agronomy Article An Analysis of the Rice-Cultivation Dynamics in the Lower Utcubamba River Basin Using SAR and Optical Imagery in Google Earth Engine (GEE) Angel James Medina Medina 1,2 , Rolando Salas López 1 , Jhon Antony Zabaleta Santisteban 1,2 , Katerin Meliza Tuesta Trauco 1 , Efrain Yury Turpo Cayo 3 , Nixon Huaman Haro 1 , Manuel Oliva Cruz 1,* and Darwin Gómez Fernández 1,4 1 Instituto de Investigación para el Desarrollo Sustentable de Ceja de Selva (INDES_CES), Universidad Nacional Toribio Rodríguez de Mendoza (UNTRM), Chachapoyas 01001, Peru; angel.medina@untrm.edu.pe (A.J.M.M.); rsalas@indes-ces.edu.pe (R.S.L.); jhon.zabaleta@untrm.edu.pe (J.A.Z.S.); 7693686132@untrm.edu.pe (K.M.T.T.); nixon.huaman.epg@untrm.edu.pe (N.H.H.); darwin.gomez@untrm.edu.pe (D.G.F.) 2 Programa de Maestría en Cambio Climático, Agricultura y Desarrollo Rural Sostenible, Escuela de Posgrado, Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas, Chachapoyas 01001, Peru 3 Programa de Doctorado en Recursos Hidricos (PDRH), Universidad Nacional Agraria La Molina, Ave. La Molina, S.N., Lima 15012, Peru; eturpo@lamolina.edu.pe 4 Centro Experimental Yanayacu, Dirección de Supervisión y Monitoreo en las Estaciones Experimentales Agrarias, Instituto Nacional de Innovación Agraria (INIA), Carretera Jaén San Ignacio KM 23.7, Jaén 06801, Peru * Correspondence: soliva@indes-ces.edu.pe; Tel.: +51-955-846-507 Abstract: One of the world’s major agricultural crops is rice (Oryza sativa), a staple food for more than half of the global population. In this research, synthetic aperture radar (SAR) and optical images are used to analyze the monthly dynamics of this crop in the lower Utcubamba river basin, Peru. Citation: Medina Medina, A.J.; Salas In addition, this study addresses the need to obtain accurate and timely information on the areas López, R.; Zabaleta Santisteban, J.A.; under cultivation in order to calculate their agricultural production. To achieve this, SAR sensor Tuesta Trauco, K.M.; Turpo Cayo, E.Y.; and Sentinel-2 optical remote sensing images were integrated using computer technology, and the Huaman Haro, N.; Oliva Cruz, M.; monthly dynamics of the rice crops were analyzed through mapping and geometric calculation of the Gómez Fernández, D. An Analysis of surveyed areas. An algorithm was developed on the Google Earth Engine (GEE) virtual platform the Rice-Cultivation Dynamics in the for the classification of the Sentinel-1 and Sentinel-2 images and a combination of both, the result Lower Utcubamba River Basin Using of which was improved in ArcGIS Pro software version 3.0.1 using a spatial filter to reduce the SAR and Optical Imagery in Google “salt and pepper” effect. A total of 168 SAR images and 96 optical images were obtained, corrected, Earth Engine (GEE). Agronomy 2024, and classified using machine learning algorithms, achieving a monthly average accuracy of 96.4% 14, 557. https://doi.org/10.3390/ and 0.951 with respect to the overall accuracy (OA) and Kappa Index (KI), respectively, in the year agronomy14030557 2019. For the year 2020, the monthly averages were 94.4% for the OA and 0.922 for the KI. Thus, Academic Editor: Gniewko Niedbała optical and SAR data offer excellent integration to address the information gaps between them, are of great importance to obtaining more robust products, and can be applied to improving agricultural Received: 30 December 2023 Revised: 5 February 2024 production planning and management. Accepted: 16 February 2024 Published: 8 March 2024 Keywords: SAR; rice; monitoring; changes Copyright: © 2024 by the authors. 1. Introduction Licensee MDPI, Basel, Switzerland. As one of the most vital crops globally, unhusked rice has had a significant impact This article is an open access article not only on human society but also on the natural environment [1]. This cereal performs distributed under the terms and conditions of the Creative Commons an essential role in subsistence [2], covering over 12 percent of the total cultivated land Attribution (CC BY) license (https:// worldwide and providing sustenance to nearly half of the global population [3]. However, creativecommons.org/licenses/by/ it is important to note that many farmers are modifying their agronomic practices, such as 4.0/). adopting new varieties and adjusting their water and fertilization management [4]. These Agronomy 2024, 14, 557. https://doi.org/10.3390/agronomy14030557 https://www.mdpi.com/journal/agronomy Agronomy 2024, 14, 557 2 of 17 changes, in some cases, stem from policy decisions related to imports, exports, and prices, directly influencing the cultivation dynamics [5]. The acquisition of accurate and timely information about areas dedicated to cultivation is essential for calculating their agricultural production [6], establishing long-term develop- ment strategies, and making decisions aimed at ensuring food security [7]. Consequently, it is necessary to carry out precise and timely mapping and monitoring of rice crops as a prerequisite for effective agricultural management and to ensure food availability [8]. Therefore, it is crucial to have effective tools to monitor fluctuations in the extent of this crop [9]. In the last decade, there has been a rapid increase in the use of satellite-based remote sensing data to map and monitor rice fields [10]. However, the precise mapping of rice is primarily hindered by the frequent occurrence of clouds in such areas during the rice- cultivation season, significantly interfering with optical remote sensing observations [11]. For this reason, extensive research has been conducted in recent years on mapping and monitoring the expansion and reality of rice crops using synthetic aperture radar (SAR) information [4,12]. SAR data provide the opportunity to obtain information about crops without restrictions caused by weather and lighting conditions and with a spatial resolu- tion of 1.5 m [13]. Time-series images, such as the data provided by Sentinel-1, are now available for free [10]; however, processing Sentinel-1 SAR data (time-series analysis) for crop monitoring can be a time-consuming task, and a cloud platform could streamline the process [8]. SAR technology, unlike optical images, has a high capability to capture and store images under cloudy conditions, light drizzle, and other meteorological phenomena [14]. Additionally, unlike optical imaging systems, SAR images are produced using microwave signals scattered back from the Earth’s surface. Sentinel-1 SAR images, the first of the five in the Sentinel series under the European Copernicus program, provide free data. Sentinel-1 consists of the satellites Sentinel-1A and Sentinel-1B, which share the same orbital plane and capture images in the C-band (approximately a 5 cm wavelength) using SAR technology (an instrument called C-SAR). This