data are used in establishing crop growth estimation model and estimate the growth stages. The daily surface reflectance data from Moderate Resolution ...
REMOTE SENSING BASED CROP GROWTH STAGE ESTIMATION MODEL Liping Di1, Eugene Genong Yu1, Zhengwei Yang2, Ranjay Shrestha1, Lingjun Kang1, Bei Zhang1, Weiguo Han1 1. Center for Spatial Information Science and Systems, George Mason University, 4400 University Drive, MSN 6E1, Fairfax, VA 22030, USA 2. National Agricultural Statistics Service, United States Department of Agriculture, 1400 Independence Ave. SW, Washington, DC 20250, USA ABSTRACT Crop growth stages are important factors for segmenting the crop growing seasons and analyzing their growth conditions against normal conditions by periods. Time series of high temporal resolution, up to daily, satellite remotely sensed data are used in establishing crop growth estimation model and estimate the growth stages. The daily surface reflectance data from Moderate Resolution Imaging Spectroradiometer (MODIS) is used as the base data to calculate indices, form condition profiles, construct crop growth model, and estimate crop growth stage. Different crops have different condition profiles. To take into consideration of crop differences, models are built on each crop type. In the United States, ten major crops have been chosen to build crop growth stage estimation models using historical date tracing back to 2000 when MODIS launched. A kernel, double sigmoid model, is used to model the single mode crop growth season. The basic core model is double sigmoid model. The Best Index Slope Extraction (BISE) is applied to pre-filter the daily crop condition index. Estimated results have reasonably high accuracy, with root mean square error less than 10% on the state level evaluation. Index Terms— crop growth stage, MODIS, Cropland Data Layer, phenology, 1. INTRODUCTION Crop growth stages are important indicators for analyzing and determining the crop growth condition. The accumulation of very high temporal resolution satellite images over decades provide solid base to form the understanding of crop growth norm, or the average or median condition at certain point of crop growing season. Time series of remote sensed data have been applied in detecting phenology and related crop growth stages. In this study, the surface reflectance data from Moderate Resolution Imaging Spectroradiometer (MODIS) has been applied to calculate indices, form condition profiles,
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construct crop growth model, and estimate crop growth stage. Models for 10 major crops in the United States have been built at pixel level to produce crop growth stage estimation. The 10 major crops are corn, cotton, barley, soybeans, sorghum, oats, peanuts, rice, and spring wheat, which are monitored and reported by the National Agricultural Statistics Service (NASS) of United States Department of Agriculture (USDA). The basic core model is double sigmoid model which is used as the kernel to model the crop growth curve of normalized difference vegetation index (NDVI) during the growing season. The Best Index Slope Extraction (BISE) has been applied to pre-filter the daily crop condition index. This paper first describes the methodology, specifically double sigmoid model, production workflow, and validation approach, followed by results from the model application on major crops in the United States and accuracy evaluation. 2. METHODOLOGY 2.1. Remote sensed data The daily MODIS Surface-Reflectance Product (MOD 09) dataset from the Terra satellite since 2000 are used in calculating NDVI. This is the base for further calculation of alternative indices and constructing crop growth models. For the estimation of crop growth stages, we used daily NDVI as the base to form crop condition profiles and constructing crop growth models for estimating crop growth stages. Crop type information is extracted from the Cropland Data Layer (CDL) of the USDA-NASS [1]. The data is available through standard geospatial Web services, primarily through OGC-compliant Web Coverage Service (WCS) (http://nassgeodata.csiss.gmu.edu)[2]. Determining the crop type of a pixel in the spatial resolution level of MODIS is complicated by the difference of resolution between MODIS and CDL. MODIS is at the nominal spatial resolution of 250 meters while CDL data are either in 30 meters or 56 meters spatial resolution depending on years. In this study, we used the geometrically registered images of MODIS and CDL data to compute the percentage
