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

A Simple RVoG Test for PolInSAR Data

In this paper, we present a simple algorithm for assessing the validity of the RVoG model for PolInSAR-based inversion techniques. This approach makes use of two important features characterizing a homogeneous random volume over a ground surface, i.e., the independence on polarization states of wave propagation through the volume and the structure of the polarimetric interferometric coherency matrix. These two features have led to two different methods proposed in the literature for retrieving the topographic phase within natural covers, i.e., the well-known line fitting procedure and the observation of the (1, 2) element of the polarimetric interferometric coherency matrix. We show that differences between outputs from both approaches can be interpreted in terms of the PolInSAR modeling based on the Freeman-Durden concept, and this leads to the definition of a RVoG/non-RVoG test. The algorithm is tested with both indoor and airborne data over agricultural and tropical forest areas.This work was supported by the Spanish Ministry of Economy and Competitiveness (MINECO) and EU FEDER under Project TEC2011-28201-C02-02

First Demonstration of Agriculture Height Retrieval With PolInSAR Airborne Data

A set of three quad-pol images acquired at the L-band in interferometric repeat-pass mode by the German Aerospace Center (DLR) with the Experimental SAR (E-SAR) system, in parallel with the AgriSAR2006 campaign, has been used to provide, for the first time with airborne data, a demonstration of the retrieval of vegetation height from agricultural crops by means of polarimetric SAR interferometry (PolInSAR)-based techniques. Despite the low frequency of the data, hence providing a weak response from the vegetation volume in contrast to the ground, accurate estimates of vegetation height at field level have been obtained over winter rape and maize fields. The same procedure does not yield valid estimates for wheat, barley, and sugar beet fields due to a mismatch with the physical model employed in the inversion and to the specific crop condition at the date of acquisition. These results show the value of the information provided by both interferometry and polarimetry for some agriculture monitoring practices.This work was supported in part by the Spanish Ministry of Science and Innovation (MICINN) and European Union FEDER under Project TEC2008-06764-C02-02 and Grant PR2009-0364 of the National Program of Human Resources Movability, by the University of Alicante under Project GRE08J01, and by Generalitat Valenciana under Projects GV/2009/079 and ACOMP/2010/082

Sentinel-1 interferometric coherence as a vegetation index for agriculture

In this study, the use of Sentinel-1 interferometric coherence data as a tool for crop monitoring has been explored. For this purpose, time series of images acquired by Sentinel-1 and 2 spanning 2017 have been analysed. The study site is an agricultural area in Sevilla, Spain, where 16 different crop species were cultivated during that year. The time series of 6-day repeat-pass coherence measured at each polarimetric channel (VV and VH), as well as their difference, have been compared to the NDVI and to the backscattering ratio (VH/VV) and other indices based on backscatter. The contribution of different decorrelation sources and the effect of the bias from the space-averaged sample coherence magnitude estimation have been evaluated. Likewise, the usage of 12 days as temporal baseline was tested. The study has been carried for three different orbits, characterised by different incidence angles and acquisition times. All results support using coherence as a measure for monitoring the crop growing season, as it shows good correlations with the NDVI (R2>0.7), and its temporal evolution fits well the main phenological stages of the crops. Although each crop shows its own evolution, the performance of coherence as a vegetation index is high for most of them. VV is generally more correlated with the NDVI than VH. For crop types characterised by low plant density, this difference decreases, with VH even showing higher correlation values in some cases. For a few crop types, such as rice, the backscattering ratio outperforms the coherence in following the growth stages of the plants. Since both coherence and backscattering are directly computed from the radar images, they could be used as complementary sources of information for this purpose. Notably, the measured coherence performs well without the need of compensating the thermal noise decorrelation or the bias due to the finite equivalent number of looks.This work was supported in part by the European Space Agency under Project SEOM-S14SCI-Land (SInCohMap), and in part by the Spanish Ministry of Science and Innovation (State Agency of Research, AEI) and the European Funds for Regional Development under Project PID2020-117303GB-C22

Remote Sensing in Agriculture: State-of-the-Art

The Special Issue on “Remote Sensing in Agriculture: State-of-the-Art” gives an exhaustive overview of the ongoing remote sensing technology transfer into the agricultural sector. It consists of 10 high-quality papers focusing on a wide range of remote sensing models and techniques to forecast crop production and yield, to map agricultural landscape and to evaluate plant and soil biophysical features. Satellite, RPAS, and SAR data were involved. This preface describes shortly each contribution published in such Special Issue

Radarkaugseire rakendused metsaüleujutuste ja põllumajanduslike rohumaade jälgimiseks

