Geography
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Item Wheat Yield Forecasting for Punjab Province from Vegetation Index Time Series and Historic Crop Statistics(MDPI, 2014-10-13) Dempewolf, Jan; Adusei, Bernard; Becker-Reshef, Inbal; Hansen, Matthew; Potapov, Peter; Khan, Ahmad; Barker, BrianPolicy makers, government planners and agricultural market participants in Pakistan require accurate and timely information about wheat yield and production. Punjab Province is by far the most important wheat producing region in the country. The manual collection of field data and data processing for crop forecasting by the provincial government requires significant amounts of time before official reports can be released. Several studies have shown that wheat yield can be effectively forecast using satellite remote sensing data. In this study, we developed a methodology for estimating wheat yield and area for Punjab Province from freely available Landsat and MODIS satellite imagery approximately six weeks before harvest. Wheat yield was derived by regressing reported yield values against time series of four different peak-season MODIS-derived vegetation indices. We also tested deriving wheat area from the same MODIS time series using a regression-tree approach. Among the four evaluated indices, WDRVI provided more consistent and accurate yield forecasts compared to NDVI, EVI2 and saturation-adjusted normalized difference vegetation index (SANDVI). The lowest RMSE values at the district level for forecast versus reported yield were found when using six or more years of training data. Forecast yield for the 2007/2008 to 2012/2013 growing seasons were within 0.2% and 11.5% of final reported values. Absolute deviations of wheat area and production forecasts from reported values were slightly greater compared to using the previous year’s or the three- or six-year moving average values, implying that 250-m MODIS data does not provide sufficient spatial resolution for providing improved wheat area and production forecasts.Item A Mapping Framework to Characterize Land Use in the Sudan-Sahel Region from Dense Stacks of Landsat Data(MDPI, 2019-03-16) Sedano, Fernando; Molini, Vasco; Azad, M. Abdul KalamWe developed a land cover and land use mapping framework specifically designed for agricultural systems of the Sudan-Sahel region. The mapping approach extracts information from inter- and intra-annual vegetation dynamics from dense stacks of Landsat 8 images. We applied this framework to create a 30 m spatial resolution land use map with a focus on agricultural landscapes of northern Nigeria for 2015. This map provides up-to-date information with a higher level of spatial and thematic detail resulting in a more precise characterization of agriculture in the region. The map reveals that agriculture is the main land use in the region. Arable land represents on average 52.5% of the area, higher than the reported national average for Nigeria (38.4%). Irrigated agriculture covers nearly 2.2% of the total area, reaching nearly 20% of the cultivated land when traditional floodplain agriculture systems are included, above the reported national average (0.63%). There is significant variability in land use within the region. Cultivated land in the northern section can reach values higher than 75%, most land suitable for agriculture is already under cultivation and there is limited land for future agricultural expansion. Marginal lands, not suitable for permanent agriculture, can reach 30% of the land at lower altitudes in the northeast and northwest. In contrast, the southern section presents lower land use intensity that results in a complex landscape that intertwines areas farms and larger patches of natural vegetation. This map improves the spatial detail of existing sources of LCLU information for the region and provides updated information of the current status of its agricultural landscapes. This study demonstrates the feasibility of multi temporal medium resolution remote sensing data to provide detailed and up-to-date information about agricultural systems in arid and sub arid landscapes of the Sahel region.Item Winter Wheat Yield Assessment from Landsat 8 and Sentinel-2 Data: Incorporating Surface Reflectance, Through Phenological Fitting, into Regression Yield Models(MDPI, 2019-07-27) Skakun, Sergii; Vermote, Eric; Franch, Belen; Roger, Jean-Claude; Kussul, Nataliia; Ju, Junchang; Masek, JeffreyA combination of Landsat 8 and Sentinel-2 offers a high frequency of observations (3–5 days) at moderate spatial resolution (10–30 m), which is essential for crop yield studies. Existing methods traditionally apply vegetation indices (VIs) that incorporate surface reflectances (SRs) in two or more spectral bands into a single variable, and rarely address the incorporation of SRs into empirical regression models of crop yield. In this work, we address these issues by normalizing satellite data (both VIs and SRs) derived from NASA’s Harmonized Landsat Sentinel-2 (HLS) product, through a phenological fitting. We apply a quadratic function to fit VIs or SRs against accumulated growing degree days (AGDDs), which affects the rate of crop development. The derived phenological metrics for VIs and SRs, namely peak, area under curve (AUC), and fitting coefficients from a quadratic function, were used to build empirical regression winter wheat models at a regional scale in Ukraine for three years, 2016–2018. The best results were achieved for the model with near infrared (NIR) and red spectral bands and derived AUC, constant, linear, and quadratic coefficients of the quadratic model. The best model yielded a root mean square error (RMSE) of 0.201 t/ha (5.4%) and coefficient of determination R2 = 0.73 on cross-validation.Item Assessing within-Field Corn and Soybean Yield Variability from WorldView-3, Planet, Sentinel-2, and Landsat 8 Satellite Imagery(MDPI, 2021-02-26) Skakun, Sergii; Kalecinski, Natacha I.; Brown, Meredith G. L.; Johnson, David M.; Vermote, Eric F.; Roger, Jean-Claude; Franch, BelenCrop yield monitoring is an important component in agricultural assessment. Multi-spectral remote sensing instruments onboard space-borne platforms such as Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) have