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Remote Sensing for Biophysical and Biochemical Property of Crops and Natural Vegetation

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 39526

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Special Issue Editors

Dr. Lea Hallik

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Guest Editor

University of Tartu
Interests: leaf optical properties; light acclimation; photosynthetic pigments; plant ecophysiology
Special Issues, Collections and Topics in MDPI journals

Prof. Tiit Nilson

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Guest Editor

Vegetation Remote Sensing Group, Tartu Observatory, Observatooriumi 1, Tõravere, Nõo parish, 61602 Tartu county, Estonia
Interests: radiative transfer models; physical models; optical ground measurements

Dr. Leonidas Toulios

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Hellenic Agricultural Organization “DEMETER, Institute of Industrial and Forage Crops, Theofrastou 1, 41335 Larissa, Greece
Interests: agriculture remote sensing; precision farming; spectral properties of soils

Dr. George P. Petropoulos

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Guest Editor

Department of Geography, Harokopio University of Athens, 176 71 Moschato, Greece
Interests: earth observation; modeling; land surface interactions; soil moisture; evapotrasnpiration; land use/cover mapping; change detection; natural hazards; floods; wildfires; sensitivity analysis; soil vegetation atmosphere transfer modeling; operational products benchmarking
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Special Issue Remote Sensing for Biophysical and Biochemical Properties of Crops is intended to bring together a wide range of contributions from different scales (from leaf level to landscape level) and different EO sensors (active and passive sensors).

Recent developments in geoinformation technology and in earth observation (EO) sensors in particular, include retrieval of seasonal trends of biochemical and biophysical land surface parameters such as foliar pigments content, leaf and soil moisture, surface roughness, albedo, fAPAR, LAI, and canopy height from EO data of different active and passive sensors and with associated uncertainties (e.g., Liang 2004; Gitelson et al 2019; Yang et al 2019). Key issues in estimating biochemical and biophysical land surface parameters are upscaling from leaf to canopy, temporal gap-filling to produce gap-free time-series, and combining data from different sensors and quantification of uncertainties. In this context, EU H2020 project MULTIPLY: “MULTIscale SENTINEL land surface information retrieval Platform” (http://www.multiply-h2020.eu/project-2 /) has created a toolbox for solving these problems. In addition, the COST Action CA17134 SENSECO “Optical synergies for spatiotemporal SENsing of Scalable ECOphysiological traits” (https://www.senseco.eu/) also addresses the same issues.

Physical radiative transfer models, statistical models, and machine learning algorithms are commonly used to retrieve biophysical and biochemical traits from remote sensing data. Phenotyping platforms as well as field studies at different scales provide valuable new data in this topic.

Specific topics for this Special Issue include but are not limited to the following:

Physical radiative transfer modeling
Statistical modeling and machine learning
Vegetation indices and other spectral transformations
Applicability of different active and passive EO sensors (including SAR, optical and thermal)
Multi-sensor synergies
Applications at different scales of proximal and remote sensing (including phenotyping platforms, drones and satellite-borne data)
Phenology, time series and gap-filling
Synergies of remote sensing, GIS, and crop growth models
Downscaling and upscaling of biophysical parameters
Uncertainty assessment of remotely sensed data
Uncertainty assessment of ground validation data (including quantified uncertainty assessment protocols for upscaling of biophysical trait measurements to sensor pixel size)

Dr. Lea Hallik
Prof. Tiit Nilson
Dr. Leonidas Toulios
Dr. George P Petropoulos
Guest Editors

References

Gitelson, A.; Viña, A; Solovchenko, A.; Arkebauer, T.; Inoue, Y. Derivation of canopy light absorption coefficient from reflectance spectra. Remote Sens. Environ. 2019, 231, 111276. https://doi.org/10.1016/j.rse.2019.111276
Liang, S. Quantitative Remote Sensing of Land Surfaces; Wiley-Interscience: Hoboken, NJ, USA,2014
Yang, P.; van der Tol, C.; Verhoef, W.; Damm, A.; Schickling, A.; Kraska, T.; Muller, O.; Rascher, U. Using reflectance to explain vegetation biochemical and structural effects on sun-induced chlorophyll fluorescence. Remote Sens. Environ. 2019, 231, 110996, https://doi.org/10.1016/j.rse.2018.11.039

