Automated Cropland Classification Algorithm (ACCA) for California Using Multi-sensor Remote Sensing

Transcription

1 Automated Cropland Classification Algorithm (ACCA) for California Using Multi-sensor Remote Sensing Zhuoting Wu, Prasad S. Thenkabail, and James P. Verdin Abstract Increasing pressure to feed the growing population with scarce water resources requires accurate and routine cropland mapping. This paper develops and implements a rule-based automated cropland classification algorithm (ACCA) using multi-sensor remote sensing data. Pixel-by-pixel accuracy assessments showed that ACCA produced an overall accuracy of 96 percent (K hat = 0.8) when tested using independent data layers. Furthermore, ACCA-generated county cropland areas showed high agreement (R-square values 0.94) when compared with three independent data sources: (a) US Department of Agriculture (USDA) cropland data layer derived cropland areas, (b) county specific crop acreage data from the Farm Service Agency, and (c) the Census of Agriculture data for the 58 counties in California. Our results demonstrate the ability of ACCA to generate cropland extent and areas over space and time, in an automated fashion with high degree of accuracies year after year, greatly contributing to food and water security analysis and decision making. Introduction The world population tripled in the Twentieth century, eventually reaching seven billion in The world population is projected to increase by another 50 percent within the next fifty years as per United Nations estimates. Rapid population growth, coupled with urbanization and industrialization, will inevitably result in increasing pressure on food and water supply. Meanwhile, recent climate change models have projected that more extreme events such as severe droughts (IPCC, 2007) will occur more frequently in the future, putting higher risks on water scarcity, and food production. We are facing a greater challenge of food security than ever before, and the first step to ensure a secure food supply is to routinely map cropland, i.e., a land cover type subjected to most rapid changes over continents such as North America and Asia (Lepers et al., 2005), from which crop growth status and water consumption can be monitored. Previously, cropland mapping has been conducted across multiple spatial scales using various methods (Ozdogan and Woodcock, 2006; Wardlow et al., 2007; Wardlow and Egbert, Zhuoting Wu is with the Western Geographic Science Center, US Geological Survey, Flagstaff, AZ 86001, and the Merriam- Powell Center for Environmental Research, Northern Arizona University, Flagstaff, AZ (zwu@usgs.gov). Prasad S. Thenkabail is with Western Geographic Science Center, US Geological Survey, Flagstaff, AZ James P. Verdin is with the USGS EROS Data Center, Sioux Falls, SD ; Thenkabail et al., 2009a and 2009b; Dheeravath, 2010; Thenkabail et al., 2010; Gumma et al., 2011; Thenkabail et al., 2011; Thenkabail and Wu, 2012). Remote sensing data serve as vital sources in delivering accurate and timely information on the area, condition, and major crop types across the globe, given its implication in food security, water use assessment, land management, trade decisions, policy, and economics. Many classification methods have been used in cropland mapping, including maximum likelihood classification (EL-Magd and Tanton, 2003), decision tree classification (Morton et al., 2006; Wardlow and Egbert, 2008; Biradar et al., 2009; Pittman et al., 2010), neural network methods (Liu et al., 2005; Atzberger and Rembold, 2010), support vector machine (Mathur and Foody, 2008), spectral unmixing techniques (Lobell and Asner, 2004; Yang et al., 2007; Chen et al., 2008), spectral matching techniques (Thenkabail et al., 2007), and spectral angle mapper (Rembold and Maselli, 2006). Most of the existing cropland mapping methods rely extensively on the human interpretation of spectral signatures, making the process labor-intensive and difficult to repeat over time and space. In addition, due to the low temporal availability of highquality and high-resolution imagery, accuracies of time seriesbased cropland classification is limited by coarse spatial resolution imagery (Wardlow and Egbert, 2008). Nevertheless, dense time series of remote sensing data have been proved to be critical in determining crop types and intensity (Lobell and Asner, 2004; Biggs et al., 2006; Wardlow et al., 2007; Thenkabail et al., 2009a and 2009b; Velpuri et al., 2009; Dheeravath et al., 2010; Lv and Liu, 2010; Shao et al., 2010; Biradar and Xiao, 2011; Thenkabail et al., 2011). The combination of high temporal resolution of MODIS and high spatial resolution of Landsat imagery allows for producing 30 m resolution cropland maps. Multiple-resolution data fusion has been used successfully in cropland mapping to improve classification accuracy (Watts et al., 2011). Some progress has been made recently in automated land-cover classification techniques such as an unsupervised algorithm called independent component analysis (Ozdogan, 2010) and a modified subspace classification method (Bagan and Yamagata, 2010). An automatic rule-based decision tree classifier such as ACCA, as proposed here, can take advantage of multiple data sources with various resolutions and perform supervised classification of cropland extent/area and Photogrammetric Engineering & Remote Sensing Vol. 80, No. 1,, pp /14/ /$3.00/ American Society for Photogrammetry and Remote Sensing doi: /PERS indd 81

