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The knowledge about nutrient dynamics in the soil is pivotal for sustainable agriculture. A comprehensive research trial can retort unanswered questions. Dynamics of nutrients sourced from organic amendment types (chicken manure, dairy manure, and MilorganiteTM) applied at different rates (0, 168, 336, 672 kg total N/ha) were monitored within and below the rootzone of collard greens cultivated on a sandy loam soil in Prairie View, TX, USA. Macro- and micronutrients (e.g., TN: total nitrogen, P: phosphorous, K: potassium, Na: sodium, Ca: calcium, Mg: magnesium, B: boron, Cu: copper, Fe: iron, and Zn: zinc) were analyzed from soil solution samples collected during six sampling periods from within and below the rootzone. As hypothesized, the organic amendment types and rates significantly (p< 0.05 and/or 0.01) affected nutrient dynamics within and below the crop rootzone. Chicken manure released significantly more TN, P, K, Na, Ca, Mg, B, Cu, and Fe than the other two amendments. The application of chicken manure and MilorganiteTM resulted in higher below-the-rootzone leachate concentration of TN, Na, Mg, and Ca than in the leachates of dairy manure. Dairy manure treatments had the lowest concentrations of TN, Ca, and Mg; whereas, MilorganiteTM had the lowest concentrations of P, K, Na, B, and Cu in the collected leachates. The higher level of P (i.e., 4% in MilorganiteTM as compared to 2 and 0.5% in chicken and dairy manures, respectively, might have reduced the formation of Vesicular-Arbuscular (VA) mycorrhizae—a fungus with the ability to dissolve the soil P, resulting in slow release of P from MilorganiteTM treatment than from the other two treatments. Patterns of nutrient dynamics varied with rain and irrigation events under the effects of the soil water and time lapse of the amendment applications’ rates and types. All the macronutrients were present within the rootzone and leached below the rootzone, except Na. The dynamic of nutrients was element-specific and was influenced by the amendments’ type and application rate.
Ripendra Awal; Almoutaz Hassan; Farhat Abbas; Ali Fares; Haimanote Bayabil; Ram Ray; Selamawit Woldesenbet. Patterns of Nutrient Dynamics within and below the Rootzone of Collard Greens Grown under Different Organic Amendment Types and Rates. Sustainability 2021, 13, 6857 .
AMA StyleRipendra Awal, Almoutaz Hassan, Farhat Abbas, Ali Fares, Haimanote Bayabil, Ram Ray, Selamawit Woldesenbet. Patterns of Nutrient Dynamics within and below the Rootzone of Collard Greens Grown under Different Organic Amendment Types and Rates. Sustainability. 2021; 13 (12):6857.
Chicago/Turabian StyleRipendra Awal; Almoutaz Hassan; Farhat Abbas; Ali Fares; Haimanote Bayabil; Ram Ray; Selamawit Woldesenbet. 2021. "Patterns of Nutrient Dynamics within and below the Rootzone of Collard Greens Grown under Different Organic Amendment Types and Rates." Sustainability 13, no. 12: 6857.
The agricultural industry is getting more data-centric and requires precise, more advanced data and technologies than before, despite being familiar with agricultural processes. The agriculture industry is being advanced by various information and advanced communication technologies, such as the Internet of Things (IoT). The rapid emergence of these advanced technologies has restructured almost all other industries, as well as advanced agriculture, which has shifted the industry from a statistical approach to a quantitative one. This radical change has shaken existing farming techniques and produced the latest prospects in a series of challenges. This comprehensive review article enlightens the potential of the IoT in the advancement of agriculture and the challenges faced when combining these advanced technologies with conventional agricultural systems. A brief analysis of these advanced technologies with sensors is presented in advanced agricultural applications. Numerous sensors that can be implemented for specific agricultural practices require best management practices (e.g., land preparation, irrigation systems, insect, and disease management). This review includes the integration of all suitable techniques, from sowing to harvesting, packaging, transportation, and advanced technologies available for farmers throughout the cropping system. Besides, this review article highlights the utilization of other tools such as unmanned aerial vehicles (UAVs) for crop monitoring and other beneficiary measures, such as optimizing crop yields. In addition, advanced programs based on the IoT are also discussed. Finally, based on our comprehensive review, we identified advanced prospects regarding the IoT, which are essential tools for sustainable agriculture.
Nawab Khan; Ram Ray; Ghulam Sargani; Muhammad Ihtisham; Muhammad Khayyam; Sohaib Ismail. Current Progress and Future Prospects of Agriculture Technology: Gateway to Sustainable Agriculture. Sustainability 2021, 13, 4883 .
AMA StyleNawab Khan, Ram Ray, Ghulam Sargani, Muhammad Ihtisham, Muhammad Khayyam, Sohaib Ismail. Current Progress and Future Prospects of Agriculture Technology: Gateway to Sustainable Agriculture. Sustainability. 2021; 13 (9):4883.
Chicago/Turabian StyleNawab Khan; Ram Ray; Ghulam Sargani; Muhammad Ihtisham; Muhammad Khayyam; Sohaib Ismail. 2021. "Current Progress and Future Prospects of Agriculture Technology: Gateway to Sustainable Agriculture." Sustainability 13, no. 9: 4883.
