Mapping seasonal rice cropland extent and area in the high cropping intensity environment of Bangladesh using MODIS 500 m data for the year 2010

Rice is the most consumed staple food in the world and a key crop for food security. Much of the world’s rice is produced and consumed in Asia where cropping intensity is often greater than 100% (more than one crop per year), yet this intensity is not sufficiently represented in many land use products. Agricultural practices and investments vary by season due to the different challenges faced, such as drought, salinity, or flooding, and the different requirements such as varietal choice, water source, inputs, and crop establishment methods. Thus, spatial and temporal information on the seasonal extent of rice is an important input to decision making related to increased agricultural productivity and the sustainable use of limited natural resources. The goal of this study was to demonstrate that hyper temporal moderate-resolution imaging spectroradiometer (MODIS) data can be used to map the spatial distribution of the seasonal rice crop extent and area. The study was conducted in Bangladesh where rice can be cropped once, twice, or three times a year.MODIS normalized difference vegetation index (NDVI) maximum value composite (MVC) data at 500 m resolution along with seasonal field-plot information from year 2010 were used to map rice crop extent and area for three seasons, boro (December/January–April), aus (April/May–June/July), and aman (July/August–November/December), in Bangladesh. A subset of the field-plot information was used to assess the pixel-level accuracy of the MODIS-derived rice area. Seasonal district-level rice area statistics were used to assess the accuracy of the rice area estimates. When compared to field-plot data, the maps of rice versus non-rice exceeded 90% accuracy in all three seasons and the accuracy of the five rice classes varied from 78% to 90% across the three seasons. On average, the MODIS-derived rice area estimates were 6% higher than the sub-national statistics during boro, 7% higher during aus, and 3% higher during the aman season. The MODIS-derived sub-national areas explained (R² values) 96%, 93%, and 96% of the variability at the district level for boro, aus, and aman seasons, respectively.The results demonstrated that the methods we applied for analysing and interpreting moderate spatial and high temporal resolution imagery can accurately capture the seasonal variability in rice crop extent and area. We discuss the robustness of the approach and highlight issues that must be addressed before similar methods are used across other areas of Asia where a mix of rainfed, irrigated, or supplemental irrigation permits single, double, and triple cropping in a single calendar year.     

Agricultural Area Diversification and Crop Water Demand Analysis: A Remote Sensing and GIS Approach

Large scale adoption of input intensive rice–wheat cropping system in the centrally located Jalandhar district of Indian Punjab has led to over-exploitation of ground water resources, intensive use of chemical fertilizers and deterioration of soil health. To overcome these shortfalls, in the present study, agricultural area diversification plan has been generated from agricultural area and crop rotation maps derived from remote sensing data (IRS P6-AWiFS and RADARSAT ScanSAR) along with few agro-physical parameters in GIS environment. Cropping system indices (area diversity, multiple cropping and cultivated land utilization) were also worked out from remote sensing data .Analysis of remote sensing data (2004–05) revealed that rice and wheat individually remained the dominant crops, occupy 57.8% and 64.9% of total agricultural area (TAA), respectively. Therefore, in the diversified plan, it is suggested that at least 39% of the current 40% TAA under rice–wheat rotation should be replaced by other low water requiring, high value and soil enriching crops, particularly in coarse textured alluvial plain having good quality ground water zones with low annual rainfall(<700 mm). This will reduce water requirement to the tune of 15,660 cm depth while stabilizing the production and profitability by crop area diversification without further degradation of natural resources.     