2 their characteristics (e.g., irrigated versus rainfed, cropping intensities, and crop types) in a logical fashion that can be repeated accurately, rapidly, and routinely over time for the area or region for which it was developed. Thus, ACCA once successfully developed for cropping season(s) in a given year for a region can be applied for the same season(s) of the same region for any other independent year, i.e., past or the future. Thus, successful development of ACCA for a region will provide a powerful opportunity to routinely, automatically, and accurately compute cropland extent/area and their characteristics (e.g., irrigated versus rainfed, cropping intensities, and crop types). Further, the ACCA concept remains the same for any area in the world, and hence can be adopted with suitable adjustments to the rule-base that best fits the regional cropland characteristics. The concept of Automated Cropland Classification Algorithm (ACCA) was first proposed, developed and implemented by Thenkabail and Wu (2012) for the Country of Tajikistan. They established that the ACCA has promising applications in generating consistent cropland cover time series and monitoring changes in cropland phenology over time corresponding to changing climate, especially for remote and/or large geographic areas. The advantage of automated algorithms is to avoid time-consuming and laborious manual interpretation and processing (Thenkabail et al., 2012; Thenkabail and Wu, 2012). Nevertheless, ACCA proposed and implemented by Thenkabail and Wu (2012) suffered from three major limitations. First, validation and reference data to develop ACCA were limited. Second, testing the ACCA for independent years was a challenge given limited reference data for the independent years. Third, applicability of the ACCA concept for other regions of the world was yet to be established. Thus, we envisioned development and implementation of ACCA for the State of California, where its full scope and capability to overcome the above limitations could be tested and demonstrated. The USDA cropland data layer is produced every year for the US and also uses remote sensing data. However, there are several differences and uniqueness in ACCA relative to USDA cropland data layer. First, the process of producing USDA cropland data layer is not automated, but involves considerable human interpretation year after year. In contrast, ACCA, once developed for a given year, generates cropland extent and areas automatically for other years. The process involved getting the remote sensing data for the independent years ready and then running the ACCA algorithm on the independent year datasets to produce cropland extent and areas. Second, ACCA is based on MODIS time-series with or without other data such as Landsat and/or secondary data. In contrast, USDA cropland data layer is produced using Landsat, Indian Remote Sensing (IRS), or other commercial imagery with extensive human interpretation for each year it is produced. Third, the idea of ACCA is to adopt and apply the similar data and methods to other parts of the world (e.g., Thenkabail and Wu, 2012). In contrast, USDA cropland data layer is limited to US. Thereby, the overarching goal of this research was to comprehensively explore, develop, test, and implement the ACCA for the entire State of California using multi-sensor remote sensing data. The specific objectives were to: (a) develop the ACCA for the State of California using multi-sensor remote sensing data cube involving MODIS time-series for one year and Landsat TM data for a selected month of the same year, taking the USDA cropland data layer as a reference knowledge base; (b) apply ACCA on data cubes of any independent year to generate ACCA-derived cropland layers of those independent years; (c) assess pixel-based accuracies and errors of ACCA-derived cropland layers for any given independent data year by comparing them with USDA cropland data layers for the corresponding year; (d) determine relationships between ACCA-generated cropland areas versus cropland areas provided by independent sources (e.g., Census of Agriculture, Farm Service Agency) for the 58 counties of California; and (e) evaluate the effectiveness of using MODIS time series only to delineate cropland using ACCA in California. Methods Study Area