Hurricanes cause severe impacts on the ecosystem, which substantially affects the carbon cycle at the local or regional scale. During the hurricanes, the loss of many vegetation/trees in the forest and agricultural lands causes more carbon to be released into the atmosphere. Studying the effects of hurricanes on the terrestrial carbon cycle, which includes gross primary product (GPP), net ecosystem exchange (NEE), heterotrophic respiration (Rh), and their interactions with land-use change, flood, and others are critical to understand the effect on the terrestrial ecosystem. The main objective of this research was to evaluate the impact of three hurricanes (Harvey, Irma, and Maria in 2017) on the carbon cycle and study the interactions among the flood events, land uses, and terrestrial carbon cycling in the state of Texas, Florida, Puerto Rico using satellite measurements. This study analyzed the GPP, NEE, and Rh distributions in the coastal climate zones in Texas, Florida, and Puerto Rico during hurricane season using Soil Moisture Active Passive (SMAP) carbon products. SMAP Carbon products (Res=9 km) were evaluated using CO2 flux data measured at EC flux site on the Prairie View A&M University Research Farm, Texas. Results showed Florida (Irma) had higher carbon emissions and lower GPP during the hurricane compared to Texas (Harvey), and Puerto Rico (Maria). For example, hurricanes Harvey (08/26/2017), Irma (09/10/2017), and Maria (09/20/2017) caused 2.6, 4.1, and 3.03 gC/m2, of carbon emissions when the recorded daily precipitations were 162, 135, and 241 mm, respectively. However, mostly carbon uptakes or low (<1 gC/m2) carbon emissions were observed on the same day in 2016 and 2018. The analysis showed that the amount of precipitation is not the only driving factor causing increased carbon emission; the characteristics of the drainage area also affect the carbon cycle and emission. Overall, the results showed that hurricanes increase carbon emissions. This study helps to understand the impact of hurricanes on the carbon cycle through analyses of spatial and temporal variations of carbon emission and uptake during the hurricane season.
Ram RayiD; Rajendra Sishodia; Yiping He; Minha Choi. The Effects of Hurricanes on the Carbon Cycle. 2020, 1 .
AMA StyleRam RayiD, Rajendra Sishodia, Yiping He, Minha Choi. The Effects of Hurricanes on the Carbon Cycle. . 2020; ():1.
Chicago/Turabian StyleRam RayiD; Rajendra Sishodia; Yiping He; Minha Choi. 2020. "The Effects of Hurricanes on the Carbon Cycle." , no. : 1.
Agriculture provides for the most basic needs of humankind: food and fiber. The introduction of new farming techniques in the past century (e.g., during the Green Revolution) has helped agriculture keep pace with growing demands for food and other agricultural products. However, further increases in food demand, a growing population, and rising income levels are likely to put additional strain on natural resources. With growing recognition of the negative impacts of agriculture on the environment, new techniques and approaches should be able to meet future food demands while maintaining or reducing the environmental footprint of agriculture. Emerging technologies, such as geospatial technologies, Internet of Things (IoT), Big Data analysis, and artificial intelligence (AI), could be utilized to make informed management decisions aimed to increase crop production. Precision agriculture (PA) entails the application of a suite of such technologies to optimize agricultural inputs to increase agricultural production and reduce input losses. Use of remote sensing technologies for PA has increased rapidly during the past few decades. The unprecedented availability of high resolution (spatial, spectral and temporal) satellite images has promoted the use of remote sensing in many PA applications, including crop monitoring, irrigation management, nutrient application, disease and pest management, and yield prediction. In this paper, we provide an overview of remote sensing systems, techniques, and vegetation indices along with their recent (2015–2020) applications in PA. Remote-sensing-based PA technologies such as variable fertilizer rate application technology in Green Seeker and Crop Circle have already been incorporated in commercial agriculture. Use of unmanned aerial vehicles (UAVs) has increased tremendously during the last decade due to their cost-effectiveness and flexibility in obtaining the high-resolution (cm-scale) images needed for PA applications. At the same time, the availability of a large amount of satellite data has prompted researchers to explore advanced data storage and processing techniques such as cloud computing and machine learning. Given the complexity of image processing and the amount of technical knowledge and expertise needed, it is critical to explore and develop a simple yet reliable workflow for the real-time application of remote sensing in PA. Development of accurate yet easy to use, user-friendly systems is likely to result in broader adoption of remote sensing technologies in commercial and non-commercial PA applications.
Rajendra P. Sishodia; Ram L. Ray; Sudhir K. Singh. Applications of Remote Sensing in Precision Agriculture: A Review. Remote Sensing 2020, 12, 3136 .
AMA StyleRajendra P. Sishodia, Ram L. Ray, Sudhir K. Singh. Applications of Remote Sensing in Precision Agriculture: A Review. Remote Sensing. 2020; 12 (19):3136.
Chicago/Turabian StyleRajendra P. Sishodia; Ram L. Ray; Sudhir K. Singh. 2020. "Applications of Remote Sensing in Precision Agriculture: A Review." Remote Sensing 12, no. 19: 3136.