Väitekirja elektrooniline versioon ei sisalda publikatsioone.Käesolev doktoritöö keskendub radarkaugseire rakenduste arendamisele kahes keerukas looduskeskkonnas: üleujutatud metsas ja põllumajanduslikel rohumaadel. Uurimistöö viidi läbi Tartu Observatooriumis, Tartu Ülikoolis, Ventspilsi Kõrgkoolis ja Aalto Ülikoolis. Töö esimene osa käsitleb X-laineala polarimeetrilise radarisignaali käitumist regulaarselt üleujutatavas metsas Soomaa näitel ning teine osa põllumajanduslike rohumaade seisundi ja polarimeetriliste ning interferomeetriliste tehisava-radari parameetrite vahelisi seoseid. 2012 kevadel Soomaa testalal TerraSAR-X andmetega läbi viidud eksperiment näitas, et topelt-peegeldusele tundlik HH-VV polarimeetriline kanal pakub tõesti kontrastsemat tagasihajumisepõhist üleujutatud metsa eristust üleujutamata metsast kui traditsiooniline HH polarimeetriline kanal. HH-VV kanali eelis HH kanali ees on seda suurem, mida madalam on mets ning raagus tingimustes lehtmetsas oli HH-VV kanali eelis HH kanali ees suurem kui okasmetsas. Lisaks on üleujutusele tundlik HH ja VV kanali polarimeetriline faasivahe, mida on soovitatud ka varasemates töödes kasutada täiendava andmeallikana üleujutuste kaardistamisel. Käesolevas doktoritöös mõõdeti polarimeetrilise X-laineala tehisava-radari HH/VV faasivahe suurenemine üleujutuste tõttu erineva kõrgusega okas- ja lehtmetsas. 2013 a vegetatsiooniperioodil korraldati Rannu test-alal välimõõtmistega toetatud eksperiment uurimaks X- ja C-laineala polarimeetrilise ning X-laineala interferomeetrilise tehisava-radari parameetrite undlikkust rohumaade tingimuste muutustele. Ilmnes, et ühepäevase vahega kogutud X-laineala tehisava-radari interferomeetriliste paaride koherentsus korreleerus rohu kõrgusega. Koherentsus oli seda madalam, mida kõrgem oli rohi - leitud seost on võimalik potentsiaalselt rakendada niitmise tuvastamiseks. TerraSAR-X ja RADARSAT-2 polarimeetriliste aegridade analüüsi tulemusel leiti kaks niitmisele tundlikku parameetrit: HH/VV polarimeetriline koherentsus ja polarimeetriline entroopia. Niitmise järel langes HH/VV polarimeetriline koherentsus järsult ning polarimeetriline entroopia tõusis järsult. Rohu tagasikasvamise faasis hakkas HH/VV polarimeetriline koherentsus aeglaselt kasvama ning entroopia aeglaselt kahanema. Täheldatud efekt oli tugevam TerraSARX X-laineala aegridadel kui RADARSAT-2 C-riba tehisava-radari mõõtmistel ning seda selgemini nähtav mida rohkem biomassi niitmise järgselt maha jäi. Leitud HH/VV polarimeetrilise koherentsuse ja polarimeetrilise entroopia käitumine vastas taimkatte osakestepilve radarikiirguse tagasihajumismudelile. Mudeli järgi põhjus- 60 tas eelnimetatud parameetrite iseloomulikku muutust rohukõrte kui dipoolide orientatsiooni ja korrastatuse muut niitmise tõttu, mis on kooskõlas meie välimõõtmiste andmetega.This thesis presents research about the application of radar remote sensing for monitoring of complex natural environments, such as flooded forests and agricultural grasslands. The study was carried out in Tartu Observatory, University of Tartu, Ventspils University College, and Aalto University. The research consists of two distinctive parts devoted to polarimetric analysis of images from a seasonal flooding of wetlands, and to polarimetric and interferometric analysis of a summer-long campaign covering eleven agricultural grasslands. TerraSAR-X data from 2012 were used to assess the use of the double-bounce scattering mechanism for improving the mapping of flooded forest areas. The study confirmed that the HH–VV polarimetric channel that is sensitive to double-bounce scattering provides increased separation between flooded and unflooded forest areas when compared to the conventional HH channel. The increase in separation increases with decreasing forest height, and it is more pronounced for deciduous forests due to the leaf-off conditions during the study. The phase difference information provided by the HH–VV channel may provide additional information for delineating flooded and unflooded forest areas. Time series of X-band (TanDEM-X and COSMO-SkyMed) and C-band (RADARSAT-2) data from 2013 were analyzed in respect to vegetation parameters collected during a field survey. The one-day repeat-pass X-band interferometric coherence was shown to be correlated to the grassland vegetation height. The coherence was also found to be potentially useful for detecting mowing events. The polarimetric analysis of TanDEM-X and RADARSAT-2 data identified two parameters sensitive to mowing events - the HH/VV polarimetric coherence magnitude and the H2α entropy. Mowing of vegetation consistently caused the coherence magnitude to decrease and the entropy to increase. The effect was more pronounced in case of X-band data. Additionally, the effect was stronger with more vegetation left on the ground after mowing. The effect was explained using a vegetation particle scattering model. The changes in polarimetric variables was shown to be caused by the change of orientation and the randomness of the vegetation

