A two-step mapping of irrigated corn with multi-temporal MODIS and Landsat analysis ready data

摘要

Timely and reliable information about irrigated croplands is important for crop water stress analysis and studies of water, energy, and food security. This study mapped irrigated and non-irrigated corn at 30 m resolution for the state of Nebraska using a two-step multi-temporal image classification of MODIS and Landsat Analysis Ready Data (ARD). Starting from the drought year of 2012, when there was a high contrast between irrigated and non-irrigated fields, we first conducted image classification using the 250 m MODIS multi-temporal NDVI data. Training pixels were automatically derived, based on counties with predominant irrigated and non-irrigated cornfields. The MODIS-derived irrigated vs. non-irrigated map was further spatially filtered to generate training data covering the entire Nebraska to support automated Landsat ARD classification, footprint-by-footprint. Three classification algorithms of multi-layer perceptron (MLP) neural network, Random Forest (RF), and Support Vector Machine (SVM) were implemented to classify all available Landsat ARD images within the growing season (i.e. May to November). Given the issues of scanline corrector (SLC) error and cloud contamination, the provisional Landsat-based classifications were finally gap-filled to generate a seamless statewise irrigation map guided by decreasing cross-validation accuracy. Pixel-wise accuracy assessments showed similar overall accuracies of 89.6%, 89.3%, and 90.0% for MLP, RF, and SVM, respectively. They are 3-6% higher than a commonly used gap-filing procedure based on valid (cloud free) pixel count for growing season images. The estimated areas of irrigated corn from Landsat-based mapping were consistent with the 2012 USDA county level census data (R-2 = 0.97 and RMSE = 37.70 km(2)). Using the 2012 Landsat-derived irrigation map and the USDA's annual Cropland Data Layer as inputs, we further developed training data for annual irrigation mapping between 2013 and 2018. Pixel-wise assessment of the 2016 map showed reasonable overall accuracies of 78.4-79.6% for three classification algorithms. The annual maps yielded R-2 of 0.94-0.98 and RMSE values of 37.70-57.62 km(2) for various mapping years compared with USDA county statistics. These results suggest that our proposed two-step analytical method has a high potential for automated annual irrigation mapping at 30 m spatial resolution (especially for the arid and semi-arid western U.S.), providing clear field boundaries and irrigation frequency information that are vitally important for accurate agricultural water use analysis.