shown to be useful for efficiently generating timely and synoptic information on the yield status of crops across regional levels. However, the coarse spatial resolution data inherent to these sensors provides little utility at the management level. Recent satellite imagery collection advances toward finer spatial resolution (down to 1 m) alongside increased observational cadence (near daily) implies information on crops obtainable at field and within-field scales to support farming needs is now possible. To test this premise, we focus on assessing the efficiency of multiple satellite sensors, namely WorldView-3, Planet/Dove-Classic, Sentinel-2, and Landsat 8 (through Harmonized Landsat Sentinel-2 (HLS)), and investigate their spatial, spectral (surface reflectance (SR) and vegetation indices (VIs)), and temporal characteristics to estimate corn and soybean yields at sub-field scales within study sites in the US state of Iowa. Precision yield data as referenced to combine harvesters’ GPS systems were used for validation. We show that imagery spatial resolution of 3 m is critical to explaining 100% of the within-field yield variability for corn and soybean. Our simulation results show that moving to coarser resolution data of 10 m, 20 m, and 30 m reduced the explained variability to 86%, 72%, and 59%, respectively. We show that the most important spectral bands explaining yield variability were green (0.560 μm), red-edge (0.726 μm), and near-infrared (NIR − 0.865 μm). Furthermore, the high temporal frequency of Planet and a combination of Sentinel-2/Landsat 8 (HLS) data allowed for optimal date selection for yield map generation. Overall, we observed mixed performance of satellite-derived models with the coefficient of determination (R2) varying from 0.21 to 0.88 (averaging 0.56) for the 30 m HLS and from 0.09 to 0.77 (averaging 0.30) for 3 m Planet. R2 was lower for fields with higher yields, suggesting saturation of the satellite-collected reflectance features in those cases. Therefore, other biophysical variables, such as soil moisture and evapotranspiration, at similar fine spatial resolutions are likely needed alongside the optical imagery to fully explain the yields.Item Considerations for AI-EO for agriculture in Sub-Saharan Africa(Institute of Physics, 2023-03-24) Nakalembe, Catherine; Kerner, HannahAdapting to and mitigating climate change while addressing food insecurity are top priorities in SubSaharan Africa that require technologies to improve rural livelihoods with minimal environmental costs [1]. Artificial intelligence (AI) offers great promise for climate-smart solutions that improve food security outcomes. While precision agriculture is often the foremost use case for AI in agriculture (e.g. automation of farm equipment or nutrient application), precision agriculture is out of reach for most African farmers due to the required capital and infrastructure. AI solutions using satellite Earth observations (EOs), which we call AI-EO, are more accessible in the near term. EO enables agricultural analyses and insights at global scales, and many datasets are freely available, making EO-based solutions affordable [2]. AI-EO-derived products such as crop type maps and yield estimates are necessary to forecast food production surpluses or deficits, inform trade, and aid decisions. These products can support policies that accelerate the design and adoption of climate-smart agriculture and impact farmer livelihoods by increasing access to actionable early warning, risk financing or insurance [3], farm inputs, markets, and costreducing interventions [2, 4]. Despite their promise, AI-EO solutions for agriculture in Africa are still limited. Most techniques are not generalizable across heterogeneous landscapes. In this paper, we describe the principal sub-fields of research in AI-EO for agriculture in Africa and discuss examples and limitations of existing work. We also propose ten considerations for future work to help increase the impact of AI-EO research in Africa.Item Effectively evaluating environmental, social, and economic outcomes in voluntary sustainability programs: Lessons from Laos(2022) Traldi, Rebecca; Silva, Julie A; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Voluntary sustainability programs (VSPs) are a subset of environmental interventions which rely on participants’ willingness to engage, rather than mandatory regulation. VSPs have been a central component of sustainable development and environmental mitigation strategies for decades, with significant investments from nongovernmental organizations (NGOs), multilaterals, and the private sector. VSPs typically aim to positively influence environmental, economic, and social outcomes, although program-specific priorities often result in an uneven focus across these three domains (also known as the three pillars of sustainability). Despite their popularity, questions regarding the value of VSPs remain unanswered. Assessments of VSPs typically do not eliminate rival explanations for program outcomes when evaluating their successes and failures, thus limiting our understanding of their effectiveness.This dissertation addresses this gap by investigating socioeconomic and environmental outcomes for agriculture and forestry VSPs. Mixed methods including systematic review, inverse probability-of-treatment weighted regression (IPWR), and inequality and polarization decomposition provide insights both at a global level, and for two national case studies in Lao People’s Democratic Republic (hereafter Laos). A wide range of datasets inform the analysis, including nationally representative poverty and expenditure surveys and land-use land cover estimates derived from remotely sensed imagery. By exploring a variety of VSPs – including agricultural and forestry voluntary sustainability standards and sustainable development projects – the study acknowledges the context-specific nature of VSP impact, while also drawing generalizable insights relevant for different types of interventions. The research findings presented in this dissertation elucidate the degree to which VSPs deliver on stated goals and objectives. First, a systematic literature review reveals that the evidence base for VSP