Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3
01 Aug 2013-International Journal of Applied Earth Observation and Geoinformation (Elsevier)-Vol. 23, Iss: 1, pp 344-351
TL;DR: Results confirm the importance of the red-edge bands on particularly Sentinel-2 for agricultural applications, because of the combination with its high spatial resolution of 20 m and linear estimators of canopy chlorophyll and N content.
About: This article is published in International Journal of Applied Earth Observation and Geoinformation.The article was published on 2013-08-01. It has received 490 citations till now. The article focuses on the topics: Spectral bands & Red edge.
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TL;DR: The study presents the preliminary results of two classification exercises assessing the capabilities of pre-operational Sentinel-2 (S2) data for mapping crop types and tree species and confirmed its expected capabilities to produce reliable land cover maps.
Abstract: The study presents the preliminary results of two classification exercises assessing the capabilities of pre-operational (August 2015) Sentinel-2 (S2) data for mapping crop types and tree species. In the first case study, an S2 image was used to map six summer crop species in Lower Austria as well as winter crops/bare soil. Crop type maps are needed to account for crop-specific water use and for agricultural statistics. Crop type information is also useful to parametrize crop growth models for yield estimation, as well as for the retrieval of vegetation biophysical variables using radiative transfer models. The second case study aimed to map seven different deciduous and coniferous tree species in Germany. Detailed information about tree species distribution is important for forest management and to assess potential impacts of climate change. In our S2 data assessment, crop and tree species maps were produced at 10 m spatial resolution by combining the ten S2 spectral channels with 10 and 20 m pixel size. A supervised Random Forest classifier (RF) was deployed and trained with appropriate ground truth. In both case studies, S2 data confirmed its expected capabilities to produce reliable land cover maps. Cross-validated overall accuracies ranged between 65% (tree species) and 76% (crop types). The study confirmed the high value of the red-edge and shortwave infrared (SWIR) bands for vegetation mapping. Also, the blue band was important in both study sites. The S2-bands in the near infrared were amongst the least important channels. The object based image analysis (OBIA) and the classical pixel-based classification achieved comparable results, mainly for the cropland. As only single date acquisitions were available for this study, the full potential of S2 data could not be assessed. In the future, the two twin S2 satellites will offer global coverage every five days and therefore permit to concurrently exploit unprecedented spectral and temporal information with high spatial resolution.
652 citations
TL;DR: In this paper, the authors present the agronomical variables and plant traits that can be estimated by remote sensing, and describe the empirical and deterministic approaches to retrieve them, and provide a synthesis of the emerging opportunities that should strengthen the role of remote sensing in providing operational, efficient and long-term services for agricultural applications.
631 citations
TL;DR: Remote sensing techniques were applied to estimate N and Chl contents of irrigated maize fertilized at five N rates and showed that at the canopy level, Chl and N contents can be accurately retrieved using green and red-edge Chl indices using near infrared (780-800 nm) and either green or red- edge spectral bands.
294 citations
Cites background or result from "Remote estimation of crop and grass..."
...This study confirms that green chlorophyll index CIgreen accurately estimates N content, which is consistent with findings for both crops and grassland systems (Clevers and Gitelson, 2013)....
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...Clevers and Gitelson (2013) focused on the potential of Sentinel-2 ( http://www.esa.int/Our_Activities/Observing_the_Earth/GMES/Sentinel-2 ) and Sentinel-3 ( http://www.esa.int/Our_Activities/Observing_ the_Earth/GMES/Sentinel-3 ) satellites for estimating total crop and grassland Chl and N…...
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...The Ocean and Land Color Instrument (OLCI) on the Sentinel-3 satellite system has one spectral band in red edge region centered at 709 nm. Clevers and Gitelson (2013) found that the CIred-edge, the green chlorophyll index, CIgreen, and the MERIS terrestrial chlorophyll index, MTCI (Table 1) were…...
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...Remote est imat ion of nitrogen and chlorophyll contents in maize at leaf and canopy levels 53 Previous studies showed that CIgreen and CIred-edge are robust for estimating canopy N content (Clevers and Kooistra, 2012; Clevers and Gitelson, 2013)....
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TL;DR: Sentinel-2 bands at 10 m spatial resolution are suitable for estimating LAI, LCC, and CCC, avoiding the need for red-edge bands that are only available at 20 m, and are an important finding for applying Sentinel-2 data in precision agriculture.
