Comparisons of Two Spatial Implementations of a Crop Model Using Remotely Sensed Observations over Southeastern United States

Global food security is one of the most pressing issues of the current century, particularly for developing nations. Agricultural simulation models can be a key component in testing new technologies, seeds and cultivars etc. However, inaccurate input information, model related errors and the mode of implementation can also add to model uncertainties. In this study, the crop model is implemented in two separate fashions: a)gridded (GriDSSAT model) and b) using random spatial ensembles (RHEAS model). This is done in the Southeastern US to evaluate and understand the modelperformance over a region data availabilities. Once the model performance is assessed, multiple satellite based earth observation parameters such as soil moisture, vegetation index etc. can be assimilated into crop models to reduce input and model related uncertainties particularly in data limited regions. In this study, the National Agricultural Statistical Services (NASS) reported yield data at county levels are used for comparison andvalidation purposes. The GriDSSAT model estimation of corn yields in comparison with the reported NASS yields showed an overall RMSD of nearly 3720 (kg/ha) whereas RMSD for the RHEAS model implementation was 3550 (kg/ha). Overall the GriDSSAT model had negative bias of nearly 2400 kg/ha (except for 2013) while RHEAS had a slight positive bias of 400 kg/ha (approx.)

Development of a Drought and Yield Assessment System in Kenya

Dependence on rainfed agriculture in a highly variable climate, renders crop and livestock production vulnerable to impacts of drought in Kenya. Stakeholders in the region have highlighted the need for timely and actionable detailed early warning information on drought and its implication on crop productivity. Here we apply the Regional Hydrological Extremes Assessment System (RHEAS) to estimate current and future drought conditions onset, severity, recovery, and duration) and expected productivity outlooks

GC13I-0857: Designing a Frost Forecasting Service for Small Scale Tea Farmers in East Africa

Kenya is the third largest tea exporter in the world, producing 10% of the world's black tea. Sixty percent of this production occurs largely by small scale tea holders, with an average farm size of 1.04 acres, and an annual net income of $1,075. According to a recent evaluation, a typical frost event in the tea growing region causes about$200 dollars in losses which can be catastrophic for a small holder farm. A 72-hour frost forecast would provide these small-scale tea farmers with enough notice to reduce losses by approximately 80 USD annually. With this knowledge, SERVIR, a joint NASA-USAID initiative that brings Earth observations for improved decision making in developing countries, sought to design a frost monitoring and forecasting service that would provide farmers with enough lead time to react to and protect against a forecasted frost occurrence on their farm. SERVIR Eastern and Southern Africa, through its implementing partner, the Regional Centre for Mapping of Resources for Development (RCMRD), designed a service that included multiple stakeholder engagement events whereby stakeholders from the tea industry value chain were invited to share their experiences so that the exact needs and flow of information could be identified. This unique event allowed enabled the design of a service that fit the specifications of the stakeholders. The monitoring service component uses the MODIS Land Surface Temperature product to identify frost occurrences in near-real time. The prediction component, currently under testing, uses the 2-m air temperature, relative humidity, and 10-m wind speed from a series of high-resolution Weather Research and Forecasting (WRF) numerical weather prediction model runs over eastern Kenya as inputs into a frost prediction algorithm. Accuracy and sensitivity of the algorithm is being assessed with observations collected from the farmers using a smart phone app developed specifically to report frost occurrences, and from data shared through our partner network developed at the stakeholder engagement meeting. This presentation will illustrate the efficacy of our frost forecasting algorithm, and a way forward for incorporating these forecasts in a meaningful way to the key decision makers - the small-scale farmers of East Africa

Book of Abstracts: Regional Knowledge Forum on Drought: Earth Observation and Climate Services for Food Security and Agricultural Decision Making in South Asia and Southeast Asia, Kathmandu, Nepal, 8-10 October 2018

