Landsat-based operational wheat area estimation model for Punjab, Pakistan

Wheat in Punjab province of Pakistan is grown during the Rabi (winter) season within a heterogeneous smallholder agricultural system subject to a range of pressures including water scarcity, climate change and variability, and management practices. Punjab is the breadbasket of Pakistan, representing over 70% of national wheat production. Timely estimation of cultivated wheat area can serve to inform decision-making in managing harvests with regard to markets and food security. The current wheat area and yield reporting system, operated by the Punjab Crop Reporting Service (CRS) delivers crop forecasts several months after harvest. The delayed production data cannot contribute to in-season decision support systems. There is a need for an alternative cost-effective, efficient and timely approach on producing wheat area estimates, in ensuring food security for the millions of people in Pakistan. Landsat data, medium spatial and temporal resolutions, offer a data source for characterizing wheat in smallholder agriculture landscapes. This dissertation presents methods for operational mapping of wheat cultivate area using within growing season Landsat time-series data. In addition to maps of wheat cover in Punjab, probability-based samples of in-situ reference data were allocated using the map as a stratifier. A two-stage probability based cluster field sample was used to estimate area and assess map accuracies. The before-harvest wheat area estimates from field-based sampling and Landsat map were found to be comparable to official post-harvest data produced by the CRS Punjab. This research concluded that Landsat medium resolution data has sufficient spatial and temporal coverage for successful wall-to-wall mapping of wheat in Punjab’s smallholder agricultural system. Freely available coarse and medium spatial resolution satellite data such as MODIS and Landsat perform well in characterizing industrial cropping systems; commercial high spatial resolution satellite data are often advocated as an alternative for characterizing fine-scale land tenure agricultural systems such as that found in Punjab. Commercial 5 m spatial resolution RapidEye data from the peak of the winter wheat growing season were used as sub-pixel training data in mapping wheat with the growing season free 30 m Landsat time series data from the 2014-15 growing season. The use of RapidEye to calibrate mapping algorithms did not produce significantly higher overall accuracies ( ± standard error) compared to traditional whole pixel training of Landsat-based 30 m data. Continuous wheat mapping yielded an overall accuracy of 88% (SE = ±4%) in comparison to 87% (SE = ±4%) for categorical wheat mapping, leading to the finding that sub-pixel training data are not required for winter wheat mapping in Punjab. Given sufficient expertise in supervised classification model calibration, freely available Landsat data are sufficient for crop mapping in the fine-scale land tenure system of Punjab. For winter wheat mapping in Punjab and other similar landscapes, training data for supervised classification may be collected directly from Landsat images with probability based stratified random sampling as reference data without the need for high-resolution reference imagery. The research concluded by exploring the use of automated models in wheat area mapping and area estimation using growing season Landsat time-series data. The automated classification tree model resulted in wheat / not wheat maps with comparable accuracies compared to results achieved with traditional manual training. In estimating area, automated wheat maps from previous growing seasons can serve as a stratifier in the allocation of current season in-situ reference data, and current growing season maps can serve as an auxiliary variable in model-assisted area estimation procedures. The research demonstrated operational implementation of robust automated mapping in generating timely, accurate, and precise wheat area estimates. Such information is a critical input to policy decisions, and can help to ensure appropriate post-harvest grain management to address situations arising from surpluses or shortages in crop production