Soil moisture (SM) and vapor pressure deficit (VPD) are key variables that affect the carbon, water, and energy cycles. Very few studies have been conducted to evaluate their impacts on site-specific ecosystems, especially in mountainous regions and plains using an integrated remote sensing and modeling approach. This study used two flux tower sites, P301 (forest–ecosystem), Prairie View A&M University (PVAMU) (grassland–ecosystem) to evaluate gross primary production (GPP), sensible heat flux (H), and latent heat flux (LE). The community land model (CLM4) and soil moisture active passive (SMAP) carbon product were employed and evaluated using the flux tower data from 2016 to 2018. From the CLM4 and SMAP estimation of GPP, the CLM4 showed better accuracy at P301, whereas SMAP performed better at PVAMU. The SMAP overestimated GPP at P301, which could be due to the coarse footprint of SMAP (9 × 9 km). CLM4 overestimated GPP during the growing season at PVAMU, which could be due to the structural and parametric uncertainties. The H at P301 showed good agreement between the CLM4 results and flux tower measurements. However, H at the PVAMU site was most likely affected by the precipitation. Moreover, at P301, VPD was effective in controlling the carbon and water fluxes (GPP and LE) with a positive partial correlation (p–value < 0.05). However, at PVAMU, SM (terrestrial control) showed substantial control over the fluxes due to year-round precipitation. Overall, the results showed that the California site was affected due to low precipitation and could be vulnerable to climate change due to a reduction in SM and high VPD during the summers. In contrast, the Texas site had high SM due to frequent and intense rainfall events due to the changing climate. For the future study, a detailed analysis of SM and VPD control at regional and global scales for various land cover types is needed.
Muhammad Umair; Daeun Kim; Ram L. Ray; Minha Choi. Evaluation of atmospheric and terrestrial effects in the carbon cycle for forest and grassland ecosystems using a remote sensing and modeling approach. Agricultural and Forest Meteorology 2020, 295, 108187 .
AMA StyleMuhammad Umair, Daeun Kim, Ram L. Ray, Minha Choi. Evaluation of atmospheric and terrestrial effects in the carbon cycle for forest and grassland ecosystems using a remote sensing and modeling approach. Agricultural and Forest Meteorology. 2020; 295 ():108187.
Chicago/Turabian StyleMuhammad Umair; Daeun Kim; Ram L. Ray; Minha Choi. 2020. "Evaluation of atmospheric and terrestrial effects in the carbon cycle for forest and grassland ecosystems using a remote sensing and modeling approach." Agricultural and Forest Meteorology 295, no. : 108187.
Climate extremes such as drought have significant impacts on agriculture, natural resources, and environment. Climate variables such as temperature and precipitation, which have key roles during a drought, directly impact crop production because they control crop growth, crop health, crop yield, and cropping system yields over time. Moreover, overall drought impact on crop yields is the combined effect of reduced or limited precipitation with increased temperature for a prolonged period, which leads to decreased soil moisture and requires adapted agricultural management practice. This chapter provides a critical and comprehensive review of recent studies about the impact of drought on crop physiology, morphology, and yields and global food security while also commenting on available genetic and agronomic tools in addressing drought stress and protecting crops under drought conditions. Furthermore, this chapter focuses on adaptation strategies to mitigate drought and crop management using sustainable and climate-smart agriculture. Best management practices that contribute to the effects of climate change related to drought adaptation and mitigation include appropriate agronomic and genetic tools for crop protection under drought conditions. This review aims to contribute to the improvement of adaptation strategies suitable for crop production under drought conditions for sustainable agricultural management practices. It focuses on the breeding of new drought-tolerant varieties of crop species, development of new approaches to secure stable yields, and selection of early maturing crop varieties and best irrigation practices. In addition, reduced tillage practices are investigated, since many sustainable agricultural management practices have not been widely adopted due to lack of access to resources, knowledge, and practical experiences.
Ram L. Ray; Peter A. Y. Ampim; Ming Gao. Crop Protection Under Drought Stress. Crop Protection Under Changing Climate 2020, 145 -170.
AMA StyleRam L. Ray, Peter A. Y. Ampim, Ming Gao. Crop Protection Under Drought Stress. Crop Protection Under Changing Climate. 2020; ():145-170.
Chicago/Turabian StyleRam L. Ray; Peter A. Y. Ampim; Ming Gao. 2020. "Crop Protection Under Drought Stress." Crop Protection Under Changing Climate , no. : 145-170.
Soil moisture is essential for water resources management, yet accurate information of soil moisture has been a challenge. The major goal was to parametrize the Modified Water Cloud Model (MWCM). The Sentinel-1A data of winter wheat crop was collected for two weeks. Concurrently, in-situ soil moisture data was collected using Time Domain Reflectometer (TDR). A parametric scheme was used for the retrieval of the VV polarization of Sentinel-1A. The effect of NDVI as a vegetation descriptors (V1 and V2) on total VV backscatter (σ 0) was analyzed. The calibration showed NDVI has the potential to influence Water Cloud Model (WCM) and vegetation descriptors; hence it is recommended to calibrate the MWCM. The coefficient of determination (R2 = 0.83) showed a good agreement between observed and estimated soil moisture. Therefore, this approach help improve soil moisture prediction, and can be applied to determine soil moisture more accurately for winter crops, grasses, and pasture lands.
