21 research outputs found
FULL POLARIMETRIC TIME SERIES IMAGE ANALYSIS FOR CROP TYPE MAPPING
Crop information and quality are not only fundamental for experts using spatial decision support systems but also have many applications in irrigation management, economic analysis for import or export, food safety, and achieving sustainable agriculture. Remote sensing is a cheap and fast way of reaching this goal. Full polarimetric SAR unlike optical sensors is an all-weather system providing geometrical and physical properties of the earthâs surface events. Due to the dynamic changes in crop properties through their phenological stages, crop type mapping has been challenging. As a result, accurate, reliable, and cost-effective crop type mapping using minimum data and processing has been the goal of the remote sensing and precision agriculture community. In this study, a new method based on time series analysis of full polarimetric SAR data combined with radar indices, polarimetric decompositions followed by the three αs extracted from H/A/α decomposition, and unsupervised H/α/Wishart classification bands as features generated from only 5 dates of RADARSAT CONSTELLATION MISSION 2 data were used for classification of crops. Applying random forest and cat boost algorithm as classifiers an accuracy of 87.4% and 75% was respectively achieved. indicating that both algorithms have promising results. Although the random forest algorithm had better results, the cat boost algorithm had less noise in each field and more homogenous farms were detected
Definition of homogenous damage zones caused by hail in agricultural crops using remote sensing data
[EN] The frequency and intensity of extreme worldwide weather events have increased in recent decades, causing enormous
economic losses. In Argentina, in the 2017-2018 agricultural season, more than 200 million dollars were allocated to protect
soy, corn and wheat crops against hail. After a storm, a field survey by an expert is required to estimate the yield losses
caused by hail, without prior information on the affected regions or their intensity. The precision in the estimation of the
damage depends to a great extent on the identification of Homogeneous Damage Zones (HDZ) within the plot, which is
further used to quantify the total damage. Currently, HDZ are delimited using in-situ visual techniques. This research
suggests developing an algorithm to define the HDZ applying Machine Learning techniques to vegetation indices derived
from Sentinel 1 and 2 data. For this purpose, 5 microwave indices (DPDD, IDPDD, VDDPI, MPDI and DPSVI) and 5
spectral indices (NDVI, EVI, SAVI, AVI and NPCRI) were tested. The most sensitive indices to changes were selected, for
both microwave and optical signals, and were in turn integrated into the damage detection model. A K-Means (k = 3)
machine learning algorithm was used to define the classes. To validate the algorithm, 38 storms that occurred between
2017 and 2020 were analyzed in 91 soybean, wheat and corn plots located in the Argentine Pampean plain. The One-Way
ANOVA model (p <0.05) was applied to each plot. The selected indices were DPSVI and NPCRI. HDZ were correctly
detected in 66.67%, 78.13% and 72.70% of the analyzed cases, for corn, wheat and soybean crops, respectively. It is
concluded that the designed algorithm allows defining efficiently HDZ caused by hail, giving transparency and precision to
the work of the expert and reducing time consuming field surveys.[ES] La frecuencia e intensidad de los eventos meteorolĂłgicos extremos, a nivel mundial, se han incrementado en las Ășltimas dĂ©cadas, provocando enormes pĂ©rdidas econĂłmicas. En Argentina, en la campaña agrĂcola 2017-2018, se destinaron mĂĄs de 200 millones de dĂłlares para proteger los cultivos soja, maĂz y trigo contra granizo. Luego de una tormenta, un perito visita el campo para estimar las mermas de rendimiento causadas por granizo, sin informaciĂłn previa de las regiones afectadas ni su intensidad. La precisiĂłn en la estimaciĂłn del daño depende en gran medida de la identificaciĂłn de Zonas HomogĂ©neas de Daños (ZHD) dentro de la parcela para ponderar el daño total. Actualmente, las ZHD se delimitan acampo con tĂ©cnicas visuales. Se propone desarrollar un algoritmo para definir las ZHD aplicando tĂ©cnicas Machine Learning a Ăndices de vegetaciĂłn calculados con datos Sentinel 1 y 2. Se procesaron y compararon 5 Ăndices de microondas (DPDD, IDPDD, VDDPI, MPDI y DPSVI) y 5 espectrales (NDVI, EVI, SAVI, AVI y NPCRI) y se seleccionĂł el mĂĄs sensible a los cambios para cada tipo de señal; ademĂĄs, se incorporaron como variable de entrada al modelo las derivadas de ambos Ăndices. Para definir las clases se empleĂł K-Means (k = 3). Para validar el algoritmo se analizaron38 tormentas ocurridas entre los años 2017 y 2020 en 91 parcelas de soja, trigo y maĂz ubicadas en la llanura pampeana argentina. Se aplicĂł a cada parcela el modelo One-Way ANOVA (p <0.05). Los Ăndices seleccionados fueron DPSVI y NPCRI. Se detectaron correctamente ZHD en un 66,67%, 78,13% y 72,70% de los casos analizados, para los cultivos de maĂz, trigo y soja, respectivamente. Se concluye que el algoritmo permite definir en forma eficiente ZHD causados por granizo dando transparencia y precisiĂłn a la labor del perito y disminuyendo el tiempo de sus tareas a campo.Sosa-Avaro, L.; Justel, A.; Molina, I. (2021). DefiniciĂłn de zonas homogĂ©neas de daño causado por granizo en cultivos agrĂcolas utilizando datos de sensores remotos. En Proceedings 3rd Congress in Geomatics Engineering. Editorial Universitat PolitĂšcnica de ValĂšncia. 278-284. https://doi.org/10.4995/CiGeo2021.2021.12737OCS27828
