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 R 2 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 (0.48) 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.

Low cost, multiscale and multi-sensor application for flooded areas mapping

“…The presence of false positives 390 hampers the use of automatic classifications of flooded areas and a visual interpretation is necessary. It is possible that flood effects and the layers of silts could have affected also the crop productivity with relative economic damages as reported in other cases (Tapia-Silva et al, 2011;Shrestha et al, 2017), but this evaluation is not the aim of this study.…”

Low cost, multiscale and multi-sensor application for flooded area mapping

“…The presence of false positives hampers the use of automatic classifications of flooded areas, and a visual interpretation is necessary. It is possible that flood effects and the layers of silts could have also affected the crop productivity with relative economic damage, as reported in other cases (Tapia-Silva et al, 2011;Shrestha et al, 2017), but this evaluation is not the aim of this study.…”

Long-Term Monitoring of Cropland Change near Dongting Lake, China, Using the LandTrendr Algorithm with Landsat Imagery

“…Vegetation indices have been regarded as better variables than individual spectral bands, in terms of monitoring land cover change, because they can reduce the impacts of external factors such as topography and atmosphere on the surface reflectance. Selecting suitable vegetation indices is critical for successfully detecting cropland conversion in our study; different vegetation indices, such as Enhanced Vegetation Index (EVI) [20] and NDVI [32,45], have been used for this purpose. Research shows that NDVI distinguishes cropland change better than other indices [46,47], but there are still some limitations to applying the LandTrendr algorithm with NDVI.…”