Kishan Singh Rawat; Sudhir Kumar Singh; Ram L. Ray; Szilard Szabo. Parameterization of the modified water cloud model (MWCM) using normalized difference vegetation index (NDVI) for winter wheat crop: a case study from Punjab, India. Geocarto International 2020, 1 -14.
AMA StyleKishan Singh Rawat, Sudhir Kumar Singh, Ram L. Ray, Szilard Szabo. Parameterization of the modified water cloud model (MWCM) using normalized difference vegetation index (NDVI) for winter wheat crop: a case study from Punjab, India. Geocarto International. 2020; ():1-14.
Chicago/Turabian StyleKishan Singh Rawat; Sudhir Kumar Singh; Ram L. Ray; Szilard Szabo. 2020. "Parameterization of the modified water cloud model (MWCM) using normalized difference vegetation index (NDVI) for winter wheat crop: a case study from Punjab, India." Geocarto International , no. : 1-14.
Vegetated land surfaces play an important role in determining the fate of carbon in the global carbon cycle. However, our understanding of the terrestrial biosphere on a global scale is subject to considerable uncertainty, especially concerning the impacts of climatic variables on the carbon cycle. Soil is a source and also a sink of CO2 exchange and helps in carbon sequestration. Agricultural management practices influence soil water dynamics, as well as carbon cycling by changing soil CO2 emission and uptake rates. The rate of soil CO2 emission varies for different crops and different organic amendments. The major goal of this study was to assess the impacts of the type and rate of organic amendment on soil CO2 emission in a collard greens crop grown in the southeast Texas environment. Thirty-six plots were developed to grow collard greens on Prairie View A&M University’s Research Farm. Three types of organic amendments (Chicken manure, Dairy manure, and Milorganite), at four levels of application (0, 168, 336, and 672 kg N/ha) were used and replicated three times. Each organic amendment type was applied to nine randomly selected plots. Three random plots were used as a control in each row. We measured daily soil CO2 emission for the first two weeks and every other day in a week during the experiment. We evaluated the effects of organic amendments and the application rates on soil CO2 emission for collard greens during two growing seasons. The results showed higher the application rates for each organic amendment, higher the CO2 emissions from the soil. The results also showed higher cumulative CO2 emissions for the soils amended with chicken manure and milorganite, but lowest for the soils amended with dairy manure. This field experiment and analyses help better understand the temporal and spatial variations of soil CO2 emission, and also help to develop best management practices to maximize carbon sequestration and to minimize soil CO2 emissions during the growth periods of collard greens under changing temperatures using different organic amendments, and application rates.
Ram L. Ray; Richard W. Griffin; Ali Fares; Almoutaz Elhassan; Ripendra Awal; Selamawit Woldesenbet; Eric Risch. Soil CO2 emission in response to organic amendments, temperature, and rainfall. Scientific Reports 2020, 10, 1 -14.
AMA StyleRam L. Ray, Richard W. Griffin, Ali Fares, Almoutaz Elhassan, Ripendra Awal, Selamawit Woldesenbet, Eric Risch. Soil CO2 emission in response to organic amendments, temperature, and rainfall. Scientific Reports. 2020; 10 (1):1-14.
Chicago/Turabian StyleRam L. Ray; Richard W. Griffin; Ali Fares; Almoutaz Elhassan; Ripendra Awal; Selamawit Woldesenbet; Eric Risch. 2020. "Soil CO2 emission in response to organic amendments, temperature, and rainfall." Scientific Reports 10, no. 1: 1-14.
The objective was to parameterize a modified water cloud model using crop coefficients (A and B). These crop coefficients were derived from Landsat-8 and Sentinel-2 data. Whereas the coefficients C and D are of soil parameters. The water cloud model was modified using crop coefficients by minimizing the RMSE between observed VVσ0 and Sentinel-1 based simulated VVσ0. The comparison with observed and simulated VV polarized σ0 showed low RMSE (0.81 dB) and strong R2 of 0.98 for NDVI-EVI combination. However, based on other possible combinations of vegetation indices VVσ0 and simulated VVσ0 do not show a good statistical agreement. It was observed that the errors in crop coefficients (A and B) are sensitive to errors in initial vegetation/canopy descriptor parameters.
Kishan Singh Rawat; Sudhir Kumar Singh; Ram L. Ray; Szilárd Szabó; Sanjeev Kumar. Parameterizing the modified water cloud model to improve soil moisture data retrieval using vegetation models. Hungarian Geographical Bulletin 2020, 69, 17 -26.
AMA StyleKishan Singh Rawat, Sudhir Kumar Singh, Ram L. Ray, Szilárd Szabó, Sanjeev Kumar. Parameterizing the modified water cloud model to improve soil moisture data retrieval using vegetation models. Hungarian Geographical Bulletin. 2020; 69 (1):17-26.
