Rick Mueller

Monitoring US agriculture: the US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program

The National Agricultural Statistics Service (NASS) of the US Department of Agriculture (USDA) produces the Cropland Data Layer (CDL) product, which is a raster-formatted, geo-referenced, crop-specific, land cover map. CDL program inputs include medium resolution satellite imagery, USDA collected ground truth and other ancillary data, such as the National Land Cover Data set. A decision tree-supervised classification method is used to generate the freely available state-level crop cover classifications and provide crop acreage estimates based upon the CDL and NASS June Agricultural Survey ground truth to the NASS Agricultural Statistics Board. This paper provides an overview of the NASS CDL program. It describes various input data, processing procedures, classification and validation, accuracy assessment, CDL product specifications, dissemination venues and the crop acreage estimation methodology. In general, total crop mapping accuracies for the 2009 CDLs ranged from 85% to 95% for the major crop categories.

Mapping global cropland and field size

A new 1 km global IIASA-IFPRI cropland percentage map for the baseline year 2005 has been developed which integrates a number of individual cropland maps at global to regional to national scales. The individual map products include existing global land cover maps such as GlobCover 2005 and MODIS v.5, regional maps such as AFRICOVER and national maps from mapping agencies and other organizations. The different products are ranked at the national level using crowdsourced data from Geo-Wiki to create a map that reflects the likelihood of cropland. Calibration with national and subnational crop statistics was then undertaken to distribute the cropland within each country and subnational unit. The new IIASA-IFPRI cropland product has been validated using very high-resolution satellite imagery via Geo-Wiki and has an overall accuracy of 82.4%. It has also been compared with the EarthStat cropland product and shows a lower root mean square error on an independent data set collected from Geo-Wiki. The first ever global field size map was produced at the same resolution as the IIASA-IFPRI cropland map based on interpolation of field size data collected via a Geo-Wiki crowdsourcing campaign. A validation exercise of the global field size map revealed satisfactory agreement with control data, particularly given the relatively modest size of the field size data set used to create the map. Both are critical inputs to global agricultural monitoring in the frame of GEOGLAM and will serve the global land modelling and integrated assessment community, in particular for improving land use models that require baseline cropland information. These products are freely available for downloading from the http://cropland.geo-wiki.org website.

Seasonal cultivated and fallow cropland mapping using MODIS-based automated cropland classification algorithm

Abstract Increasing drought occurrences and growing populations demand accurate, routine, and consistent cultivated and fallow cropland products to enable water and food security analysis. The overarching goal of this research was to develop and test automated cropland classification algorithm (ACCA) that provide accurate, consistent, and repeatable information on seasonal cultivated as well as seasonal fallow cropland extents and areas based on the Moderate Resolution Imaging Spectroradiometer remote sensing data. Seasonal ACCA development process involves writing series of iterative decision tree codes to separate cultivated and fallow croplands from noncroplands, aiming to accurately mirror reliable reference data sources. A pixel-by-pixel accuracy assessment when compared with the U.S. Department of Agriculture (USDA) cropland data showed, on average, a producer’s accuracy of 93% and a user’s accuracy of 85% across all months. Further, ACCA-derived cropland maps agreed well with the USDA Farm Service Agency crop acreage-reported data for both cultivated and fallow croplands with R -square values over 0.7 and field surveys with an accuracy of ≥ 95 % for cultivated croplands and ≥ 76 % for fallow croplands. Our results demonstrated the ability of ACCA to generate cropland products, such as cultivated and fallow cropland extents and areas, accurately, automatically, and repeatedly throughout the growing season.

Web service-based vegetation condition monitoring system - VegScape

Timely, frequent, high resolution, fully geospatial covered crop vegetation condition information throughout the season is critical to decision making in both public and private sectors that concern agricultural policy, production, food security, and food prices. This paper presents a new interactive Web service-based vegetation condition monitoring system - VegScape. This system automatically obtains and preprocesses near real-time 250m MODIS daily surface reflectance data for better spatial and temporal resolutions, and generates geospatially various vegetation condition indices for timely crop condition. The VegScape not only offers the online interactive map operations, data dissemination, crop condition statistics, charting and graphing, and comparison analysis, but also provides Web services such on-demand vegetation condition maps and statistics for uses in other applications. This system delivers dynamic user experiences and geospatial crop condition information for decision support with its comprehensive capabilities through standard geospatial Web services in a publicly accessible online environment.

Intercomparison of Soil Moisture, Evaporative Stress, and Vegetation Indices for Estimating Corn and Soybean Yields Over the U.S.

This paper presents an intercomparative study of 12 operationally produced large-scale datasets describing soil moisture, evapotranspiration (ET), and/or vegetation characteristics within agricultural regions of the contiguous United States (CONUS). These datasets have been developed using a variety of techniques, including, hydrologic modeling, satellite-based retrievals, data assimilation, and survey/in-field data collection. The objectives are to assess the relative utility of each dataset for monitoring crop yield variability, to quantitatively assess their capacity for predicting end-of-season corn and soybean yields, and to examine the evolution of the yield-index correlations during the growing season. This analysis is unique both with regards to the number and variety of examined yield predictor datasets and the detailed assessment of the water availability timing on the end-of-season crop production during the growing season. Correlation results indicate that over CONUS, at state-level soil moisture and ET indices can provide better information for forecasting corn and soybean yields than vegetation-based indices such as normalized difference vegetation index. The strength of correlation with corn and soybean yields strongly depends on the interannual variability in yield measured at a given location. In this case study, some of the remotely derived datasets examined provide skill comparable to that of in-situ field survey-based data—further demonstrating the utility of these remote sensing-based approaches for estimating crop yield.

