Sami Khanal

An overview of current and potential applications of thermal remote sensing in precision agriculture

Abstract Precision agriculture (PA) utilizes tools and technologies to identify in-field soil and crop variability for improving farming practices and optimizing agronomic inputs. Traditionally, optical remote sensing (RS) that utilizes visible light and infrared regions of the electromagnetic spectrum has been used as an integral part of PA for crop and soil monitoring. Optical RS, however, is slow in differentiating stress levels in crops until visual symptoms become noticeable. Surface temperature is considered to be a rapid response variable that can indicate crop stresses prior to their visual symptoms. By measuring estimates of surface temperature, thermal RS has been found to be a promising tool for PA. Compared to optical RS, applications of thermal RS for PA have been limited. Until recently (i.e., before the advancement of low cost RS platforms such as unmanned aerial systems (UAVs)), the availability of high resolution thermal images was limited due to high acquisition costs. Given recent developments in UAVs, thermal images with high spatial and temporal resolutions have become available at a low cost, which has increased opportunities to understand in-field variability of crop and soil conditions useful for various agronomic decision-making. Before thermal RS is adopted as a routine tool for crop and environmental monitoring, there is a need to understand its current and potential applications as well as issues and concerns. This review focuses on current and potential applications of thermal RS in PA as well as some concerns relating to its application. The application areas of thermal RS in agriculture discussed here include irrigation scheduling, drought monitoring, crop disease detection, and mapping of soil properties, residues and tillage, field tiles, and crop maturity and yield. Some of the issues related to its application include spatial and temporal resolution, atmospheric conditions, and crop growth stages.

Implications of biofuel policy-driven land cover change for rainfall erosivity and soil erosion in the United States

Large‐scale conversion of traditional agricultural cropping systems to biofuel cropping systems is predicted to have significant impact on the hydrologic cycle. Changes in the hydrologic cycle lead to changes in rainfall and its erosive power, and consequently soil erosion that will have onsite impacts on soil quality and crop productivity, and offsite impacts on water quality and quantity. We examine regional change in rainfall erosivity and soil erosion resulting from biofuel policy‐induced land use/land cover (LULC) change. Regional climate is simulated under current and biofuel LULC scenarios for the period 1979–2004 using the Weather Research Forecast (WRF) model coupled to the NOAH land surface model. The magnitude of change in rainfall erosivity under the biofuel scenario is 1.5–3 times higher than the change in total annual rainfall. Over most of the conterminous United States (~56%), the magnitude of the change in erosivity is between −2.5% and +2.5%. A decrease in erosivity of magnitude 2.5–10% is predicted over 23% of the area, whereas an increase of the same magnitude is predicted over 14% of the area. Corresponding to the changes in rainfall erosivity and crop cover, a decrease in soil loss is predicted over 60% of the area under the biofuel scenario. In Kansas and Oklahoma, the states in which a large fraction of land area is planted with switchgrass under the biofuel scenario, soil loss is estimated to decrease 12% relative to the baseline. This reduction in soil loss is due more to changes in the crop cover factor than changes in rainfall or rainfall erosivity. This indicates that the changes in LULC, due to future cellulosic biofuel feedstock production, can have significant implications for regional soil and water resources in the United States and we recommend detailed investigation of the trade‐offs between land use and management options.

A techno-environmental overview of a corn stover biomass feedstock supply chain for cellulosic biorefineries

ABSTRACT Corn stover is the primary feedstock choice of the first generation of cellulosic biorefineries in the Midwestern US due to its abundance in the region. The technically sound, economic and environment-friendly supply system of this feedstock is inevitable for the commercial success and proliferation of these biorefineries. Such supply systems involve different aspects of stover production, harvesting, collection, handling, transportation, storage and preprocessing. This review discusses the environmental concerns of stover collection for biofuels production, and provides an update on the technological status of the corn stover feedstock supply system. The major environmental concerns of corn stover collection discussed here include its impact on soil nitrogen availability and crop requirements, changes in crop productivity, soil erosion and change in soil organic carbon, soil fertility, carbon sequestration, nutrient emissions and disease pressures. The cellulosic biorefinery feedstock quality requirements and different supply chain components are also thoroughly discussed. This review concludes by presenting a perspective on near-term and future corn stover-based feedstock supply systems with the potential to be commercially implemented. Although the extent of stover removal from the field is highly debated, there is a consensus that some fraction of biomass removal would help lower both agronomic production cost and environmental impact.