Chicago/Turabian StyleKishan Singh Rawat; Sudhir Kumar Singh; Ram L. Ray; Szilárd Szabó; Sanjeev Kumar. 2020. "Parameterizing the modified water cloud model to improve soil moisture data retrieval using vegetation models." Hungarian Geographical Bulletin 69, no. 1: 17-26.
Sentinel-1 and Landsat-8 data were used to retrieve soil moisture from top soil surface (0–5 cm depth) at agricultural land (area under wheat crop). After pre-processing of satellite data and removal of vegetation influence (σ°veg) using Water Cloud Model (WCM), total backscattering coefficient (σ°total) and Normalized Difference Vegetation Index (NDVI) were used to simulate backscattering from soil (σ°soil). Modified Dubois Model (MDM) and Topp's Model were used to retrieve soil moisture using ε. Further, modelled soil moisture was evaluated using in situ soil moisture measurements and a Time Domain Reflectometer during Sentinel-1 overpass (24 January, 25 February and 13 March 2018). Statistical tests showed that an integrated approach has potential to improve soil moisture estimates over the vegetated/cropped area for agricultural and hydrological studies.
Kishan Singh Rawat; Sudhir Kumar Singh; Ram Lakhan Ray. An integrated approach to estimate surface soil moisture in agricultural lands. Geocarto International 2019, 1 -19.
AMA StyleKishan Singh Rawat, Sudhir Kumar Singh, Ram Lakhan Ray. An integrated approach to estimate surface soil moisture in agricultural lands. Geocarto International. 2019; ():1-19.
Chicago/Turabian StyleKishan Singh Rawat; Sudhir Kumar Singh; Ram Lakhan Ray. 2019. "An integrated approach to estimate surface soil moisture in agricultural lands." Geocarto International , no. : 1-19.
Climate change and variability, soil types and soil characteristics, animal and microbial communities, and photosynthetic plants are the major components of the ecosystem that affect carbon sequestration potential of any location. This study used NASA’s Soil Moisture Active Passive (SMAP) Level 4 carbon products, gross primary productivity (GPP), and net ecosystem exchange (NEE) to quantify their spatial and temporal variabilities for selected terrestrial ecosystems across Texas during the 2015–2018 study period. These SMAP carbon products are available at 9 km spatial resolution on a daily basis. The ten selected SMAP grids are located in seven climate zones and dominated by five major land uses (developed, crop, forest, pasture, and shrub). Results showed CO2 emissions and uptake were affected by land-use and climatic conditions across Texas. It was also observed that climatic conditions had more impact on CO2 emissions and uptake than land-use in this state. On average, South Central Plains and East Central Texas Plains ecoregions of East Texas and Western Gulf Coastal Plain ecoregion of Upper Coast climate zones showed higher GPP flux and potential carbon emissions and uptake than other climate zones across the state, whereas shrubland on the Trans Pecos climate zone showed lower GPP flux and carbon emissions/uptake. Comparison of GPP and NEE distribution maps between 2015 and 2018 confirmed substantial changes in carbon emissions and uptake across Texas. These results suggest that SMAP carbon products can be used to study the terrestrial carbon cycle at regional to global scales. Overall, this study helps to understand the impacts of climate, land-use, and ecosystem dynamics on the terrestrial carbon cycle.
Ram L. Ray; Ademola Ibironke; Raghava Kommalapati; Ali Fares. Quantifying the Impacts of Land-Use and Climate on Carbon Fluxes Using Satellite Data across Texas, U.S. Remote Sensing 2019, 11, 1733 .
AMA StyleRam L. Ray, Ademola Ibironke, Raghava Kommalapati, Ali Fares. Quantifying the Impacts of Land-Use and Climate on Carbon Fluxes Using Satellite Data across Texas, U.S. Remote Sensing. 2019; 11 (14):1733.
Chicago/Turabian StyleRam L. Ray; Ademola Ibironke; Raghava Kommalapati; Ali Fares. 2019. "Quantifying the Impacts of Land-Use and Climate on Carbon Fluxes Using Satellite Data across Texas, U.S." Remote Sensing 11, no. 14: 1733.
Evapotranspiration (ET) is a critical component of the global hydrological cycle, and it has a large impact on water resource management as it affects the availability of freshwater resources. It is important to understand the hydrological cycle for the water resources planning and management. This study used Moderate Resolution Imaging Spectroradiometer (MODIS) satellite derived ET, and potential evapotranspiration (PET) and Tropical Rainfall Measuring Mission (TRMM) satellite derived precipitation datasets to assess the spatial and temporal distributions of ET, PET, and precipitation during the study period at Three Gorges Reservoir (TGR) region. Based on the topographic variations and land-use/land-cover distributions, the study region which includes five counties of Hubei Province and nineteen counties of Chongqing Municipality was divided into four study zones. The ET and precipitation data were evaluated using in situ observations. The ET, PET, and precipitation data were compared to analyze the spatial and long-term (2001-2016) temporal distributions of average annual ET, PET, and precipitation, and to understand the relationships between them in the study region. The results showed that each selected zone had highest ET at the counties with the Yangtze River passing through whereas lowest at the counties which were located away from the river. Results also showed increasing trends in ET and PET from south-west to north-east in the study region. Analysis showed TGR had a significant impact on spatial and temporal distributions of ET and PET in the study region. Therefore, this study helps to understand the impact of TGR on spatial and temporal distributions of ET and PET during and after the construction.