Advances in Radar Remote Sensing of Agricultural Crops: A Review
There are enormous advantages of a review article in the field of emerging technology like radar remote sensing applications in agriculture. This paper aims to report select recent advancements in the field of Synthetic Aperture Radar (SAR) remote sensing of crops. In order to make the paper comprehensive and more meaningful for the readers, an attempt has also been made to include discussion on various technologies of SAR sensors used for remote sensing of agricultural crops viz. basic SAR sensor, SAR interferometry (InSAR), SAR polarimetry (PolSAR) and polarimetric interferometry SAR (PolInSAR). The paper covers all the methodologies used for various agricultural applications like empirically based models, machine learning based models and radiative transfer theorem based models. A thorough literature review of more than 100 research papers indicates that SAR polarimetry can be used effectively for crop inventory and biophysical parameters estimation such are leaf area index, plant water content, and biomass but shown less sensitivity towards plant height as compared to SAR interferometry. Polarimetric SAR Interferometry is preferable for taking advantage of both SAR polarimetry and SAR interferometry. Numerous studies based upon multi-parametric SAR indicate that optimum selection of SAR sensor parameters enhances SAR sensitivity as a whole for various agricultural applications. It has been observed that researchers are widely using three models such are empirical, machine learning and radiative transfer theorem based models. Machine learning based models are identified as a better approach for crop monitoring using radar remote sensing data. It is expected that the review article will not only generate interest amongst the readers to explore and exploit radar remote sensing for various agricultural applications but also provide a ready reference to the researchers working in this field
Estimating Global Ecosystem Isohydry/Anisohydry Using Active and Passive Microwave Satellite Data
The concept of isohydry/anisohydry describes the degree to which plants regulate their water status, operating from isohydric with strict regulation to anisohydric with less regulation. Though some species level measures of isohydry/anisohydry exist at a few locations, ecosystem-scale information is still largely unavailable. In this study, we use diurnal observations from active (Ku-Band backscatter from QuikSCAT) and passive (X-band vegetation optical depth (VOD) from Advanced Microwave Scanning Radiometer on EOS Aqua) microwave satellite data to estimate global ecosystem isohydry/anisohydry. Here diurnal observations from both satellites approximate predawn and midday plant canopy water contents, which are used to estimate isohydry/anisohydry. The two independent estimates from radar backscatter and VOD show reasonable agreement at low and middle latitudes but diverge at high latitudes. Grasslands, croplands, wetlands, and open shrublands are more anisohydric, whereas evergreen broadleaf and deciduous broadleaf forests are more isohydric. The direct validation with upscaled in situ species isohydry/anisohydry estimates indicates that the VOD-based estimates have much better agreement than the backscatter-based estimates. The indirect validation with prior knowledge suggests that both estimates are generally consistent in that vegetation water status of anisohydric ecosystems more closely tracks environmental fluctuations of water availability and demand than their isohydric counterparts. However, uncertainties still exist in the isohydry/anisohydry estimate, primarily arising from the remote sensing data and, to a lesser extent, from the methodology. The comprehensive assessment in this study can help us better understand the robustness, limitation, and uncertainties of the satellite-derived isohydry/anisohydry estimates. The ecosystem isohydry/anisohydry has the potential to reveal new insights into spatiotemporal ecosystem response to droughts
A 3-D Full-Wave Model to Study the Impact of Soybean Components and Structure on L-Band Backscatter
Microwave remote sensing offers a powerful tool for monitoring the growth of
short, dense vegetation like soybean. As the plants mature, changes in their
biomass and 3-D structure impact the electromagnetic (EM) backscatter signal.