results in a temporal resolution of 6 days for both satellites and 12 days for each. The C-SAR instrument operates at wavelengths unaffected by cloud cover or a lack of illumination, allowing data acquisition in areas of interest during the day or night and under all weather conditions [15]. In 2015, Google launched an environmental management and climate change-fighting tool called Google Earth Engine (GEE). This is a free platform that hosts petabytes of data spanning over 40 years of remote sensing data, including Landsat; MODIS; the Advanced Very High-Resolution Radiometer from the National Oceanic and Atmospheric Administration (NOAA AVHRR); Sentinels 1, 2, 3, and 5; and data from the Advanced Land Observing Satellite (ALOS). It is a cloud-based platform that enables the parallel processing of global-scale geospatial data using Google’s cloud infrastructure [16]. GEE can be controlled using an internet-accessible Application Programming Interface (API) and an associated web-based Interactive Development Environment (IDE), allowing for rapid prototyping and visualization of the results [16]. Due to the flexibility of accessing GEE and the availability of the SAR images, there has been an increase in multitemporal analyses of water bodies [17,18] and land use changes, among other applications [19,20]. Examining changes in land cover, especially the expansion of rice cultivation, is crucial. Therefore, for this research, we propose classifying Sentinel-1 SAR images and Sentinel-2 optical images, jointly and by means of machine learning algorithms, based on the retrospective values for elements in the SAR images to analyze the monthly dynamics of land cover in the lower Utcubamba river basin. To achieve this, integration of these two sensors was performed, and large-scale image classification of the Sentinel-1 SAR and Sentinel-2 optical remote sensing images was successfully carried out using the GEE cloud computing technology. With this, we analyzed the monthly dynamics of rice cultivation by mapping and geometrically calculating the areas studied. Agronomy 2024, 14, x FOR PEER REVIEW 3 of 18 Agronomy 2024, 14, 557 computing technology. With this, we analyzed the monthly dynamics of rice cultivatio3n of 17 by mapping and geometrically calculating the areas studied. 22.. Maatteerriiaallss aanndd Meetthhooddss 22..11.. Sttudy Arreea Thhee ssttudyy aarreeaa iiss ssiittuuaatetedd bbetewtweeene nthteh ededpeapratmrtemnetsn otsf oAfmAamzoanzaosn aansda CndajaCmaajarcma.a rca. AAmaazzoonnaass,, whheerree moosstt ooff tthhee sstutuddyy araerae aisi slolcoactaetde,d is, iisn innonrtohrwthewsteersnte PrnerPue, rwui,thw aitnh aarneaa rea ooff aabboouutt 33,9,93355,1,14488..5577 hhaa.. TThhee rreeggiioonn isism moossttlylyc coovveerreeddb byyi mimppeenneettrraabbllee trtrooppiiccaall ffoorreessttsst hat rtehmata rienmuanine xupnleoxrpedlor[e2d1 ][,2A1]m, Aamzoanzaosnacso mcopmrpisreissesse sveevnenp prorovvinincceess aanndd hhaass aa ppooppuulalatitoionn of aorfo aurnodun4d2 942,498,438i3n ihnahbaibtiatnantsts[ 2[222]].. TThhee ootthheerr ppoorrtitoionn inin CCajaajmamaracarc, aal,saol sino ninornthowrtehswt PeesrtuP, eru, wwiitthh aann aarreeaa ooff aabboouutt 33,,229955,3,35500.1.10 0hha,a c, ocnosnisstiss tosfo 1f31 p3rpovroinvciensc easnda nhdash aa spaoppuolpatuiolant ioofn of aapppprrooxxiimmaatteellyy 11,,445544,,221177 iinnhhaabbitiatanntst s[2[22]2.] . TThhee ssttuuddyy aarreeaa iinncclluuddeess zzoonnese sinin BaBgaugau,a U, tUcutcbuabmabmab, aan, dan JdaéJna épnropvrionvceins,c aess ,daespdicetepdic ted iinn FFiigguurree 11. .ThTeh perpevreavilianilgi nclgimclaitme iant ethiins rtehgisiorne igsi “ohnoits an“dh odtrya”n d[23d]r, yw”it[h2 a3l]t,itwuditehs raaltnigtu- des rianngg firnogmf r4o0m0 t4o0 101t0o0 1m1 0a0bomvea bseoav leevseeal. level. Figure 1. Location: (A) Republic of Peru, (B) Departments of Amazonas and Cajamarca, (C) Study aFriegaubree t1w. eLeoncatthioenp: r(oAv)i nRceepsuobfliJca oénf ,PBeraug,u (aB,) aDnedpUarttcmuebnatms bofa .Amazonas and Cajamarca, (C) Study area between the provinces of Jaén, Bagua, and Utcubamba. 2.2. Methodological Design 2.2. Methodological Design Figure 2 illustrates the flow chart used to determine the dynamics of rice cultivation in the loFwigeur rUet 2c uilbluamstrbaatersiv tehre bflaoswin cdhuarritn ugs2e0d1 t9oa dnedte2r0m2i0nues tihneg dimynaagmesicfsr oomf rtihcee cSuelntitvinaetilo-1n and Sienn tthien elol-w2emr Uistsciuobnasmonbat hrieveGrE bEaspinla dtfuorrimng. 2T0h1e9 panrodc 2e0s2s0s tuasritnegd imwaitghesa fmroamn uthael dSeenlitminietla-tion o1f atnhde sSteundtiynealr-e2a m, fiossllioownse odnb tyhet hGeEiEn tproladtfuocrtmio. nThoef SpAroRceasns dstOarptetdic waliitmh aa gmeasn, iunaclo dreploimra-tion oitfaatuioxni loiaf rtyhev astruiadbyl easr,esap, efoclklolewreedd ubyct itohne ,inantrdodinuccltuiosnio onf oSfApRo alyngdo Onsptfiocralc ilmasasgifiecs,a itniocno.r- poraTtihone oclfa asusixfiilciaartyio vnarwiaabslepse, rsfpoercmkleed reudsuinctgioRn,a annddo mincFluosrieosnt o(Rf pFo) lwygitohnsS feonrt classcation. inel-1 ifio- nly, SentinTehle- 2cloansslyifi, caantidona wcoams bpienrafotiromnedof ubsointhg, Rtaaknidnogmin Ftooraecstc o(RuFn)t wthiatht tSheinstsintuedl-1y oansslyu,m es tSheantttihneesl-e2 doantlay,a raendal wa caoyms abvinaailtaiobnle o. fT bhoetho,p ttaimkianlgc ilnatsos iaficccaotuionnt wthaast ethviasl ustautdedy ,arsessuumlteins g in tthheatf othlleoswe dinagtam araep asl.wTahyes acvlaasilsaifibleed. Timhea ogpetsimwaelr celapsrsoificceastsieodn iwnaAs ervcGalIuSatPerdo, ;rtehsuelmtining iimn um mthaep fpoilnlogwuinngit mcoanpssi.d Tehreed clfaosrstifiheisds itmudagyews wereerae rperaoscleasrsgeedr itnh aAnrcoGnIeS hPercot;a trhee. Amrienaims summa ller tmhaanpptihnigs uwneitr ecognesnideerraeldiz feodr tthoisa svtuoiddy owveerree asrteimasa ltairognera tnhdani monper hoevcetatrhee. Avriesausa slmizaaltlieorn of etahcahn othnies; wfienrael glye,ntehrealiezxetde ntot aovfogidro owvethresatnimdatthioens atangde ismopfroevaec hthme voinsuthalwizaetrieond oetfe eramchin ed. Principio del formulario. Agronomy 2024, 14, x FOR PEER REVIEW 4 of 18 Agronomy 2024, 14, 557 one; finally, the extent of growth and the stages of each month were determined. Princip4ioo f 17 del formulario. FiFgiugruere2 .2.M Meeththooddoollooggiiccaall ddeessiiggnn.. 2.23..3D. Deleilmimitiatattioionn ooff tthhee Study Area TThhees tsutuddyya arreeaa wass manualllly deelliimiitteedd uussiningg SeSnentitnienle-2l- 2opotpictaicl aiml iamgaegrye rtyo mtoamrka trhket he rirciecep plolotstsi nin2 2001199a anndd 22002200.. Thhiiss prroocceedduurree wwaas scacarrrireide douotu ttot oavaovidoi pdrpecriescioisnio enrreorrrso arnsda nd cocnonfufusisoionni nint hthee aallggoorriitthhm ssiinnccee tthhee rreessuultltss mmaayy bbe eaafffefcetcetde dini na asusbusbesqeuqeunet netvaelvuaaltuioanti on anaanlaylysissis[ 2[244].].T Thhiiss pprroocceedduurree waass eexxeeccuutteedd inin AArcrcGGISIS PPror ososfotwftwaraer veevrseirosnio 3n.03.1.0. .1. 