Deducing Rice Crop Dynamics and Cultural Types of Bangladesh Using Geospatial Techniques

Rice crop occupies an important aspect of food security and also contributes to global warming via GHGs emission. Characterizing rice crop using spatial technologies holds the key for addressing issues of global warming and food security as different rice ecosystems respond differently to the changed climatic conditions. Remote sensing has become an important tool for assessing seasonal vegetation dynamics at regional and global scale. Bangladesh is one of the major rice growing countries in South Asia. In present study we have used remote sensing data along with GIS and ancillary map inputs in combination to derive seasonal rice maps, rice phenology and rice cultural types of Bangladesh. The SPOT VGT S10 NDVI data spanning Aus, Aman and Boro crop season (1^st May 2008 to 30^th April 2009) were used, first for generating the non-agriculture mask through ISODATA clustering and then to generate seasonal rice maps during second classification. The spectral rice profiles were modelled and phenological parameters were derived. NDVI growth profiles were modelled and crop calendar was derived. To segregate the rice cultural types of Bangladesh into IPCC rice categories, we used elevation, irrigated area, interpolated rainfall maps and flood map through logical modelling in GIS. The results indicated that the remote sensing derived rice area was 9.99 million ha as against the reported area of 11.28 million ha. The wet and dry seasons accounted for 64% and 36 % of the rice area, respectively. The flood prone, drought prone and deep water categories account for 7.5%, 5.56% and 2.03%, respectively. The novelty of current findings lies in the spatial outcome in form of seasonal and rice cultural type maps of Bangladesh which are helpful for variety of applications.     

Analysis of cropping pattern changes in Bathinda district,Punjab

A study was conducted in the Bathinda district of Punjab state for mapping the cropping pattern and crop rotation, monitoring long term changes in cropping pattern by using the satellite based remote sensing data along other spatial and non-spatial collateral data. Multi-date IRS LISS I and IRS WiFS sensor data have been used for this study. Cropping pattern maps and crop rotation maps were generated for the years 1988-89 and 1998-99. The present study has shown the increase of cropping intensity significantly, mainly due to increase in rice area. However, crop diversity has decreased mainly due to decline in the area under the minor crops like pearl millet, gram, rapeseed/ mustard. There is increase in area coverage of cotton-wheat and rice-wheat rotation, at the expense of the minor crops.     

Vegetation图像植被指数与实测水稻叶面积指数的关系

水稻的叶面积指数 (LAI)是水稻生长的一项重要参数 ,与水稻的生物量与产量直接相关。利用 1999年在江苏省江宁县实测的水稻叶面积指数与同期Vegetation/SPOT的植被指数作了对比分析 ,结果发现同期的LAI与植被指数表现相近的变化特征 ,两者具有良好的相关关系。     

Identification of Aman Rice Crop in Bangladesh Using Temporal C-Band SAR – A Feasibility Study

In-season rice area estimation using C-band Synthetic Aperture Radar (SAR) data from RADARSAT-1 is being done in India for more than a decade. Decision rule based models in backscatter domain have been calibrated and validated using extensive field data and a long term backscatter signature bank of rice fields has been developed. Since the rice crop growing environment in India is a diverse one in the world having all the rice cultural types, the rice backscatter is quite exhaustive. This paper highlights the results of classification of rice lands in Bangladesh using the signature bank of India. The results showed that the Aman rice crop of Bangladesh has a typical temporal backscatter of shallow and intermediate rice fields of that of West Bengal state. The mean backscatter of the intermediate/deep water fields in southern Bangladesh was ?19?dB, while that of shallow cultural types mostly in northern Bangladesh was ?17?dB. The signature of the rice crop in Southern Bangladesh matched well with that of Gangetic West Bengal, particularly that of the 24 Parganas, Howrah and Hughli districts. The signature of rice crop in the Sub-Himalayan West Bengal particularly that of Dinajpur and Maldah districts matched well with that of the northern area of Bangladesh. State level rice area estimated using the selected models was found with in 5% deviation from that of the reported acreage.     

