Comparing Sentinel-2A and Landsat 7 and 8 Using Surface Reflectance over Australia

Flood, Neil

doi:10.3390/rs9070659

Cited by 81 publications

(77 citation statements)

References 21 publications

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“…Previous studies have reported a significant agreement between Sentinel-2 and Landsat-8 data for continuous monitoring of global land surface [35][36][37][38]. However, the combined use of these two sensors or the transition of a model developed with a particular sensor to another, poses a number of challenges due to the difference in the orbital, spatial, and spectral configurations [35].…”

Section: Introductionmentioning

confidence: 99%

Integrating Landsat-8 and Sentinel-2 Time Series Data for Yield Prediction of Sugarcane Crops at the Block Level

Rahman

Robson

2020

Remote Sensing

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Early prediction of sugarcane crop yield at the commercial block level (unit area of a single crop of the same variety, ratoon or planting date) offers significant benefit to growers, consultants, millers, policy makers, crop insurance companies and researchers. This current study explored a remote sensing based approach for predicting sugarcane yield at the block level by further developing a regionally specific Landsat time series model and including individual crop sowing (or previous seasons' harvest) date. For the Bundaberg growing region of Australia this extends over a five months period, from July to November. For this analysis, the sugarcane blocks were clustered into 10 groups based on their specific planting or ratoon commencement date within the specified five months period. These clustered or groups of blocks were named 'bins'. Cloud free (<20%) satellite data from the polar-orbiting Landsat-8 (launched 2013), Sentinel-2A (launched 2015) and Sentinel-2B (launched 2017) sensors were acquired over the cane growing region in Bundaberg (area of 32,983 ha), from the growing season starting in July 2014, with the average green normalised difference vegetation index (GNDVI) derived for each block. The number of images acquired for each season was defined by the number of cloud free acquisitions. Using the Simple Linear Machine Learning (ML) algorithm, the extracted Landsat derived GNDVI values for each of the blocks were converted to Sentinel GNDVI. The average GNDVI of each 'bin' was plotted and a quadratic model was fitted through the time series to identify the peak growth stage defined as the maximum GNDVI value. The model derived maximum GNDVI values for each of the bins were then regressed against the average actual yield (t·ha -1 ) achieved for the respective bin over the five growing years, producing strong correlations (R 2 = 0.92 to 0.99). The quadratic curves developed for the different bins were shifted according to the specific planting or ratoon date of an individual block allowing for the peak GNDVI value of the block to be calculated, regressed against the actual block yield (t·ha -1 ) and the prediction of yield to be made. To validate the accuracies of the 10 time series algorithms representing each of the 10 bins, 592 individual blocks were selected from the Bundaberg region during the 2019 harvest season. The crops were clustered into the appropriate bins with the respective algorithm applied. From a Sentinel image acquired on the 5 May 2019, the prediction accuracies were encouraging (R 2 = 0.87 and RMSE = 11.33 (t·ha -1 )) when compared to actual harvested yield, as reported by the mill. The results presented in this paper demonstrate significant progress in the accurate prediction of sugarcane yield at the individual sugarcane block level using a remote sensing, time-series based approach.

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Section: Introductionmentioning

confidence: 99%

Integrating Landsat-8 and Sentinel-2 Time Series Data for Yield Prediction of Sugarcane Crops at the Block Level

Rahman

Robson

2020

Remote Sensing

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“…Though differences in spatial resolution and spectral bands are key considerations that must be addressed, incorporating Sentinel-2 data would notably improve the temporal repeat time of assessments, providing more opportunities for cloud-free acquisitions [43][44][45]. Enhanced temporal frequency of observations could also improve historic model fitting.…”

Section: Toward An Integrated Disturbance Monitoring Systemmentioning

confidence: 99%

Near-Real-Time Monitoring of Insect Defoliation Using Landsat Time Series

Pasquarella

Bradley

Woodcock

2017

Forests

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Introduced insects and pathogens impact millions of acres of forested land in the UnitedStates each year, and large-scale monitoring efforts are essential for tracking the spread of outbreaks and quantifying the extent of damage. However, monitoring the impacts of defoliating insects presents a significant challenge due to the ephemeral nature of defoliation events. Using the 2016 gypsy moth (Lymantria dispar) outbreak in Southern New England as a case study, we present a new approach for near-real-time defoliation monitoring using synthetic images produced from Landsat time series. By comparing predicted and observed images, we assessed changes in vegetation condition multiple times over the course of an outbreak. Initial measures can be made as imagery becomes available, and season-integrated products provide a wall-to-wall assessment of potential defoliation at 30 m resolution. Qualitative and quantitative comparisons suggest our Landsat Time Series (LTS) products improve identification of defoliation events relative to existing products and provide a repeatable metric of change in condition. Our synthetic-image approach is an important step toward using the full temporal potential of the Landsat archive for operational monitoring of forest health over large extents, and provides an important new tool for understanding spatial and temporal dynamics of insect defoliators.

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“…The routine use of Sentinel 2A and Sentinel 2B increments the temporal and spatial resolutions providing up to two images per week in the study area with a spatial resolution greater than 100 m 2 (0.01 ha). In addition to this, the compatibility of the NDVI derived from Landsat 8 and Sentinel 2 [36] simplified the calculation procedures. A virtual constellation based on Landsat 8 and Sentinel 2 is being applied during the current campaign of irrigation advice.…”

Section: Discussionmentioning

confidence: 99%

Irrigation Performance Assessment in Table Grape Using the Reflectance-Based Crop Coefficient

et al. 2017

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Abstract:In this paper, we present the results of our study on the operational application of the reflectance-based crop coefficient for assessing table grape irrigation requirements. The methodology was applied to provide irrigation advice and to assess the irrigation performance. The net irrigation water requirements (NIWR) simulated using the reflectance-based basal crop coefficient were provided to the farmer during the growing season and compared with the actual irrigation volumes applied. Two treatments were implemented in the field, increasing and reducing the irrigation doses by 25%, respectively, compared to the regular management. The experiment was carried out in a commercial orchard during three consecutive growing seasons in Northern Chile. The NIWR based on the model was approximately 900 mm per season for the orchard at tree maturity. The experimental results demonstrate that the regular irrigation applied covered only 76% of the NIWR for the whole season, and the analysis of monthly and weekly accumulated values indicates several periods of water shortage. The regular management system tended to underestimate the water requirements from October to January and overestimate the water requirements after harvest from February to April. The level of the deficit of water was quantified using such plant physiological parameters as stem water potential, vegetative development (coverage), and fruit productivity. The estimated NIWR was roughly covered in the treatment where the irrigation dose was increased, and the analyses of the crop production and fruit quality point to the relative advantage of this treatment. Finally, we conclude that the proposed approach allows the analysis of irrigation performance on the scale of commercial fields. These analytic capabilities are based on the well-demonstrated relationship of the crop evapotranspiration with the information provided by satellite images, and provide valuable information for irrigation management by identifying periods of water shortage and over-irrigation.

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Comparing Sentinel-2A and Landsat 7 and 8 Using Surface Reflectance over Australia

Cited by 81 publications

References 21 publications

Integrating Landsat-8 and Sentinel-2 Time Series Data for Yield Prediction of Sugarcane Crops at the Block Level

Integrating Landsat-8 and Sentinel-2 Time Series Data for Yield Prediction of Sugarcane Crops at the Block Level

Near-Real-Time Monitoring of Insect Defoliation Using Landsat Time Series

Irrigation Performance Assessment in Table Grape Using the Reflectance-Based Crop Coefficient

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