2.24..4D. Dataatasesett ToToo botbatianinS ASRARd adtaa,taS,e Snetinnteinl-e1l-i1m iamgaegseisn iInn tIenrtfeerrfoemroemtreitcriWc iWdeid-sew-sawthat(hIW (I)Wm) omdoedwe ere usweedr,e wuhseicdh, warheicahv aarilea bavleaiilnabGleE iEn. GThEeE.t eTmhep toermalproersaol lruetsioolnutwioans w12asd 1a2y sd,auytsi,l iuztiinligzicnrgo ss- pocrlaorsisz-paotiloanrizVaHtionan VdHV aVn,din VaVd, dinit iaodndtiotiobno ttho Abostche nAdsicnegnd(iAng) a(nAd) aDneds cDeensdceinngdi(nDg) (flDi)g ht diflriegchtito dnisre[c2t5io],nos b[2ta5i]n, ionbgtatihneincgo mtheb icnoamtiboinnsatVioHnAs V, VHVAA, ,VVVHA,D V, HanDd, aVnVdD V.VD. ToToo botbatianino opptitcicaalld daatatai ninG GEEEE,, SSeennttiinneell--22 iimmaaggeess wweerree iimmppoorrtteedd wwitihth aa ssppaatitaila lrerseos-olu- tioluntioofn 1o0f m10. mA.d Addidtiiotinoanlalyll,yf,o fromr mononththssw whheenn tthheerree wwaass nnoo aavvaailialabbiliiltiyty anadn/do/r ohrighhi gchloculdo ud cocvoevreargagee, ,L Laannddssaatt--88 iimmaaggeess wwiitthh aa ssppaattiiaall rreessoolulutitoionn oof f303 0mm wwereer echcohsoesne. n. 2.5. Processing in GEE Several studies have assessed land cover dynamics at various spatial scales, leveraging the capabilities of GEE. GEE provides access to data and advanced analytical techniques for big data. In the specific cartography of rice fields, both the research community and operators often utilize spatial information. Cloud-based platforms like GEE facilitate access to medium- and high-resolution satellite data, such as images from sensors including Multi Agronomy 2024, 14, 557 5 of 17 Spectral Instrument (MSI), Thematic Mapper (TM), Enhanced Thematic Mapper (ETM), and Operational Land Imager and Thermal Infrared Sensor (OLI.TIRS) [13]. The efficiency of the GEE platform is highlighted in executing complex workflows for processing satellite data required in large-scale applications, such as crop mapping [26]. We also made the code generated during the execution of this research available to promote the transparency and reproducibility of our results: https://code.earthengine.google.com/0811b809ed89944 f6750cb197e8e5509 (accessed on 23 July 2023). 2.5.1. Image Grouping and Filtering All available images from Sentinel-1, Sentinel-2, and Landsat 8 were grouped and filtered based on date ranges corresponding to the specific month under consideration. In this research, the results were analyzed on a monthly basis, covering individual months from January to December of 2019 and/or 2020. 2.5.2. Incorporation of Auxiliary Variables Vegetation and water indices, based on reflectance data from each spectral band, were employed. Among various multispectral indices, the Normalized Difference Vegetation Index (NDVI) [27] is commonly used to monitor vegetation health, land use planning, and ecosystem monitoring [28]. Additionally, the Modified Normalized Difference Water Index (MNDWI) [29–31], known for its effectiveness in delineating water and providing a relatively constant thresh- old compared to other water indices, was applied [32]. It is considered the most accurate available [33] and was selected as a feature for contour extraction in water bodies [34]. Table 1 details the formulas for the aforementioned indices. Table 1. Spectral indices used as auxiliary variables. Index Description For(mula ) Source NDVI Normalized Difference Vegetation Index NDVI = NIR−RED [27]NIR+RED MNDWI Modified Normalized Difference Water Index MNDWI ρGreen−ρSWIR= Green SWIR [31]ρ +ρ 2.5.3. Cluster Generation The purpose of cluster analysis is to group data based on their similarity [35]. To achieve this, the algorithm automatically normalizes the input numerical attributes and employs Euclidean distance to measure distances between groups, aiming to minimize the variation (distances) within the groups [10]. In this research, the GEE platform “ee.Algorithms.Image.Segmentation.SNIC” was utilized to generate the respective clusters. 2.5.4. Speckle Reduction Filter Application The noise present in images, commonly known as speckle, is generated during the SAR image-generation process and has a significant impact on image quality and target- extraction capabilities [36]. One strategy to reduce this speckle involves multitemporal stack aggregation, allowing reduction without compromising spatial resolution. This approach contrasts with the more common speckle-filtering practice, which involves comparing neighboring pixels in a single-date image and often results in a loss of spatial resolution [37]. Therefore, the GEE platform function “ee.Image.focal_median” was used for speckle reduction to enhance and refine the image quality of Sentinel-1. 2.5.5. Image Classification Prior to classification, training samples (polygons) were utilized to generate monthly maps placed in distinct zones representing various stages of rice cultivation. For the purposes of this research, they were separated into 4 major groups. Agronomy 2024, 14, 557 6 of 17 The first stage is referred to as “Stage 1” (flooded areas), the second stage is referred to as “Stage 2” (areas with rice cultivation one to two months old), the third stage is referred to as “Stage 3” (areas with mature rice cultivation), and finally, the fourth stage is named “Stage 4” (areas covered with rice fields ready to be harvested). Subsequently, it was decided to use the Random Forest (RF) classifier in a pixel-based classification to categorize the selected images [38]. The RF, a nonparametric machine learning classifier, was used for the task of land cover classification by visual or digital interpretation [39]. For this research, three data groups were classified: the first ones resulting from the Sentinel-1 mission (SAR data), the second comprising optical data from Sentinel-2 and/or Landsat 8 based on availability, and finally, the third group consisting of the combination of SAR and Optical data. 2.5.6. Evaluation of Accuracy Metrics In order to measure the accuracy of the classification results and provide a compre- hensive assessment of the algorithm’s performance, we calculated two common statistical indicators that offer precision metrics for Land Use and Land Cover (LULC) maps [40]. These indicators are Overall Accuracy (OA) and Kappa Index (KI). 2.5.7. Export of Maps Generated in GEE Finally, the maps for all processed months were exported in Geotiff format. Firstly, they were exported as “LULC,” a dataset that provided information on land distribution and use, i.e., the various rice coverages. Secondly, they were exported as bands of the visible light spectrum, a dataset that, when combined, produces a visual representation similar to what we would see with the naked eye from space. The combination of both formats aided in the visual analysis conducted in the post-classification phase. 