Abstract: Leaf area index (LAI) and chlorophyll content, at leaf and canopy level, are important variables for agricultural applications because of their crucial role in photosynthesis and in plant functioning. The goal of this study was to test the hypothesis that LAI, leaf chlorophyll content (LCC), and canopy chlorophyll content (CCC) of a potato crop can be estimated by vegetation indices for the first time using Sentinel-2 satellite images. In 2016 ten plots of 30 × 30 m were designed in a potato field with different fertilization levels. During the growing season approximately 10 daily radiometric field measurements were used to determine LAI, LCC, and CCC. These radiometric determinations were extensively calibrated against LAI2000 and chlorophyll meter (SPAD, soil plant analysis development) measurements for potato crops grown in the years 2010–2014. Results for Sentinel-2 showed that the weighted difference vegetation index (WDVI) using bands at 10 m spatial resolution can be used for estimating the LAI (R2 of 0.809; root mean square error of prediction (RMSEP) of 0.36). The ratio of the transformed chlorophyll in reflectance index and the optimized soil-adjusted vegetation index (TCARI/OSAVI) showed to be a good linear estimator of LCC at 20 m (R2 of 0.696; RMSEP of 0.062 g·m−2). The performance of the chlorophyll vegetation index (CVI) at 10 m spatial resolution was slightly worse (R2 of 0.656; RMSEP of 0.066 g·m−2) compared to TCARI/OSAVI. Finally, results showed that the green chlorophyll index (CIgreen) was an accurate and linear estimator of CCC at 10 m (R2 of 0.818; RMSEP of 0.29 g·m−2). Results for CIgreen were better than for the red-edge chlorophyll index (CIred-edge, R2 of 0.576, RMSE of 0.43 g·m−2). Our results show that Sentinel-2 bands at 10 m spatial resolution are suitable for estimating LAI, LCC, and CCC, avoiding the need for red-edge bands that are only available at 20 m. This is an important finding for applying Sentinel-2 data in precision agriculture.
242 citations
Cites background or methods from "Remote estimation of crop and grass..."
...The second one is also interesting using Sentinel-2, since it avoids the need for red-edge bands that are only available at 20 m [13]....
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...Both indices can be evaluated using Sentinel-2 data [13]....
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...However, since errors in the estimates of LAI and LCC will accumulate in this way, it is preferred to use specific indices for estimating CCC, like the CIred-edge and the CIgreen [6,13]....
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TL;DR: In this paper, a satellite retrieval, solar-induced fluorescence (SIF) of chlorophyll, provides for the first time a direct measurement related to plant photosynthetic activity (i.e. electron transport rate).
Abstract: Large-scale monitoring of crop growth and yield has important value for forecasting food production and prices and ensuring regional food security. A newly emerging satellite retrieval, solar-induced fluorescence (SIF) of chlorophyll, provides for the first time a direct measurement related to plant photosynthetic activity (i.e. electron transport rate). Here, we provide a framework to link SIF retrievals and crop yield, accounting for stoichiometry, photosynthetic pathways, and respiration losses. We apply this framework to estimate United States crop productivity for 2007-2012, where we use the spaceborne SIF retrievals from the Global Ozone Monitoring Experiment-2 satellite, benchmarked with county-level crop yield statistics, and compare it with various traditional crop monitoring approaches. We find that a SIF-based approach accounting for photosynthetic pathways (i.e. C3 and C4 crops) provides the best measure of crop productivity among these approaches, despite the fact that SIF sensors are not yet optimized for terrestrial applications. We further show that SIF provides the ability to infer the impacts of environmental stresses on autotrophic respiration and carbon-use-efficiency, with a substantial sensitivity of both to high temperatures. These results indicate new opportunities for improved mechanistic understanding of crop yield responses to climate variability and change.
236 citations
References
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TL;DR: In this paper, a wide range of leaf chlorophyll levels were established in field-grown corn (Zea mays L.) with the application of 8 N levels: 0, 12.5%, 25, 50, 75, 100, 125, and 150% of the recommended rate.
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TL;DR: In this paper, a combined modeling and indices-based approach is presented to predict the crop chlorophyll content from remote sensing data while minimizing LAI (vegetation parameter) influence and underlying soil background effects.
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