The State of California (32 N to 42 N, 114 W to 124 W) is one of the most productive agricultural regions in the world, and a top ranked producer and exporter of agricultural products (USDA, 2009). California produces over 250 different crops, and is the sole producer of 12 different commodities (e.g., almonds, artichokes, raisins, kiwifruit, olives, pistachios, walnuts) in the US. Agriculture is an important sector in California s economy. In 2010, California s 81,700 farms and ranches generated 37.5 billion USD products revenue. Based on a five-year cropland mask (2007 to 2011) using USDA cropland data layers, about 11.6 million acres ( 12 percent of California s land surface area) is farmland, of which 80 percent gets cropped every year. According to the 2007 Census of Agriculture, there was roughly eight million irrigated acres in California, and crop production and yields are directly linked to water use by irrigation. Farming accounts for about 80 percent of the total water usage of the state (Canessa et al., 2011), and therefore spatially precise cropland mapping has great implications on water use management, state economy, and livelihood of its people. Remote Sensing Data Compilation The ACCA was developed using multi-sensor remote sensing data for the year 2010, using the USDA cropland data layer for the same year as the knowledge base. The year 2010 was selected randomly as a year to develop ACCA. Five other independent years (2007, 2008, 2009, 2011, and 2012) were selected to test the applicability of the algorithm. Remote sensing data used for developing the ACCA included high temporal resolution MODIS data and high spatial resolution Landsat Thematic Mapper-5 (TM-5) data, both of which were compiled into a remote sensing data cube (Plate 1), akin to a hyperspectral data cube. Such data cubes were generated for the algorithm development for the randomly selected base year 2010 (Plate 1), as well as five additional independent years 2007, 2008, 2009, 2011, and We also explored the use of secondary data such as precipitation, temperature, elevation, and slope in the data cubes, as explored and used by Thenkabail and Wu (2012), but they did not improve the cropland mapping outcome or accuracy; therefore they were dropped from the data cubes used in developing ACCA. The uniformly flat terrain of California s Central Valley agricultural area and near 90 percent of cropland being irrigated make these secondary datasets redundant. However, such data are highly useful elsewhere (Thenkabail and Wu, 2012). MODIS Terra 250 m surface reflectance eight-day composite data were obtained through US Geological Survey (USGS) Land Process Distributed Active Archive Center (LPDAAC; throughout the year of MODIS surface reflectance data (band 1 and band 2) were used to calculate normalized difference vegetation index (NDVI) by using the equation below: NDVI (band 2 band 1) / (band 2 band 1). The calculated NDVI ( 1 to 1) was then converted to 8-bit scaled NDVI, which ranges from 0 to 255. In addition, Landsat TM-5 surface reflectance data were obtained through indd 82

3 Plate 1. Remote sensing data cube of year 2010 for California compiled from MODIS and Landsat TM-5 data. The front display image is the MODIS Terra surface reflectance derived yearly total NDVI. USGS Landsat surface reflectance products. In California, July has the least cloud coverage throughout all scenes and is also a crucial month during the growing season. Therefore a total of 36 scenes of Landsat TM-5 surface reflectance data were acquired for 15 July through 31 July , covering the entire State of California. Upon acquiring both MODIS and Landsat data, a data cube was generated by stacking all the MODIS and Landsat data layers together, in which MODIS data were resampled to match Landsat spatial resolution of 30 m (Plate 1). The importance of data layers in the data cubes (e.g., Plate 1) was determined based on the amount of cropped area delineated by each data layer by applying a set of rules through ACCA (described later). A layer is considered most important when it captures the maximum spatial extent and/ or other cropland characteristics (e.g., crop type, irrigated or rainfed) when compared with a reference data layer such as a USDA reference cropland layer as in this study. The process of selecting the important data layers was performed by iterations of trial and error, coding and testing. Based on this criterion, the MODIS yearly total NDVI (the additive sum of all 12 monthly NDVI) delineated 51 percent of the total cropped area and, thereby, constituted the single most important data layer. Then, by adding August NDVI, an additional 20 percent of the cropped area was identified. Thereby, the algorithm development process (Figure 1) started with using the yearly indd 83 total NDVI to delineate cropland, followed by using NDVI from critical months out of the year to map