This backscatter information holds valuable insights into crop health and
yield, prompting the need for a comprehensive understanding of how structural
and biophysical properties of soybeans as well as soil characteristics
contribute to the overall backscatter signature. In this study, a full-wave
model is developed for simulating L-band backscatter from soybean fields.
Leveraging the ANSYS High-Frequency Structure Simulator (HFSS) framework, the
model solves for the scattering of EM waves from realistic 3-D structural
models of soybean, explicitly incorporating the interplant scattering effects.
The model estimates of backscatter match well with the field observations from
the SMAPVEX16-MicroWEX and SMAPVEX12, with average differences of 1-2 dB for
co-pol and less than 4 dB for cross-pol. Furthermore, the model effectively
replicates the temporal dynamics of crop backscatter throughout the growing
season. The HFSS analysis revealed that the stems and pods are the primary
contributors to HH-pol backscatter, while the branches contribute to VV-pol,
and leaves impact the cross-pol signatures. In addition, a sensitivity study
with 3-D bare soil surface resulted in an average variation of 8 dB in co- and
cross-pol, even when the root mean square height and correlation length were
held constant
Robust machine learning techniques for rice crop variables estimation using multiangular bistatic scattering coefficients
The present study is designed to explore the potential of bistatic scattering coefficients (Ïâ°) and machine learning algorithms for the estimation of rice crop variables using ground-based multiangular, multitemporal, and dual-polarized bistatic scatterometer data. The bistatic scatterometer measurements are carried out at eight different growth stages of the rice crop in the angular range of incidence angle 20 deg to 70 deg for HH- and VV-polarization at 10-GHz frequency in the specular direction with an azimuthal angle (Ïââ=ââ0). Several field measurements are taken for the measurement of rice crop variables, such as vegetation water content, leaf area index, and plant height at its various growth stages. Machine learning algorithmsâsuch as fuzzy inference system (FIS), support vector machine for regression (SVR), and generalized linear model (GLM)âare used to estimate the rice crop variables using bistatic scatterometer data. The linear regression analysis is carried out for the evaluation of the multiangular, multitemporal, and dual-polarized datasets for the selection of optimum incidence angle and polarization for accurate estimation of rice crop variables. The highest value of the coefficient of determination (R2) is found at 30-deg incidence angle for VV-polarization. The sensitivity of copolarized ratio of Ïâ°ââ with the rice crop variable is also evaluated using linear regression analysis for the estimation of rice crop variables. The highest value of R^2 is found to be at 35-deg incidence angle between the copolarized ratio of Ïâ°ââ and rice crop variables. The performance of SVR model is found superior in comparison to the FIS and GLM at VV-polarization and the copolarized ratio of Ïâ°ââ for the estimation of rice crop variables. However, the copolarized ratio of Ïâ°ââ is found superior to VV-polarized bistatic scatterometer data for the estimation of rice crop variables
Influence of Incidence Angle in the Correlation of C-band Polarimetric Parameters with Biophysical Variables of Rain-fed Crops
A multi-temporal field experiment was conducted within the Soil Measurement Stations Network of the University of Salamanca (REMEDHUS) in Spain in order to retrieve useful crop information. The objective of this research was to evaluate the potential of polarimetric observations for crop monitoring by exploiting a time series of 20 quad-pol RADARSAT-2 images at different incidence angles (i.e. 25°, 31°, and 36°) during an entire growing season of rain-fed crops, from February to July 2015. The time evolution of 6 crop biophysical variables was gathered from the field measurements, whereas 10 polarimetric parameters were derived from the images. Thus, a subsequent correlation analysis between both datasets was performed. The study demonstrates that the backscattering ratios (HH/VV and HV/VV), the normalized correlation between HH and VV (γHHVV), and the dominant alpha angle (α1), showed significant and relevant correlations with several biophysical variables such as biomass, height, or leaf area index (LAI) at incidence angles of 31° or 36°. The joint use of data acquired with different beams could be exploited effectively to increase the refresh rate of information about crop condition with respect to a single incidence acquisition scheme.This study was supported by the Spanish Ministry of Economy and Competitiveness and the Spanish Ministry of Science, Innovation and Universities, [Projects ESP2015-67549-C3-3, ESP2017-89463-C3-3-R, and TEC2017-85244-C2-1-P] and the European Regional Development Fund (FEDER)
