Remote Sensing Applications in Sugarcane Cultivation: A Review

Abstract

A large number of studies have been published addressing sugarcane management and monitoring to increase productivity and production as well as to better understand landscape dynamics and environmental threats. Building on existing reviews which mainly focused on the crop’s spectral behavior, a comprehensive review is provided which considers the progress made using novel data analysis techniques and improved data sources. To complement the available reviews, and to make the large body of research more easily accessible for both researchers and practitioners, in this review (i) we summarized remote sensing applications from 1981 to 2020, (ii) discussed key strengths and weaknesses of remote sensing approaches in the sugarcane context, and (iii) described the challenges and opportunities for future earth observation (EO)-based sugarcane monitoring and management. More than one hundred scientific studies were assessed regarding sugarcane mapping (52 papers), crop growth anomaly detection (11 papers), health monitoring (14 papers), and yield estimation (30 papers). The articles demonstrate that decametric satellite sensors such as Landsat and Sentinel-2 enable a reliable, cost-efficient, and timely mapping and monitoring of sugarcane by overcoming the ground sampling distance (GSD)-related limitations of coarser hectometric resolution data, while offering rich spectral information in the frequently recorded data. The Sentinel-2 constellation in particular provides fine spatial resolution at 10 m and high revisit frequency to support sugarcane management and other applications over large areas. For very small areas, and in particular for up-scaling and calibration purposes, unmanned aerial vehicles (UAV) are also useful. Multi-temporal and multi-source data, together with powerful machine learning approaches such as the random forest (RF) algorithm, are key to providing efficient monitoring and mapping of sugarcane growth, health, and yield. A number of difficulties for sugarcane monitoring and mapping were identified that are also well known for other crops. Those difficulties relate mainly to the often (i) time consuming pre-processing of optical time series to cope with atmospheric perturbations and cloud coverage, (ii) the still important lack of analysis-ready-data (ARD), (iii) the diversity of environmental and growth conditions—even for a given country—under which sugarcane is grown, superimposing non-crop related radiometric information on the observed sugarcane crop, and (iv) the general ill-posedness of retrieval and classification approaches which adds ambiguity to the derived information.

Mapping Sugarcane in Central India with Smartphone Crowdsourcing

“…We made such categorizations based on the number, timing, and magnitude of the peaks in the time-series NDVI throughout a year (see Figure A2 for more details). Given that it is difficult to distinguish sugarcane from other perennial crops based on NDVI alone [10,13,14] and the majority of perennial crops in the basin are sugarcane, we assumed that the perennial crops identified by our unsupervised method were sugarcane.…”

“…The features used to identify the four groups are as follows. We tested the NDVI threshold based on the approximate range of values from existing studies [10,49]; however, we saw substantial misclassification in our study region (e.g., identifying trees as sugarcane). Adopting a threshold of 0.4 reduced this misclassification.…”

“…Although satellite data present an opportunity for sugarcane mapping, major issues general to any crop mapping and specific to sugarcane mapping in India still remain [8][9][10]. For example, ground-truth data are still required to train, or at least validate, models that can relate remote sensing data to crop types.…”

“…In India, crop mapping efforts have mostly focused on rice, an important staple food crop which occupies the largest share of cultivated land in the country and plays a key role in the country's groundwater depletion problem [11,12], with sugarcane receiving less attention. Some phenological characteristics of sugarcane also make mapping it quite challenging, such as a long and variable growing period of 12-18 months, different sowing and harvesting periods for different sugarcane varieties, and an extended harvest period lasting several months [10,13,14].…”

Pyramid Information Distillation Attention Network for Super-Resolution Reconstruction of Remote Sensing Images

“…High-resolution (HR) remote sensing imagery can provide rich and detailed information about ground features and this has led to it being widely used in various tasks, including urban surveillance, forestry inspection, disaster monitoring, and military object detection [1]. However, it is difficult to guarantee the clarity of remote sensing images because it can be restricted by the imaging hardware, transmission conditions, and other factors.…”