Streamflow impacts of biofuel policy-driven landscape change.

Likely changes in precipitation (P) and potential evapotranspiration (PET) resulting from policy-driven expansion of bioenergy crops in the United States are shown to create significant changes in streamflow volumes and increase water stress in the High Plains. Regional climate simulations for current and biofuel cropping system scenarios are evaluated using the same atmospheric forcing data over the period 1979–2004 using the Weather Research Forecast (WRF) model coupled to the NOAH land surface model. PET is projected to increase under the biofuel crop production scenario. The magnitude of the mean annual increase in PET is larger than the inter-annual variability of change in PET, indicating that PET increase is a forced response to the biofuel cropping system land use. Across the conterminous U.S., the change in mean streamflow volume under the biofuel scenario is estimated to range from negative 56% to positive 20% relative to a business-as-usual baseline scenario. In Kansas and Oklahoma, annual streamflow volume is reduced by an average of 20%, and this reduction in streamflow volume is due primarily to increased PET. Predicted increase in mean annual P under the biofuel crop production scenario is lower than its inter-annual variability, indicating that additional simulations would be necessary to determine conclusively whether predicted change in P is a response to biofuel crop production. Although estimated changes in streamflow volume include the influence of P change, sensitivity results show that PET change is the significantly dominant factor causing streamflow change. Higher PET and lower streamflow due to biofuel feedstock production are likely to increase water stress in the High Plains. When pursuing sustainable biofuels policy, decision-makers should consider the impacts of feedstock production on water scarcity.

Integration of high resolution remotely sensed data and machine learning techniques for spatial prediction of soil properties and corn yield

Abstract Widespread adoption of precision agriculture requires timely acquisition of low-cost, high quality soil and crop yield maps. Integration of remotely sensed data and machine learning algorithms offers cost-and time-effective approach for spatial prediction of soil properties and crop yield compared to conventional approaches. The objectives of this study were to: (i) evaluate the role of remotely sensed images; (ii) compare the performance of various machine learning algorithms; and (iii) identify the importance of remotely sensed image-derived variables, in spatial prediction of soil properties and corn yield. This study integrated field based data on five soil properties (i.e., soil organic matter (SOM), cation exchange capacity (CEC), magnesium (Mg), potassium (K), and pH) and yield monitor based corn yield data with multispectral aerial images and topographic data, both collected in 2013, from seven fields at the Molly Caren Farm near London, Ohio. Digital elevation model data, at a resolution of 1 m, was used to derive topographic properties of the fields. Multispectral images collected at bare-soil conditions, at a resolution 0.30 m, were used to derive soil and vegetation indices. Models developed for prediction of soil properties and corn yield using linear regression (LM) and five machine learning algorithms (i.e., Random Forest (RF); Neural Network (NN); Support Vector Machine (SVM) with radial and linear kernel functions; Gradient Boosting Model (GBM); and Cubist (CU)) were evaluated in terms of coefficient of determination (R 2 ) and root mean square error (RMSE). Machine learning algorithms were found to outperform LM algorithm for most of the times with a higher R 2 and lower RMSE. Based on models for seven fields, on average, NN provided the highest accuracy for SOM (R 2  = 0.64, RMSE = 0.44) and CEC (R 2  = 0.67, RMSE = 2.35); SVM for K (R 2  = 0.21, RMSE = 0.49) and Mg (R 2  = 0.22, RMSE = 4.57); and GBM for pH (R 2  = 0.15, RMSE = 0.62). For corn yield, RF consistently outperformed other models and provided higher accuracy (R 2  = 0.53, RMSE = 0.97). Soil and vegetation indices based on bare-soil imagery played a more significant role in demonstrating in-field variability of corn yield and soil properties than topographic variables. The accuracy of the models developed for prediction of soil properties and corn yield observed in this study suggested that the approach of integrating remotely sensed data and machine learning algorithms are promising for mapping soil properties and corn yield at a local scale, which can be useful in locating areas of potential concerns and implementing site-specific farming practices.

