Wheat Planted and Harvested Areas, Photosynthesis and Grain Production in the Contiguous United States from 2008–2018

2021 ◽  
Vol 13 (9) ◽  
pp. 1735
Author(s):  
Xiaocui Wu ◽  
Xiangming Xiao ◽  
Jean Steiner ◽  
Zhengwei Yang ◽  
Yuanwei Qin ◽  
...  
Keyword(s):  
United States ◽  
Winter Wheat ◽  
Great Plains ◽  
Gross Primary Production ◽  
The United States ◽  
Grain Production ◽  
Wheat Grain ◽  
Global Food Security ◽  
The Usa ◽  
Data Layer

Winter wheat is a main cereal crop grown in the United States of America (USA), and the USA is the third largest wheat exporter globally. Timely and reliable in-season forecast and year-end estimation of winter wheat grain production in the USA are needed for regional and global food security. In this study, we assessed the consistency between the agricultural statistical reports and satellite-based data for winter wheat over the contiguous US (CONUS) at both the county and national scales. First, we compared the planted area estimates from the National Agricultural Statistics Service (NASS) and the Cropland Data Layer (CDL) from 2008-2018. Second, we investigated the relationship between gross primary production (GPP) estimated by the vegetation photosynthesis model (VPM) and grain production from the NASS. Lastly, we explored the in-season utility of GPPVPM in monitoring seasonal production. Strong spatiotemporal consistency of planted areas was found between the NASS and CDL datasets. However, in the Southern Great Plains, both the CDL and NASS planted acreage were noticeable larger (>20%) than the NASS harvested area, where some winter wheat fields were used as forage for cattle grazing. County-level GPPVPM was linearly related with grain production of winter wheat, with an R2 value of 0.68 across the CONUS. The relationships between grain production and GPPVPM in those counties without a substantial difference (<20%) between planted and harvested area were much stronger and their harvest index (HIGPP) values ranged from 0.2-0.3. GPPVPM in May could explain about 70%-90% of the variance of winter wheat grain production. Our findings highlight the potential of GPPVPM in winter wheat monitoring, especially for those high harvested/planted ratio, which could provide useful data to guide planning and marketing for decision makers, stakeholders, and the public.

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