Mapping of peanut crops in Queensland, Australia, using time-series PROBA-V 100-m normalized difference vegetation index imagery

Abstract

Mapping of peanut crops is essential in supporting peanut production, yield prediction, and commodity forecasting. While ground-based surveys can be used over small areas, the development of remote-sensing technologies could provide rapid and inexpensive crop area estimates with high accuracy over large regions. Some of these recent earth observation satellite systems, such as the Project for On-Board Autonomy Vegetation (PROBA-V), have the advantage of increased spatial and temporal resolution. With a study area located in the South Burnett region, Queensland, Australia, the primary aim of this study was to assess the ability of timeseries PROBA-V 100-m normalized difference vegetation index (NDVI) for peanut crop mapping. Two datasets, i.e., PROBA-V NDVI time-series imagery and the corresponding phenological parameters generated from TIMESAT data analysis technique, were classified using maximum likelihood classification, spectral angle mapper, and minimum distance classification algorithms. The results show that among all methods used, the application of MLC in PROBA-V NDVI time series produced very good overall accuracy, i.e., 92.75%, with producer and user accuracy of each class ≥78.79%. For all algorithms tested, the mapping of peanut cropping areas produced satisfactory classification results, i.e., 75.95% to 100%. Our study confirmed that the use of finer resolution 100 m of PROBA-V imagery (i.e., relative to MODIS 250-m data) has contributed to the success of mapping peanut and other crops in the study area.

Land Cover Classification of Nine Perennial Crops Using Sentinel-1 and -2 Data

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The Performance of Random Forest Classification Based on Phenological Metrics Derived from Sentinel-2 and Landsat 8 to Map Crop Cover in an Irrigated Semi-arid Region

“…Thirteen phenological parameters, which represent the main phenological events, were derived from phenological profiles. Several studies that used these parameters to identify and discriminate crop types at the pixel level have been based on coarse resolution satellite data, including AVHRR data [32], MODIS data [38,44], and PROBAV datasets [20,27]. However, the high temporal and spatial resolution of USGS Landsat 8 OLI and Copernicus Sentinel 2 sensors has opened up important opportunities for extracting these phenological metrics at finer spatial resolution.…”

Change Detection Techniques Based on Multispectral Images for Investigating Land Cover Dynamics

“…Combining images has also been used to improve the interpretation of seasonal changes and their drivers. The trend of VI [295] Phenological change [296,297] Exploring anthropogenic and climate effects on vegetation [298] Monthly Phenological metrics and productivity extraction [299] Assessing land degradation/recovery [300] MODIS Vegetation Index 16-day The extraction of phenological metrics for: -rice crops [301] -forest [302] -vegetation affected by insect infestation [303] -abandoned agriculture [304] -complex ecosystems [305] -spring ephemerals [306] Mapping seasonality metrics [307] MODIS Surface Reflectance 8-day Improving ecosystem classification [302] Mapping fractional forest cover [308] Tracking seasonal change in coniferous forest [309] Monitoring cotton stages [310] Delineating vegetation phenology [311] Ecosystem assessment [312] Validating spring green-up [313] MODIS LAI 8-day Smoothing and gap-filling data [314] Modelling land surface and climate [315] MERIS 10-day Estimating start of season (SOS) [316] Extracting phenological information for deciduous forest [317] AMSR-E 4-day Estimating SOS and ecoregion variability [318] HJ-1 data Irregular Estimating crop phenology [319] PROBA-V Daily Mapping peanut cultivation [320] SPOT-4 5-day Scheduling irrigation [321] Sentinel-2 5-day Mapping macrophyte phenology at a lake [322] Combination of various sensors 8-day Studying deciduous forest carbon flux [323] 16-day Impact of illumination on seasonal metrics [324] 5-day Exploring macrophyte seasonal dynamics [322] 4.6. Dynamic Simulation of Changes Simulation of future land cover changes has been an interest of many remote sensing users for land and ecosyst...…”