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of each crop type in a MODIS pixel. Only pixels that have more 90% of certain crop type are considered in this study. These pixels are called pure pixels of certain crop type. For example, if a MODIS pixel has a 92% corn area coverage by overlapping it over the high resolution 30-meter CDL data, it is assigned as a corn pixel at the MODIS spatial resolution level. This pixel is called a pure corn pixel at the MODIS spatial resolution level. In this project, mixed pixels are not considered for simplifying the study. For example, a MODIS pixel covering 30% corn, 40% soybean, and 30% non-agricultural land is not considered in this project. 2.2. Double sigmoid model At each pixel, the crop type was determined using CDL data. For each crop type, the time series of daily NDVI are collected and used as the base to build up the crop condition profiles[3]. The data are further filtered using BISE algorithm to remove abnormal data (e.g. cloudy day’s data). The remaining daily NDVI of a year are truncated to the growing season by examining if they are falling in between the starting and ending dates of that specific crop. The dates vary from one agricultural statistical district (ASD) to another. The dates are primarily extracted from USDA NASS crop date reports. The remaining ‘good’ NDVI data of each are then fed into double sigmoid model to formulate the annual crop growth model. This is then used to estimate the crop growth stage using a trained and optimized threshold for specific pixel and specific crop. The following the core of double sigmoid equation. ଵ ଵ ܻൌ షభ െ షమ ଵା
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Actual surveyed dataset available from USDA NASS are used to assess the accuracy of crop growth stages. Summary of pixel-level crop growth data are summarized to county level and state level. The state level is publically verifiable using published NASS data (http://quickstats.nass.usda.gov/ ). The summary of surveyed data at the state level is expressed as percentages of a given crop at different stages. For example, the surveyed summary for Indiana has 62% in silking stage of the 29th week in 2013. The estimated stages of all corn pixels in Indiana in 2013 are summarized to get the percentage. The derived percentage is compared to the surveyed summary. The error estimation is evaluated for all the years since 2000 when the first MODIS data are available. All states and all year data and their deviation are used to estimate their root mean square error. 3. RESULTS AND DISCUSSIONS 3.1. Model optimization Historical data are used in optimizing the models. The parameters can be optimized are threshold selection and lower bound of ‘normal’ NDVI value. Stepwise, gradient descent optimization was applied to find the optimal parameters. Figure shows one example outputs of modeling threshold selection for silking stage of corn. The historical data used are between 2000 and 2012. The figure shows the optimal threshold for dough stage of corn in this region is around local threshold 87% of the range of NDVI.
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A geospatial workflow was implemented to extract data from the data server of NASA[4]. The data are then populated into the crop growth model, matched and filtered using CDL, smoothed using BISE algorithm, and modeled using double sigmoid model. The resulted models are used to estimate crop growth stage using local threshold algorithm. The results are published in standard geospatial Web services for easy access, disseminating, and rendering. In this study, data are stored and persisted in the Web Coverage Service (WCS). The OGC Web Map Service (WMS) is then used to render the results from WCS. This allows the rendering and displaying of the results in the Web-based environment while WCS is made available for users to extract the actual data through standard Web interface.
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Where, ܻ is the model output, ݐis time in Julian day, ଵ and ଶ are positions for both sigmoid functions, and ݓଵ and ݓଶ are width for both sigmoid functions.
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Figure 1. Threshold optimization – corn silking stage threshold of Iowa State
As the single mode double sigmoid model is used to model the complete growing season, we have two types of threshold value – one for those before the mode and another for those after the mode. Before the mode, the pixel is evaluated to be true for the estimate stage if its local ratio of NDVI value is larger than the threshold value. After the mode, the pixel is evaluated to be true for the estimate stage if its local ratio of NDVI value is less than the threshold value. The reason for such evaluation strategy is that the double sigmoid model ensures the monotonic increase trend
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before the mode and the monotonic decrease trend after the mode. Different crop and different area, some stage occurs after the mode and some stage occurs before the mode. In Iowa state, the dough stage occurs after the mode. Figure 2 shows one example outputs of modeling threshold selection for dough stage of corn. From the RMS curve of different trials with different local thresholds, it is obvious that T88% is the optimized threshold which leads to the least RMS for dough stage estimation in Iowa State. Notice the prefix T used here which differentiates it from the threshold before the mode. This means the threshold value will be used in reverse order to evaluate and estimate the stage of pixels when the stage estimation program is running against their ratio of NDVI.
online following open geospatial Web service specifications. The public access URL is at http://dss.csiss.gmu.edu/CropGrowth/ . Figure 3 shows the crop growth data portal. This showed the percentage of corn at the dough at county level. The greenness of the color shades reflects the percentage of corn at dough stage.