Integrating aerial images for in-season nitrogen management in a corn field

Abstract Methods of determining in-season corn (Zea mays L.) nitrogen (N) requirements and yield estimates are needed for designing a resource-efficient corn production system that is both profitable and environmentally sustainable. The objectives of this study were to examine: (1) the role of spectral signatures of corn plants obtained by aerial images in examining the yield variability across various N treatments, (2) whether the images could be used to guide in-season N management decisions, and to predict in-season corn yield and corn yield loss, and (3) the influence of spatial resolution of imagery on the accuracy of corn yield prediction models. Twenty-four treatments evaluated were the combinations of eight fertilization times (at-planting (A), pre-planting (P)∗A, P∗A∗mid-season (M), P∗A∗late-season (L), PAML, AM, AL, and AML) and three at-planting N rates (11, 45, and 78 kg N ha−1). Visual and thermal images were collected from manned aircraft and geo-corrected for the analyses. Vegetation indices and ratios were derived from three waveband combinations of visual images, and they were examined in relation to yield. Two linear regression models - model 1 (based solely on imagery) and model 2 (based on imagery and information about elevation and N fertilizer application rate), were tested on their performances (in terms of coefficient of determination (R2) and root mean square error (RMSE)) for in-season corn yield prediction at four spatial resolutions (0.35, 0.5, 1, and 2 m px−1). Among individual wavebands, and vegetation indices and ratio, plant pigment ratio (PPR) at early growth stages were highly correlated to corn yield, particularly in the field that received limited N application. The correlation improved as the corn growth stage progressed, but weakened towards the end of the growing season. There were significant differences in PPR values between the treatments receiving the least and the most N application, and it was the amount of N applied at planting that created the most significant differences. The models for 0.35 to 1 m px−1 spatial resolutions did not show significant improvements in R2 over the lowest ground resolutions (2 m px−1) (differences in R2 ≤ 0.05). The model 2 showed higher R2 (up to 0.64 at tasseling stage) and lower RMSE than model 1. These results indicate that the models developed integrating spectral and spatial information from aerial imagery with the information about elevation and N application rate help improve in-season corn yield estimates under different N management practices.

A GIS analysis of the spatial relation between evapotranspiration and pan evaporation in the United States

Despite increases in global temperature, studies have observed a decrease in evaporation in the Northern Hemisphere. To examine whether a decrease in pan evaporation also indicates decreased evapotranspiration (ET), ET rates were modelled in a geographic information system by integrating climatic data and water-balance data from 1997 to 2007. Average monthly ET values were compared with National Climatic Data Center pan-evaporation (PE) data. PE and ET were significantly related, but the degree of significance and the direction of the relation (positive or negative) varied across eco-divisions and seasons. Thus, decreased pan evaporation does not necessarily imply that ET will decrease as well.

Assessment of Impacts of Hurricane Katrina on Net Primary Productivity in Mississippi

Abstract Southern forests contribute significantly to the carbon sink for the atmospheric carbon dioxide (CO2) associated with the anthropogenic activities in the United States. Natural disasters like hurricanes are constantly threatening these forests. Hurricane winds can have a destructive impact on natural vegetation and can adversely impact net primary productivity (NPP). Hurricane Katrina (23–30 August 2005), one of the most destructive natural disasters in history, has affected the ecological balance of the Gulf Coast. This study analyzed the impacts of different categories of sustained winds of Hurricane Katrina on NPP in Mississippi. The study used the Carnegie–Ames–Stanford Approach (CASA) model to estimate NPP by using remote sensing data. The results indicated that NPP decreased by 14% in the areas hard hit by category 3 winds and by 1% in the areas hit by category 2 winds. However, there was an overall increase in NPP, from 2005 to 2006 by 0.60 Tg of carbon, in Mississippi. The authors found t...