Spectral matching techniques (SMTs) and automated cropland classification algorithms (ACCAs) for mapping croplands of Australia using MODIS 250-m time-series (2000–2015) data

Abstract

Mapping croplands, including fallow areas, are an important measure to determine the quantity of food that is produced, where they are produced, and when they are produced (e.g. seasonality). Furthermore, croplands are known as water guzzlers by consuming anywhere between 70% and 90% of all human water use globally. Given these facts and the increase in global population to nearly 10 billion by the year 2050, the need for routine, rapid, and automated cropland mapping year-after-year and/or season-after-season is of great importance. The overarching goal of this study was to generate standard and routine cropland products, year-after-year, over very large areas through the use of two novel methods: (a) quantitative spectral matching techniques (QSMTs) applied at continental level and (b) rulebased Automated Cropland Classification Algorithm (ACCA) with the ability to hind-cast, now-cast, and future-cast. Australia was chosen for the study given its extensive croplands, rich history of agriculture, and yet nonexistent routine yearly generated cropland products using multitemporal remote sensing. This research produced three distinct cropland products using Moderate Resolution Imaging Spectroradiometer (MODIS) 250-m normalized difference vegetation index 16-day composite time-series data for 16 years: 2000 through 2015. The products consisted of: (1) cropland extent/areas versus cropland fallow areas, (2) irrigated versus rainfed croplands, and (3) cropping intensities: single, double, and continuous cropping. An accurate reference cropland product (RCP) for the year 2014 (RCP2014) produced using QSMT was used as a knowledge base to train and develop the ACCA algorithm that was then applied to the MODIS time-series data for the years 2000-2015. A comparison between the ACCA-derived cropland products

Irrigation History Estimation Using Multitemporal Landsat Satellite Images: Application to an Intensive Groundwater Irrigated Agricultural Watershed in India

“…Moderate Resolution Imaging Spectroradiometer's (MODIS), Terra mission and Landsat satellite products are well known for land use and land cover studies [6,[36][37][38][39]. However, the available high temporal frequency satellite products (MODIS) are of coarse (250 m or more) to medium (Landsat (30 m)) spatial resolution.…”

Agricultural Expansion and Intensification in the Foothills of Mount Kenya: A Landscape Perspective

“…However, many studies focus on the global or national scale, using multi-temporal MODIS satellite data that are too coarse to capture small-scale irrigated plots or regional changes [27][28][29]. More recently, scholars have begun to explore phenological profiles from Landsat time series, which has led to more accurate and more detailed cropland identification and change assessments [30][31][32].…”

Nominal 30-m Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine

“…Although the human capacity for interpreting images is remarkable, visual interpretation is subjective, time-consuming, and expensive on large area. A number of cropland cover datasets on a global scale have been developed, mostly at a coarse resolution of 1-km [8,[12][13][14]. Others have mapped cropland as one class in their land cover products at MODIS resolution [15][16][17][18][19].…”