Ranjay Shrestha

GEOSS Component and Service Registry: Design, Implementation and Lessons Learned

Petabytes of Earth science data have been accumulated through space- and air-borne Earth observation programs during the last several decades. The data are valuable both scientifically and socioeconomically. The value of these data could be further increased significantly if the data from these programs can be easily discovered, accessed, integrated, and analyzed. The Global Earth Observation System of Systems (GEOSS) is addressing this need. In particular, the GEOSS Component and Service Registry maintains the descriptive information about available Earth Observation resources that are self-nominated by providers. This registry provides the following capabilities for data providers: user registration, resource registration, and service interface registration. It enables data users to discover these resources through the dedicated graphical user interfaces. It also exposes machine-to-machine interfaces to other GEOSS core components. The captured functional requirements, system design and implementation details of this registry are introduced in this paper, followed by the analysis of the registered resources and the system accessing information. The discussion on the strengths of this registry, the limitations in its current form, and the lessons learned from the system maintenance and upgrade over the last five years may be useful to others building a centralized managed resource catalog to enable a large-scale integrated system following a system-of-systems approach.

Regression model to estimate flood impact on corn yield using MODIS NDVI and USDA cropland data layer

Abstract Flood events and their impact on crops are extremely significant scientific research issues; however, flood monitoring is an exceedingly complicated process. Flood damages on crops are directly related to yield change, which requires accurate assessment to quantify the damages. Various remote sensing products and indices have been used in the past for this purpose. This paper utilizes the moderate resolution imaging spectroradiometer (MODIS) weekly normalized difference vegetation index (NDVI) product to detect and further quantify flood damages on corn within the major corn producing states in the Midwest region of the US. County-level analyses were performed by taking weighted average of all pure corn pixels (>90%) masked by the United States Department of Agriculture (USDA) Cropland Data Layer (CDL). The NDVI-based time-series difference between flood years and normal year (median of years 2000–2014) was used to detect flood occurrences. To further measure the impact of the flood on corn yield, regression analysis between change in NDVI and change in corn yield as independent and dependent variables respectively was performed for 30 different flooding events within growing seasons of the corn. With the R2 value of 0.85, the model indicates statistically significant linear relation between the NDVI and corn yield. Testing the predictability of the model with 10 new cases, the average relative error of the model was only 4.47%. Furthermore, small error (4.8%) of leave-one-out cross validation (LOOCV) along with smaller statistical error indicators (root mean square error (RMSE), mean absolute error (MAE), and mean bias error (MBE)), further validated the accuracy of the model. Utilizing the linear regression approach, change in NDVI during the growing season of corn appeared to be a good indicator to quantify the yield loss due to flood. Additionally, with the 250 m MODIS-based NDVI, these yield losses can be estimated up to field level.

Use of Geographically Weighted Regression Model for Exploring Spatial Patterns and Local Factors Behind NDVI-Precipitation Correlation

Normalized Difference Vegetation Index (NDVI)-precipitation correlation has long been studied. In previous studies, the correlation was usually based on global regression model, which assumed such correlation be constant across the space. However, NDVI-precipitation correlation is spatially dependent and affected by local factors (e.g., soil background). In this paper, geographically weighted regression model is utilized to analyze the NDVI-precipitation correlation on three land use types (i.e., 1) grassland, 2) fallow/idle land, and 3) winter wheat land) within U.S. central great plain area. Results suggest that geographically weighted regression model has better performances than global regression models. Specifically, higher average R2 (0.81) and lower proportion (9%) of residuals with spatial autocorrelation has been achieved under geographically weighted regression in comparison with lower average R2 (0.68) and higher proportion (38%) of residual with spatial autocorrelation under global regression models. In addition, the spatially dependent correlation between NDVI and precipitation has been revealed with geographically weighted regression model. From the north to south, the increasing unit rate of NDVIs change with precipitation has been found through spatially varying regression slopes. Moreover, local factors affecting NDVI-precipitation correlation, such as soil permeability and thickness, have been identified through analyzing the local goodness of fitting under geographically weighted regression model. In summary, unveiled spatial patterns of NDVI-precipitation correlation provide another perspective for studying correlations between NDVI and climatic factors. This work should also be helpful to better understand crop responses to precipitation in agricultural management.

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.

Evaluation of assimilated SMOS Soil Moisture data for US cropland Soil Moisture monitoring

Remotely sensed soil moisture data can provide timely, objective and quantitative crop soil moisture information with broad geospatial coverage and sufficiently high resolution observations collected throughout the growing season. This paper evaluates the feasibility of using the assimilated ESA Soil Moisture Ocean Salinity (SMOS) Mission L-band passive microwave data for operational US cropland soil surface moisture monitoring. The assimilated SMOS soil moisture data are first categorized to match with the United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) survey-based weekly soil moisture observation data, which are ordinal. The categorized assimilated SMOS soil moisture data are compared with NASSs survey-based weekly soil moisture data for consistency and robustness using visual assessment and rank correlation. Preliminary results indicate that the assimilated SMOS soil moisture data highly co-vary with NASS field observations across a large geographic area. Therefore, SMOS data have great potential for US operational cropland soil moisture monitoring.

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.