Heterogeneously sensed imagery radiometric response normalization for citrus grove change detection

Citrus grove change detection is of great importance to citrus production inventory monitoring. Using remotely sensed imagery to detect the land use and land coverage is one of the most widely-used, cost-effective approaches. However, there is little published research on citrus grove change detection using remotely sensed multi-spectral imagery, especially for those acquired by heterogeneous sensors. The purpose of this paper is to investigate the effectiveness of the citrus change detection based on the histogram matching normalization to the heterogeneously sensed imagery. In this paper, it is found that different reference image and band selection will result in different normalization performance. Based on this finding, a concept of finding optimal reference image and best spectral band for normalization in terms of the minimum Manhattan distance measure is presented. In this paper, the comparison of change detection results of unnormalized and histogram matching normalized images is presented. The experimental results show that histogram matching normalization significantly improves the image differencing based change detection results of the heterogeneously sensed citrus images, and the optimal reference image and band found with proposed optimization algorithm gives the best change detection results.

Data partnership synergy: The Cropland Data Layer

The US Department of Agriculture (USDA)/National Agricultural Statistics Service (NASS) has generated the Cropland Data Layer (CDL) product for more than twelve years, providing annual geospatial updates of the agricultural landscape across the US Heartland. The CDL program delivers acreage estimates based on regression modeling for decision support and provides a crop-specific geospatial dataset for the public domain. This model produces acreage estimates for statisticians and key decision makers in NASS Field Offices and the Agricultural Statistics Board, the official statistical reporting unit of USDA. The CDL program has grown incrementally as collaborative partnerships and technological efficiencies have increased via reengineering both the classification and estimation process. The CDL is now operational in 19 states for 2008 covering the major corn, soybeans, cotton, and wheat areas. Additionally, the CDL is generated multiple times during the growing season. This allows the program to take advantage of updated satellite imagery and updated farmer reported ground data, in consideration for the crop reports that NASS releases in June, August, September, and October. Satellite data have been used successfully for years by the CDL program to accurately identify crop types and produce acreage estimates at the state, district, and county levels. Continued expansion of the CDL program would be impossible without leveraging both satellite and ground truth data partnerships. The USDA/Foreign Agricultural Service/Satellite Image Archive (SIA) provides year round coverage of all major growing areas, while the USDA/Farm Services Agency (FSA) provides farmer reported agricultural specific ground truth. These data sharing partnerships are synergized by the CDL to provide a crop specific land cover classification utilizing regression tree software derived from two major inputs; 1) 56 meter multispectral imagery from Resourcesat-1 AWiFS and 2) ground truth training data from the FSA, Common Land Unit Program. Additionally, 3) ancillary datasets are incorporated into the classification method to improve non-agricultural land cover, including; The National Elevation Dataset; the 2001 National Land Cover Dataset (NLCD), the NLCD Imperviousness and Forest Canopy products. The current CDL product is a comprehensive land cover inventory produced operationally in-season annually.

Tests with VHR images for the identification of olive trees and other fruit trees in the European Union

In the context of the Common Agricultural Policy (CAP) there is a strong interest of the European Commission for counting and individually locating fruit trees. An automatic counting algorithm developed by the JRC (OLICOUNT) was used in the past for olive trees only, on 1m black and white orthophotos but with limits in case of young trees or irregular groves. This study investigates the improvement of fruit tree identification using VHR images on a large set of data in three test sites, one in Creta (Greece) and one in the south-east of France with a majority of olive trees and associated fruit trees, and the last one in Florida on citrus trees. OLICOUNT was compared with two other automatic tree counting, applications, one using the CRISP software on citrus trees and the other completely automatic based on regional minima (morphological image analysis). Additional investigation was undertaken to refine the methods. This paper describes the automatic methods and presents the results derived from the tests.

SMAP DATA for cropland soil moisture assessment — A case study

Timely, frequent, and complete cropland soil moisture information, acquired throughout the growing season is critical for agricultural policy, production, food security, and food prices. The NASA Soil Moisture Active and Passive (SMAP) mission provides a reliable data source for cropland soil moisture assessment. This paper presents a case study of using SMAP soil moisture data products for agriculture land soil moisture assessment. A prototype application of interactive Web service based SMAP soil moisture data visualization, dissemination and analytics based on VegScape is used. In the study, we propose to temporally aggregate SMAP data for a better crop soil moisture assessment. The case study assesses Iowas soil moisture status using SMAP data and compares it with the NOAA precipitation record and NASS published soil moisture survey results for a late September period in Iowa. The high correlation between the SMAP and NOAA observations is found. Moreover, we find that the SMAP results are generally consistent with NASS published survey results. The preliminary results of the study indicate the SMAP data have great potential for agricultural soil moisture assessment applications.

Web service-based SMAP soil moisture data visualization, dissemination and analytics based on vegscape framwork

Timely, frequent, crop vegetation condition information, with complete geospatial coverage acquired throughout the growing season is critical for public and private sector decision making that concerns agricultural policy, production, food security, and food prices. The NASA Soil Moisture Active and Passive (SMAP) mission provides such a reliable data source for cropland soil moisture assessment. This paper presents a prototype of an interactive Web service based SMAP soil moisture visualization, dissemination and analytics system for US soil moisture monitoring based on the VegScape framework. This system automatically retrieves and preprocesses SMAP soil moisture data for US cropland soil moisture condition monitoring and assessment. The prototype takes advantage of the VegScapes service oriented architecture and adds a new component for SMAP soil moisture. It reuses existing VegScape visualization, dissemination and analytical functionalities and tools. The prototype inherits the capabilities of interactive map operations, data dissemination, statistical tabulating and charting, comparison analysis, and various Web services.