Satellite-based investigation of flood-affected rice cultivation areas in Chao Phraya River Delta,Thailand

The occurrence of catastrophic floods in Thailand in 2011 caused significant damage to rice agriculture. This study investigated flood-affected rice cultivation areas in the Chao Phraya River Delta (CRD) rice bowl, Thailand using time-series moderate resolution imaging spectroradiometer (MODIS) data. The data were processed for 2008 (normal flood year) and 2011, comprising four main steps: (1) data pre-processing to construct time-series MODIS vegetation indices (VIs), to filter noise from the time-series VIs by the empirical mode decomposition (EMD), and to mask out non-agricultural areas in respect to water-related cropping areas; (2) flood-affected area classification using the unsupervised linear mixture model (ULMM); (3) rice crop classification using the support vector machines (SVM); and (4) accuracy assessment of flood and rice crop mapping results. The comparisons between the flood mapping results and the ground reference data indicated an overall accuracy of 97.9% and Kappa coefficient of 0.62 achieved for 2008, and 95.7% and 0.77 for 2011, respectively. These results were reaffirmed by close agreement (R² > 0.8) between comparisons of the two datasets at the provincial level. The crop mapping results compared with the ground reference data revealed that the overall accuracies and Kappa coefficients obtained for 2008 were 88.5% and 0.82, and for 2011 were 84.1% and 0.76, respectively. A strong correlation was also found between MODIS-derived rice area and rice area statistics at the provincial level (R² > 0.7). Rice crop maps overlaid on the flood-affected area maps showed that approximately 16.8% of the rice cultivation area was affected by floods in 2011 compared to 4.9% in 2008. A majority of the flood-expanded area was observed for the double-cropped rice (10.5%), probably due to flood-induced effects to the autumn–summer and rainy season crops. Information achieved from this study could be useful for agricultural planners to mitigate possible impacts of floods on rice production.     

中国农情遥感速报系统

介绍了中国农情遥感速报系统的建设情况 ,系统内容包括农作物长势监测、农作物种植面积监测、农作物单产预测与粮食产量估算、作物时空结构监测和粮食供需平衡预警等。简要介绍了 1998年以来中国农情遥感速报系统在监测内容与监测范围、监测频率、技术发展以及质量控制与过程检验体系建立等方面的进展 ,并就中国农情遥感速报系统的发展方向提出了展望。     

Discriminating transplanted and direct seeded rice using Sentinel-1 intensity data

Improved rice crop and water management practices that make the sustainable use of resources more efficient are important interventions towards a more food secure future. A remote sensing-based detection of different rice crop management practices, such as crop establishment method (transplanting or direct seeding), can provide timely and cost-effective information on which practices are used as well as their spread and change over time as different management practices are adopted. Establishment method cannot be easily observed since it is a rapid event, but it can be inferred from resulting observable differences in land surface characteristics (i.e. field condition) and crop development (i.e. delayed or prolonged stages) that take place over a longer time. To examine this, we used temporal information from Synthetic Aperture Radar (SAR) backscatter to detect differences in field condition and rice growth, then related those to crop establishment practices in Nueva Ecija (Philippines). Specifically, multi-temporal, dual-polarised, C-band backscatter data at 20m spatial resolution was acquired from Sentinel-1A every 12 days over the study area during the dry season, from November 2016 to May 2017. Farmer surveys and field observations were conducted in four selected municipalities across the study area in 2017, providing information on field boundaries and crop management practices for 61 fields. Mean backscatter values were generated per rice field per SAR acquisition date. We matched the SAR acquisition dates with the reported dates for land management activities and with the estimated dates for when the crop growth stages occurred. The Mann-Whitney U test was used to identify significant differences in backscatter between the two practices during the land management activities and crop growth stages. Significant differences in cross-polarised, co-polarised and band ratio backscatter values were observed in the early growing season, specifically during land preparation, crop establishment, rice tillering and stem elongation. These findings indicate the possibility to discriminate crop establishment methods by SAR at those stages, suggesting that there is more opportunity for discrimination than has been presented in previous studies. Further testing in a wider range of environments, seasons, and management practices should be done to determine how reliably rice establishment methods can be detected. The increased use of dry and wet direct seeding has implications for many remote sensing-based rice detection methods that rely on a strong water signal (typical of transplanting) during the early season.     