2.6. Post-Processing Sorting The products generated in GEE were imported into ArcGIS Pro version 3.0.1 software in order to improve the quality of each monthly map; a spatial filter was applied to reduce the pepper salt effect (areas smaller than one hectare was generalized), and the calculation of areas for each rice stage evaluated was performed. For this purpose, toolbox functions were used, such as Majority Filter, Raster to Polygon, Eliminate, Dissolve, and Calculate Geometry. 2.7. Shapiro–Wilk Test A comparison between the years 2019 and 2020 was carried out with the objec- tive of identifying statistical differences. To establish the normality of the data, the Shapiro–Wilk test was applied, using the null hypothesis (H0) with a p-value < 0.05, indicating that the sample follows a normal distribution, and the alternative hypothesis (H1) with a p-value > 0.05 that contradicts the normality of the data [41]. Subsequently, a t-test for paired samples was performed, where H0 is accepted if the p-value > 0.05, indicating that the mean value of the years is equal, and H1 is accepted if the p-value < 0.05, suggesting that the mean values of the years are different. All this was performed in R 3.3.0 software, using the ggplot 2 library [41]. 3. Results 3.1. Distribution and Availability of Data Figure 3 details the monthly distribution and availability of SAR and Optical data used in this study. It can be observed that, generally, a greater amount of SAR data can be obtained from the GEE platform. In total, 79 and 89 SAR images were obtained for the years 2019 and 2020, respectively. Similarly, 52 and 44 Optical images were acquired for the years 2019 and 2020, respectively. Agronomy 2024, 14, x FOR PEER REVIEW 7 of 18 3. Results 3.1. Distribution and Availability of Data Figure 3 details the monthly distribution and availability of SAR and Optical data used in this study. It can be observed that, generally, a greater amount of SAR data can be obtained from the GEE platform. In total, 79 and 89 SAR images were obtained for the Agronomy 2024, 14, 557 years 2019 and 2020, respectively. Similarly, 52 and 44 Optical images were acquire7do ffo1r7 the years 2019 and 2020, respectively. 12 9 6 3 0 1 2 3 4 5 6 7 8 9 10 11 12 Month 12 9 6 3 0 1 2 3 4 5 6 7 8 9 10 11 12 Month Sentinel-1 Sentinel-2 FFiigguurree 33.. SSAARR aanndd OOppttiiccaall iimmaaggeess aavvaaiillaabbllee ffoorr 22001199 aanndd 22002200.. 3.2. Accuracy Metrics and LULC Maps For the year 2019, as seen in Figure 4, the classifification using Sentinel-1 data for the OA metriic obttaaiinneedd tthheel olowweesstta acccuuraraccyyv vaalulueei nint htheem monotnhtho foFf eFberburaurayr,yw, iwthitah par epcriescioisnioonf 7o0f .7909.%99,%an, dantdh etheig hhiegshteasct caucrcaucryaciny Jiann Juanaruya,rwy,i twh iathp are pcriseicoinsionf 9o4f. 2974%.2.7%Re. gRaergdainrdginthge tKheI mKIe tmriec,trtihce, tlohwe elostwaecsctu araccyurwacays awcahsie avcehdieinveAdu ignu Astu, wguitsht, awviatlhu ea ovfa0lu.6e1 4o7f, 0a.n6d14t7h,e ahnidgh tehset ahcigcuhreasct yacwcuasraactyta winaesd aittnaJianneuda irny ,Jawniutharayp, wreictihsi ao nproefc0is.9io1n91 o.f 0.9191. SSiimiillaarrlly,, tthee ccllaassssiifificcaattiion ussiing onlly SSeenttiineell--22 daattaa fforr tthee OA meettrriicc obttaaiineed tthee llooweesstt aaccccuurraaccyy vvaalluuee iinn tthhee moonntthh ooff JJuullyy,, wiitthh aa pprreecciissiioonn ooff 7733..0022%,, aannd tthhee hhiigghheesstt aaccccuurraaccyy iinn Maayy,, wiitthh aa pprreecciissiioonn ooff 9977..7722%.. RReeggaarrddiinngg tthhee KII meettrriicc,, tthhee llooweesstt aaccccuurraaccyy waass aacchhiieevveedd iinn JJuullyy,, wiitthh aa vvaalluuee ooff 00..66442200,, aanndd tthhee hhiigghheesstt aaccccuurraaccyy waass aattttaaiinneedd iinn Maayy,, wwiitthh aa pprreecciissiioonn ooff 00..99667766.. Finally, the classification using the combination of Sentinel-1 and Sentinel-2 data for the OA metric obtained the lowest accuracy value in the month of November, with a precision of 90.51%, and the highest accuracy in March, with a precision of 100%. Regarding the KI metric, the lowest accuracy was achieved in November, with a value of 0.8720, and the highest accuracy was attained in March, with a precision of 1. Images 2020 Images 2019 Agronomy 2024, 14, x FOR PEER REVIEW 8 of 18 Finally, the classification using the combination of Sentinel-1 and Sentinel-2 data for the OA metric obtained the lowest accuracy value in the month of November, with a pre- cision of 90.51%, and the highest accuracy in March, with a precision of 100%. Regarding Agronomy 2024, 14, 557 the KI metric, the lowest accuracy was achieved in November, with a value of 0.87208, oafn1d7 the highest accuracy was attained in March, with a precision of 1. 1 0.8 0.6 0.4 0.2 0 1 2 3 4 5 6 7 8 9 10 11 12 Month 1 0.8 0.6 0.4 0.2 0 1 2 3 4 5 6 7 8 9 10 11 12 Month Sentinel-1 Sentinel-2 Sentinel 1 and 2 FFiigguurree 44.. AAccccuurraaccyym meetrtircicssf ofor r2 0210919fo froSr eSnetninteinl-e1l-o1n olyn,lSye, nSteinnetiln-2elo-n2 loy,nalyn,d acnodm cboinmebdinSeedn tSineenlt-i1naenl-d1 Saenndt iSneenl-t2incella-s2s cifilacsastiifiocnast.ions. Similarlly,,f foorrt thheey yeeaarr2 2002200, ,a sass hshowownnin inF iFgiugruer5e, 5t,h tehcel acslasisfiscifiactiaotniouns uinsginogn olynSlye nSteinetil-- 1nedla-1ta dfaotrat fhoer OthAe mOAet rmiceotrbitca ionbetdaitnheedl othwee lsotwacecsut raaccyurvaaclyu evainluteh einm thoen tmhonf tOhc otof bOecrt,owbietrh, awpitrhe ac ipsiroencisoifon65 o.f6 765%.6, 7a%nd, atnhde theig hhiegshteasct cauccraucryaciyn inJa Jnaunaurayr,yw, witihtha ap prreecciissiioonn of 90..72%.. Regarding the KI metric, the lowest accuracy was achieved in February, with a value of 0.6295, and the highest accuracy was attttained in January,, wiitthh aa prreecciissiioonn ooff 00..88771166.. Similarly,, the ccllaassssiifificcaattiioonn uussiningg oonnlyly SeSnetnintienle-2l- 2dadta tfaorf othr et hOeAO mAetmriect oribctaoibnteadin tehde tlhowe leoswt aecsctuarcaccuyr vacayluvea ilnu tehien mthoenmtho onf tDheocfeDmebceerm, wbeitrh, wa pitrheacispiroenc iosfi o6n8.o71f %68, .a7n1%d t,haen hdigthhe- hesigt haecsctuarcacuyr ainc yJuinneJu, nwei,twh iath parepcriescioisnio onf o9f59.653.6%3%. R. eRgeagradridnign gththe eKKI Imeettrriicc,, the llowest accuracy was achieved in December, with a value of 0.5810, and the highest accuracy was attained in June, with a precision of 0.9359. Finally, the classification using the combination of Sentinel-1 and Sentinel-2 data for the OA metric obtained the lowest accuracy value in the month of October, with a precision of 89.47%, and the highest accuracy in January, with a precision of 98.40%. Regarding the KI metric, the lowest accuracy was achieved in October, with a value of 0.8523, and the highest accuracy was attained in January, with a precision of 0.9780. Kappa Index Overall Accuracy Agronomy 2024, 14, x FOR PEER REVIEW 9 of 18 accuracy was achieved in December, with a value of 0.5810, and the highest accuracy was attained in June, with a precision of 0.9359. Finally, the classification using the combination of Sentinel-1 and Sentinel-2 data for the OA metric obtained the lowest accuracy value in the month of October, with a preci- sion of 89.47%, and the highest accuracy in January, with a precision of 98.40%. Regarding Agronomy 2024, 14, 557 the KI metric, the lowest accuracy was achieved in October, with a value of 0.85239, aofn1d7 the highest accuracy was attained in January, with a precision of 0.9780. 