A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform

© 2018 The Author(s) Mapping high resolution (30-m or better) cropland extent over very large areas such as continents or large countries or regions accurately, precisely, repeatedly, and rapidly is of great importance for addressing the global food and water security challenges. Such cropland extent products capture individual farm fields, small or large, and are crucial for developing accurate higher-level cropland products such as cropping intensities, crop types, crop watering methods (irrigated or rainfed), crop productivity, and crop water productivity. It also brings many challenges that include handling massively large data volumes, computing power, and collecting resource intensive reference training and validation data over complex geographic and political boundaries. Thereby, this study developed a precise and accurate Landsat 30-m derived cropland extent product for two very important, distinct, diverse, and large countries: Australia and China. The study used of eight bands (blue, green, red, NIR, SWIR1, SWIR2, TIR1, and NDVI) of Landsat-8 every 16-day Operational Land Imager (OLI) data for the years 2013–2015. The classification was performed by using a pixel-based supervised random forest (RF) machine learning algorithm (MLA) executed on the Google Earth Engine (GEE) cloud computing platform. Each band was time-composited over 4–6 time-periods over a year using median value for various agro-ecological zones (AEZs) of Australia and China. This resulted in a 32–48-layer mega-file data-cube (MFDC) for each of the AEZs. Reference training and validation data were gathered from: (a) field visits, (b) sub-meter to 5-m very high spatial resolution imagery (VHRI) data, and (c) ancillary sources such as from the National agriculture bureaus. Croplands versus non-croplands knowledge base for training the RF algorithm were derived from MFDC using 958 reference-training samples for Australia and 2130 reference-training samples for China. The resulting 30-m cropland extent product was assessed for accuracies using independent validation samples: 900 for Australia and 1972 for China. The 30-m cropland extent product of Australia showed an overall accuracy of 97.6% with a producer's accuracy of 98.8% (errors of omissions = 1.2%), and user's accuracy of 79% (errors of commissions = 21%) for the cropland class. For China, overall accuracies were 94% with a producer's accuracy of 80% (errors of omissions = 20%), and user's accuracy of 84.2% (errors of commissions = 15.8%) for cropland class. Total cropland areas of Australia were estimated as 35.1 million hectares and 165.2 million hectares for China. These estimates were higher by 8.6% for Australia and 3.9% for China when compared with the traditionally derived national statistics. The cropland extent product further demonstrated the ability to estimate sub-national cropland areas accurately by providing an R2 value of 0.85 when compared with province-wise cropland areas of China. The study provides a paradigm-shift on how cropland maps are produced using multi-date remote sensing. These products can be browsed at www.croplands.org and made available for download at NASA's Land Processes Distributed Active Archive Center (LP DAAC) https://www.lpdaac.usgs.gov/node/1282

Using satellite remote sensing and hydrologic modeling to improve understanding of crop management and agricultural water use at regional to global scales.

Thesis (Ph. D.)--Boston UniversityCroplands are essential to human welfare. In the coming decades , croplands will experience substantial stress from climate change, population growth, changing diets, urban expansion, and increased demand for biofuels. Food security in many parts of the world therefore requires informed crop management and adaptation strategies. In this dissertation, I explore two key dimensions of crop management with significant potential to improve adaptation pathways: irrigation and crop calendars. Irrigation, which is widely used to boost crop yields, is a key strategy for adapting to changes in drought frequency and duration. However, irrigation competes with household, industrial, and environmental needs for freshwa t er r esources. Accurate information regarding irrigation patterns is therefore required to develop strategies that reduce unsustainable water use. To address this need, I fused information from remote sensing, climate datasets, and crop inventories to develop a new global database of rain-fed, irrigated, and paddy croplands. This database describes global agricultural water management with good realism and at higher spatial resolution than existing maps. Crop calendar management helps farmers to limit crop damage from heat and moisture stress. However, global crop calendar information currently lacks spatial and temporal detail. In the second part of my dissertation I used remote sensing to characterize global cropping patterns annually, from 2001-2010, at 0.08 degree spatial resolution. Comparison of this new dataset with existing sources of crop calendar data indicates that remote sensing is able to correct substantial deficiencies in available data sources. More importantly, the database provides previously unavailable information related to year-to-year variability in cropping patterns. Asia, home to roughly one half of the Earth's population, is expected to experience significant food insecurity in coming decades. In the final part of my dissertation, I used a water balance model in combination with the data sets described above to characterize the sensitivity of agricultural water use in Asia to crop management. Results indicate that water use in Asia depends strongly on both irrigation and crop management, and that previous studies underestimate agricultural water use in this region. These results support policy development focused on improving the resilience of agricultural systems in Asia

Assessing the Impacts of Land-Use and Climate Change for Water Resource Management