Multi temporal ERS-1 SAR data for identification of rice crop

Microwave sensors having all-weather capabilities provide an opportunity to monitor rice grown in monsoon season. An attempt has been made to identify rice crop using multitemporal ERS-1 SAR data in C-band (5.3 GHz). Data acquired on August 15 (D1), September 19 (D2), October 24 (D3) and November 28 (D4) 1993 were taken. Combinations of data acquired on different dates were used for identification of rice crop. Single-date IRS-1B LISS II data in visible and NIR bands acquired on October 23, 1993 was also used for comparison of estimated rice area. Analysis of the results has shown that a combination of SAR data acquired at the tillering (August), booting (September) and heading (October) stages of rice crop enabled identification and area estimation of rice crop grown under lowland conditions. Single-date SAR data acquired in the month of October was found to be better for identification of rice compared to other dates.     

Evaluation of RADARSAT ScanSAR synthetic aperture radar data for rice crop inventory and monitoring

Radarsat ScanSAR Narrow (SN2) data acquired on July 24 and August 17, 1997 were used to analyse the signature of rice crop in West Bengal, India. The analysis showed that the lowland practice of cultivation gives a distinct signature to rice due to the initial water background. The relatively stable backscatter from water bodies in temporal data enhanced the separability of rice fields from water using two date data. Around 94 per cent classification accuracy was achieved for rice crop using two date data. It was feasible to discriminate rice sub-classes based on their planting period like early and late crop. The analysis indicates the suitability of ScanSAR data for large area rice crop monitoring as it has a wide swath of 300 km.     

Detection and estimation of mixed paddy rice cropping patterns with MODIS data

In this paper, we developed a more sophisticated method for detection and estimation of mixed paddy rice agriculture from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. Previous research demonstrated that MODIS data can be used to map paddy rice fields and to distinguish rice from other crops at large, continental scales with combined Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI) analysis during the flooding and rice transplanting stage. Our approach improves upon this methodology by incorporating mixed rice cropping patterns that include single-season rice crops, early-season rice, and late-season rice cropping systems. A variable EVI/LSWI threshold function, calibrated to more local rice management practices, was used to recognize rice fields at the flooding stage. We developed our approach with MODIS data in Hunan Province, China, an area with significant flooded paddy rice agriculture and mixed rice cropping patterns. We further mapped the aerial coverage and distribution of early, late, and single paddy rice crops for several years from 2000 to 2007 in order to quantify temporal trends in rice crop coverage, growth and management systems. Our results were validated with finer resolution (2.5 m) Satellite Pour l’Observation de la Terre 5 High Resolution Geometric (SPOT 5 HRG) data, land-use data at the scale of 1/10,000 and with county-level rice area statistical data. The results showed that all three paddy rice crop patterns could be discriminated and their spatial distribution quantified. We show the area of single crop rice to have increased annually and almost doubling in extent from 2000 to 2007, with simultaneous, but unique declines in the extent of early and late paddy rice. These results were significantly positive correlated and consistent with agricultural statistical data at the county level (P < 0.01).     

全国作物种植结构快速调查技术与应用

现有种植结构的分析都是基于统计数据 ,时效性低及精度差 ,难以及时为各级政府部门提供决策支持。以“中国农情遥感速报系统”使用的GVG农情采样系统和样条采样框架为基础 ,提出了快速获取全国农作物种植结构的技术方法 ,并以 2 0 0 2年为例 ,开展全国夏粮和秋粮种植结构的调查与现状分析。全国夏粮的粮经比例为 5 8%∶2 1% ,秋粮的粮经比例为 79%∶14 % ,粮食作物仍然占有较大的比例。调查表明 ,全国范围的种植结构在时间和空间上变化很大。黑龙江省的大豆种植成数最高 ,达到38% ,是中国的大豆主产区 ;吉林和辽宁两省的春玉米种植成数相差不大 ,高达 71% ;黄淮海地区夏粮以种植冬小麦为主 ,种植成数高达 97% (河北省 ) ,秋粮以夏玉米为主 ,种植成数高达 82 % (河南 ) ;以长江为界 ,冬小麦和油料在长江南北的种植成数变化很大 ,长江以北冬小麦与油料并重 ,以南以油料为主。秋粮则以中晚稻为主 ,种植成数均超过 6 6 % ;华南夏粮和秋粮均以水稻为主 ,其中广东的蔬菜瓜果的种植成数高达 2 9% ;西南地区的秋粮以中稻和夏玉米为主 ,其中云南省的棉麻糖的种植成数高达19% ,说明云南省仍然是中国的烟草大省。经济发达或邻近经济发达地区的省份的蔬菜瓜果的种植成数较大 ,如天津市高达 34%。     

