Abstract
Indonesia has the most favorable climates for agriculture because of its location in the tropical climatic zones. The country has several commodities to support economics growth that are driven by key export commodities—e.g., oil palm, rubber, paddy, cacao, and coffee. Thus, identifying the main commodities in Indonesia using spatially-explicit tools is essential to understand the precise productivity derived from the agricultural sectors. Many previous studies have used predictions developed using binary maps of general crop cover. Here, we present national commodity maps for Indonesia based on remote sensing data using Google Earth Engine. We evaluated a machine learning algorithm—i.e., Random Forest to parameterize how the area in commodity varied in Indonesia. We used various predictors to estimate the productivity of various commodities based on multispectral satellite imageries (36 predictors) at 30-meters spatial resolution. The national commodity map has a relatively high accuracy, with an overall accuracy of about 95% and Kappa coefficient of about 0.90. The results suggest that the oil palm plantation was the highest commodity product that occupied the largest land of Indonesia. However, this study also showed that the land area in rubber, rice paddies, and cacao commodities was underestimated due to its lack of training samples. Improvement in training data collection for each commodity should be done to increase the accuracy of the commodity maps. The commodity data can be viewed online (website can be found in the end of conclusions). This data can further provide significant information related to the agricultural sectors to investigate food provisioning, particularly in Indonesia.
Highlights
Agricultural industries are an integral part of the Earth’s ecology and play a significant role in human livelihoods [1,2]
Accuracy assessment for the overall model was measured by the error matrices which were used to estimate the user’s accuracy and the producer’s accuracy
Accuracy assessment for the overall model was measured by the error matrices which were used to estimate the user’s accuracy withon and the Random Forest (RF) algorithm number trees gives the highest accuracy based overall producer’s
Summary
Agricultural industries are an integral part of the Earth’s ecology and play a significant role in human livelihoods [1,2]. Indonesia has the most favorable climates in the world for agriculture [2]. The commodities contribute around 60 per cent of all exports from. Following the healthy growth shown by exports to Indonesia over the years, exports from. Indonesia has several commodities that support the economics growth such as palm oil, rubber, coffee, cacao, and paddy. Existing croplands feed this country’s population and play a key role in the nations’ economic income, with many food products such as rice, coffee, tea, cocoa, and palm oil exported to many countries around the world [2]. The main crop is rice, and the Indonesian government has emphasized increasing the national agricultural self-sufficiency by developing the infrastructures and subsidies for rice production [4]
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