Sustainable management of water resources is a challenging interdisciplinary problem requiring the integration of fields such as hydrology, ecology, sociology, and public policy. In the past decade, there has been a great effort to understand how issues such as climate change and land-use change for biofuel feedstock production will affect water resources. This dissertation assesses the impacts of climate change and land-use change for water resource management in Kansas using an interdisciplinary approach and tools such as the Soil and Water Assessment Tool (SWAT), social surveys, and geospatial analysis. The SWAT model is used to simulate corn and grain sorghum biofuel-based land-use scenarios to assess water quality impacts and sustainability indicators in the Perry Lake and the Kanopolis Lake watersheds in Kansas. Modeling results suggest that corn scenarios produced significantly greater water quality impacts than grain sorghum scenarios, but that corn had a much higher crop yield, particularly in the Perry Lake watershed, and thus can provide more ethanol production potential per land, water, and nutrient input, which are efficiency metrics often used in agricultural studies. Overall, grain sorghum may be a more sustainable feedstock crop in drier climates and corn may be more sustainable in wetter climates. The sustainability measures utilized in this study allow for comparison between crops and between watersheds, yet they are typically not included in the current biofuel-based land-use analyses. This study shows the potential of integrating water quality analysis with sustainability indicators to develop a richer assessment of the trade-offs and benefits of landscape change for biofuel feedstock development. The impact of climate change was assessed in three ways: first, with a review of the potential climate change impacts for reservoirs and a discussion of the potential in-lake and watershed management strategies for mitigation; second, with a social survey that explores perceptions of Kansas water managers towards climate change and planning for climate impacts; and third, with a study of the influence of reservoir management on greenhouse gas emissions from a tributary of the Three Gorges Reservoir in China. The review of climate change impacts for reservoirs found that the sustainability of reservoir services will be threatened by climate change, but that there are a variety of management tools that may be able to mitigate impacts. The social survey demonstrated that anthropogenic climate change is a contentious issue within the state of Kansas, but that water managers believe it is important to consider future climate change in their planning efforts. Survey results, along with a review of key Kansas water management plans, suggest that Kansas water managers are indeed responsive to climate variability and are starting to integrate climate variability into planning efforts. The study of reservoir greenhouse gas emissions suggest that both CO2 and CH4 fluxes were influenced by reservoir water level and exhibited distinct patterns that correspond to the reservoir operation cycle. Over 90% of CO2 effluxes occurred during the high water period, whereas the 58% of CH4 effluxes occurred during the low water period. Results suggest that reservoir operations altered the hydraulic retention time, which along with water temperature, controlled the synthesis and decomposition of carbon in the backwater system

Nominal 30-m Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine

A satellite-derived cropland extent map at high spatial resolution (30-m or better) is a must for food and water security analysis. Precise and accurate global cropland extent maps, indicating cropland and non-cropland areas, are starting points to develop higher-level products such as crop watering methods (irrigated or rainfed), cropping intensities (e.g., single, double, or continuous cropping), crop types, cropland fallows, as well as for assessment of cropland productivity (productivity per unit of land), and crop water productivity (productivity per unit of water). Uncertainties associated with the cropland extent map have cascading effects on all higher-level cropland products. However, precise and accurate cropland extent maps at high spatial resolution over large areas (e.g., continents or the globe) are challenging to produce due to the small-holder dominant agricultural systems like those found in most of Africa and Asia. Cloud-based geospatial computing platforms and multi-date, multi-sensor satellite image inventories on Google Earth Engine offer opportunities for mapping croplands with precision and accuracy over large areas that satisfy the requirements of broad range of applications. Such maps are expected to provide highly significant improvements compared to existing products, which tend to be coarser in resolution, and often fail to capture fragmented small-holder farms especially in regions with high dynamic change within and across years. To overcome these limitations, in this research we present an approach for cropland extent mapping at high spatial resolution (30-m or better) using the 10-day, 10 to 20-m, Sentinel-2 data in combination with 16-day, 30-m, Landsat-8 data on Google Earth Engine (GEE). First, nominal 30-m resolution satellite imagery composites were created from 36,924 scenes of Sentinel-2 and Landsat-8 images for the entire African continent in 2015–2016. These composites were generated using a median-mosaic of five bands (blue, green, red, near-infrared, NDVI) during each of the two periods (period 1: January–June 2016 and period 2: July–December 2015) plus a 30-m slope layer derived from the Shuttle Radar Topographic Mission (SRTM) elevation dataset. Second, we selected Cropland/Non-cropland training samples (sample size = 9791) from various sources in GEE to create pixel-based classifications. As supervised classification algorithm, Random Forest (RF) was used as the primary classifier because of its efficiency, and when over-fitting issues of RF happened due to the noise of input training data, Support Vector Machine (SVM) was applied to compensate for such defects in specific areas. Third, the Recursive Hierarchical Segmentation (RHSeg) algorithm was employed to generate an object-oriented segmentation layer based on spectral and spatial properties from the same input data. This layer was merged with the pixel-based classification to improve segmentation accuracy. Accuracies of the merged 30-m crop extent product were computed using an error matrix approach in which 1754 independent validation samples were used. In addition, a comparison was performed with other available cropland maps as well as with LULC maps to show spatial similarity. Finally, the cropland area results derived from the map were compared with UN FAO statistics. The independent accuracy assessment showed a weighted overall accuracy of 94%, with a producer’s accuracy of 85.9% (or omission error of 14.1%), and user’s accuracy of 68.5% (commission error of 31.5%) for the cropland class. The total net cropland area (TNCA) of Africa was estimated as 313 Mha for the nominal year 2015. The online product, referred to as the Global Food Security-support Analysis Data @ 30-m for the African Continent, Cropland Extent product (GFSAD30AFCE) is distributed through the NASA’s Land Processes Distributed Active Archive Center (LP DAAC) as (available for download by 10 November 2017 or earlier): https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30AFCE.001 and can be viewed at https://croplands.org/app/map. Causes of uncertainty and limitations within the crop extent product are discussed in detail