Multiple forecasts of kharif rice in orissa state-four year experience of fasal pilot study

Considering the requirement of multiple pre-harvest crop forecasts, the concept of Forecasting Agricultural output using Space, Agrometeorology and Land based observations (FASAL) has been formulated. Development of procedure and demonstration of this technique for four in-season forecasts for kharif rice has been carried out as a pilot study in Orissa State since 1998. As the availability of cloud-free optical remote sensing data during kharif season is very poor for Orissa state, multi-date RADARSAT SCANSAR data were used for acreage estimation of kharif rice. Meteorological models have been developed for early assessment of acreage and prediction of yield at mid and late crop growth season. Four in-season forecasts were made during four kharif seasons (1998-2001); the first forecast of zone level rice acreage at the beginning of kharif crop season using meteorological models, second forecast of district level acreage at mid growth season using two-date RADARSAT SCANSAR data and yield using meteorological models, third forecast at late growth season of district level acreage using three-date RADARSAT SCANSAR data and yield using meteorological models and revised forecast incorporating field observations at maturity. The results of multiple forecasts have shown rice acreage estimation and yield prediction with deviation up to 14 and 11 per cent respectively. This study has demonstrated the potential of FASAL concept to provide inseason multiple forecasts using data of remote sensing, meteorology and land based observations.     

基于TM影像的沙化土地动态变化研究——以榆林市榆阳区河口水库周边地区为例

以榆林市榆阳区河口水库周边地区1999年和2004年两期TM影像为基础数据,在ERDAS IMAGINE平台上,通过对研究区遥感影像进行预处理、建立解译标志、人工判读并分类等,得到了河口水库周边地区沙化土地动态变化分布图。结果表明,1999年到2004年期间,河口水库水域面积明显增大。周边5km范围内,沙化土地明显逆转,部分流动沙地和半固定沙地向固定沙地转化,使耕地面积减少,林地增加。     

农作物单产预测的运行化方法

提出了适于运行化农作物单产预测的方法。即以农作物单产区划为基础 ,通过搜集不同地区不同作物的单产预测模型 ,分析每个模型的空间适用范围 ,并从模型参数等角度筛选模型 ,然后利用这些模型进行气象站点的作物单产预测 ,并以NDVI分布图为参考数据将点上的单产数据空间外推到区域尺度。借助耕地分布估计区域水平的农作物单产。最后以 2 0 0 3年冬小麦为例 ,进行了全国 10个省的冬小麦平均单产估算 ,花费了较少的人力和时间 ,符合运行化遥感估产要求     

Methane Emission Pattern of Indian Rice-Ecosystems

The rice land is linked to the climate change due to its methane emission potential. The systems of growing rice and associated soil and crop management practices that have evolved are varied and complex. However, from the methane emission point of view, water regime is a crucial parameter. According to IPCC guidelines the rice ecosystem need to be categorized into four strata for methane emission study. The remote sensing based stratification map previously developed was used for in-situ weekly/monthly measurements of methane emission from the representative ecosystems, samples were collected and analysed using gas chromatography following the IPCC standards for three consecutive years; 2003, 2004 and 2005. This paper highlights the results of methane emission measurement and pattern from rice lands of India based on in-situ measurements. The CH₄ emission pattern of irrigated crop in dry season showed a steady increase in the beginning which peaks during flowering stage, decreasing gradually thereafter. The results were consistent for different varieties and across the years. The emission pattern of irrigated wet season crop showed two peaks. The emission pattern also showed the influence of crop variety as well as year (of observation). The mean emission coefficient derived from all categories and all samples (n = 471) weighted for the Indian rice crop was 74.05 + 43.28 kg/ha.     