Dual polarimetric radar vegetation index for crop growth monitoring using sentinel-1 SAR data

Sentinel-1 Synthetic Aperture Radar (SAR) data have provided an unprecedented opportunity for crop monitoring due to its high revisit frequency and wide spatial coverage. The dual-pol (VV-VH) Sentinel-1 SAR data are being utilized for the European Common Agricultural Policy (CAP) as well as for other national projects, which are providing Sentinel derived information to support crop monitoring networks. Among the Earth observation products identified for agriculture monitoring, indicators of vegetation status are deemed critical by end-user communities. In literature, several experiments usually utilize the backscatter intensities to characterize crops. In this study, we have jointly utilized the scattering information in terms of the degree of polarization and the eigenvalue spectrum to derive a new vegetation index from dual-pol (DpRVI) SAR data. We assess the utility of this index as an indicator of plant growth dynamics for canola, soybean, and wheat, over a test site in Canada. A temporal analysis of DpRVI with crop biophysical variables (viz., Plant Area Index (PAI), Vegetation Water Content (VWC), and dry biomass (DB)) at different phenological stages confirms its trend with plant growth dynamics. For each crop type, the DpRVI is compared with the cross and co-pol ratio (σVH0/σVV0) and dual-pol Radar Vegetation Index (RVI = 4σVH0/(σVV0 + σVH0)), Polarimetric Radar Vegetation Index (PRVI), and the Dual Polarization SAR Vegetation Index (DPSVI). Statistical analysis with biophysical variables shows that the DpRVI outperformed the other four vegetation indices, yielding significant correlations for all three crops. Correlations between DpRVI and biophysical variables are highest for canola, with coefficients of determination (R2) of 0.79 (PAI), 0.82 (VWC), and 0.75 (DB). DpRVI had a moderate correlation (R2≳ 0.6) with the biophysical parameters of wheat and soybean. Good retrieval accuracies of crop biophysical parameters are also observed for all three crops.This work was supported by the Spanish Ministry of Science, Innovation and Universities, the State Agency of Research (AEI) and the European Funds for Regional Development (EFRD) under Project TEC2017-85244-C2-1-P

Polarimetric Synthetic Aperture Radar

This open access book focuses on the practical application of electromagnetic polarimetry principles in Earth remote sensing with an educational purpose. In the last decade, the operations from fully polarimetric synthetic aperture radar such as the Japanese ALOS/PalSAR, the Canadian Radarsat-2 and the German TerraSAR-X and their easy data access for scientific use have developed further the research and data applications at L,C and X band. As a consequence, the wider distribution of polarimetric data sets across the remote sensing community boosted activity and development in polarimetric SAR applications, also in view of future missions. Numerous experiments with real data from spaceborne platforms are shown, with the aim of giving an up-to-date and complete treatment of the unique benefits of fully polarimetric synthetic aperture radar data in five different domains: forest, agriculture, cryosphere, urban and oceans

Cartografía de alta resolución de la cubierta del suelo y clasificación de los cultivos en la cuenca del Loukkos (norte de Marruecos): Un enfoque que utiliza las series temporales de SAR Sentinel-1