Literature review of the remote sensing of natural resources

Abstracts of 596 documents related to remote sensors or the remote sensing of natural resources by satellite, aircraft, or ground-based stations are presented. Topics covered include general theory, geology and hydrology, agriculture and forestry, marine sciences, urban land use, and instrumentation. Recent documents not yet cited in any of the seven information sources used for the compilation are summarized. An author/key word index is provided

Proceedings of 2011 Kentucky Water Resources Annual Symposium

This conference was planned and conducted as part of the state water resources research annual program with the support and collaboration of the Department of the Interior, U.S. Geological Survey and the University of Kentucky Research Foundation, under Grant Agreement Number 06HQGR0087. The views and conclusions contained in this document and presented at the symposium are those of the abstract authors and presenters and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the U.S. Government or other symposium organizers and sponsors

Agricultural Land Use, Watershed Characteristics, and Hydrological Forces Contributing to the Impairment of a Shallow Lake in the Western Corn Belt Ecoregion

The Lake Titlow watershed (approximately 35,000 acres) in south-central Minnesota is part of the Minnesota River Basin. The lake is listed in the draft 2010 Clean Water Act Section 303d for nutrient pollution, eutrophication, and biological indicators for impairment of aquatic life and recreational use. Over 90 percent of pre-settlement wetlands are currently drained for agricultural land use. The Lake Titlow watershed is over 80 percent row crops and land use is implicated as a primary cause of impairment in the lake. Water samples were collected from the Lake Titlow tributaries McLeod-Sibley Judicial Ditch Number 18 (JD18), Sibley County Ditch Number 18 (CD18), and Ditch 250 (D250) during 2009 and 2010 and were analyzed for total suspended solids (TSS), total phosphorus (TP), and nitrate-nitrite nitrogen (NOx). Investigative methods included continuous recording stream stage and through the use of rating curves, discharge. Runoff, sediment loads, and nutrient loads were then determined from the field data. Four rain gauges collected precipitation each year and were used to assess the impact of precipitation on runoff and loading. Four characteristic precipitation events were selected for each of the calendar years 2009 and 2010 to estimate the loads of sediment and nutrients to the lake and more fully understand the specific roles that land use, hydrologic soil group, slope, and precipitation play with regard to causing sediment and nutrient loading in the lake. Results indicate runoff and loads are significant and highly variable by position within the watershed, areas referred to herein as subsheds. The row crop land use, soils characteristics, and precipitation do contribute to overall runoff and loads; however, they do not control subshed variability. Although the low-sloping land surfaces of the watershed should not contribute to overall runoff and loads, results indicate that subtle slope changes in the JD18Lo and CD18Lo subsheds could contribute to the variability of loads seen in these portions of the watershed. The location and type of best management practices to implement is debatable because the results of this study indicate that large runoffs and loads could originate within any given subshed during any given rainstorm event. This study was unable to precisely identify the root cause of the variability in subshed runoff and loading. Therefore, it is suggested to look at other factors (e.g., antecedent soil moisture, rainfall intensity, mass wasting, etc.) to explain the subshed variability in the sediment and nutrient loading in future studies of this lakeshed