Ze-Zhong Ma; Ram L Ray; Yi-Ping He. Assessing the spatiotemporal distributions of evapotranspiration in the Three Gorges Reservoir Region of China using remote sensing data. Journal of Mountain Science 2018, 15, 2676 -2692.
AMA StyleZe-Zhong Ma, Ram L Ray, Yi-Ping He. Assessing the spatiotemporal distributions of evapotranspiration in the Three Gorges Reservoir Region of China using remote sensing data. Journal of Mountain Science. 2018; 15 (12):2676-2692.
Chicago/Turabian StyleZe-Zhong Ma; Ram L Ray; Yi-Ping He. 2018. "Assessing the spatiotemporal distributions of evapotranspiration in the Three Gorges Reservoir Region of China using remote sensing data." Journal of Mountain Science 15, no. 12: 2676-2692.
A landslide susceptibility mapping study was performed using dynamic hillslope hydrology. The modified infinite slope stability model that directly includes vadose zone soil moisture (SM) was applied at Cleveland Corral, California, US and Krishnabhir, Dhading, Nepal. The variable infiltration capacity (VIC-3L) model simulated vadose zone soil moisture and the wetness index hydrologic model simulated groundwater (GW). The GW model predictions had a 75% NASH-Sutcliffe efficiency when compared to California’s in-situ GW measurements. The model performed best during the wet season. Using predicted GW and VIC-3L vadose zone SM, the developed landslide susceptibility maps showed very good agreement with mapped landslides at each study region. Previous quasi-dynamic model predictions of Nepal’s hazardous areas during extreme rainfall events were enhanced to improve the spatial characterization and provide the timing of hazardous conditions.
Ram L. Ray; Jennifer M. Jacobs; Ellen M. Douglas. Modeling regional landslide susceptibility using dynamic soil moisture profiles. Journal of Mountain Science 2018, 15, 1807 -1824.
AMA StyleRam L. Ray, Jennifer M. Jacobs, Ellen M. Douglas. Modeling regional landslide susceptibility using dynamic soil moisture profiles. Journal of Mountain Science. 2018; 15 (8):1807-1824.
Chicago/Turabian StyleRam L. Ray; Jennifer M. Jacobs; Ellen M. Douglas. 2018. "Modeling regional landslide susceptibility using dynamic soil moisture profiles." Journal of Mountain Science 15, no. 8: 1807-1824.
Assessment of Land Surface Models (LSMs) at heterogeneous terrain and climate regimes is essential for understanding complex hydrological and biophysical parameterization. This study utilized the two LSMs, Community Land Model (CLM 4.0) and three layer Variable Infiltration Capacity (VIC-3L), to estimate the interaction between land surface and atmosphere by means of energy fluxes including net radiation (R), sensible heat flux (H), latent heat flux (LE), and ground heat flux (G). The modeled energy fluxes were analyzed at two sites: Freeman Ranch-2 (FR2) located in the lowland region of Texas (272m), and Providence 301 (P301) located on the mountains of Sierra Nevada in California (2015m) from 2003 to 2013. R was underestimated by CLM with bias -25.06Wm due to its snow hydrology scheme at P301. LE was overestimated by the VIC during summer precipitation and had a positive bias of 5.51Wm, whereas CLM showed a negative bias of -6.58Wm at the FR2 site. G was considered as a residual term in CLM, which caused weak performance at P301, while VIC calculated G as a function of soil temperature, depth, and hydraulic conductivity. In addition, The MOD16 showed similar results with models at FR2; however, at P301, they yielded a correlation value of 0.85 and 0.21 for LSMs and MOD16, respectively. The later has lower correlation with in situ specifically in summer season caused by erroneous biophysical or meteorological inputs to the algorithms. The sensitivity analysis between soil moisture and turbulent fluxes, exhibited negative trend (especially for LE at P301) due to topography and snow cover. The results from this study are conducive to improvements in models and satellite based characterization of water and energy fluxes, especially at rugged terrain with high elevation, where observational experiments are difficult to conduct.
Muhammad Umair; Daeun Kim; Ram L. Ray; Minha Choi. Estimating land surface variables and sensitivity analysis for CLM and VIC simulations using remote sensing products. Science of The Total Environment 2018, 633, 470 -483.
AMA StyleMuhammad Umair, Daeun Kim, Ram L. Ray, Minha Choi. Estimating land surface variables and sensitivity analysis for CLM and VIC simulations using remote sensing products. Science of The Total Environment. 2018; 633 ():470-483.
Chicago/Turabian StyleMuhammad Umair; Daeun Kim; Ram L. Ray; Minha Choi. 2018. "Estimating land surface variables and sensitivity analysis for CLM and VIC simulations using remote sensing products." Science of The Total Environment 633, no. : 470-483.