seasonal croplands. The final data layers included in the data cube (Plate 1; in the order of importance) are as follows: 1. MODIS yearly total (cumulative) 250 m NDVI; 2. MODIS 250 m August NDVI; 3. MODIS 250 m June NDVI; 4. MODIS 250 m July NDVI; 5. MODIS 250 m September NDVI; 6. MODIS cumulative April to July 250 m NDVI; 7. Landsat July band 4 scaled surface reflectance; 8. MODIS cumulative January to April 250 m NDVI; and 9. MODIS cumulative May to September 250 m NDVI. Reference Cropland Layer The most routinely available and spatially-explicit cropland maps of the entire California are annual USDA cropland data layers. The USDA cropland data layer uses combination of remote sensing and ground data to generate croplands and have an accuracy of over 90 percent (Boryan et al., 2011; Agricultural ground data for generating the USDA cropland data layer were acquired every growing season from the farmers reporting, and spatially-explicit non-agricultural data were sampled from the national land cover dataset (NLCD) and then 83

4 Figure 1. ACCA rules for determining croplands in California. The rules were established using MODIS and Landsat data for the year MODIS monthly scaled NDVI ranges from 0 to 255; Landsat TM-5 surface reflectance has a scale factor of All five set of rules were combined into one single algorithm, written in the ERDAS Imagine modeler 2011 (.gmd file). incorporated into the USDA cropland data layer. Although the NLCD was not updated yearly, it has been proven that by using current satellite imagery, the classifier has accurately identified land-cover and land-use change such as urban expansion, agricultural land conversion, and forest clearing (Boryan et al., 2011). Thereby, USDA cropland data layers were taken as reference cropland layers for developing an ACCA to map the total cropland extent as well as the spatially-explicit cropped area distribution. USDA cropland data layers for California were available since 2007, and we obtained all six years of available data from 2007 to 2012, among which, year 2010 was used for developing ACCA, and the rest were used as independent years to test the accuracy of ACCA-derived cropland layers. ACCA Development ACCA development involved writing series of rules (Figure 1) using multi-sensor remote sensing data (Plate 1) to generate an ACCA-derived cropland layer that replicated the reference USDA cropland data layer. Contrary to the previous ACCA development in Tajikistan where we had to generate our own reference cropland layer first (Thenkabail and Wu, 2012), we took advantage of well-established and independently-developed USDA cropland data layers for California. We chose the year 2010, randomly, to develop an ACCA. The goal of ACCA (Figure 1) was to separate croplands from non-croplands and map the spatial extent of croplands. Once this was achieved, the ACCA-derived cropland map was used to compute cropland area statistics for the 58 counties of California. The process of developing the ACCA rules (Figure 1) involved three major steps: 1. setting certain thresholds (e.g., MODIS yearly cumulative total NDVI; Figure 1), 2. running the ACCA on the data cube (Plate 1), and 3. spatially comparing the ACCA-derived cropland layer for year 2010 with the reference USDA cropland data layer of The threshold of ACCA rules (e.g., MODIS August NDVI 200; Figure 1b) was determined by trial and error, where a rule was set when 90 percent of the ACCA-derived cropland matched pixel-by-pixel with the USDA cropland data layers. Given such a test involved comparing millions of 30 m by 30 m pixels (0.09 hectare units) from the entire cropland extent of California, it is quite a robust measure. The goal of each rule in ACCA (Figure 1) was to capture as much cropland area as possible and eliminate non-croplands at the same time. Typically, any single rule (e.g., MODIS yearly total NDVI 2350: Figure 1a) will only map a certain portion of croplands seen in the reference cropland layer, and therefore a series of rules (Figure 1) were written until nearly all croplands seen in the reference USDA cropland layer were captured in the ACCAderived cropland layer. Overall, the final ACCA is comprised of five sets of rules (Figure 1). Algorithm 1 resolved 51 percent of California s total cropland area, and Algorithm 2 delineating additional 20 percent of California s total cropland area. Algorithm 3, 4, and 5 separated out additional 8 percent, 6 percent, and 5 percent of California s total cropland area, respectively. Each algorithm is applied on the remaining area left from previous algorithms, so no overlap cropland area occurred among the five algorithms. Thus, all five sets of rules, compiled together, delineated 90 percent of total cropland area. The ACCA rules were coded in ERDAS Imagine Modeler 2011 (in.gmd file format). It needs to be noted that the ACCA (Figure 1) made use of all the selected data