Concurrent use of active and passive microwave remote sensing data for monitoring of rice crop

Estimation of crop area, growth and phenological information is very important for monitoring of agricultural crops. However, judicious combination of spatial and temporal data from different spectral regions is necessary to meet the requirement. This study highlights the use of active microwave QuikSCAT Ku-band scatterometer and Special Sensor Microwave/Imager (SSM/I) passive microwave radiometer data to derive information on important phenological phases of rice crop. The wetness index, a weekly composite product derived using brightness temperatures from 19, 37 and 85 GHz channels of SSM/I, was used to identify the puddling period. Ku-band scatterometer data provided the signal of transplanted rice seedlings since they acts as scatterers and increases the backscattering. Dual peak nature of temporal backscatter curve around the heading stage of rice crop was observed in Ku-band. The decrease of backscatter after first peak was associated with the threshold value of 60% crop canopy cover. The symmetric (Gaussian) and asymmetric (lognormal) curve fits were attempted to derive the date of initiation of the heading phase. The temporal signature from each of these sensors was found to complement each other in crop growth monitoring. Image showing pixel-wise timings of heading stage revealed the differences exists in various parts of the study area.     

Remote sensing based agricultural drought assessment in Palar basin of Tamil Nadu state,India

Agricultural drought has been a recurrent phenomenon in many parts of India. Remote sensing plays a vital role in real time monitoring of the agricultural drought conditions over large area, there by effectively supplementing the ground mechanism. Conventional drought monitoring is based on subjective data. The satellite based monitoring such as National Agricultural Drought Assessment and Monitoring System (NADAMS) is based on the crop condition, which is an integrated effect of soil, effective rainfall, weather, etc. Drought causes changes in the external appearance of vegetation, which can clearly be identified (by their changed spectral response) and judged using satellite sensors through the use of vegetation indices. These indices are functions of rate of growth of the plants and are sensitive to the changes of moisture stress in vegetation. The satellite based drought assessment methodology was developed based on relationship obtained between previous year’s Normalised Difference Vegetation Index (NDVI) profiles with corresponding agricultural performance available at district/block level. Palar basin, one of the major river basins in Tamil Nadu state was selected as the study area. The basin covers 3 districts, which contain 44 blocks. Wide Image Field Sensor (WiFS) of 188m spatial resolution from Indian Remote Sensing Satellite (IRS) data was used for the analysis. Satellite based vegetation index NDVI, was generated for Samba and Navarai seasons in the years 1998 and 1999. An attempt has been made to estimate the area under paddy. It was also observed that, there was reduction in the crop area as well as vigour in the vegetation in both Samba and Navarai seasons in 1999 when compared with 1998. Drought severity maps were prepared in GIS environment giving blockwise agricultural water deficiency status.     

利用Sentinel-2A数据提取长江中下游丘陵地带农作物种植信息

长江中下游丘陵地带地块细小破碎、种植结构复杂,导致作物遥感光谱特征相互纠缠,信息精确提取困难等。本文基于Sentinel-2A数据提出了多特征组合优化的丘陵地带农作物种植结构精确识别方法。首先获取研究区内主要农作物的关键物候特征信息;然后计算其光谱特征、纹理特征、地形特征值,构建原始特征集;最后采用随机森林方法对特征进行重要性排序,对原始特征集进行特征变量优化,并选择优化后的组合特征进行监督分类提取出研究区农作物信息。试验结果表明,相较于单变量特征,通过多特征优化组合分类总体精度和Kappa系数分别从80.4%和0.748提高到96.3%和0.954,有效地提高了南方丘陵地带农作物分类精度,算法稳定性较强。在南方丘陵地带农作物的识别过程中,进行特征变量优化后的地形特征与纹理特征能显著提高分类精度。