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Keywords

geoinformatics
remote sensing
proximal sensing
geospatial data
vegetation
agriculture
plant traits
ecophysiology
operational products
quantified measurement uncertainties

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Published Papers (9 papers)

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30 pages, 9388 KiB

Open AccessArticle

Leaf Age Matters in Remote Sensing: Taking Ground Truth for Spectroscopic Studies in Hemiboreal Deciduous Trees with Continuous Leaf Formation

by Eva Neuwirthová, Andres Kuusk, Zuzana Lhotáková, Joel Kuusk, Jana Albrechtová and Lea Hallik

Remote Sens. 2021, 13(7), 1353; https://doi.org/10.3390/rs13071353 - 1 Apr 2021

Cited by 11 | Viewed by 2687

Abstract

We examined the seasonal changes in biophysical, anatomical, and optical traits of young leaves, formed throughout the vegetative season due to sylleptic growth, and mature leaves formed by proleptic growth in spring. Leaf developmental categories contribute to the top-of-canopy reflectance and should be considered when taking ground truth for remote sensing studies (RS). Deciduous tree species, Betula pendula, Populus tremula, and Alnus incana, were sampled from May to October 2018 in an Estonian hemiboreal forest. Chlorophyll and carotenoid content were detected biochemically; leaf anatomical traits (leaf, palisade, and spongy mesophyll thickness) were measured on leaf cross-sections; leaf reflectance was measured by a spectroradiometer with an integrating sphere (350–2500 nm). Biophysical and anatomical leaf traits were related to 64 vegetation indices (VIs). Linear models based on VIs for all tested leaf traits were more robust if both juvenile and mature leaves were included. This study provides information on which VIs are interchangeable or independent. Pigment and leaf thickness sensitive indices formed PC1; water and structural trait related VIs formed an independent group associated with PC3. Type of growth and leaf age could affect the validation of biophysical and anatomical leaf trait retrieval from the optical signal. It is, therefore, necessary to sample both leaf developmental categories—young and mature—in RS, especially if sampling is only once within the vegetation season. Full article

(This article belongs to the Special Issue Remote Sensing for Biophysical and Biochemical Property of Crops and Natural Vegetation)

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20 pages, 14384 KiB

Open AccessArticle

Improving the Retrieval of Crop Canopy Chlorophyll Content Using Vegetation Index Combinations

by Qi Sun, Quanjun Jiao, Xiaojin Qian, Liangyun Liu, Xinjie Liu and Huayang Dai

Remote Sens. 2021, 13(3), 470; https://doi.org/10.3390/rs13030470 - 29 Jan 2021

Cited by 34 | Viewed by 4611

Abstract

Estimates of crop canopy chlorophyll content (CCC) can be used to monitor vegetation productivity, manage crop resources, and control disease and pests. However, making these estimates using conventional ground-based methods is time-consuming and resource-intensive when deployed over large areas. Although vegetation indices (VIs), derived from satellite sensor data, have been used to estimate CCC, they suffer from problems related to spectral saturation, soil background, and canopy structure. A new method was, therefore, proposed for combining the Medium Resolution Imaging Spectrometer (MERIS) terrestrial chlorophyll index (MTCI) and LAI-related vegetation indices (LAI-VIs) to increase the accuracy of CCC estimates for wheat and soybeans. The PROSAIL-D canopy reflectance model was used to simulate canopy spectra that were resampled to match the spectral response functions of the MERIS carried on the ENVISAT satellite. Combinations of the MTCI and LAI-VIs were then used to estimate CCC via univariate linear regression, binary linear regression and random forest regression. The accuracy using the field spectra and MERIS data was determined based on field CCC measurements. All the MTCI and LAI-VI combinations for the selected regression techniques resulted in more accurate estimates of CCC than the use of the MTCI alone (field spectra data for soybeans and wheat: R² = 0.62 and RMSE = 77.10 μg cm⁻²; MERIS satellite data for soybeans: R² = 0.24 and RMSE = 136.54 μg cm⁻²). The random forest regression resulted in better accuracy than the other two linear regression models. The combination resulting in the best accuracy was the MTCI and MTVI2 and random forest regression, with R² = 0.65 and RMSE = 37.76 μg cm⁻² (field spectra data) and R² = 0.78 and RMSE = 47.96 μg cm⁻² (MERIS satellite data). Combining the MTCI and a LAI-VI represents a further step towards improving the accuracy of estimation CCC based on multispectral satellite sensor data. Full article