layers in the data cube (Plate 1). The data cube can also include gridded secondary data (i.e., precipitation, temperature, soils, evapotranspiration). However, for California s cropland delineation, MODIS NDVI time-series along with Landsat TM-5 band 4 data for the critical month of July were found to be optimal. ACCA-derived Croplands for Independent Years Once the ACCA was developed, it was applied on data cubes of several individual independent years (2007, 2008, 2009, 2011, and 2012) of California in order to test its accuracy and performance. The individual year data cubes also used MODIS NDVI time series and Landsat TM-5 (except for 2012), akin to the data cube of year 2010 (Plate 1) that was used to develop the algorithm (Figure 1). Landsat-5 TM data for July was not available for 2012 and hence was not used, which resulted in use of only MODIS data for Once the individual independent year data cubes were composed and ready, the ACCA that was already developed earlier for year 2010 (Figure 1) was directly applied on these individual independent year data cubes (2007, 2008, 2009, 2011, and 2012) to automatically and rapidly generate ACCA-derived cropland layers for each of these independent years indd 84

5 Accuracy Assessment We adopted two approaches for accuracy assessment. First, pixel-by-pixel accuracy (Congalton et al., 1998; Congalton and Green, 2009) was established by comparing ACCA-derived cropland layers with reference cropland layers (a.k.a. USDA cropland data layers) for five independent years (2007, 2008, 2009, 2011, and 2012). This approach allowed us to determine the strength of ACCA in automatically computing cropland spatial extent. Second, the performance of ACCA was also tested using the cropland area statistics of the 58 counties of California, which are of great interest to multiple governmental and nongovernmental agencies and organizations. For this purpose, cropland areas of the 58 counties of California were computed for six years (2007, 2008, 2009, 2010, 2011, and 2012) using ACCA, which were then compared with corresponding county area statistics from three independent data sources: 1. USDA cropland data layer derived cropland areas, 2. Crop Acreage Data from the Farm Service Agency reported by individual farmers ( area=newsroom&subject=landing&topic=foi-er-fri-cad), and Census of Agriculture for California (only 2007 in this case, since such census is conducted only once every five years). Results First, we report ACCA-derived cropland layers for the year of algorithm development (2010) as well as for five independent years (2007, 2008, 2009, 2011, and 2012). Second, we present pixel-based error matrix results of the same five independent data layers mentioned above. Third, we compare the cropland areas of the 58 counties from ACCA-derived products with three other independent sources. ACCA-derived Cropland Layers The ACCA (Figure 1) was applied on data cubes (Plate 1) of six years from 2007 to 2012, among which, 2010 was used for ACCA development and is presented here first. ACCA-derived cropland map of 2010 (Figure 2a) showed pronounced resemblance to the USDA cropland data layer in 2010 (Figure 2b) throughout the northern, central and southern parts of California. ACCA was then applied on five independent years (2007, 2008, 2009, 2011, and 2012) and outputs were then compared with corresponding cropland layers of these years from the USDA. We illustrate ACCA-derived cropland layer of 2007 (Figure 3a) versus USDA cropland data layer of 2007 (Figure 3b). Results from the other four years (2008, 2009, 2011, and 2012) exhibited similar patterns (see the accuracy assessment below). Pixel-based Error Matrix and Accuracy Assessment An important test of the applicability of ACCA is to see how well it works for the independent years. The ACCA-derived cropland layer of 2007, an independent year, was compared with the reference USDA cropland data layer of the same year (Figure 3) through a pixel-by-pixel based accuracy and error assessment. The results showed an overall accuracy of 97 percent (with K hat of 0.8, Table 1), demonstrating that the ACCA can achieve high level of accuracy even when applied on independent data layers. In order to test the robustness of ACCA, four other independent years were selected (2008, 2009, 2011, and 2012). It is important to note that these include years from the past (2008 and 2009) and years of future (2011 and 2012) relative to ACCA development year (2010). We could not use years before 2007 since USDA started producing CDL reference datasets only from The results are presented in Table 2. In general, the overall accuracies were 96 percent (with K hat of 0.8). The producer s accuracies were 84 percent and user s accuracies 71 percent across all years for croplands only (Table 2). The errors of omissions were 16 percent, clearly implying that cropland areas are well accounted for even during independent years. The errors of commissions were 29 percent, indicating that certain non-croplands (a) (b) Figure 2. Croplands of California for year 2010 from (a) ACCA-derived, and (b) USDA reference cropland data layer, both with three zoom-in views for the northern, central and southern part of California indd 85

