Rice Crop Mapping Using Sentinel-1 Time Series Images (Case Study: Mazandaran, Iran)

Abstract

Abstract. Policymaking and planning agricultural improvement require accurate and timely information and statistics. In Iran, collecting and acquiring agricultural statistics is often done in the traditional methods. Related studies have proved that these methods mostly contain some mistakes. Multi-temporal acquisition strategies of remotely sensed data provide an opportunity to improve rice monitoring and mapping. Studying and monitoring rice paddies in vast areas is limited by the presence of cloud cover, the spatial and temporal resolution of optical sensors, and the lack of open access or systematic Radar data. Sentinel-1 satellite data, which are free to access and has a high quality of spatial and temporal resolution, can provide a great opportunity for monitoring crop products, especially rice. In this study, Sigma Nought, Gamma Nought and Beta Nought time series of Sentinel-1 data in VV, VH and VV+VH polarizations were employed for extracting areas under rice cultivation in the region of Mazandaran province, Iran. These satellite data are taken regularly every 12 days, according to the season of the region, from March 21st to September 22nd of 2018. In this study, in order to specify the rice paddies area, several fieldworks were randomly carried out for two weeks, and field data were collected as well. Field data including rice paddies areas and non-rice areas were collected as ‘Test and Train data set’ and then the Random Forrest (RF) algorithm was carried out to determine the rice paddies area. The classification result was validated using test samples. The accuracy of all classifications results are over 80% and the best result is related to Sigma Nought and gamma Nought of VH polarization, with an accuracy of 91.37%. The results showed a high capability to evaluate and monitor rice production at moderate levels in a vast area which is regularly exposed to the cloud cover.

Citrus orchard mapping in Juybar, Iran: Analysis of NDVI time series and feature fusion of multi-source satellite imageries

“…have periodic or non-periodic NDVI changes, the time series of NDVI is extracted from 2016 to 2019. In addition, sometimes, using the term "time-series" for the case in which a series of input images is processed is common (Farhadi et al, 2022;Saadat et al, 2019). In the current study, to obtain propitious features that could be applicable for citrus orchards extraction, analysis and processing of a series/set of input satellite images is required to be able to remove poor pixels (noisy pixels, cloud pixels, etc.).…”

“…Our personal information about the growing rice calendar as well as ground truth information from the red-marked land indicates that it is definitely rice cropland and no other crops are raised in the mentioned cropland during the year except rice (Saadat et al, 2019). In this methodology, all other crops like rice that have periodic cultivation calendars are shown in non-white colors.…”

“…Having fertilized soil, good weather, high precipitation, relatively high sunny days, 1 etc. leads to the cultivation of a variety of crops all over Mazandaran, as the first-rank province in Iran's agriculture (Mirkarimi et al, 2013;Saadat et al, 2019). In developing countries like Iran, the extent and distribution of agricultural croplands and quality/quantity estimations of cultivated crops have been traditionally managed for many years, but today, the implementation of up-to-date methods and techniques to monitor farms and natural resources is necessary and inevitable.…”

“…Hao et al (2018) improved a neural network-based algorithm for crop mapping (including orchards) using Sentinel-derived Normalized Difference Vegetation Index (NDVI) time series for Hebei province, China. In three distinct but relatively similar studies, Clauss et al (2018), Bazzi et al (2019), and Saadat et al (2019) provided rice crop maps for their case studies using Sentinel-1time series analysis. In another case, Chen et al (2019aChen et al ( , 2019b implemented an automatic orchard mapping method using Landsat satellite images time series.…”

Evaluating the efficiency of coarser to finer resolution multispectral satellites in mapping paddy rice fields using GEE implementation

“…Rice crop, which is the second major crop of Kharif season in terms of area after cotton, is the second most consumed staple food in southern Punjab after wheat 14 . For effective management of rice production and demand, accurate satellite instruments need to be identified so that effective mapping techniques can be implemented in the region 15 . Government officials, agronomists, and researchers often require accurate and timely crop information for producing statistical reports, policy implementation, and advancement in precision agriculture 15 .…”