(This article belongs to the Special Issue Remote Sensing for Biophysical and Biochemical Property of Crops and Natural Vegetation)

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21 pages, 5061 KiB

Open AccessArticle

Improving Soil Thickness Estimations Based on Multiple Environmental Variables with Stacking Ensemble Methods

by Xinchuan Li, Juhua Luo, Xiuliang Jin, Qiaoning He and Yun Niu

Remote Sens. 2020, 12(21), 3609; https://doi.org/10.3390/rs12213609 - 3 Nov 2020

Cited by 32 | Viewed by 4022

Abstract

Spatially continuous soil thickness data at large scales are usually not readily available and are often difficult and expensive to acquire. Various machine learning algorithms have become very popular in digital soil mapping to predict and map the spatial distribution of soil properties. Identifying the controlling environmental variables of soil thickness and selecting suitable machine learning algorithms are vitally important in modeling. In this study, 11 quantitative and four qualitative environmental variables were selected to explore the main variables that affect soil thickness. Four commonly used machine learning algorithms (multiple linear regression (MLR), support vector regression (SVR), random forest (RF), and extreme gradient boosting (XGBoost) were evaluated as individual models to separately predict and obtain a soil thickness distribution map in Henan Province, China. In addition, the two stacking ensemble models using least absolute shrinkage and selection operator (LASSO) and generalized boosted regression model (GBM) were tested and applied to build the most reliable and accurate estimation model. The results showed that variable selection was a very important part of soil thickness modeling. Topographic wetness index (TWI), slope, elevation, land use and enhanced vegetation index (EVI) were the most influential environmental variables in soil thickness modeling. Comparative results showed that the XGBoost model outperformed the MLR, RF and SVR models. Importantly, the two stacking models achieved higher performance than the single model, especially when using GBM. In terms of accuracy, the proposed stacking method explained 64.0% of the variation for soil thickness. The results of our study provide useful alternative approaches for mapping soil thickness, with potential for use with other soil properties. Full article

(This article belongs to the Special Issue Remote Sensing for Biophysical and Biochemical Property of Crops and Natural Vegetation)

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19 pages, 5459 KiB

Open AccessArticle

Assessment of Cornfield LAI Retrieved from Multi-Source Satellite Data Using Continuous Field LAI Measurements Based on a Wireless Sensor Network

by Lihong Yu, Jiali Shang, Zhiqiang Cheng, Zebin Gao, Zixin Wang, Luo Tian, Dantong Wang, Tao Che, Rui Jin, Jiangui Liu, Taifeng Dong and Yonghua Qu

Remote Sens. 2020, 12(20), 3304; https://doi.org/10.3390/rs12203304 - 11 Oct 2020

Cited by 25 | Viewed by 3302

Abstract

Accurate and continuous monitoring of leaf area index (LAI), a widely-used vegetation structural parameter, is crucial to characterize crop growth conditions and forecast crop yield. Meanwhile, advancements in collecting field LAI measurements have provided strong support for validating remote-sensing-derived LAI. This paper evaluates the performance of LAI retrieval from multi-source, remotely sensed data through comparisons with continuous field LAI measurements. Firstly, field LAI was measured continuously over periods of time in 2018 and 2019 using LAINet, a continuous LAI measurement system deployed using wireless sensor network (WSN) technology, over an agricultural region located at the Heihe watershed at northwestern China. Then, cloud-free images from optical satellite sensors, including Landsat 7 the Enhanced Thematic Mapper Plus (ETM+), Landsat 8 the Operational Land Imager (OLI), and Sentinel-2A/B Multispectral Instrument (MSI), were collected to derive LAI through inversion of the PROSAIL radiation transfer model using a look-up-table (LUT) approach. Finally, field LAI data were used to validate the multi-temporal LAI retrieved from remote-sensing data acquired by different satellite sensors. The results indicate that good accuracy was obtained using different inversion strategies for each sensor, while Green Chlorophyll Index (CIgreen) and a combination of three red-edge bands perform better for Landsat 7/8 and Sentinel-2 LAI inversion, respectively. Furthermore, the estimated LAI has good consistency with in situ measurements at vegetative stage (coefficient of determination R² = 0.74, and root mean square error RMSE = 0.53 m² m⁻²). At the reproductive stage, a significant underestimation was found (R² = 0.41, and 0.89 m² m⁻² in terms of RMSE). This study suggests that time-series LAI can be retrieved from multi-source satellite data through model inversion, and the LAINet instrument could be used as a low-cost tool to provide continuous field LAI measurements to support LAI retrieval. Full article