6 (a) (b) Figure 3. Croplands of California for year 2007 from (a) ACCA-derived, and (b) USDA cropland data layer, both with three zoom-in views for the northern, central and southern part of California. TABLE 1. ERROR MATRIX (NO. OF PIXELS) FROM COMPARISON BETWEEN ACCA-DERIVED CROPLAND LAYER OF 2007 VERSUS USDA CROPLAND DATA LAYER (CDL) OF 2007 ACCA-derived U S D A Cropland Non-cropland Row total Producer s accuracy Errors of Omission Cropland 25,281,774 4,244,544 29,526,318 86% 14% Non-cropland 9,334, ,300, ,634,872 98% 2% Column total 34,616, ,545,043 C D L User s accuracy 73% 99% Errors of Commission 27% 1% Overall accuracy 97% K hat 0.8 TABLE 2. SUMMARY OF ACCURACIES AND ERRORS OF ACCA-DERIVED CROPLAND LAYERS OF CALIFORNIA FOR FIVE INDEPENDENT YEARS: 2007, 2008, 2009, 2011, AND 2012 WHEN COMPARED WITH CORRESPONDING DATA FROM REFERENCE USDA CROPLAND DATA LAYERS Year Producer s accuracy User s accuracy Overall accuracy Errors of omission Errors of commission % 73% 97% 14% 27% % 71% 96% 16% 29% % 72% 97% 16% 28% % 71% 97% 10% 29% % 73% 96% 14% 27% were mixed into croplands. This is mainly as a result of (a) some uncertainty issues involved with reference training data (which itself is 90 percent accurate), and (b) existence of fallow farms where, at times, significant grasses or other natural vegetation exist. It is possible to further reduce these errors, if we have greater accuracies in reference data and greater ground data on vegetative fallow farmland. Overall, results in Table 1, Table 2, and Figure 3 clearly imply that ACCA algorithm is robust across years. We also need to point out that ACCA worked very well in this heavily-irrigated cropped areas of California where inter-annual climate variability had little effect on cropland growth from year to year with irrigation acting as insurance against climate variability. Area-based Comparison with County-level Cropland Area Statistics County-level cropland areas derived from the ACCA was regressed against the area statistics from three other indd 86

7 independent data sources: (a) US Department of Agriculture (USDA) cropland data layer derived cropland areas, (b) county specific crop acreage data from the Farm Service Agency, and (c) the Census of Agriculture data, for the 58 counties in California. First, the ACCA-derived cropland areas of the 58 counties were compared with reference cropland areas of these counties derived from USDA cropland data layer, and illustrated for two years: 2010 (Figure 4) which is an algorithm development year and 2007 (Figure 5) an independent year. The R-squares and slopes of these two years, as well as four other independent years (2008, 2009, 2011, and 2012), are presented in Table 3. Linear regressions between the ACCA-derived cropland areas and USDA cropland data layers for the 58 counties in California showed R-square values 0.94 and slopes close to 1 (p 0.001) across all six years. Second, ACCA-derived county-level cropland areas for 2007 agreed very well with the corresponding areas from the 2007 Census of Agriculture (Figure 5). We were able to compare only with one independent year of 2007, because the census data are only made available once every five years for our study duration. The 2012 Census of Agriculture data will be released in February Third, the crop acreage data from the Farm Service Agency (FSA) were collected directly from farmers of the 58 counties for four years (2009, 2010, 2011, and 2012), based on availability. Out of these four years, three years were for independent years. In each year, 90 percent farmers reported areas (personal communication with Rick Mueller from USDA NASS). The ACCA-derived cropland areas provided an R-square value of 0.83 when compared with FSA reported cropland areas (Table 3). Based on the above three rigorous approaches, we can clearly state that ACCAgenerated cropland area statistics are reliable and robust. (a) (b) Figure 4. ACCA-derived county-level cropland areas for California in the year 2010, in comparison with cropland areas derived from other sources for 2010: (a) USDA cropland data layer, and (b) crop acreage data from the Farm Service Agency. (a) (b) Figure 5. ACCA-derived county-level cropland areas for California in the year 2007, in comparison with cropland areas derived from other sources for 2007: (a) USDA cropland data layer, and (b) 2007 Census of Agriculture indd 87