Ătude comparative dâindices de vĂ©gĂ©tation radar Ă plusieurs frĂ©quences et de lâindice de vĂ©gĂ©tation optique (NDVI) pour le suivi de la croissance des cultures
De nos jours, la tĂ©lĂ©dĂ©tection contribue Ă©normĂ©ment dans le domaine de lâagriculture. La possibilitĂ© dâacquisition des mesures en tout temps et la non sensibilitĂ© aux perturbations atmosphĂ©riques sont des avantages reconnus Ă la tĂ©lĂ©dĂ©tection radar. Cette Ă©tude a pour objectif dâeffectuer une analyse comparative des indices radar, Ă savoir lâindice de vĂ©gĂ©tation radar (RVI) et lâindice de vĂ©gĂ©tation radar Ă double polarisation (IVRDvv) dans trois frĂ©quences (L, C et X) et de lâindice de vĂ©gĂ©tation par diffĂ©rence normalisĂ©e (NDVI) utilisĂ© en tĂ©lĂ©dĂ©tection multispectrale optique dans un contexte de suivi de la croissance des cultures de blĂ©, de canola, de maĂŻs et de soja. Pour y parvenir, ces indices de vĂ©gĂ©tation radar (RVI et IVRDvv) calculĂ©s Ă plusieurs frĂ©quences et lâindice optique (NDVI) sont utilisĂ©s pour effectuer un suivi temporel de la croissance de ces quatre cultures. Dâune part, lâefficacitĂ© des indices de vĂ©gĂ©tation radar Ă traduire la quantitĂ© de la biomasse vĂ©gĂ©tale disponible est analysĂ©e en dĂ©terminant lâindice et la frĂ©quence les mieux adaptĂ©s au suivi de la croissance de chaque type de culture. Dâautre part, la corrĂ©lation des indices de vĂ©gĂ©tation radar (RVI et IVRDvv) et le NDVI par rapport Ă la quantitĂ© de la biomasse vĂ©gĂ©tale est utilisĂ©e pour apprĂ©cier lâusage de ces indices de vĂ©gĂ©tation radar comme alternative Ă lâutilisation du NDVI dans un contexte de suivi de la croissance des cultures de blĂ©, de canola, de maĂŻs et de soja.
Les indices radar RVI (indice de vĂ©gĂ©tation radar) et IVRDvv (indice de vĂ©gĂ©tation radar Ă double polarisation) ont Ă©tĂ© calculĂ©s sur la base dâimages acquises sur les sites des campagnes de terrain SMAP Validation Experiment 2012 (SMAPVEX12) et SMAP Validation Experiment 2016 in Manitoba (SMAPVEX16-MB) situĂ©s au Sud du Manitoba. Les donnĂ©es de biomasse vĂ©gĂ©tale ainsi que lâindice de surface foliaire (LAI) ont Ă©tĂ© recueillis directement sur le terrain durant ces deux campagnes. Les donnĂ©es radar en bande L proviennent de la campagne SMAPVEX12, elles sont acquises par un Uninhabited Aerial Vehicule Synthetic Aperture Radar UAVSAR; celles utilisĂ©es en bande C et X ont Ă©tĂ© acquises durant la campagne SMAPVEX16-MB par les satellites Radarsat-2 et TerraSAR-X, respectivement. Les donnĂ©es optiques proviennent des images de Sentinelle-2.
Le suivi de la croissance des cultures de blĂ©, de canola, de maĂŻs et de soja sur une base temporelle a permis de remarquer lâinefficacitĂ© de la bande L Ă Ă©valuer la croissance des plantes. Le coefficient de rĂ©trodiffusion dans cette bande est contrĂŽlĂ© par les paramĂštres de surface et particuliĂšrement lâhumiditĂ© du sol plutĂŽt que la biomasse vĂ©gĂ©tale. Les indices de vĂ©gĂ©tation radar en bandes C et X ont prĂ©sentĂ© de bons rĂ©sultats qui traduisent lâĂ©volution de la quantitĂ© de la biomasse vĂ©gĂ©tale disponible; la bande X Ă©tant toutefois beaucoup mieux corrĂ©lĂ©e Ă la biomasse vĂ©gĂ©tale. Pour le blĂ©, la quantitĂ© de biomasse vĂ©gĂ©tale est mieux corrĂ©lĂ©e Ă lâIVRDvv en bande X (R = 0,9) que le NDVI (R = 0,7). De mĂȘme, pour la culture de canola, la quantitĂ© de la biomasse disponible est lĂ©gĂšrement mieux corrĂ©lĂ©e Ă lâIVRDvv en bande X (R =0,96) quâau NDVI (R=0,9). Dâautre part, le RVI et lâIVRDvv en bande C pour les cultures de maĂŻs et de soja a montrĂ© des fortes corrĂ©lations avec le NDVI (R = 0,9). Ces rĂ©sultats montrent que dans un contexte de suivi de la croissance des vĂ©gĂ©taux, les indices de vĂ©gĂ©tation radar en bande C et X sont une alternative Ă lâindice de vĂ©gĂ©tation par diffĂ©rence normalisĂ©e utilisĂ© en tĂ©lĂ©dĂ©tection optique