Satellite remote sensing has become a popular tool to analyze agricultural drought through terrestrial vegetation health conditions using the normalized difference vegetation index (NDVI). Drought monitoring techniques using remote sensing-based drought indices assume that vegetation conditions vary year-to-year due to prevailing weather conditions (e.g., precipitation and temperature), and current conditions are evaluated based on the deviation from the long-term statistics such as mean, minimum, or maximum. However, the rotation between agricultural crops (e.g., corn and soybeans) implies that this assumption may not hold, as each crop type may have distinct phenological variability across the growing season. In this study, the effect of crop rotation between corn and soybeans on the accuracy of the NDVI-based agricultural drought monitoring was investigated in Iowa, USA. The vegetation condition index (VCI), which is derived from NDVI, was selected to demonstrate the impact of crop rotation. The standard precipitation index (SPI) and official crop yield statistics were used as independent validation of the drought information acquired by these indices. The results suggested that the NDVI alone was not able to distinguish drought-related vegetation stress from vegetation changes caused by crop rotation between corn and soybeans. It was found that the integration of land cover with NDVI greatly improved the agricultural drought information obtained by the VCI over the crop-rotated agricultural fields in Iowa.

The Cropland Data Layer (CDL) product provides categorized land-cover information encompassing the whole contiguous United States. Using the latest Web and geospatial interoperability technologies, CropScape was built to visualize, query, disseminate, and analyze historical and current CDL data interactively and intuitively over the Internet. CropScape can be utilized as a useful online mapping educational tool and an important source of free geospatial data and geospatial Web services in GIS education. This article introduces the CDL product and functions of CropScape, and presents several specific examples of cropland exploring, thematic map creation, natural disaster assessment, crop planting patterns discovery, geospatial data mashup, and geospatial Web application development to demonstrate CropScape’s capabilities.

During the last decade, the use of the normalized difference vegetation index (NDVI) for drought monitoring applications has drawn many criticisms, mainly because a number of drivers such as land-cover/land-use change, pest infestation, and flooding may depress the NDVI, further causing false drought identification. In this study, the impacts of land-cover change on the NDVI-derived satellite drought indicator, the vegetation condition index (VCI), are presented. It was found that the VCI is sensitive to changes in land cover, especially deforestation, the land cover changes from evergreen and deciduous forests to other land-cover classes. However, because the scale of land-cover changes was very small across the study area, only trivial drought alerts were observed in the VCI-based drought maps during non-drought years. Because drought is a large-scale climate event, it is reasonable to neglect these alerts. Besides, when the VCI was averaged to climate division scale, the results obtained through the VCI method were in good agreement with those acquired by the meteorological data-based drought indices such as the Palmer drought severity index and standardized precipitation index.

There is an urgent need but remains a very challenging problem to provide worldwide users with timely, on-demand, and ready-to-use agricultural drought data and information. The latest advances in Web service, geospatial interoperability and cyber-infrastructure technologies and the availability of near real-time global remote sensing data promise a solution to the problem. This paper presents the research study of the state of the art, methodology and approaches to building an open, interoperable, on-demand Web service system for global agriculture drought monitoring and forecasting. The implemented system, named as Global Agriculture Drought Monitoring and Forecasting System (GADMFS), overcomes most limitations of current agriculture drought information systems in the world and significantly improves global agriculture drought monitoring, prediction and analysis with advanced geospatial Web service technology.