8 TABLE 3. SUMMARY OF LINEAR REGRESSIONS BETWEEN ACCA-DERIVED COUNTY-LEVEL CROPLAND AREAS FOR CALIFORNIA (58 COUNTIES IN TOTAL) AND OTHER DATA SOURCES INCLUDING (A) CENSUS OF AGRICULTURE (2007), (B) USDA CROPLAND DATA LAYERS (2007 TO 2012), AND (C) CROP ACREAGE DATA FROM THE FARM SERVICE AGENCY (FSA, ) Year USDA cropland data layer FSA crop acreage data 2007 Census of Agriculture Slope R-square Slope R-square Slope R-square NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA Reference data not available Applicability of ACCA Using MODIS Data Only Compared to Landsat data, MODIS data have the advantage of high temporal coverage and fewer issues with clouds with the time-composite products, which can be very valuable in many parts of the world. The original ACCA developed for California used Landsat band 4 (near-infrared band) to delineate cropland (e.g., Algorithm 4 in Figure 1). We replaced the Landsat band 4 data with MODIS band 2 (near infrared band) reflectance data and re-run the algorithms for years 2007, 2008, 2009, 2010, and 2011 and achieved the same level of accuracy (Table 4) when compared with the earlier results (Table 2) which used Landsat data along with MODIS (Figure 1). We attempted this because of the year 2012, when Landsat TM-5 data were discontinued, so we had to run the 2012 ACCA solely based on MODIS data. However, the results of Table 4 re-affirmed that the ACCA algorithm can be run without Landsat data, solely using MODIS data and still achieve equally good results. Discussion The uniqueness of ACCA is the ability to compute cropland spatial extent and areas automatically, rapidly, and accurately on independent data layers year after year. Once the ACCA is developed for one year, it can be applied on independent years to compute cropland extent and areas to generate cropland time series. The ACCA-derived croplands were computed in less than an hour on a Dell Precision T7400 desktop for the entire State of California once the remote sensing data cube of the year of interest is compiled, eliminating the further need to code ACCA algorithm. Further, ACCA is run automatically without any need for human interpretation once the datasets required for the data cube (e.g., Plate 1) are ready, making it labor-efficient and rapid to generate cropland map time series for any past or future years. For example, in this study we generated ACCA based on year 2010 data, but then applied the same ACCA to generate cropland extent and areas for the past years (2007, 2008, 2009) and future years (2011 and 2012), without need for any interpretation but by applying the ACCA developed for year 2010 on data cubes of other years. A more robust ACCA can be derived if we use data from distinct years: normal, wet, and dry years. However, this is not a requirement for the heavily irrigated area like California where irrigation insures against non-normal years. The accuracies of ACCA-derived croplands of this study are comparable to other remote sensing and survey-based cropland products. The overall accuracies of ACCA-derived cropland layers were 96 percent (with K hat of 0.8) with producer s and user s accuracies of 80 percent on average for croplands alone. In addition, the cropland areas at county level agreed well with other cropland data sources with slopes close to 1 and R-square values 0.95 on average. It needs to be noted that the ACCA developed for California can be used year after year repeatedly for the region it was developed (i.e., the State of California). For other geographic areas, the concept of ACCA remains the same. However, the ACCA rules need to be changed to suit that region. Further, other remote sensing and/or secondary data may be needed in complex agroecosystems, or when dealing with other crop characteristics (e.g., crop types, irrigated versus rainfed). For example, the ACCA concept was first proposed, developed, and implemented for Tajikistan (Thenkabail and Wu, 2012) and the same concept is adopted successfully for California. Yet, additional data layers such as elevation and slope were used in Tajikistan to facilitate mapping irrigated versus rainfed croplands as well as mapping croplands in various elevations and slopes. These secondary data layers were not useful in California especially given that the California s Central Valley is relatively flat, and therefore elevation or slopes play very minimal role. In other agroecosystems, we may have better ACCA performance if we use additional secondary data layers such as soils, precipitation, TABLE 4. SUMMARY OF ACCURACIES AND ERRORS OF ACCA-DERIVED CROPLAND LAYERS OF CALIFORNIA USING MODIS DATA ONLY FOR YEARS 2007 TO 2011 Year Producer s accuracy User s accuracy Overall accuracy Errors of omission Errors of commission % 72% 97% 12% 28% % 71% 97% 13% 29% % 72% 97% 16% 28% % 73% 97% 9% 27% % 71% 97% 12% 29% indd 88