impact remains limited, with some geographies, sustainability outcomes, and project types receiving more inquiry and evaluation than others. Second, an IPWR analysis suggests that agriculture and forestry VSPs have achieved some success in generating positive outcomes – specifically, for poverty and forest cover. However, variations in project focus and design bring different results. For example, food security and livelihoods programs which prioritize local socioeconomic well-being can generate significant co-benefits for environmental outcomes, and resource management projects can positively impact forest cover. Conversely, the forest management projects considered here do not achieve significant benefits for poverty or forest cover – presumably due to challenges like land tenure insecurity, insufficient participant incentives, and persistent drivers of deforestation (illegal logging, large-scale concessions). Finally, an assessment of economic inequality and polarization associated with the Laos rubber boom demonstrates the importance of assessing how VSPs influence economic inequality. It also indicates that VSPs must address inequality’s systemic drivers – including dispossession from land and forest resources, lacking worker protections, livelihood vulnerability, and barriers for smallholders – to maximize potential benefits. Overall, this dissertation research provides an example of how evidence synthesis, quasi-experimental methods, and consideration of economic, social, and environmental sustainability can deepen our understanding of VSPs.Item Factors Influencing Remote Sensing Measurements of Winter Cover Crops(2016) Prabhakara, Kusuma; Justice, Christopher O; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Winter cover crops are an essential part of managing nutrient and sediment losses from agricultural lands. Cover crops lessen sedimentation by reducing erosion, and the accumulation of nitrogen in aboveground biomass results in reduced nutrient runoff. Winter cover crops are planted in the fall and are usually terminated in early spring, making them susceptible to senescence, frost burn, and leaf yellowing due to wintertime conditions. In addition to remote sensing imagery, advances have been made in the use of proximal sensors integrated with GPS for on-field measurements, and the comparability of such measurements between platforms, as well as based on processing level is important. Cover crop growth on six fields planted to barley, rye, ryegrass, triticale or wheat was measured over the 2012-2013 winter growing season. There was a strong relationship between the Normalized Difference Vegetation Index (NDVI) and percent groundcover (r2 =0.93) suggesting that date restrictions effectively eliminate yellowing vegetation from analysis. The Triangular Vegetation Index (TVI) was most accurate in estimating high ranges of biomass (r2=0.86), while NDVI did not experience a clustering of values in the low and medium biomass ranges but saturated in the higher range (>1500 kg/ha). Accounting for index saturation, senescence, and frost burn on leaves can greatly increase the accuracy of estimates of percent groundcover and biomass for winter cover crops. Surface reflectance measurements were more correlated with proximal sensors compared to top of atmosphere, with intercepts closer to zero, regression slopes nearer to the 1 to 1 line, and less variance between measured values. NDVI was highly correlated with percent vegetative groundcover, though surface reflectance products did not necessarily improve the relationships. When the Scattering for Arbitrarily Inclined Leaves (SAIL) model was used with measured field variables reflective of realistic winter cover crop scenarios, there were not large differences between NDVI despite differences in residue cover and moisture. At low LAI, NDVI is not capable of differentiating between residue and vegetative cover.Item A GENERALIZED APPROACH TO WHEAT YIELD FORECASTING USING EARTH OBSERVATIONS: DATA CONSIDERATIONS, APPLICATION, AND RELEVANCE.(2012) Becker-Reshef, Inbal; Justice, Christopher C; Vermote, Eric; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In recent years there has been a dramatic increase in the demand for timely, comprehensive global agricultural intelligence. The issue of food security has rapidly risen to the top of government agendas around the world as the recent lack of food access led to unprecedented food prices, hunger, poverty, and civil conflict. Timely information on global crop production is indispensable for combating the growing stress on the world's crop production, for stabilizing food prices, developing effective agricultural policies, and for coordinating responses to regional food shortages. Earth Observations (EO) data offer a practical means for generating such information as they provide global, timely, cost-effective, and synoptic information on crop condition and distribution. Their utility for crop production forecasting has long been recognized and demonstrated across a wide range of scales and geographic regions. Nevertheless it is widely acknowledged that EO data could be better utilized within the operational monitoring systems and thus there is a critical need for research focused on developing practical robust methods for agricultural monitoring. Within this context this dissertation focused on advancing EO-based methods for crop yield forecasting and on demonstrating the potential relevance for adopting EO-based crop forecasts for providing timely reliable agricultural intelligence. This thesis made contributions to this field by developing and testing a robust EO-based method for wheat production forecasting at state to national scales using available and easily accessible data. The model was developed in Kansas (KS) using coarse resolution normalized difference vegetation index (NDVI) time series data in conjunction with out-of-season wheat masks and was directly applied in Ukraine to assess its transferability. The model estimated yields within 7% in KS and 10% in Ukraine of final estimates 6 weeks prior to harvest. The relevance of adopting such methods to provide timely reliable information to crop commodity markets is demonstrated through a 2010 case study.