1 0.8 0.6 0.4 0.2 0 1 2 3 4 5 6 7 8 9 10 11 12 Month 1 0.8 0.6 0.4 0.2 0 1 2 3 4 5 6 7 8 9 10 11 12 Month Sentinel-1 Sentinel-2 Sentinel 1 y 2 FFiigguurree 55.. 22002200 aaccccuurraaccyym meetrtirciscsf ofrorth teheS eSnetnintienl-e1l-o1n olyn,lSye, nSteinnetiln-2elo-2n loy,nalyn,d acnodm cboinmebdinSeedn tSineenlt-i1naenl-d1 and Sentinel-2 classifications. Sentinel-2 classifications. IInn tthhiiss rreeggaarrdd,, tthhee bbeesstt ccllaassssiifificcaattiioonn ffoorr tthhee eexxppoorrtt ooff LLUULLCC mmaappss wwaass sseelleecctteedd.. FFoorr aallll mmoonntthhss,, tthhee ccllaassssiifificcaattiioonnss ooff tthhee ccoommbbiinnaattiioonn ooff SSeennttiinneell--11 aanndd SSeennttiinneell--22 iimmaaggeess wweerree cchhoosseenn ssiinnccee tthheeyy ggeenneerraallllyy aacchhiieevveedd hhiigghheerr aaccccuurraaccyy ffoorr bbootthh ccaallccuullaatteedd mmeettrriiccss.. TTaabbllee 22 ddeettaaiillss tthhee aavveerraaggee aaccccuurraaccyy vvaalluueess ffoorr bbootthh yyeeaarrss aanndd aallll tthhrreeee ccllaassssiifificcaattiioonnss.. TThhee bbeesstt ccllaassssiifificcaattiioonnss weerree oobbttaainineeddi nint htheec ocommbbininataitoinono foSf eSnetnintienle-1l-a1n adndSe Snetninteinl-e2l,-2w, iwthitahn aOnA OoAf 9o6f .946%.4a%n danKdI oKfI0 o.9f 501.9f5o1r tfhore tyheea ry2ea01r 920a1n9d aanndO aAn oOfA94 o.4f %94a.4n%d KanI dof K0.I9 o2f2 0f.o9r2t2h feoyr ethare 2y0e2a0r. 2020. Table 2. Overall average accuracy metrics for both years. Sentinel-1 Sentinel-2 Sentinel 1 and 2 Year OA KI OA KI OA KI 2019 83.3% 0.771 87.3% 0.826 96.4% 0.951 2020 79.7% 0.717 85.6% 0.803 94.4% 0.922 Kappa Index Overal Accuracy Agronomy 2024, 14, 557 10 of 17 3.3. Mapping of Classified Rice Areas Figures 6 and 7 display classified maps of rice areas for every month in the years 2019 Agronomy 2024, 14, x FOR PEERa RnEVdIE2W0 20, created from the combination of SAR and Optical images, show11c aosf i1n8 g the four stages (stage 1, stage 2, stage 3, and stage 4). st FiguFriegu6r.e M6. aMpappipnigngo offr riiccee aarreeaass, ,frformom JanJaunaruya troy DteoceDmebceerm 20b1e9r. 2019. Agronomy 2024, 14, 557 Agronomy 2024, 14, x FOR PEER REVIEW 12 of 18 11 of 17 FigurFeig7u.reM 7a. Mppaipnpginogf orfi rciecea arreeaass,, ffrroomm JaJnaunaurayr tyo tDoecDeemcbeemr 2b0e2r0.2 020. 3.4. Geometric Attributes Figure 8 depicts the variation in areas (hectares) resulting from the combination of SAR and Optical images for Stage 1, Stage 2, Stage 3, and Stage 4. It can be observed that for water-covered areas, there is no abrupt monthly change compared to the other stages. For Stage 2, there is a significant monthly variation, with March 2019 and February 2020 registering the highest changes. This suggests that the majority of newly planted areas are Agronomy 2024, 14, x FOR PEER REVIEW 13 of 18 3.4. Geometric Attributes Figure 8 depicts the variation in areas (hectares) resulting from the combination of SAR and Optical images for Stage 1, Stage 2, Stage 3, and Stage 4. It can be observed that Agronomy 2024, 14, 557 for water-covered areas, there is no abrupt monthly change compared to the other s1t2aogfe1s7. For Stage 2, there is a significant monthly variation, with March 2019 and February 2020 registering the highest changes. This suggests that the majority of newly planted areas are ttyypiiccaalllly cultivated in the fifirrsstt qquuaarrtteerr ooff eeaacchh ccaalelennddaar ryyeaera.r F. oFro trhteh ephpehneonlolgoigcaicl asltasgtaeg oef ohfahrvarevste s(tS(tSagtaeg 4e)4 i)ni nrirciec ecucultlitvivataitoionn, ,iti tisis oobbsseerrvveedd tthaatt producers typically harvest ttheeiirr pplloottss dduurriinnggt thheem oonntthhsso offJ Juullyyt tooD Deecceemmbbeerrf foorrb booththy yeeaarrss. . 18,000.00 15,000.00 12,000.00 9,000.00 6,000.00 3,000.00 0.00 Stage 1 Stage 2 Stage 3 Stage 4 FFiigguurree 88.. AArreeaa vvaarriiaattiioonn iinn hheeccttaarreess ffoorr SSttaaggee1 1,,S Sttaaggee2 2,,S Sttaaggee3 3,,a annddS Sttaaggee4 4.. TTaabbllee3 3p preresesenntstst htheeo voevrearlallslu smummarayrfyo froera echacshtu sdtuieddieyde ayre.aCr.o Cncoenrcneinrnginhgar hvaersvt evsatl uveasl-, iut ecsa,n itb ceaonb bser ovbesderthvaetdt thhearte tihsenroe issi gnnoi fisciganitfivcarniat tvioanriabteiotwn ebeentwtheeentw thoey tewaros ,yweairths, awtiothta al otof t1a0l8 o,8f 0100.84,08100h.e4c0t1a rheescftoaretsh efoyre tahre2 y0e1a9ra 2n0d191 1a0n,d12 171.207,122h7.e2c7t2a rheescftoarreths efoyre tahre2 y0e2a0r. 2020. TTaabbllee3 3..E Essttiimaatteedda arreeaa( (hhaa))f foorrt thheey yeeaarrss2 2001199a anndd2 2002200. . 201290 19 20220020 SStataggee 11 17,1078,90.8997.947 4 252,254,32.43385.385 SStataggee 22 81,8619,16.9818.868 6 808,606,26.65211.511 SStataggee 3 54,5941,19.1415.475 7 464,764,97.40904.004 SStataggee 44 1081,0880,08.0400.410 1 1110,1102,71.22772.272 33..55.. Shaapiirroo––Wiillkk Teesstt TTaabbllee 44 sshhoowss tthhee rreessuullttss ooff tthhee SShaapiirroo––Wiillkkt teesstt ffoorr tthhee yyeeaarrss 22001199 aanndd 22002200,, whhiicchh eevvalluattess tthe norrmalliitty off tthee ddaattaa.. Forr tthe yearr 2019,, a p--vallue off 0..05646 iiss obttaiined,, sslliigghhttllyy hhiigghheerr tthhaann tthhee ssiiggnniifificcaannccee lleevveell ooff 00..0055,, whiicch iindiiccaatteess tthaatt tthee nullll hypottheessiiss tthhaatt tthhee ddiissttrriibbuuttiioonn ooff tthhee ddaattaa iiss nnoorrmaall iiss aacccceepptetedd. .SiSmimilailralryl,y f,ofro rthteh eyeyaera 2r022002, 0t,hteh pe- pv-avlauleu eisi s0.00.00508528121, ,wwhhicichh isis lelessss ththaann 00.0.055,, iinnddiiccaattiinngg tthhaatt tthhee ddaattaa aallssoo hhaavvee nnoorrmaalliittyy ffoorr tthhaatt yyeeaarr.. TTaabbllee4 4..S Shhaappiirroo––Wiliklkt etesst.t. SShhaappiirroo––Wilk NoorrmmaaliltiytyT eTset st YYeeaarr 2019 YYeeaarr 2020 ddaattaa:: ddff__1199$$VVaaluluee ddaattaa:: ddff__2200$$VVaaluluee WW == 00..9955337755,, pp--vvaaluluee= =0 0.0.506546646 WW == 00..9922881133,, pp--vvaaluluee= =0 0.0.00508528121 In Table 5, given the high p-value and the confidence interval that includes 1, the null hypothesis cannot be