Most climate change impacts are linked to terrestrial vegetation productivity, carbon stocks and land use change. Changes in land use and climate drive the dynamics of terrestrial carbon cycle. These carbon cycle dynamics operate at different spatial and temporal scales. Quantification of the spatial and temporal variability of carbon flux has been challenging because land-atmosphere-carbon exchange is influenced by many factors, including but not limited to, land use change and climate change and variability. The study of terrestrial carbon cycle, mainly gross primary product (GPP), net ecosystem exchange (NEE), soil organic carbon (SOC) and ecosystem respiration (Re) and their interactions with land use and climate change, are critical to understanding the terrestrial ecosystem. The main objective of this study was to examine the interactions among land use, climate change and terrestrial carbon cycling in the state of Texas using satellite measurements. We studied GPP, NEE, Re and SOC distributions for five selected major land covers and all ten climate zones in Texas using Soil Moisture Active Passive (SMAP) carbon products. SMAP Carbon products (Res=9 km) were compared with observed CO2 flux data measured at EC flux site on Prairie View A&M University Research Farm. Results showed the same land cover in different climate zones has significantly different carbon sequestration potentials. For example, cropland of the humid climate zone has higher (-228 g C/m2) carbon sequestration potentials than the semiarid climate zone (-36 g C/m2). Also, shrub land in the humid zone and in the semiarid zone showed high (-120 g C/m2) and low (-36 g C/m2) potentials of carbon sequestration, respectively, in the state. Overall, the analyses indicate CO2 storage and exchange respond differently to various land covers, and environments due to differences in water availability, root distribution and soil properties.
Ram Ray; Ali Fares; Ripendra Awal; Eric Risch; Yiping He. Exploring the Interactions between Land Use, Climate Change and Carbon Cycle using Satellite Measurements. 2018, 1 .
AMA StyleRam Ray, Ali Fares, Ripendra Awal, Eric Risch, Yiping He. Exploring the Interactions between Land Use, Climate Change and Carbon Cycle using Satellite Measurements. . 2018; ():1.
Chicago/Turabian StyleRam Ray; Ali Fares; Ripendra Awal; Eric Risch; Yiping He. 2018. "Exploring the Interactions between Land Use, Climate Change and Carbon Cycle using Satellite Measurements." , no. : 1.
To plan the proper irrigation strategy for the maximum crop yield in precision agriculture, many research works have been done extensively on how to estimate the soil moisture content. As the population and availability of thermal infrared sensors grow, their abilities to recognize the heat signatures and small differences in soil temperature help to estimate the soil moisture precisely. Since Convolutional Neural Network (CNN) has made a great success on the image recognition, we propose a scheme that integrates the thermal images captured by the sensors mounted on drones along with in situ measurements of the farm area and a CNN-based regression model to estimate the soil moisture. This model is used to estimate the soil moisture content through the soil temperature represented by thermal infrared images. Our experimental results show that our model performs better than the typical Deep Neural Network (DNN) with the test datasets, which in turn provides the more general estimation.
Remilekun Sobayo; Hsiang-Huang Wu; Ram Ray; Lijun Qian. Integration of Convolutional Neural Network and Thermal Images into Soil Moisture Estimation. 2018 1st International Conference on Data Intelligence and Security (ICDIS) 2018, 207 -210.
AMA StyleRemilekun Sobayo, Hsiang-Huang Wu, Ram Ray, Lijun Qian. Integration of Convolutional Neural Network and Thermal Images into Soil Moisture Estimation. 2018 1st International Conference on Data Intelligence and Security (ICDIS). 2018; ():207-210.
Chicago/Turabian StyleRemilekun Sobayo; Hsiang-Huang Wu; Ram Ray; Lijun Qian. 2018. "Integration of Convolutional Neural Network and Thermal Images into Soil Moisture Estimation." 2018 1st International Conference on Data Intelligence and Security (ICDIS) , no. : 207-210.
Increased crop yield is required to meet the needs of future population growth, but drought causes significant yield reductions for rainfed and irrigated crops. This study evaluates the impact of drought on crop yield and cropping area over 10 climate zones in Texas from 2008 to 2016. It also depicts the spatiotemporal distribution of crop yield and cropping area changes at each climate zone across the state. We analyzed the impact of drought on crop yields and cropping areas before and after the 2011 severe drought using annual crop yields of four major crops. Results show that drought had a greater impact on winter wheat (Triticum aestivum L.) and corn (Zea mays L.) and lesser impact on cotton (Gossypium spp.) and sorghum [Sorghum bicolor (L.) Moench] production across Texas. Cotton and corn hectarages were reduced during the drought period and increased after that, whereas winter wheat hectarage was reduced in the northern climate zones and increased in the southern climate zones before the drought. Results also indicate that drought impact on crop production may be reduced by replacing water-demanding crops such as corn with drought-tolerant crops such as sorghum and expanding irrigation hectarage during drought periods. It may be beneficial for Texas agricultural production to increase the hectarage of sorghum and other grains especially during drought periods. This study provides valuable information that can be used to adopt appropriate measures to cope with future drought challenges in drought-prone regions. Copyright © 2018. . Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.
