Monitoring Irrigation Events and Crop Dynamics Using Sentinel-1 and Sentinel-2 Time Series

Ma, Can; Johansen, Kasper; McCabe, Matthew F.

doi:10.3390/rs14051205

Cited by 12 publications

(5 citation statements)

References 74 publications

(114 reference statements)

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“…The specific dates for acquiring the Sentinel-1 images used in this study are 30 August 2020 and 11 September 2020. Equipped with a C-band dual-polarized SAR sensor, the Sentinel-1 satellite is capable of transmitting and receiving signals in both vertical transmit-vertical receive (VV) and vertical transmit-horizontal receive (VH) polarizations [55]. The primary mode of data acquisition utilized in forest AGB studies was the interferometric wide (IW) mode.…”

Section: Sar Data Acquisitions and Preprocessingmentioning

confidence: 99%

A Novel Framework for Forest Above-Ground Biomass Inversion Using Multi-Source Remote Sensing and Deep Learning

Zhang,

Zhou,

Zhang

et al. 2024

Forests

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The estimation of forest above-ground biomass (AGB) can be significantly improved by leveraging remote sensing (RS) and deep learning (DL) techniques. In this process, it is crucial to obtain appropriate RS features and develop a suitable model. However, traditional methods such as random forest (RF) feature selection often fail to adequately consider the complex relationships within high-dimensional RS feature spaces. Moreover, challenges related to parameter selection and overfitting inherent in DL models may compromise the accuracy of AGB estimation. Therefore, this study proposes a novel framework based on freely available Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 optical data. Firstly, we designed new indices through the formula analogous with vegetation index calculation to integrate multidimensional spectral and structural information. Then, leveraging the simplicity of computational principles, a pigeon-inspired optimization algorithm (PIO) was introduced into a bi-directional long short-term memory neural network (PIO-BiLSTM), which achieved the set objective function through repeated iteration and validation to obtain the optimal model parameters. Finally, to verify the framework’s effect, we conducted experiments in two different tree species and compared another seven classical optimization algorithms and machine learning models. The results indicated that the new indices significantly improved the inversion accuracy of all models in both categories, and the PIO-BiLSTM model achieved the highest accuracy (Category-1: R2 = 0.8055, MAE = 8.8475 Mg·ha−1, RMSE = 12.2876 Mg·ha−1, relative RMSE = 18.1715%; Category-2: R2 = 0.7956, MAE = 1.7103 Mg·ha−1, RMSE = 2.2887 Mg·ha−1, relative RMSE = 9.3000%). Compared with existing methods, the proposed framework greatly reduced the labor costs in parameter selection, and its potential uncertainty also decreased by up to 9.0%. Furthermore, the proposed method has a strong generalization ability and is independent of tree species, indicating its great potential for future forest AGB inversion in wider regions with diverse forest types.

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Section: Sar Data Acquisitions and Preprocessingmentioning

confidence: 99%

A Novel Framework for Forest Above-Ground Biomass Inversion Using Multi-Source Remote Sensing and Deep Learning

Zhang,

Zhou,

Zhang

et al. 2024

Forests

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“…There are various methods to determine spectral water indices, often derived from processes involving the reflectance or digital values of two or more spectral bands, such as ratios, differences, normalizations, and multiplications (Table 3) (Li et al, 2016). For monitoring crop dynamics, satellite remote sensing offers multiple approaches, including the use of vegetation and water indices calculated from multispectral band ratios (Ferrant et al, 2017;Ma et al, 2022). In this study, we utilized Sentinel-2 and Landsat data from months 1, 2, 4, and 5, with the exception of month 3.…”

Section: Water and Crop Growth Index Data Preparationmentioning

confidence: 99%

“…This implies that expanding and intensifying irrigated agriculture will be necessary to keep up with this escalating demand (Massari et al, 2021). The increasing demand for irrigation water to sustain agricultural production has become a crucial challenge for arid and semi-arid regions worldwide, exacerbated by climate change and population growth (Ma et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…Researchers have employed both optical and radar data to generate maps that distinguish between irrigated and non-irrigated areas. Optical data includes temporal series of vegetation indices such as normalized difference vegetation indices (NDVI) (Maselli et al, 2020;Toureiro et al, 2017), normalized water indices like NDWI (Ma et al, 2022), and modified normalized difference water index (MNDWI). These indices have been used because they can classify irrigated and non-irrigated crops (Singh et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Assessing how irrigation practices and soil moisture affect crop growth through monitoring Sentinel-1 and Sentinel-2 data

Ibrahim,

Rasul,

Abdullah

2023

Environ Monit Assess

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This study authorizes processes and approaches using optical and microwave data to determine the availability of water in the study area at any given moment. This will aid in identifying the optimal time and location for irrigation to enhance crop growth. For this purpose, a set of spectral vegetation parameters (from Sentinel-2), soil moisture (from Sentinel-1), evapotranspiration, and surface temperature (from Landsat-8) were used, along with field data on water content and irrigation timing. The results showed that both NDVI and NDMI are highly sensitive to moisture, making them the best indices for determining the timing and location of irrigation. This research contributes to sustainable agricultural development. It has implications for farmers, policymakers, and researchers in optimizing irrigation schedules, developing policies for sustainable agriculture, and enhancing crop productivity while conserving water resources. This approach can be particularly useful in regions facing water scarcity, where the efficient use of water resources is crucial for sustainable agricultural development

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“…Agricultural land cover monitoring is performed for various purposes such as yield forecasting [1][2][3], precision farming [4,5], and control of direct agricultural subsidies and sustainable development [6,7]. In Europe, the Integrated Administration Control System (IACS) has been established to manage direct payments in agriculture and monitor the type of agriculture land cover and its area, as well as "cross-compliance" [8].…”

Section: Introductionmentioning

confidence: 99%

Agriculture Land Cover Identification Using One-Shot Airborne Hyperspectral Images: case study of small parcels, Poland

Hejmanowska¹,

Kramarczyk²

2023

Preprint

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This study aimed to investigate the possibility of using one-shot hyperspectral airborne images to recognize crops for an area with many small plots. The results showed that unsupervised clustering methods could classify crops with an accuracy of 80%, which improved to 90% when restricted to only grain crops, using a single airborne hyperspectral recording. However, additional layers such as NDVI, DTM, slope, and aspect did not improve classification accuracy. For comparison, the accuracy of clustering time series Sentinel-2 images with NDVI layers and DTM-derived data yielded an accuracy of: 74% ,Sentinel-2 time series 68% and single one registration before harvest - 39%. The results of the random forest classification were slightly less accurate due to a lack of sufficient reference data. However, it is challenging to verify the reported accuracy of crop recognition in the literature above 90% due to differences in analysis methodologies, reference data selection, pixel/object approaches, metric choice, and calculation formulas used.

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Monitoring Irrigation Events and Crop Dynamics Using Sentinel-1 and Sentinel-2 Time Series

Cited by 12 publications

References 74 publications

A Novel Framework for Forest Above-Ground Biomass Inversion Using Multi-Source Remote Sensing and Deep Learning

A Novel Framework for Forest Above-Ground Biomass Inversion Using Multi-Source Remote Sensing and Deep Learning

Assessing how irrigation practices and soil moisture affect crop growth through monitoring Sentinel-1 and Sentinel-2 data

Agriculture Land Cover Identification Using One-Shot Airborne Hyperspectral Images: case study of small parcels, Poland

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