Zhengwei Yang

Deriving 2011 cultivated land cover data sets using usda National Agricultural Statistics Service historic Cropland Data Layers

This paper describes the method used to derive 30 meter resolution 2011 US cultivated data sets based on multi-year National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) data. This paper presents different sets of rules (models) to build the cultivated data sets, and a comparison of the resulting cultivated data set accuracies to the accuracies of the original CDL input data. Nine models to create 2011 cultivated data sets for nine US states are tested. Each model provides a set of rules for merging pixels of multi-year (2007-2011) CDL data. The cultivated data accuracy was assessed against in situ 2011 Farm Service Agency (FSA) Common Land Unit (CLU) data. It was found that accuracies were close among the cultivated data generated using the different models. The strongest models for all states achieved overall (producer and user) accuracies greater than 94% for cultivated and non cultivated categories.

Vegetation condition indices for crop vegetation condition monitoring

NDVI maps have been proven valuable in providing a spatially complete view of crops vegetation condition, which manifests disastrous events such as massive flood and drought. It is virtually impossible to obtain from ground survey data. This paper uses NASA MODIS 250m resolution, daily surface reflectance data for crop condition monitoring. The NDVI provides an absolute metrics for vegetation condition. However, a relative measurement of the current vegetation condition against a reference vegetation condition is critical for understanding, interpreting and quantifying the current vegetation condition. In this paper, a new NDVI based vegetation condition index is presented to measure the vegetation condition with respect to the “normal condition”, which is characterized by historical average. The proposed new vegetation condition index is empirically compared with several other vegetation indices to evaluate its effectiveness. Its advantages and utility for crop vegetation condition measurement are evidenced by the preliminary results.

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.

Crop phenology date estimation based on NDVI derived from the reconstructed MODIS daily surface reflectance data

Crop phenological stage estimation based on remote sensing data is critical for evaluating crop progress, condition and crop yield. However, the coarse spatial and temporal resolutions of multi-day composited data products limit the phenology estimation accuracy. The finer resolutions mean more variations in the data. To solve this dilemma, this paper proposes to use NDVI and its derivatives derived from the 250m MODIS daily surface reflectance data MOD09GQ to estimate crop phenology stages. In this paper, the contaminated data of MOD09GQ are first filtered out using quality flag and cloud information from MOD09GA. The missing data are reconstructed with linear interpolation. To remove noise and to generate differentiable NDVI curve, a new temporally and spatially iterative smoothing procedure that uses Savitzky-Golay filter and area averaging is proposed and the double logistic function fitting method is also presented as a comparison. The phenology stages such as emerged, maturity, and harvest dates are detected from the NDVI curve and its derivatives while other phenological stages that are not characterized by NDVI and its derivatives are indirectly derived from all known information. The initial experimental results indicate that the overall mean error of phenological stage estimation is less than 2 weeks for both corn and soybean, which are better than the results produced using temporal composited products as reported by existing papers. The experimental results for corn and soybean phenological estimation also indicate that different denoising techniques may lead to different results on diverse land cover types.

Deriving crop specific covariate data sets from multi-year NASS geospatial cropland data layers

The National Agricultural Statistics Service (NASS) Area Sampling Frames (ASFs) are based on the stratification of US land cover by percent cultivation. Recently, an automated stratification method based on the NASS Cropland Data Layer (CDL) was developed to efficiently and objectively stratify US land cover. This method achieved higher accuracies in all cultivated strata with statistical significance at a 95% confidence level. This paper proposed to develop crop specific covariate data based on 2007 - 2010 CDLs. Crop (corn, soybeans, wheat and cotton) and non crop (forest, urban and water) covariate data were derived and validated for six states. Producer and user accuracies for the covariate data sets were based on independent 2011 Farm Service Agency Common Land Unit data and 2011 CDLs. Non crop covariate data were validated using the National Land Cover Data 2006. Covariate data were used within NASS to conduct substratification of the 2013 Oklahoma ASF.

US national cropland soil moisture monitoring using SMAP

This paper investigates at the pre-launch stage the feasibility of using NASA SMAP mission results for US national operational crop soil moisture monitoring. The purpose of using remote sensed SMAP data for crop soil moisture monitoring is to eliminate data collection subjectivity, reduce cost, increase cropland soil moisture monitoring data consistency, and operational efficiency. In this paper, the SMAP simulated data product time series, such as L2_SM_A, L3_SM_A/P, L4_SM, L1C-S0_HiRes are first evaluated for their suitability for NASS operational cropland soil moisture monitoring by comparing SMAP results with the NASS survey based weekly soil moisture observation data for their consistency and robustness. The preliminary results illustrate that SMAP products have the potential for NASS operational use at least for county level soil moisture statistics. This paper also explores a technical route to build a Web-service based interactive soil moisture monitoring system for map visualization, dissemination, and analysis based on SMAP results.

Remote sensing based crop growth stage estimation model

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.

Enhancing Agricultural Geospatial Data Dissemination and Applications Using Geospatial Web Services

There are many important publicly available agricultural geospatial data products for the agriculture-related research, applications, and educational outreach programs. The traditional data distribution method cannot fully meet users on-demand geospatial data needs. This paper presents interoperable, standard-compliant Web services developed for geospatial data access, query, retrieval, statistics, mapping, and comparison. Those standard geospatial Web services can be integrated in scientific workflows to accomplish specific tasks or consumed over the Web to create value-added new geospatial application by users. In addition, this paper demonstrates, via real world use cases, applications of those services and potential impacts on facilitating geospatial Cropland Data Layer (CDL) retrieval, analysis, visualization, dissemination and integration in agricultural industry, government, research, and educational communities. This paper also shows that the geospatial Web service approach helps improve the reusability, interoperability, dissemination, and utilization of agricultural geospatial data. It allows for integrating multiple online applications and different geospatial data sources, and enables automated retrieving and delivery of agricultural geospatial information for decision-making support.

A study of MODIS and AWiFS multisensor fusion for crop classification enhancement

Accurate, robust, timely and complete remote sensing-based crop classification results are critical to the mission of the National Agricultural Statistics Service (NASS), United States Department of Agriculture. However, due to cloud coverage and limited budget, in many cases, there are not enough quality AWiFS image data available for performing a reliable multitemporal crop classification. To solve this problem, extra image data from other sensors are sought for fusing with AWiFS images for temporal compensation while preserving the high spatial and spectral resolutions. This paper attempts to assess the crop classification accuracy enhancement with AWiFS and MODIS multisensor, multispectral and intertemporal fusion. Three different image fusion methods: principal component analysis (PCA), intensity-hue-saturation (IHS) and image band stacking (IBS) are applied to perform intertemporal image fusion between the 56m AWiFS and the 8-day composited reflectance MODIS data (Red and NIR bands only) with 250m resolution from NASA to incorporate more spectral dynamic information from MODIS images for better crop classification. To make the two-band MODIS data applicable to IHS fusion, this paper proposes a novel combined fusion process, in which the MODIS green band is replaced with the AWiFS green band to create a new multispectral image for IHS transformation. The fused image from AWiFS and MODIS images, together with the original AWiFS multispectral image, are then fed into the decision tree classifier for multitemporal crop classifications in accordance with different fusion methods and temporal combinations. The crop classification accuracies of various classification experiments are assessed with respect to different image fusion methods and different temporal combinations and compared with the reference single AWiFS classification results. The experimental results indicate that properly using the fusion of intertemporal MODIS and AWiFS data improves the crop classification accuracy in large crop area when enough fused temporal images are used.