Ram L. Ray; Ali Fares; Eric Risch. Effects of Drought on Crop Production and Cropping Areas in Texas. Agricultural & Environmental Letters 2018, 3, 170037 .
AMA StyleRam L. Ray, Ali Fares, Eric Risch. Effects of Drought on Crop Production and Cropping Areas in Texas. Agricultural & Environmental Letters. 2018; 3 (1):170037.
Chicago/Turabian StyleRam L. Ray; Ali Fares; Eric Risch. 2018. "Effects of Drought on Crop Production and Cropping Areas in Texas." Agricultural & Environmental Letters 3, no. 1: 170037.
The main goal of this study was to evaluate four major remote sensing soil moisture (SM) products over the state of Texas. These remote sensing products are: (i) the Advanced Microwave Scanning Radiometer—Earth Observing System (AMSR-E) (2002–September 2011); (ii) the Soil Moisture Ocean Salinity system (SMOS, 2010–present); (iii) AMSR2 (2012–present); and (iv) the Soil Moisture Active Passive system (SMAP, 2015–present). The quality of the generated SM data is influenced by the accuracy and precision of the sensors and the retrieval algorithms used in processing raw data. Therefore, it is important to evaluate the quality of these satellite SM products using in situ measurements and/or by inter-comparing their data during overlapping periods. In this study, these two approaches were used where we compared each satellite SM product to in situ soil moisture measurements and we also conducted an inter-comparison of the four satellite SM products at 15 different locations in Texas over six major land cover types (cropland, shrub, grassland, forest, pasture and developed) and eight climate zones along with in situ SM data from 15 Mesonet, USCRN and USDA-NRCS Scan stations. Results show that SM data from SMAP had the best correlation coefficients range from 0.37 to 0.92 with in situ measurements among the four tested satellite surface SM products. On the other hand, SM data from SMOS, AMSR2 and AMSR-E had moderate to low correlation coefficients ranges with in situ data, respectively, from 0.24–0.78, 0.07–0.62 and 0.05–0.52. During the overlapping periods, average root mean square errors (RMSEs) of the correlations between in situ and each satellite data were 0.13 (AMSR-E) and 0.13 (SMOS) cm3/cm3 (2010–2011), 0.16 (AMSR2) and 0.14 (SMOS) cm3/cm3 (2012–2016) and 0.13, 0.16, 0.14 (SMAP, AMSR2, SMOS) cm3/cm3 (2015–2016), respectively. Despite the coarser spatial resolution of all four satellite products (25–36 km), their SM measurements are considered reasonable and can be effectively used for different applications, e.g., flood forecasting, and drought prediction; however, further evaluation of each satellite product is recommended prior to its use in practical applications.
Ram L. Ray; Ali Fares; Yiping He; Marouane Temimi. Evaluation and Inter-Comparison of Satellite Soil Moisture Products Using In Situ Observations over Texas, U.S. Water 2017, 9, 372 .
AMA StyleRam L. Ray, Ali Fares, Yiping He, Marouane Temimi. Evaluation and Inter-Comparison of Satellite Soil Moisture Products Using In Situ Observations over Texas, U.S. Water. 2017; 9 (6):372.
Chicago/Turabian StyleRam L. Ray; Ali Fares; Yiping He; Marouane Temimi. 2017. "Evaluation and Inter-Comparison of Satellite Soil Moisture Products Using In Situ Observations over Texas, U.S." Water 9, no. 6: 372.
Ram L. Ray; Ali Fares; Ripendra Awal; Eric Risch. ASSESSING THE EFFECTS OF CHANGE IN IMPERVIOUS AREAS ON FLOODING IN TEXAS. 51st Annual GSA South-Central Section Meeting - 2017 2017, 1 .
AMA StyleRam L. Ray, Ali Fares, Ripendra Awal, Eric Risch. ASSESSING THE EFFECTS OF CHANGE IN IMPERVIOUS AREAS ON FLOODING IN TEXAS. 51st Annual GSA South-Central Section Meeting - 2017. 2017; ():1.
Chicago/Turabian StyleRam L. Ray; Ali Fares; Ripendra Awal; Eric Risch. 2017. "ASSESSING THE EFFECTS OF CHANGE IN IMPERVIOUS AREAS ON FLOODING IN TEXAS." 51st Annual GSA South-Central Section Meeting - 2017 , no. : 1.
Daeun Kim; Ram L. Ray; Seokkoo King; Minha Choi. Estimation of Land Surface Energy Fluxes using CLM and VIC model. Journal of Wetlands Research 2016, 18, 166 -172.
AMA StyleDaeun Kim, Ram L. Ray, Seokkoo King, Minha Choi. Estimation of Land Surface Energy Fluxes using CLM and VIC model. Journal of Wetlands Research. 2016; 18 (2):166-172.
Chicago/Turabian StyleDaeun Kim; Ram L. Ray; Seokkoo King; Minha Choi. 2016. "Estimation of Land Surface Energy Fluxes using CLM and VIC model." Journal of Wetlands Research 18, no. 2: 166-172.