[EN] Remote  sensing  has  become  more  and  more  a  reliable  tool  for  mapping  land  cover  and  monitoring  cropland. Much of the work done in this field uses optical remote sensing data. In Morocco, active remote sensing data remain under-exploited despite their importance in monitoring spatial and temporal dynamics of land cover and crops even during cloudy weather. This study aims to explore the potential of C-band Sentinel-1 data in the production of a high-resolution land cover mapping and crop classification within the irrigated Loukkos watershed agricultural landscape in northern Morocco. The work was achieved by using 33 dual-polarized images in vertical-vertical  (VV)  and  vertical-horizontal  (VH)  polarizations.  The  images  were  acquired  in  ascending  orbits  between  April 16 and October 25, 2020, with the purpose to track the backscattering behavior of the main crops and other land  cover  classes  in  the  study  area.  The  results  showed  that  the  backscatter  increased  with  the  phenological  development  of  the  monitored  crops  (rice,  watermelon,  peanuts,  and  winter  crops),  strongly  for  the  VH  and  VV  bands, and slightly for the VH/VV ratio. The other classes (water, built-up, forest, fruit trees, permanent vegetation, greenhouses, and bare lands) did not show significant variation during this period. Based on the backscattering analysis and the field data, a supervised classification was carried out, using the Random Forest Classifier (RF) algorithm.  Results  showed  that  radiometric  characteristics  and  6  days  time  resolution  covered  by  Sentinel-1  constellation gave a high classification accuracy by dual-polarization with Radar Ratio (VH/VV) or Radar Vegetation Index and textural features (between 74.07% and 75.19%). Accordingly, this study proves that the Sentinel-1 data provide useful information and a high potential for multi-temporal analyses of crop monitoring, and reliable land cover mapping which could be a practical source of information for various purposes in order to undertake food security issues.[ES] La teledetección se ha convertido en una herramienta cada vez más fiable para cartografiar la cubierta vegetal y controlar las tierras de cultivo. Gran parte de los trabajos realizados en este campo utilizan datos ópticos de teledetección. Además, en Marruecos, los datos de teledetección activa siguen estando infrautilizados, a pesar de su importancia para el seguimiento de la dinámica espacial y temporal de la cubierta vegetal y de los cultivos, incluso con tiempo nublado. Este estudio tiene como objetivo explorar el potencial de los datos de la banda C de Sentinel-1 en la producción de una cartografía de alta resolución de la cubierta del suelo y la clasificación de los cultivos dentro del paisaje agrícola de la cuenca del Loukkos de regadío en el norte de Marruecos. Este trabajo se ha realizado utilizando 33 imágenes de doble polarización vertical-vertical (VV) y vertical-horizontal (VH). Las imágenes fueron adquiridas en órbitas ascendentes entre el 16 de abril y el 25 de octubre de 2020, con el propósito de rastrear el comportamiento de retrodispersión de los principales cultivos y otras clases de cobertura del suelo en el área de estudio. Los gráficos obtenidos muestran que la retrodispersión aumenta con el desarrollo fenológico de los tres cultivos monitorizados (arroz, sandía, cacahuetes, cultivos de invierno), fuertemente para las bandas VH y VV, y ligeramente para el ratio VH/VV. Las otras clases (agua, edificado, bosque, árboles frutales, vegetación permanente, invernaderos y tierras desnudas) no muestran una variación significativa durante este periodo. A partir del análisis de retrodispersión y de los datos de campo, se llevó a cabo una clasificación supervisada, utilizando el  algoritmo  Random Forest Classifier (RF). Los resultados muestran que las características radiométricas y la resolución temporal para los 6 días cubiertos por la constelación Sentinel-1 dan una alta precisión de clasificación por polarización dual con Ratio de Radar (VH/VV) o Índice de Vegetación de Radar y características de la textura (entre  74,07%  y  75,17%).  En  consecuencia,  este  estudio  demuestra  que  los  datos  de  Sentinel-1  proporcionan  información útil y un alto potencial para los análisis multitemporales de seguimiento de los cultivos, así como una cartografía fiable de la cubierta terrestre que debería ser una fuente de información práctica para para varios propósitos a fin de acometer cuestiones de seguridad alimentaria.Nizar, EM.; Wahbi, M.; Ait Kazzi, M.; Yazidi Alaoui, O.; Boulaassal, H.; Maatouk, M.; Zaghloul, MN.... (2022). High Resolution Land Cover Mapping and Crop Classification in the Loukkos Watershed (Northern Morocco): An Approach Using SAR Sentinel-1 Time Series. Revista de Teledetección. (60):47-69. https://doi.org/10.4995/raet.2022.17426OJS47696

Estimating biophysical variables of pasture cover using sentinel-1 data

Over the years, different optical remote sensing platforms and data have been used to estimate aboveground pasture biomass in a variety of landscapes, both heterogeneous and homogenous and at varying spatial scales. Optical methods are often confounded by target visibility, namely presence of cloud cover and haze, and are constrained to daylight conditions. In this study, we used the synthetic aperture radar data from the European Space Agency Sentinel-1 mission to estimate pasture biomass, sward height and leaf area index of a complex extensive grazing ‘farmscape’ comprising of a range of grass vegetation communities We observed that the quality of digital elevation model used in radar data pre-processing significantly influences the ability of eigenvector scattering decomposition in estimating biomass, sward height and leaf area index