rejected. Therefore, the variance of both years is significantly equal. jan-19 feb-19 mar-19 apr-19 may-19 jun-19 jul-19 aug-19 sep-19 oct-19 nov-19 dec-19 jan-20 feb-20 mar-20 apr-20 may-20 jun-20 jul-20 aug-20 sep-20 oct-20 nov-20 dec-20 Agronomy 2024, 14, x FOR PEER REVIEW 14 of 18 Agronomy 2024, 14, 557 In Table 5, given the high p-value and the confidence interval that includes 1, th13e onfu17ll hypothesis cannot be rejected. Therefore, the variance of both years is significantly equal. TTaabblele5 5. .F Ft etessttt otoc coommppaarreet wtwoov vaarriaianncceess. . F FTTeesstt ttoo CCoomppaarreeT TwwooV aVriaarnicaensces datad: ata: df_d1f9_$1V9a$luVeaalunde danf_d2 0d$fV_a2l0u$eValue F = 1F.0 =7 219.0729 num df = 47num df = 47 denom def n=o4m7 df = 47 p-vaalluuee= =0 0.8.1801404 altearnltaetrivneathiyvpeo hthyepsoist:hesis: true ratiotorfuvea rraiatnioce osfi svnaoritaenqcueasl tiso n1ot equal to 1 95 9p5e rpceenrtceconnt ficdoennfciedeinntcerev ianl:terval: Upper LimUit pper Limit Lower lLimowit er limit sample estimates: 0.6014441 1.913864 sample estimates: 0.6014441 1.913864 ratio of variances 1.072885 ratio of variances 1.072885 Figure 9 displays the statistical difference between the “Stages” in the years 2019 and 2020F, iwguitrhe t9hed hisepcltayresst ohne tshtae txis-taixciasl adnidff etrheen pc-evableutewse oen tthhee y“-aSxtaisg.e Ist” isi nobtsheervyeeda rtsha2t0 t1h9e asntadti2s0ti2c0a,l wdiiffthertehneceh eisc tmariensimonalt hfoer xe-acxhis satangde tahnedp y-vealru, ewshoincht hsuegyg-easxtis .a Int oisrmobasl edrivsetrdi- tbhuatitohne osft atthiest dicaatlad. ifference is minimal for each stage and year, which suggests a normal distribution of the data. FFiigguurree9 9. .S Sttaattiissttiiccaalld dififffeerreenncceeb beetwtweeeennt hthee“ “SStataggeess””i nin2 2001199a anndd2 2002200. . 4. Discussion 4. Discussion Obtaining accurate and timely crop data on a global scale is crucial for ensuring food securiOtybtaanindinsugs atcacinuarabtlee adnedv etilmopemlye cnrto.pT dhaetua soeno af gsaloteblalli tsecsafloe risr ecmruoctiaels feonr seinnsguprirnogv ifdoeosd asneceucrointyo manidca slumstoaninitaobrlien gdeavlteelornpamtievnet.c oTmhep uarseed otfo sathteellcitoens vfeonr trieomnaoltem seetnhsoidngo fporonv-siditees inansp eeccotnioonm, wichali cmhorenqituoirriensge axlpteernnsaivtievhe ucmomanpareresodu troc ethsea ncodncvoennstuiomneasl ma seitghnoidfi coafn otna-msioteu innt- osfpteimctieo[n4,2 w].hich requires expensive human resources and consumes a significant amount of timThei s[4s2t]u. dy emphasizes the importance of combining data from Optical and SAR imageTshtihsr ostuugdhyt ehme mphualtsiifzuensc tthioen iaml ppolarttfaonrmce ooff GcoEmEbdiunriningg dtahteag frroowmt hOpphtiacsaels aonfdr iScAe Rcu ilmti-- vaagteiosn th(ir.oeu.,gvhe gthetea mtivuel,tirfeupnrcotdiouncatli vpel,aatfnodrm oatfu GraEtEio dnu).riHnogw theev egrr,opwrethv ipohuasssetsu odfi ersic[e2 8c,u4l3t]i- dveavtieolonp (ei.de.,v veeggeetatatitoivne,a rnedprfaordmuclatinvde,m anodn imtoartinugramtioenth).o Hdsowuseivnegr,r pemreovtieousse nstsuindgie, sr e[l2y8i,n4g3] sdoelevleyloopnedO pvteigceatlaitmioang aesndsu fcahrmalsaLnadn mdsoantitaonrdinSge mnteintheol-d2sd uastianbga sreesm. oStiem sielanrsliyn,go, trheelryirneg- sseoalreclhy [o4n,7 ,O13p,4ti4c,a4l5 ]imhaasgeems spulocyhe adsS Lenantidnseal-t1 atnimde Sseenrtieinse(lS-A2 Rdadtaatbaa),sepsr.i mSiamriillyarfolyr, motahpepri nrge- asnedarcmho [n4i,t7o,1ri3n,4g4,r4ic5e] hcaros pemstpalnodyse,di dSeenttiifnyeiln-1g tliamned suersieesa n(SdAcRo vdeart,a)a,n pdriimdeanritliyf yfionrg mwaap-- tperincgo vaenrda gme.oTnhitiosriinmgp rroicvee cdrothpe svtanlidsa,t iiodnenatnifdyisnugi talabnildit yusoef tahnedr ecosuvletrs,, adnedm iodnesntrtiaftyiningg a positive correlation in crop yield prediction. In recent years, there have been successful efforts to integrate various datasets, such as Landsat, Sentinel-1, Sentinel-2, and PlanetScope (PL) [42,46–49]; the latter has become Agronomy 2024, 14, 557 14 of 17 available more recently on the GEE platform [16]. This combination produces more robust results compared to using a single data source, and it generally achieves higher accuracy. For example, a study by [50] classified a highly diverse agricultural region for 2020 and 2021, achieving accuracies of 95.2%. That research study concluded that integrating both satellite datasets enhanced overall accuracy by 2.94%. In this research, integrated data from Sentinel-1 and Sentinel-2 were used to classify rice-cultivation dynamics, and this combination yielded the best results in terms of accuracy, as measured by OA and KI metrics. The highest precision results for both years were between 97.8% and 100%. This is similar to the findings of [50], who concluded that the combination of Sentinel-1 and Sentinel-2 data enables accurate early mapping of crops in the studied area, achieving an OA of up to 95.02%. The scenarios reveal that the monthly temporal series offers superior performance in terms of classification accuracy compared to individual images within monthly windows. Similarly, ref. [40], through a comparative analysis, determined that achieving precise mapping of crop types at a regional level involves using a combination of multiple images with single-moment characteristics and images derived from temporal series of Sentinel-1 and Sentinel-2, resulting in outstanding performance. This approach enables precise mapping of various crop types with a resolution of 10 m for extensive areas. The OA and Kappa Coefficient (KC) turned out to be 84.15% and 0.80, respectively. Another example is the research by Vizzari [42], which aimed to evaluate the advantages of land cover classification by integrating PlanetScope (PL) datasets with Sentinel-2 and Sentinel-1 data. The integration of PL data with S2 and S1 datasets improved overall accuracy results for PB and OB (82 versus 67% and 91 versus 82%, respectively). 