Asia has some of the highest peaks in the world as well as several low-lying plains and coastal areas. River basins in Asia – such as the Ganges-Brahmaputra, Indus, Mekong, and Yangtze –provide water that sustains agriculture and livelihoods in the region. Agriculture in this region accounts for two-thirds of the global agricultural gross domestic product (GDP). In a changing climate, this agricultural productivity will likely be effected negatively as changes in rainfall patterns and intensity affect agricultural production – especially in marginal rain-fed areas. There has been considerable progress on agro-climatic monitoring and modelling using remotely sensed information in combination with field knowledge to counter these negative impacts. Lessons from this work can provide a basis for sustainable crop management, and provide farmers with alternative options to adapt to changes in climate and cope with the impacts. SERVIR, a joint initiative between the United States Agency for International Development (USAID) and the National Aeronautics and Space Administration (NASA), helps developing countries integrate publicly available earth observation information and geospatial technologies into decision-making to address critical challenges in food security, water resources, weather and climate, land use, and natural disasters. As part of the SERVIR global network, underpinning regional knowledge exchange on the use of Earth observation tools and technologies in decision-making processes is crucial to our remit. This forum will help build synergies among national, regional, and international initiatives to share knowledge on emerging issues, and take us a step closer to providing solutions for national institutions and local communities. The forum brings together researchers and practitioners from Asia and beyond to discuss the emerging potential of Earth observation information and climate modelling to reduce climate related vulnerabilities in the agriculture sector in south and southeast Asia. Climate change is affecting the whole watershed, from mountain to sea. Vulnerable communities need help in adapting to and mitigating negative impact from these changes. We are continually striving to strengthen our programmes to promote resilient communities and strengthen ecosystem service in the region. We hope to achieve this through regional and international partnerships to further enhance regional capacities and develop a relevant knowledge base by using the best science and technology in Earth observation and climate sciences

Flood inundation mapping- Kerala 2018; Harnessing the power of SAR, automatic threshold detection method and Google Earth Engine.

Flood inundation maps provide valuable information towards flood risk preparedness, management, communication, response, and mitigation at the time of disaster, and can be developed by harnessing the power of satellite imagery. In the present study, Sentinel-1 Synthetic Aperture RADAR (SAR) data and Otsu method were utilized to map flood inundation areas. Google Earth Engine (GEE) was used for implementing Otsu algorithm and processing Sentinel-1 SAR data. The results were assessed by (i) calculating a confusion matrix; (ii) comparing the submerge water areas of flooded (Aug 2018), non-flooded (Jan 2018) and previous year's flooded season (Aug 2016, Aug 2017), and (iii) analyzing historical rainfall patterns to understand the flood event. The overall accuracy for the Sentinel-1 SAR flood inundation maps of 9th and 21st August 2018 was observed as 94.3% and 94.1% respectively. The submerged area (region under water) classified significant flooding as compared to the non-flooded (January 2018) and previous year's same season (August 2015-2017) classified outputs. Summing up, observations from Sentinel-1 SAR data using Otsu algorithm in GEE can act as a powerful tool for mapping flood inundation areas at the time of disaster, and enhance existing efforts towards saving lives and livelihoods of communities, and safeguarding infrastructure and businesses

Evaluating Ecosystem Services for the Expansion of Irrigation on Agricultural Land

Managing water resources requires consideration of both environmental and socio-economic benefits to effectively balance the benefits and costs. This includes identifying ecosystem services (ES) of concern and how to evaluate the project or proposed changes effect on these ES. The purpose of this effort is to describe methods to evaluate ecosystem services to provide expanded irrigation to existing agricultural lands in Alabama and the potential application to other areas. A case study has been undertaken on the Middle Alabama watershed in central Alabama and methods have been developed and applied to evaluate ES in terms of how irrigated versus rainfed fields will affect sediment retention, fertilizer usage and the effect of the subsequent discharges of sediment and nitrogen from fertilizer on water quality. The results of case studies in the Middle Alabama watershed indicate positive ES benefits from sustainable agricultural practices and the irrigation of agricultural lands versus rainfed fields. We anticipate these methods will be applicable to other watersheds outside the southeast region too

Towards Developing a Customized Composite Drought Index for Pakistan: A Framework for Understanding Drought in an Agricultural Context

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