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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #369497

Research Project: Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems

Location: Hydrology and Remote Sensing Laboratory

Title: Monitoring crop water content for corn and soybean fields through data fusion of MODIS and Landsat measurements in Iowa

item XU, C. - George Mason University
item QU, J.J. - George Mason University
item HAO, X. - George Mason University
item Cosh, Michael
item ZHU, Z. - Us Geological Survey (USGS)
item GUTENBERG, L. - George Mason University

Submitted to: Agricultural Water Management
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/1/2019
Publication Date: 1/20/2020
Citation: Xu, C., Qu, J., Hao, X., Cosh, M.H., Zhu, Z., Gutenberg, L. 2020. Monitoring crop water content for corn and soybean fields through data fusion of MODIS and Landsat measurements in Iowa. Agricultural Water Management. 227:105844.

Interpretive Summary: Monitoring vegetation water content is difficult on a large scale without incorporating satellite products which are not optimized for agricultural management. It is often necessary to combine satellite products to achieve a product that has the adequate spatial and temporal resolutions to satisfy agricultural monitoring requirements. A study was conducted on two essential crops in the U.S., corn and soybeans, to help develop methods for water content assessment. A data fusion method ultimately produced a highly correlated product derived from both the Landsat satellite platform and the Moderate Resolution Imaging Spectroradiometer (MODIS). This result will prove useful for agricultural drought monitoring, crop yield forecasting, as well as soil moisture estimation.

Technical Abstract: Vegetation water content (VWC) is of vital significance to many applications in agriculture, such as crop yield estimation and precision irrigation. However, previous studies mainly estimate VWC at either high spatial resolution or temporal resolution due to the limitation of space-borne observation systems. In this paper, we target on monitoring daily plant VWC as well as canopy VWC at 30 m high spatial resolution with the fusion of optical measurements from the Land Remote Sensing Satellite (Landsat) Operational Land Imager (OLI) and the Moderate Resolution Imaging Spectroradiometer (MODIS) in an agricultural area. The study area locates in the central of Iowa, the United States (USA), the study period ranges from August 1 to August 17, 2016. Landsat 8 OLI observations are with 16-day revisit cycle and 30-m spatial resolution, MODIS daily surface reflectance product used in this study has 500-m spatial resolution. Six Landsat OLI images and thirty MODIS images were used to estimate daily remotely sensed reflectance data at 30m first. Then, the fused satellite measurements were used to calculate Normalized Difference Water Index (NDWI). Ground-based plant VWC and canopy VWC measurements were collected during the Soil Moisture Active Passive Validation Experiment (SMAPVEX16) to calibrate and validate the model for plant VMC and canopy VWC estimation separately with the NDWI. The results of validation with in-situ measurements showed an R2 of 0.44 for the corn plant VWC, an R2 of 0.66 for the corn canopy VWC; and an R2 of 0.78 for the soybean plant VWC, an R2 of 0.85 for the soybean canopy VWC, respectively.