9 and temperature. In addition, the low availability of cloudfree or near-cloud-free coverage of Landsat imagery could potentially limit the performance of ACCA, although MODIS alone can achieve comparable high accuracy in mapping cropland, and therefore different regions may require different remote sensing data cubes for ACCA development and implementation. Given the richness and reliability of reference cropland data layers available for California, we were able to develop and test a more robust ACCA for California. As a result, we were able to generate time series of ACCA-derived croplands for the past (2007 to 2009), present (2010, base year) and future (2011 to 2012, relative to 2010) when the imagery was available. The ACCA algorithms can be further strengthened with a better knowledge base such as accurate ground data and other reference data. The accuracy of ACCA is often tightly tied to the accuracy of the reference cropland data layers. The errors and uncertainties inherent in the reference data layers carry over to ACCA-derived products, and can significantly influence uncertainties in ACCA-derived cropland layers. This is the main cause of lower user s accuracies (and higher errors of commission) found in Table 2. For example, the ACCA captures croplands in any year by taking MODIS monthly/yearly spectral signature time-series into consideration whereas the USDA cropland data layers are usually primed for the main growing season trained from the farm-based ground data (Boryan et al., 2011). Thereby, the ACCA development can be improved and strengthened by more reliable and well-dispersed field data across space and time. For other geographic regions in the world, reliable training data are needed to implement the ACCA concept. Furthermore, the ACCA can be expanded to include crop types (e.g., dominant crop types), cropping intensities (e.g., single, double, triple), and other characteristics such as watering regimes (e.g., irrigated versus rainfed). Such expansion will require greater volume and type of remote sensing data (e.g., Landsat data throughout the year, non-optimal remote sensing data) as well as secondary data (e.g., elevation, precipitation, evapotranspiration, temperature). This will be realistic with successful launch of Landsat-8 on 11 February Conclusions This research developed and demonstrated a unique and robust automated cropland classification algorithm (ACCA) for the State of California to routinely, rapidly, and accurately compute cropland extent and areas by combining multi-sensor remote sensing data. The ACCA developed in this research, successfully and accurately produced cropland extent and areas for one base year and five independent years, and demonstrated an ability to generate cropland time series for an entire state over a six-year period in an automated fashion. The overall accuracies of ACCA-derived cropland layers of all five independent years for California was 96 percent when compared to reference cropland layers obtained from the USDA. The ACCA-derived cropland layers provide reliable and consistent cropland area statistics over a six-year period (2007 to 2012) for 58 counties of California, with R-square values of 0.95 when compared with multiple independent data sources. The ACCA is applicable to the region where it is developed and can be applied repeatedly year after year. The value and uniqueness of the ACCA is that once the remote sensing data cube of the year of interest is compiled, accurate cropland extent and areas can be achieved on a Dell desktop in less than an hour for the State of California, without any need for human interaction, making it automatic, rapid and reliable. This research is expected to make significant contribution to the Group on Earth Observation Global Agricultural Monitoring (GEO GLAM), USGS global cropland initiative ( NASA MEASURES project of global food security-support analysis data at 30 m (GFSAD30), and similar initiatives where reliable, consistent, rapid, and routine cropland mapping is expected year after year, thus contributing to water and food security analysis and planning, and facilitating agrarian decision-making. Acknowledgments This work is supported by the US Geological Survey s (USGS) WATERSMART (Sustain and Manage America s Resources for Tomorrow) project and Famine Early Warning Network (FEWSNET) project. Special thanks to Dr. Eric Evenson and Dr. James Rowland of USGS for their insights, support, and collaboration. Inputs and comments on algorithm development from the team members of the USGS Powell Center working group on global croplands ( current_projects.php#globalcroplandmembers) are deeply appreciated. Review comments from Dr. John Jones and Dr. Chandra Giri of USGS, and three anonymous journal reviewers are acknowledged. Funding support from USGS Geographic Analysis and Monitoring (GAM) and Land Remote Sensing (LRS) programs are gratefully acknowledged. The use of trade, product, or firm names is for descriptive purposes only and does not constitute endorsement by the US Government. References Atzberger, C., and F. 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