Estimating cotton water consumption using a time series of Sentinel-2 imagery

Rozenstein, Offer; Haymann, Nitai; Kaplan, Gregoriy; Tanny, Josef

doi:10.1016/j.agwat.2018.05.017

Cited by 70 publications

(39 citation statements)

References 40 publications

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“…Several studies have illustrated the relevance of individual Sentinel-2 images for environmental applications, including soil organic mapping [22,23], soil moisture mapping using the synergy of Sentinel-1 (radar) and Sentinel-2 data [24,25], and Mediterranean seagrass mapping [26]. The importance of the multitemporal dimension of Sentinel-2 data was also evidenced for environmental applications, including the detection of precursory motions before landslide failures [27], the evaluation of the extent of forest fires [28], the estimation of vegetation phenological stages [29], water use by cotton [30], and the improvement of crop type mapping [31]. In Soil Science, the multitemporal dimension may allow to (i) increase the probability of image acquisition in clear sky conditions during periods with consistent bare soil coverage over cultivated areas (e.g., between October and November in Mediterranean areas, during the plowing time) and (ii) provide several repetitions of spectral measurements of the surface.…”

Section: Introductionmentioning

confidence: 99%

Use of Sentinel-2 Time-Series Images for Classification and Uncertainty Analysis of Inherent Biophysical Property: Case of Soil Texture Mapping

et al. 2019

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The Sentinel-2 mission of the European Space Agency (ESA) Copernicus program provides multispectral remote sensing data at decametric spatial resolution and high temporal resolution. The objective of this work is to evaluate the ability of Sentinel-2 time-series data to enable classification of an inherent biophysical property, in terms of accuracy and uncertainty estimation. The tested inherent biophysical property was the soil texture. Soil texture classification was performed on each individual Sentinel-2 image with a linear support vector machine. Two sources of uncertainty were studied: uncertainties due to the Sentinel-2 acquisition date and uncertainties due to the soil sample selection in the training dataset. The first uncertainty analysis was achieved by analyzing the diversity of classification results obtained from the time series of soil texture classifications, considering that the temporal resolution is akin to a repetition of spectral measurements. The second uncertainty analysis was achieved from each individual Sentinel-2 image, based on a bootstrapping procedure corresponding to 100 independent classifications obtained with different training data. The Simpson index was used to compute this diversity in the classification results. This work was carried out in an Indian cultivated region (84 km2, part of Berambadi catchment, in the Karnataka state). It used a time-series of six Sentinel-2 images acquired from February to April 2017 and 130 soil surface samples, collected over the study area and characterized in terms of texture. The classification analysis showed the following: (i) each single-date image analysis resulted in moderate performances for soil texture classification, and (ii) high confusion was obtained between neighboring textural classes, and low confusion was obtained between remote textural classes. The uncertainty analysis showed that (i) the classification of remote textural classes (clay and sandy loam) was more certain than classifications of intermediate classes (sandy clay and sandy clay loam), (ii) a final soil textural map can be produced depending on the allowed uncertainty, and iii) a higher level of allowed uncertainty leads to increased bare soil coverage. These results illustrate the potential of Sentinel-2 for providing input for modeling environmental processes and crop management.

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Section: Introductionmentioning

confidence: 99%

Use of Sentinel-2 Time-Series Images for Classification and Uncertainty Analysis of Inherent Biophysical Property: Case of Soil Texture Mapping

et al. 2019

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“…The cotton water demand is different at different stages of cotton growth and development [78]. Moreover, the spectral features of water indices reflected water stress to a certain extent [24]. Due to the self-regulation mechanism of crop, the sensitivity of crop to water stress are reflected on its growth and development [79].…”

Section: Discussionmentioning

confidence: 99%

“…The indices using visible and near-infrared bands were developed to predict crop water stress due to their positive correlations with stomatal conductance and leaf water potential [22,23]. Many studies have suggested that these indices are nearly linear related to stem water potential [24,31,52,53]. The red edge-based vegetation indices were also compared in the study, due to their sensitive to chlorophyll content and leaf area index (LAI) [54,55].…”

Section: Linear-regression-based Methodsmentioning

confidence: 99%

“…Moreover, the water absorption bands in the 1300-2500 nm region, which can eliminate variations caused by leaf internal structure and leaf dry matter content, are most sensitive to the leaf water content of most crops [56]. The indices created by the combination of the NIR and the SWIR bands can substantially improve the accuracy of retrieving the vegetation water content [57], which were widely used to monitor changes in stem water potential in the literature [24,25,58]. Table 2.…”