5. Conclusions This research confirmed that Optical and SAR data offer excellent integration to address information gaps between them and are of great importance to obtaining more robust products. Unfortunately, classification methods using only Sentinel-1 SAR data are challenging to handle. Therefore, it is necessary to work by combining them with optical data from Sentinel-2 or Landsat 8 to achieve better results. This combination was conducted on a monthly and metric basis to ensure a sufficient amount of updated data without interference from clouds. A total of 79 SAR images were obtained in 2019 and 89 in 2020; likewise, the quantity of optical images was 52 in 2019 and 44 in 2020. It is worth noting that a semi-automatic methodology was applied to process optical and SAR images using the GEE platform for the years 2019 and 2020 in the provinces of Utcubamba, Bagua, and Jaén. The spatial dynamics of rice cultivation were successfully determined, achieving an average monthly precision of 96.4% and 0.951 for OA and KI, respectively, in the year 2019. For the year 2020, the monthly averages were 94.4% for OA and 0.922 for KI. In another way, the harvest results were 108,800.401 hectares for the year 2019 and 110,127.272 hectares for the year 2020. This indicates and reaffirms that, during the first year of the pandemic (2020), producers and the cultivated lands of basic food items in the Peruvian family basket were crucial for survival, as they continued their activities without interruptions. Finally, the proposed approach is accurate and cost-effective, as the data on the platforms used are open source and freely available. Author Contributions: Conceptualization, A.J.M.M. and D.G.F.; Data curation, A.J.M.M., E.Y.T.C., R.S.L., N.H.H. and J.A.Z.S.; Formal analysis, A.J.M.M., D.G.F. and E.Y.T.C.; Investigation, A.J.M.M., R.S.L., J.A.Z.S., K.M.T.T., E.Y.T.C., N.H.H., M.O.C. and D.G.F.; Methodology, A.J.M.M., R.S.L., J.A.Z.S., K.M.T.T., E.Y.T.C., N.H.H., M.O.C. and D.G.F.; Project administration, R.S.L. and M.O.C.; Resources, R.S.L. and M.O.C.; Software, A.J.M.M., R.S.L., E.Y.T.C. and D.G.F.; Supervision, R.S.L.; Validation, K.M.T.T. and N.H.H.; Visualization, A.J.M.M., D.G.F. and K.M.T.T.; Writing—original draft, A.J.M.M. and D.G.F.; Writing—review and editing, J.A.Z.S. and E.Y.T.C. All authors have read and agreed to the published version of the manuscript. Agronomy 2024, 14, 557 15 of 17 Funding: This research was funded by the Public Investment Project “Creation of a Geomatics and Remote Sensing Laboratory of the National University Toribio Rodríguez of Mendoza of Amazonas” GEOMATICA, (CUI N◦ 2255626). The APC was funded by the vice chancellor’s office of Research of the National University Toribio Rodriguez of Mendoza of Amazonas. Data Availability Statement: The data generated for the development of this research are available at the following address: https://code.earthengine.google.com/0811b809ed89944f6750cb197e8e5509 (accessed on 23 July 2023). Acknowledgments: The authors acknowledge and appreciate the support of the INDES-CES of the National University Toribio Rodríguez of Mendoza of Amazonas (UNTRM). Conflicts of Interest: The authors declare no conflicts of interest. References 1. Ni, R.; Tian, J.; Li, X.; Yin, D.; Li, J.; Gong, H.; Zhang, J.; Zhu, L.; Wu, D. An Enhanced Pixel-Based Phenological Feature for Accurate Paddy Rice Mapping with Sentinel-2 Imagery in Google Earth Engine. ISPRS J. Photogramm. Remote Sens. 2021, 178, 282–296. [CrossRef] 2. Kuenzer, C.; Knauer, K. Remote Sensing of Rice Crop Areas. Int. J. Remote Sens. 2013, 34, 2101–2139. [CrossRef] 3. Yang, H.; Pan, B.; Wu, W.; Tai, J. Field-Based Rice Classification in Wuhua County through Integration of Multi-Temporal Sentinel-1A and Landsat-8 OLI Data. Int. J. Appl. Earth Obs. Geoinf. 2018, 69, 226–236. [CrossRef] 4. Phan, H.; Le Toan, T.; Bouvet, A.; Nguyen, L.D.; Duy, T.P.; Zribi, M. Mapping of Rice Varieties and Sowing Date Using X-Band SAR Data. Sensors 2018, 18, 316. [CrossRef] 5. Nelson, A.; Setiyono, T.; Rala, A.B.; Quicho, E.D.; Raviz, J.V.; Abonete, P.J.; Maunahan, A.A.; Garcia, C.A.; Bhatti, H.Z.M.; Villano, L.S.; et al. Towards an Operational SAR-Based Rice Monitoring System in Asia: Examples from 13 Demonstration Sites across Asia in the RIICE Project. Remote Sens. 2014, 6, 10773–10812. [CrossRef] 6. You, N.; Dong, J. Examining Earliest Identifiable Timing of Crops Using All Available Sentinel 1/2 Imagery and Google Earth Engine. ISPRS J. Photogramm. Remote Sens. 2020, 161, 109–123. [CrossRef] 7. de Bem, P.P.; de Carvalho Júnior, O.A.; de Carvalho, O.L.F.; Gomes, R.A.T.; Guimarāes, R.F.; Pimentel, C.M.M. Irrigated Rice Crop Identification in Southern Brazil Using Convolutional Neural Networks and Sentinel-1 Time Series. Remote Sens. Appl. Soc. Environ. 2021, 24, 100627. [CrossRef] 8. Dineshkumar, C.; Kumar, J.S.; Nitheshnirmal, S. Rice Monitoring Using Sentinel-1 Data in the Google Earth Engine Platform. Multidiscip. Digit. Publ. Inst. Proc. 2019, 24, 4. [CrossRef] 9. Onojeghuo, A.O.; Blackburn, G.A.; Wang, Q.; Atkinson, P.M.; Kindred, D.; Miao, Y. Mapping Paddy Rice Fields by Applying Machine Learning Algorithms to Multi-Temporal Sentinel-1A and Landsat Data. Int. J. Remote Sens. 2018, 39, 1042–1067. [CrossRef] 10. Rudiyanto; Minasny, B.; Shah, R.M.; Soh, N.C.; Arif, C.; Setiawan, B.I. Automated Near-Real-Time Mapping and Monitoring of Rice Extent, Cropping Patterns, and Growth Stages in Southeast Asia Using Sentinel-1 Time Series on a Google Earth Engine Platform. Remote Sens. 2019, 11, 1666. [CrossRef] 11. Xiao, W.; Xu, S.; He, T. Mapping Paddy Rice with Sentinel-1/2 and Phenology-, Object-Based Algorithm—A Implementation in Hangjiahu Plain in China Using Gee Platform. Remote Sens. 2021, 13, 990. [CrossRef] 12. Mosleh, M.K.; Hassan, Q.K.; Chowdhury, E.H. Application of Remote Sensors in Mapping Rice Area and Forecasting Its Production: A Review. Sensors 2015, 15, 769–791. [CrossRef] 13. Onojeghuo, A.O.; Miao, Y.; Blackburn, G.A. Deep ResU-Net Convolutional Neural Networks Segmentation for Smallholder Paddy Rice Mapping Using Sentinel 1 SAR and Sentinel 2 Optical Imagery. Remote Sens. 2023, 15, 1517. [CrossRef] 14. Stendardi, L.; Karlsen, S.R.; Niedrist, G.; Gerdol, R.; Zebisch, M.; Rossi, M.; Notarnicola, C. Exploiting Time Series of Sentinel-1 and Sentinel-2 Imagery to Detect Meadow Phenology in Mountain Regions. Remote Sens. 2019, 11, 542. [CrossRef] 15. Del Pilar García Rodríguez, M.; De Évora, A.S.P. Study of Degraded Areas Throughimages Obtained Froma UAV (Drone) and the ESA Sentinel Satellite. An. Geogr. La Univ. Complut. 2020, 40, 55–71. [CrossRef] 16. Gorelick, N.; Hancher, M.; Dixon, M.; Ilyushchenko, S.; Thau, D.; Moore, R. Google Earth Engine: Planetary-Scale Geospatial Analysis for Everyone. Remote Sens. Environ. 2017, 202, 18–27. [CrossRef] 17. White, L.; Brisco, B.; Dabboor, M.; Schmitt, A.; Pratt, A. A Collection of SAR Methodologies for Monitoring Wetlands. Remote Sens. 2015, 7, 7615–7645. [CrossRef] 18. DeLancey, E.R.; Kariyeva, J.; Cranston, J.; Brisco, B. Monitoring Hydro Temporal Variability in Alberta, Canada with Multi- Temporal Sentinel-1 SAR Data. Can. J. Remote Sens. 2018, 44, 1–10. [CrossRef] 19. Mutanga, O.; Kumar, L. Google Earth Engine Applications. Remote Sens. 2019, 11, 591. [CrossRef] 20. Talema, T.; Hailu, B.T. Mapping Rice Crop Using Sentinels (1 SAR and 2 MSI) Images in Tropical Area: A Case Study in Fogera Wereda, Ethiopia. Remote Sens. Appl. Soc. Environ. 2020, 18, 100290. [CrossRef] Agronomy 2024, 14, 557 16 of 17 21. Briceño, N.B.R.; Castillo, E.B.; Torres, O.A.G.; Oliva, M.; Tafur, D.L.; Gurbillón, M.