(This article belongs to the Special Issue Remote Sensing for Biophysical and Biochemical Property of Crops and Natural Vegetation)

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24 pages, 6256 KiB

Open AccessArticle

Detecting Montane Flowering Phenology with CubeSat Imagery

by Aji John, Justin Ong, Elli J. Theobald, Julian D. Olden, Amanda Tan and Janneke HilleRisLambers

Remote Sens. 2020, 12(18), 2894; https://doi.org/10.3390/rs12182894 - 7 Sep 2020

Cited by 13 | Viewed by 4714

Abstract

Shifts in wildflower phenology in response to climate change are well documented in the scientific literature. The majority of studies have revealed phenological shifts using in-situ observations, some aided by citizen science efforts (e.g., National Phenology Network). Such investigations have been instrumental in quantifying phenological shifts but are challenged by the fact that limited resources often make it difficult to gather observations over large spatial scales and long-time frames. However, recent advances in finer scale satellite imagery may provide new opportunities to detect changes in phenology. These approaches have documented plot level changes in vegetation characteristics and leafing phenology, but it remains unclear whether they can also detect flowering in natural environments. Here, we test whether fine-resolution imagery (<10 m) can detect flowering and whether combining multiple sources of imagery improves the detection process. Examining alpine wildflowers at Mt. Rainier National Park (MORA), we found that high-resolution Random Forest (RF) classification from 3-m resolution PlanetScope (from Planet Labs) imagery was able to delineate the flowering season captured by ground-based phenological surveys with an accuracy of 70% (Cohen’s kappa = 0.25). We then combined PlanetScope data with coarser resolution but higher quality imagery from Sentinel and Landsat satellites (10-m Sentinel and 30-m Landsat), resulting in higher accuracy predictions (accuracy = 77%, Cohen’s kappa = 0.39). The model was also able to identify the timing of peak flowering in a particularly warm year (2015), despite being calibrated on normal climate years. Our results suggest PlanetScope imagery holds utility in global change ecology where temporal frequency is important. Additionally, we suggest that combining imagery may provide a new approach to cross-calibrate sensors to account for radiometric irregularity inherent in fine resolution PlanetScope imagery. The development of this approach for wildflower phenology predictions provides new possibilities to monitor climate change effects on flowering communities at broader spatiotemporal scales. In protected and tourist areas where the flowering season draws large numbers of visitors, such as Mt. Rainier National Park, the modeling framework presented here could be a useful tool to manage and prioritize park resources. Full article

(This article belongs to the Special Issue Remote Sensing for Biophysical and Biochemical Property of Crops and Natural Vegetation)

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18 pages, 5829 KiB

Open AccessArticle

Retrieving Crop Leaf Chlorophyll Content Using an Improved Look-Up-Table Approach by Combining Multiple Canopy Structures and Soil Backgrounds