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Figure 3. Crop growth stage geospatial portal - corn stage map – 31st week of 2012
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Figure 2. Threshold optimization – corn dough stage threshold of Iowa State
This optimization process repeats for each of the stages. The results for corn stage estimation thresholds in Iowa State are {67%, 87%, T88%, T86%, T81%}, which corresponds to emerged stage, silking stage, dough stage, dented stage, and mature stage. The same optimization strategy is applied to estimate the two management stages: planted and harvested. Planted stage is before mode while harvested stage is after mode. The optimal thresholds for planted stage and harvested stage in Iowa are 61% and T64%. The optimization process repeats the similar procedure for each state. Each state gets a set of optimal thresholds. The thresholds may be different from crop to crop. They may be different from state to state. For example, the optimal thresholds for corn stages in Mississippi State are {Planted: 68%, Emerged: 72%, Silking: 90%, Doughing: T82%, Dented: T80%, Mature: T78%, Harvested: T76%}. They are quite different from Iowa State. The reason may be because of their difference in meteorological conditions due to location difference. 3.2. Products and services The geospatial Web services are not only used in feeding data into the geospatial workflow for production but also adopted in publishing and rending the results. The results of all states on 10 major crops are estimated using the approach described in Section 2. The results are published and served
The interactive map of the crop growth stages can be useful to quickly show the distribution of crop at different stages. As shown in Figure 3, major production states are North Dakota, South Dakota, Minnesota, Wisconsin, Ohio, Michigan, Nebraska, Kansas, Missouri, Kentucky, Oklahoma, Colorado, Texas, Pennsylvania, North Carolina, South Carolina, Alabama, Delaware, Georgia, Louisiana, Maryland, New Jersey, New Mexico, Utah, West Virginia, and Washington. Most corn growing counties in the southern part show greener than those in the northern state. This can be interpreted as follows: in the 31st week of 2012, corns in most corn growing counties reach dough stage while those in northern counties have not reach dough stage.
Figure 4. Crop growth stage geospatial portal - corn stage map – 40th week of 2012
Figure 4 shows the percentage map of dough stage at county level for the 40th week of 2012. All corn growing counties have uniformly green shade. This means that all counties pass the dough stage in the 40th week of 2012. 3.3. Accuracy
Table 1 shows the results of accuracy assessment. The accuracy is based on the deviation from the surveyed
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percentage report on all stages of each crop. The table shows that the overall error is relatively low. Most are only 10% deviation from what the surveyed stage percentages. Corn stage estimation show good results of 10%. It has a large coverage in the States. Survey data for corn stages are most complete in many states since 1997 that covers the years of MODIS lifespan of 2000 to current year. Enough survey data helps in the model optimization to select the best models for estimating corn stages. The error for barley is relatively higher than the rest, which has RMS of 14%. The reason may be because of the limited availability of dataset for training the barley model. Rice show extremely good results which may be related to limited number of stages to be estimated and the strong signature of stages. Table 1. Crop growth stage estimation accuracy Crop
Stages
Corn
5 Phenological stages: emerged, silking, dough, dent, mature; 2 management stages: planted and harvested 3 Phenological stages: squaring, setting bolls, bolls opening; 2 management stages: planted and harvested 2 Phenological stages: emerged, headed; 2 management stages: planted and harvested 4 Phenological stages: emerged, blooming, setting pods, dropping leaves; 2 management stages: planted and harvested 3 Phenological stages: headed, coloring, mature; 2 management stages: planted and harvested 2 Phenological stages: emerged, headed; 2 management stages: planted and harvested 1 Phenological stages: pegging; 2 management stages: planted and harvested 2 Phenological stages: emerged, headed; 2 management stages: planted and harvested 2 Phenological stages: emerged, headed; 2 management stages: planted and harvested
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inclusion of estimating the two stages is for the completeness of the report that meets the requirement of NASS. The accuracy would increase if the two management stages are included in the evaluation. As shown in Table 1, the crop growth stages for different crops are different. Corn has the most stages among all the crops under study. In general, the accuracy shows that the less the stages are, the more accurate the estimate is while the less survey validation data is required. The most availability of corn survey data compensates the difficult to estimate many stages. 4. CONCLUSIONS A remote sensing based, double sigmoid crop growth model was developed, implemented, optimized, and verified using historical time series of NDVI datasets derived from MODIS observations. The results showed reasonable accuracy, of average around 10%. Open geospatial Web services facilitate the data serving, the data production, the information presentation, and the product dissemination. 5. REFERENCES
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[1] Boryan, C., Yang, Z., Mueller, R., Craig, M., 2011. Monitoring US agriculture: the US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program. Geocarto International, Vol. 26, Issue 5, pp. 341–358.
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[2] Han, W., Yang, Z., Di, L., Yue, P., 2014. A geospatial Web service approach for creating on-demand Cropland Data Layer thematic maps. Transactions of the ASABE, Vol. 57, Issue. 1, pp. 239-247.
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[3] Yu, G., L. Di, Z. Yang, Z. Chen, and B. Zhang, “Crop condition assessment using high temporal resolution satellite images,” in 2012 First International Conference on AgroGeoinformatics (Agro-Geoinformatics), 2012, pp. 1–6.
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[4] Yu, G., P. Zhao, L. Di, A. Chen, M. Deng, and Y. Bai, “BPELPower—A BPEL execution engine for geospatial web services,” Computers & Geosciences, 2012, Vol. 47, pp. 87-101.
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Table 1 also lists all the stages to be estimated. All crops have two management stages – planted stage and harvested stage. The estimation of planted stage and harvested stage are difficult since they are not always related to the natural growth stages of crops but the management practice. The
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