Exploring the Potential of Mapping Cropping Patterns on Smallholder Scale Croplands Using Sentinel-1 SAR Data

Abstract

It is of paramount importance to have sustainable agriculture since agriculture is the backbone of many nations' economic development. Majority of agricultural professionals rarely capture the cropping patterns necessary to promote Good Agricultural Practises. Objective of this research is to explore the potential of mapping cropping patterns occurring on different field parcels on small-scale farmlands in Zimbabwe. The first study location under investigation are the International Maize and Wheat Improvement Center (CIMMYT) research station and a few neighboring fields, the second is Middle Sabi Estate. Fourier time series modeling was implemented to determine the trends befalling on the two study sites. Results reveal that Sentinel-1 synthetic aperture radar (SAR) time series allow detection of subtle changes that occur to the crops and fields respectively, hence can be utilized to detect cropping patterns on small-scale farmlands. Discrimination of the main crops (maize and soybean) grown at CIMMYT was possible, and crop rotation was synthesized where sowing starts in November. A single cropping of early and late crops was observed, there were no winter crops planted during the investigation period. At Middle Sabi Estate, single cropping on perennial sugarcane fields and triple cropping of fields growing leafy vegetables, tomatoes and onions were observed. Classification of stacked images was used to derive the crop rotation maps representing what is practised at the farming lands. Random forest classification of the multi-temporal image stacks achieved overall accuracies of 99% and 95% on the respective study sites. In conclusion, Sentinel-1 time series can be implemented effectively to map the cropping patterns and crop rotations occurring on small-scale farming land. We recommend the use of Sentinel-1 SAR multi-temporal data to spatially explicitly map cropping patterns of single-, double-and triple-cropping systems on both small-scale and large-scale farming areas to ensure food security.

Delineating Smallholder Maize Farms from Sentinel-1 Coupled with Sentinel-2 Data Using Machine Learning

“…These results are similar to those of Abubakar et al [53], who observed an OA of 78.9% when mapping smallholder maize using Sentinel-1 data by applying SVM. However, Useya and Chen [54] reported an OA of 46% with RF and 40% with K-means classification when mapping smallholder maize farms and other crops with Sentinel-1 single-date data. The poor performance of the Sentinel-1 C-band data could be because of its shorter wavelength, which decreases canopy penetration in comparison to L-band SAR, which has a longer wavelength [55,56].…”

Mapping Crop Types in Southeast India with Smartphone Crowdsourcing and Deep Learning

“…Sentinel-2 was chosen for its high spatial resolution and public availability, and prior work has shown that optical features can be used to distinguish crop types [14,18,27,41]. Sentinel-2A was first launched by the European Space Agency (ESA) in June 2015 as part of the European Union's Copernicus Programme for Earth observation, and captures high-resolution (10-60 m) optical imagery to serve a wide range of scientific applications on land and in coastal waters.…”

Using Sentinel-1, Sentinel-2, and Planet Imagery to Map Crop Type of Smallholder Farms

“…Yet, to date, relatively little work has been carried out to map crop types in smallholder systems compared to other agricultural characteristics, such as cropped area and yield. The work that does exist to map smallholder crop types using readily available imagery has largely focused on using Sentinel 1 and 2 [9,[12][13][14]], yet it is likely that micro-satellite data could improve classification accuracies given their higher spatial resolution, which reduces the effect of mixed pixels at field edges [15]. To the best of our knowledge, only two studies have examined the benefits that Planet micro-satellite data provide when mapping smallholder crop types, and these two studies were conducted in Africa [16,17].…”