Section: Linear-regression-based Methodsmentioning

confidence: 99%

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Continuous Monitoring of Cotton Stem Water Potential using Sentinel-2 Imagery

et al. 2020

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Monitoring cotton status during the growing season is critical in increasing production efficiency. The water status in cotton is a key factor for yield and cotton quality. Stem water potential (SWP) is a precise indicator for assessing cotton water status. Satellite remote sensing is an effective approach for monitoring cotton growth at a large scale. The aim of this study is to estimate cotton water stress at a high temporal frequency and at a large scale. In this study, we measured midday SWP samples according to the acquisition dates of Sentinel-2 images and used them to build linear-regression-based and machine-learning-based models to estimate cotton water stress during the growing season (June to August, 2018). For the linear-regression-based method, we estimated SWP based on different Sentinel-2 spectral bands and vegetation indices, where the normalized difference index 45 (NDI45) achieved the best performance (R 2 = 0.6269; RMSE = 3.6802 (-1*swp (bars))). For the machine-learning-based method, we used random forest regression to estimate SWP and received even better results (R 2 = 0.6709; RMSE = 3.3742 (-1*swp (bars))). To find the best selection of input variables for the machine-learning-based approach, we tried three different data input datasets, including (1) 9 original spectral bands (e.g., blue, green, red, red edge, near infrared (NIR), and shortwave infrared (SWIR)), (2) 21 vegetation indices, and (3) a combination of original Sentinel-2 spectral bands and vegetation indices. The highest accuracy was achieved when only the original spectral bands were used. We also found the SWIR and red edge band were the most important spectral bands, and the vegetation indices based on red edge and NIR bands were particularly helpful. Finally, we applied the best approach for the linear-regression-based and the machine-learning-based methods to generate cotton water potential maps at a large scale and high temporal frequency. Results suggests that the methods developed here has the potential for continuous monitoring of SWP at large scales and the machine-learning-based method is preferred.have devastating impacts on the environment [3]. For agriculture, drought can endanger food security which can have critical effects on the economy, geopolitics, and society [5]. Drought can also negatively affect agriculture production especially in arid and semiarid regions [6]. Therefore, detecting crop water stress is of vital importance to improve agricultural quality and productivity.Cotton is cultivated in arid and semiarid regions and is the most important fiber crop in the world [7]. Cotton is susceptible to water stress in all growth stages, particularly during the flowering and boll development stages [8]. Moreover, water deficit is an important factor limiting growth and development in cotton, including plant height, leaf and root size, as well as economic yield and fiber quality [8]. To better understand the cotton's response and adaption to water stress, continuous monitoring of cotton water status in large a...

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“…On the other hand, regarding irrigation strategies, neural network techniques were applied on satellite images to manage a central pivot irrigation system in Colorado (USA) [17]. The use of several red and red-edge bands for predicting the crop coefficient, Kc, in cotton was studied in [18]. These prediction models were used for near-real-time irrigation decision support systems.…”

Section: Introductionmentioning

confidence: 99%

An Automatic Non-Destructive Method for the Classification of the Ripeness Stage of Red Delicious Apples in Orchards Using Aerial Video

et al. 2019

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The estimation of the ripening state in orchards helps improve post-harvest processes. Picking fruits based on their stage of maturity can reduce the cost of storage and increase market outcomes. Moreover, aerial images and the estimated ripeness can be used as indicators for detecting water stress and determining the water applied during irrigation. Additionally, they can also be related to the crop coefficient (Kc) of seasonal water needs. The purpose of this research is to develop a new computer vision algorithm to detect the existing fruits in aerial images of an apple cultivar (of Red Delicious variety) and estimate their ripeness stage among four possible classes: unripe, half-ripe, ripe, and overripe. The proposed method is based on a combination of the most effective color features and a classifier based on artificial neural networks optimized with genetic algorithms. The obtained results indicate an average classification accuracy of 97.88%, over a dataset of 8390 images and 27,687 apples, and values of the area under the ROC (receiver operating characteristic) curve near or above 0.99 for all classes. We believe this is a remarkable performance that allows a proper non-intrusive estimation of ripening that will help to improve harvesting strategies.

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Estimating cotton water consumption using a time series of Sentinel-2 imagery

Cited by 70 publications

References 40 publications

Use of Sentinel-2 Time-Series Images for Classification and Uncertainty Analysis of Inherent Biophysical Property: Case of Soil Texture Mapping

Use of Sentinel-2 Time-Series Images for Classification and Uncertainty Analysis of Inherent Biophysical Property: Case of Soil Texture Mapping

Continuous Monitoring of Cotton Stem Water Potential using Sentinel-2 Imagery

An Automatic Non-Destructive Method for the Classification of the Ripeness Stage of Red Delicious Apples in Orchards Using Aerial Video

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