Á.B.; Corroto, F.; López, R.S.; Rascón, J. Morphometric Prioritization, Fluvial Classification, and Hydrogeomorphological Quality in High Andean Livestock Micro- Watersheds in Northern Peru. ISPRS Int. J. Geo-Inf. 2020, 9, 305. [CrossRef] 22. Instituto Nacional de Estadistica e Informatica. Available online: https://m.inei.gob.pe/estadisticas/indice-tematico/poblacion- y-vivienda/ (accessed on 28 September 2023). 23. Castillo, E.B.; Turpo Cayo, E.Y.; De Almeida, C.M.; López, R.S.; Rojas Briceño, N.B.; Silva López, J.O.; Gurbillón, M.Á.B.; Oliva, M.; Espinoza-Villar, R. Monitoring Wildfires in the Northeastern Peruvian Amazon Using Landsat-8 and Sentinel-2 Imagery in the GEE Platform. ISPRS Int. J. Geo-Inf. 2020, 9, 564. [CrossRef] 24. Souza, C.M.; Shimbo, J.Z.; Rosa, M.R.; Parente, L.L.; Alencar, A.A.; Rudorff, B.F.T.; Hasenack, H.; Matsumoto, M.; Ferreira, L.G.; Souza-Filho, P.W.M.; et al. Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine. Remote Sens. 2020, 12, 2735. [CrossRef] 25. Gómez Fernández, D.; Salas López, R.; Rojas Briceño, N.B.; Silva López, J.O.; Oliva, M. Dynamics of the Burlan and Pomacochas Lakes Using SAR Data in GEE, Machine Learning Classifiers, and Regression Methods. ISPRS Int. J. Geo-Inf. 2022, 11, 534. [CrossRef] 26. Shelestov, A.; Lavreniuk, M.; Kussul, N.; Novikov, A.; Skakun, S. Exploring Google Earth Engine Platform for Big Data Processing: Classification of Multi-Temporal Satellite Imagery for Crop Mapping. Front. Earth Sci. 2017, 5, 1–10. [CrossRef] 27. Tsakmakis, I.D.; Gikas, G.D.; Sylaios, G.K. Integration of Sentinel-Derived NDVI to Reduce Uncertainties in the Operational Field Monitoring of Maize. Agric. Water Manag. 2021, 255, 106998. [CrossRef] 28. Liu, Z.; Chen, Y.; Chen, C. Analysis of the Spatiotemporal Characteristics and Influencing Factors of the NDVI Based on the GEE Cloud Platform and Landsat Images. Remote Sens. 2023, 15, 4980. [CrossRef] 29. Liu, D.; Chen, N.; Zhang, X.; Wang, C.; Du, W. Annual Large-Scale Urban Land Mapping Based on Landsat Time Series in Google Earth Engine and OpenStreetMap Data: A Case Study in the Middle Yangtze River Basin. ISPRS J. Photogramm. Remote Sens. 2020, 159, 337–351. [CrossRef] 30. Zurqani, H.A.; Post, C.J.; Mikhailova, E.A.; Schlautman, M.A.; Sharp, J.L. Geospatial Analysis of Land Use Change in the Savannah River Basin Using Google Earth Engine. Int. J. Appl. Earth Obs. Geoinf. 2018, 69, 175–185. [CrossRef] 31. Du, Y.; Zhang, Y.; Ling, F.; Wang, Q.; Li, W.; Li, X. Water Bodies’ Mapping from Sentinel-2 Imagery with Modified Normalized Difference Water Index at 10-m Spatial Resolution Produced by Sharpening the Swir Band. Remote Sens. 2016, 8, 354. [CrossRef] 32. Feyisa, G.L.; Meilby, H.; Fensholt, R.; Proud, S.R. Automated Water Extraction Index: A New Technique for Surface Water Mapping Using Landsat Imagery. Remote Sens. Environ. 2014, 140, 23–35. [CrossRef] 33. Gulácsi, A.; Kovács, F. Sentinel-1-Imagery-Based High-Resolution Water Cover Detection on Wetlands, Aided by Google Earth Engine. Remote Sens. 2020, 12, 1614. [CrossRef] 34. Zhang, M.; Chen, F.; Tian, B. An Automated Method for Glacial Lake Mapping in High Mountain Asia Using Landsat 8 Imagery. J. Mt. Sci. 2018, 15, 13–24. [CrossRef] 35. Nafarin, N.A.; Novitasari, N. Relationship between Normalized Difference Vegetation Index (NDVI) and Rice Growth Phases in Danda Jaya Swamp Irrigation Area Regency Barito Kuala. IOP Conf. Ser. Earth Environ. Sci. 2023, 1184, 012019. [CrossRef] 36. Cui, J.; Guo, Y.; Xu, Q.; Li, D.; Chen, W.; Shi, L.; Ji, G. Extraction of Information on the Flooding Extent of Agricultural Land in Henan Province Based on Multi-Source Remote Sensing Images and Google Earth Engine. Agronomy 2023, 13, 355. [CrossRef] 37. Lindsay, E.; Frauenfelder, R.; Ruther, D.; Nava, L.; Rubensdotter, L.; Strout, J.; Nordal, S. Multi-Temporal Satellite Image Composites in Google Earth Engine for Improved Landslide Visibility: A Case Study of a Glacial Landscape. Remote Sens. 2022, 14, 2301. [CrossRef] 38. Zeng, J.; Tan, M.L.; Tew, Y.L.; Zhang, F.; Wang, T.; Samat, N.; Tangang, F.; Yusop, Z. Optimization of Open-Access Optical and Radar Satellite Data in Google Earth Engine for Oil Palm Mapping in the Muda River Basin, Malaysia. Agriculture 2022, 12, 1435. [CrossRef] 39. Breiman, L.E.O. Random Forests. Mach. Learn. 2001, 45, 5–32. [CrossRef] 40. Wang, X.; Fang, S.; Yang, Y.; Du, J.; Wu, H. A New Method for Crop Type Mapping at the Regional Scale Using Multi-Source and Multi-Temporal Sentinel Imagery. Remote Sens. 2023, 15, 2466. [CrossRef] 41. Romero, M. Comparación de Medias En Grupos Apareados o Dependientes. Enfermería Trab. 2013, 3, 118–123. 42. Vizzari, M. PlanetScope, Sentinel-2, and Sentinel-1 Data Integration for Object-Based Land Cover Classification in Google Earth Engine. Remote Sens. 2022, 14, 2628. [CrossRef] 43. Wu, J.; Jin, S.; Zhu, G.; Guo, J. Monitoring of Cropland Abandonment Based on Long Time Series Remote Sensing Data: A Case Study of Fujian Province, China. Agronomy 2023, 13, 1585. [CrossRef] 44. Singha, C.; Swain, K.C. Rice Crop Growth Monitoring with Sentinel 1 SAR Data Using Machine Learning Models in Google Earth Engine Cloud. Remote Sens. Appl. Soc. Environ. 2023, 32, 101029. [CrossRef] 45. da Costa Freitas, C.; de Souza Soler, L.; Sant’Anna, S.J.S.; Dutra, L.V.; Dos Santos, J.R.; Mura, J.C.; Correia, A.H. Land Use and Land Cover Mapping in the Brazilian Amazon Using Polarimetric Airborne P-Band SAR Data. IEEE Trans. Geosci. Remote Sens. 2008, 46, 2956–2970. [CrossRef] 46. DeVries, B.; Huang, C.; Armston, J.; Huang, W.; Jones, J.W.; Lang, M.W. Rapid and Robust Monitoring of Flood Events Using Sentinel-1 and Landsat Data on the Google Earth Engine. Remote Sens. Environ. 2020, 240, 111664. [CrossRef] Agronomy 2024, 14, 557 17 of 17 47. Xue, H.; Xu, X.; Zhu, Q.; Yang, G.; Long, H.; Li, H.; Yang, X.; Zhang, J.; Yang, Y.; Xu, S.; et al. Object-Oriented Crop Classification Using Time Series Sentinel Images from Google Earth Engine. Remote Sens. 2023, 15, 1353. [CrossRef] 48. Tian, F.; Wu, B.; Zeng, H.; Zhang, X.; Xu, J. Efficient Identification of Corn Cultivation Area with Multitemporal Synthetic Aperture Radar and Optical Images in the Google Earth Engine Cloud Platform. Remote Sens. 2019, 11, 629. [CrossRef] 49. De Alban, J.D.T.; Connette, G.M.; Oswald, P.; Webb, E.L. Combined Landsat and L-Band SAR Data Improves Land Cover Classification and Change Detection in Dynamic Tropical Landscapes. Remote Sens. 2018, 10, 306. [CrossRef] 50. Saad El Imanni, H.; El Harti, A.; Hssaisoune, M.; Velastegui-Montoya, A.; Elbouzidi, A.; Addi, M.; El Iysaouy, L.; El Hachimi, J. Rapid and Automated Approach for Early Crop Mapping Using Sentinel-1 and Sentinel-2 on Google Earth Engine; A Case of a Highly Heterogeneous and Fragmented Agricultural Region. J. Imaging 2022, 8, 316. [CrossRef] Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.