by Xiaojin Qian and Liangyun Liu

Remote Sens. 2020, 12(13), 2139; https://doi.org/10.3390/rs12132139 - 3 Jul 2020

Cited by 22 | Viewed by 3243

Abstract

Leaf chlorophyll content (LCC) is a pivotal parameter in the monitoring of agriculture and carbon cycle modeling at regional and global scales. ENVISAT MERIS and Sentinel-3 OLCI data are suitable for use in the global monitoring of LCC because of their spectral specifications (covering red-edge bands), wide field of view and short revisit times. Generally, remote sensing approaches for LCC retrieval consist of statistically- and physically-based models. The physical approaches for LCC estimation require the use of radiative transfer models (RTMs), which are more robust and transferrable than empirical models. However, the operational retrieval of LCC at large scales is affected by the large variability in canopy structures and soil backgrounds. In this study, we proposed an improved look-up-table (LUT) approach to retrieve LCC by combining multiple canopy structures and soil backgrounds to deal with the ill-posed inversion problem caused by the lack of prior knowledge on canopy structure and soil-background reflectance. Firstly, the PROSAIL-D model was used to simulate canopy spectra with diverse imaging gometrics, canopy structures, soil backgrounds and leaf biochemical contents, and the canopy spectra were resampled according to the spectral response functions of ENVISAT MERIS and Sentinel-3 OLCI instruments. Then, an LUT that included 25 sub-LUTs corresponding to five types of canopy structure and five types of soil background was generated for LCC estimation. The mean of the best eight solutions, rather than the single best solution with the smallest RMSE value, was selected as the retrieval of each sub-LUT. The final inversion result was obtained by calculating the mean value of the 25 sub-LUTs. Finally, the improved LUT approach was tested using simulations, field measurements and ENVISAT MERIS satellite data. A simulation using spectral bands from the MERIS and Sentinel-3 OLCI simulation datasets yielded an R² value of 0.81 and an RMSE value of 10.1 μg cm⁻². Validation performed well with field-measured canopy spectra and MERIS imagery giving RMSE values of 9.9 μg cm⁻² for wheat and 9.6 μg cm⁻² for soybean using canopy spectra and 8.6 μg cm⁻² for soybean using MERIS data. The comparison with traditional chlorophyll-sensitive indices showed that our improved LUT approach gave the best performance for all cases. Therefore, these promising results are directly applicable to the use of ENVISAT MERIS and Sentinel-3 OLCI data for monitoring of crop LCC at a regional or global scale. Full article

(This article belongs to the Special Issue Remote Sensing for Biophysical and Biochemical Property of Crops and Natural Vegetation)

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17 pages, 1943 KiB

Open AccessArticle

Novel Combined Spectral Indices Derived from Hyperspectral and Laser-Induced Fluorescence LiDAR Spectra for Leaf Nitrogen Contents Estimation of Rice

by Lin Du, Jian Yang, Bowen Chen, Jia Sun, Biwu Chen, Shuo Shi, Shalei Song and Wei Gong

Remote Sens. 2020, 12(1), 185; https://doi.org/10.3390/rs12010185 - 4 Jan 2020

Cited by 5 | Viewed by 3450

Abstract

Spectra of reflectance (S_r) and fluorescence (S_f) are significant for crop monitoring and ecological environment research, and can be used to indicate the leaf nitrogen content (LNC) of crops indirectly. The aim of this work is to use the S_r-S_f features obtained with hyperspectral and laser-induced fluorescence LiDAR (HSL, LIFL) systems to construct novel combined spectral indices (NCI_H-F) for multi-year rice LNC estimation. The NCI_H-F is in a form of FWs*

Φ

+ GSIs*

Φ

, where

Φ

is the S_r-S_f features, and FWs and GSIs are the feature weights and global sensitive indices for each characteristic band. In this study, the characteristic bands were chosen in different ways. Firstly, the S_r-S_f characteristics which can be the intensity or derivative variables of spectra in 685 and 740 nm, have been assigned as the

Φ

value in NCI_H-F formula. Simultaneously, the photochemical reflectance index (PRI) formed with 531 and 570 nm was modified based on a variant spectral index, called PRI_fraction, with the S_f intensity in 740 nm, and then compared its potential with NCI_H-F on LNC estimation. During the above analysis, both NCI_H-F and PRI_fraction values were utilized to model rice LNC based on the artificial neural networks (ANNs) method. Subsequently, four prior bands were selected, respectively, with high FW and GSI values as the ANNs inputs for rice LNC estimation. Results show that FW- and GSI-based NCI_H-F are closely related to rice LNC, and the performance of previous spectral indices used for LNC estimation can be greatly improved by multiplying their FWs and GSIs. Thus, it can be included that the FW- and GSI-based NCI_H-F constitutes an efficient and reliable constructed form combining HSL (S_r) and LIFL (S_f) data together for rice LNC estimation. Full article

(This article belongs to the Special Issue Remote Sensing for Biophysical and Biochemical Property of Crops and Natural Vegetation)

