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

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 time-series 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

Improved crop classification using multitemporal RapidEye data

Land Use/Land Cover (LU/LC) of agricultural areas derived from remotely sensed data still remains very challenging. With regard to the rising availability and the improving spatial resolution of satellite data, multitemporal analyses become increasingly important for remote sensing investigations. Even crops with similar spectral behaviour can be separated by adding spectral information of different phenological stages. Hence, the potential of multi-date RapidEye data for classifying numerous agricultural classes was investigated in this study. In an agricultural area in Northern Israel two complete crop cycles 2013 and 2014 with two cultivation periods each were investigated. In order to avoid a high number of classification runs, a pre-procedure was tested to get the multitemporal data set which provides best spectral separability. Therefore, Jeffries-Matusita (JM) measure was used in order to obtain the best multitemporal setting of all available images within one cultivation period. Eight classifiers were applied to compare the potential of separating crops. The three algorithms Maximum Likelihood (ML), Random Forest (RF) and Support Vector Machine (SVM) outperformed by far the other classifiers with Overall Accuracies higher than 90 %. The processing time of ML and RF, however, was significantly shorter compared to SVM, in fact by a factor of five to seven

Remote sensing of peanut cropping areas and modelling of their future geographic distribution and disease risks

Peanut or groundnut (Arachis hypogaea L), one of the most important oil seed crops, faces several challenges due to climate change. The unfavourable climate in Australia, as a result of high climate variability, could easily affect peanut production. For example, the incidence of drought stress will increase the likelihood of one of the major problems in the peanut industry, i.e. aflatoxin. In addition, if the climate changes as projected, shifts in geographic distribution of peanut crops and the associated diseases are inevitable. In view of these concerns, this study set the following objectives: 1) to assess the effectiveness of PROBA-V imagery in mapping peanut crops; 2) to study the effects of climate change on the future geographic distribution of peanut crops in Australia; and 3) to examine the effects of climate change on the future distribution of aflatoxin in peanut crops, and to locate high risk areas of aflatoxin in the future areas of peanut crop production. In this study, the area of peanut crop mapping was the South Burnett region in Queensland, while the area of future geographic distribution of peanut crops and aflatoxin covered the entire continent of Australia. To address the first objective, the peanut crop areas were mapped using time-series PROBA-V NDVI by stacking time-series imagery and generating the phenological parameter imagery. Three classification algorithms were used: maximum likelihood classification (MLC), spectral angle mapper (SAM), and minimum distance classification (Min). The results reveal that the overall accuracy of mapping using time-series imagery outweighed phenological parameter imagery, although both datasets performed very well in mapping peanut crops. MLC application in the time-series imagery dataset produced the best result, i.e. overall accuracy of 92.75%, with producer and user accuracy of each class ≥ 78.79%. Specifically for peanut crops, all the algorithms tested produced satisfactory results (≥75.95% of producer and user accuracy), except for the producer accuracy of Min algorithm. Overall, PROBA-V imagery can provide satisfactory results in mapping peanut crops in the study area. For the second objective, the effects of climate change in the potential future geographic distribution of peanut crops in Australia for 2030, 2050, 2070, and 2100 were studied using the CLIMEX program (a Species Distribution Model) under Global Climate Models (GCMs) of CSIRO-Mk3.0 and MIROC-H. The results show an increase in unsuitable areas for peanut cultivation in Australia throughout the projection years for the two GCMs. However, the CSIRO-Mk3 projection of unsuitable areas for 2100 was higher (76% of Australian land) than MIROC-H projection (48% of Australian land). Both GCMs agreed that some current peanut cultivation areas will become unsuitable in the future, while only limited areas will still remain suitable for peanut cultivation. The present study confirms the effects of climate change on the suitability of peanut growing areas in the future. In the third objective, the impacts of climate change on future aflatoxin distribution in Australia and the high risk areas of aflatoxin incidence in the projected future distribution of peanut crops were examined. The projected future distribution of aflatoxin for 2030, 2050, 2070, and 2100 was also modelled using CLIMEX under CSIRO-Mk3.0 and MIROC-H GCMs. The results demonstrated that only a small portion of the Australian continent will be optimal/suitable for aflatoxin persistence, due to the incidence of heat and dry stresses. The map overlay results between the future projections of aflatoxin and peanut crops resulted in small areas of low aflatoxin risk in the future projected areas of peanut crops. It is projected that most of the current peanut cultivation areas will have a high aflatoxin risk, while others will no longer be favourable for peanut cultivation in the future. This study has clearly demonstrated the ability of PROBA-V satellite imagery in mapping peanut crops. It has also demonstrated that climate change incidence will affect the suitability areas of future geographical distribution of peanut crops and the associated aflatoxin disease. This study provides strategic information on current peanut growing areas, future suitable areas for peanut crops in Australia, and future high risk areas of aflatoxin incidence. This information will provide valuable contributions to the long-term planning of peanut cultivation in the country