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Review

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14 pages, 1863 KiB

Open AccessReview

A Review of Remote Sensing Challenges for Food Security with Respect to Salinity and Drought Threats

by Wen Wen, Joris Timmermans, Qi Chen and Peter M. van Bodegom

Remote Sens. 2021, 13(1), 6; https://doi.org/10.3390/rs13010006 - 22 Dec 2020

Cited by 40 | Viewed by 6182

Abstract

Drought and salinity stress are considered to be the two main factors limiting crop productivity. With climate change, these stresses are projected to increase, further exacerbating the risks to global food security. Consequently, to tackle this problem, better agricultural management is required on the basis of improved drought and salinity stress monitoring capabilities. Remote sensing makes it possible to monitor crop health at various spatiotemporal scales and extents. However, remote sensing has not yet been used to monitor both drought and salinity stresses simultaneously. The aim of this paper is to review the current ability of remote sensing to detect the impact of these stresses on vegetation indices (VIs) and crop trait responses. We found that VIs are insufficiently accurate (0.02 ≤ R² ≤ 0.80) to characterize the crop health under drought and salinity stress. In contrast, we found that plant functional traits have a high potential to monitor the impacts of such stresses on crop health, as they are more in line with the vegetation processes. However, we also found that further investigations are needed to achieve this potential. Specifically, we found that the spectral signals concerning drought and salinity stress were inconsistent for the various crop traits. This inconsistency was present (a) between studies utilizing similar crops and (b) between investigations studying different crops. Moreover, the response signals for joint drought and salinity stress overlapped spectrally, thereby significantly limiting the application of remote sensing to monitor these separately. Therefore, to consistently monitor crop responses to drought and salinity, we need to resolve the current indeterminacy of the relationships between crop traits and spectrum and evaluate multiple traits simultaneously. Using radiative transfer models (RTMs) and multi-sensor frameworks allow monitoring multiple crop traits and may constitute a way forward toward evaluating drought and salinity impacts. Full article

(This article belongs to the Special Issue Remote Sensing for Biophysical and Biochemical Property of Crops and Natural Vegetation)

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Other

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17 pages, 7416 KiB

Open AccessLetter

Separability of Mowing and Ploughing Events on Short Temporal Baseline Sentinel-1 Coherence Time Series

by Kaupo Voormansik, Karlis Zalite, Indrek Sünter, Tanel Tamm, Kalev Koppel, Tarvi Verro, Agris Brauns, Dainis Jakovels and Jaan Praks

Remote Sens. 2020, 12(22), 3784; https://doi.org/10.3390/rs12223784 - 18 Nov 2020

Cited by 24 | Viewed by 5434

Abstract

Short temporal baseline regular Synthetic Aperture Radar (SAR) interferometry is a tool well suited for wide area monitoring of agricultural activities, urgently needed in European Union Common Agricultural Policy (CAP) enforcement. In this study, we demonstrate and describe in detail, how mowing and ploughing events can be identified from Sentinel-1 6-day interferometric coherence time series. The study is based on a large dataset of 386 dual polarimetric Sentinel-1 VV/VH SAR and 351 Sentinel-2 optical images, and nearly 2000 documented mowing and ploughing events on more than 1000 parcels (average 10.6 ha, smallest 0.6 ha, largest 108.5 ha). Statistical analysis revealed that mowing and ploughing cause coherence to increase when compared to values before an event. In the case of mowing, the coherence increased from 0.18 to 0.35, while Sentinel-2 NDVI (indicating the amount of green chlorophyll containing biomass) at the same time decreased from 0.75 to 0.5. For mowing, there was virtually no difference between the polarisations. After ploughing, VV-coherence grew up to 0.65 and VH-coherence to 0.45, while NDVI was around 0.2 at the same time. Before ploughing, both coherence and NDVI values were very variable, determined by the agricultural management practices of the parcel. Results presented here can be used for planning further studies and developing mowing and ploughing detection algorithms based on Sentinel-1 data. Besides CAP enforcement, the results are also useful for food security and land use change detection applications. Full article

(This article belongs to the Special Issue Remote Sensing for Biophysical and Biochemical Property of Crops and Natural Vegetation)

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