Mapping Crop Cycles in China Using MODIS-EVI Time Series

As the Earth’s population continues to grow and demand for food increases, the need for improved and timely information related to the properties and dynamics of global agricultural systems is becoming increasingly important. Global land cover maps derived from satellite data provide indispensable information regarding the geographic distribution and areal extent of global croplands. However, land use information, such as cropping intensity (defined here as the number of cropping cycles per year), is not routinely available over large areas because mapping this information from remote sensing is challenging. In this study, we present a simple but efficient algorithm for automated mapping of cropping intensity based on data from NASA’s (NASA: The National Aeronautics and Space Administration) MODerate Resolution Imaging Spectroradiometer (MODIS). The proposed algorithm first applies an adaptive Savitzky-Golay filter to smooth Enhanced Vegetation Index (EVI) time series derived from MODIS surface reflectance data. It then uses an iterative moving-window methodology to identify cropping cycles from the smoothed EVI time series. Comparison of results from our algorithm with national survey data at both the provincial and prefectural level in China show that the algorithm provides estimates of gross sown area that agree well with inventory data. Accuracy assessment comparing visually interpreted time series with algorithm results for a random sample of agricultural areas in China indicates an overall accuracy of 91.0% for three classes defined based on the number of cycles observed in EVI time series. The algorithm therefore appears to provide a straightforward and efficient method for mapping cropping intensity from MODIS time series data

Time tracking of different cropping patterns using Landsat images under different agricultural systems during 1990-2050 in Cold China

Rapid cropland reclamation is underway in Cold China in response to increases in food demand, while the lack analyses of time series cropping pattern mappings limits our understanding of the acute transformation process of cropland structure and associated environmental effects. The Cold China contains different agricultural systems (state and private farming), and such systems could lead to different cropping patterns. So far, such changes have not been revealed yet. Based on the Landsat images, this study tracked cropping information in five-year increments (1990-1995, 1995-2000, 2000-2005, 2005-2010, and 2010-2015) and predicted future patterns for the period of 2020-2050 under different agricultural systems using developed method for determining cropland patterns. The following results were obtained: The available time series of Landsat images in Cold China met the requirements for long-term cropping pattern studies, and the developed method exhibited high accuracy (over 91%) and obtained precise spatial information. A new satellite evidence was observed that cropping patterns significantly differed between the two farm types, with paddy field in state farming expanding at a faster rate (from 2.66 to 68.56%) than those in private farming (from 10.12 to 34.98%). More than 70% of paddy expansion was attributed to the transformation of upland crop in each period at the pixel level, which led to a greater loss of upland crop in state farming than private farming (9505.66 km(2) vs. 2840.29 km(2)) during 1990-2015. Rapid cropland reclamation is projected to stagnate in 2020, while paddy expansion will continue until 2040 primarily in private farming in Cold China. This study provides new evidence for different land use change pattern mechanisms between different agricultural systems, and the results have significant implications for understanding and guiding agricultural system development

Northward expansion of paddy rice in northeastern Asia during 2000-2014.

Paddy rice in monsoon Asia plays an important role in global food security and climate change. Here we documented annual dynamics of paddy rice areas in the northern frontier of Asia, including Northeastern (NE) China, North Korea, South Korea, and Japan, from 2000-2014 through analysis of satellite images. The paddy rice area has increased by 120% (2.5 to 5.5 million ha) in NE China, in comparison to a decrease in South Korea and Japan, and the paddy rice centroid shifted northward from 41.16 °N to 43.70 °N (~310 km) in this period. Market, technology, policy, and climate together drove the rice expansion in NE China. The increased use of greenhouse nurseries, improved rice cultivars, agricultural subsidy policy, and a rising rice price generally promoted northward paddy rice expansion. The potential effects of large rice expansion on climate change and ecological services should be paid more attention in the future

Using satellite remote sensing and hydrologic modeling to improve understanding of crop management and agricultural water use at regional to global scales.

Thesis (Ph. D.)--Boston UniversityCroplands are essential to human welfare. In the coming decades , croplands will experience substantial stress from climate change, population growth, changing diets, urban expansion, and increased demand for biofuels. Food security in many parts of the world therefore requires informed crop management and adaptation strategies. In this dissertation, I explore two key dimensions of crop management with significant potential to improve adaptation pathways: irrigation and crop calendars. Irrigation, which is widely used to boost crop yields, is a key strategy for adapting to changes in drought frequency and duration. However, irrigation competes with household, industrial, and environmental needs for freshwa t er r esources. Accurate information regarding irrigation patterns is therefore required to develop strategies that reduce unsustainable water use. To address this need, I fused information from remote sensing, climate datasets, and crop inventories to develop a new global database of rain-fed, irrigated, and paddy croplands. This database describes global agricultural water management with good realism and at higher spatial resolution than existing maps. Crop calendar management helps farmers to limit crop damage from heat and moisture stress. However, global crop calendar information currently lacks spatial and temporal detail. In the second part of my dissertation I used remote sensing to characterize global cropping patterns annually, from 2001-2010, at 0.08 degree spatial resolution. Comparison of this new dataset with existing sources of crop calendar data indicates that remote sensing is able to correct substantial deficiencies in available data sources. More importantly, the database provides previously unavailable information related to year-to-year variability in cropping patterns. Asia, home to roughly one half of the Earth's population, is expected to experience significant food insecurity in coming decades. In the final part of my dissertation, I used a water balance model in combination with the data sets described above to characterize the sensitivity of agricultural water use in Asia to crop management. Results indicate that water use in Asia depends strongly on both irrigation and crop management, and that previous studies underestimate agricultural water use in this region. These results support policy development focused on improving the resilience of agricultural systems in Asia

A low-cost rice mapping remote sensing based algorithm

Egypt faces a great challenge, limited water resources and increasing water demand. The agriculture sector consumes about 83% of the available water resources. The main water-consuming crop planted in summer is rice. Thus for any better water resources management, rice mapping is required. Remote sensing can be utilized for rice mapping. This will potentially save money and effort. The most differentiating feature of rice is being flooded in the transplanting period. Xiao (2005) developed a rice mapping algorithm by studying the dynamics of three vegetation indices, the Land surface water index (LSWI), the normalized difference vegetation index (NDVI), and the Enhanced vegetation Index (EVI). The key assumption is that a moisture sensitive index, as LSWI, will capture the flooding of rice and will temporal lily exceeds or approaches NDVI, or EVI, thus signaling rice transplanting. Xiao utilized MODIS (500 m spatial resolution, twice a day temporal resolution) free satellite imagery. However, its coarse resolution combined with Egypt heterogeneous and fragmented land ownership raised the need for the algorithm modification. In the current research a low-cost rice-mapping algorithm was developed. The accuracy of rice mapping from MODIS satellite imagery was enhanced by making use of LANDSAT imagery. This was achieved by developing a novel decision tree classifier that classifies land cover into its four main classes namely: vegetation, desert, bare land or urban, and water utilizing LANDSAT imagery. The non-vegetation area is then used to refine the rice area calculated from MODIS. Another challenge of rice mapping from MODIS is that in rice fields the reflectance is a combination of water, vegetation, soil, and ditches thus not always the LSWI will exceed the EVI or the NDVI as proposed in the literature, but instead it will approach it in the transplanting period. In order to reflect this, a ∆-parameter was introduced. The adopted criteria for rice mapping was LSWI + ∆\u3e NDVI or LSWI + ∆\u3e EVI. The ∆-parameter was obtained as best fit for each rice-growing region. The ∆-parameter is different for EVI and NDVI. The ∆EVI for Kafrelsheikh and Dumyat was found to be 0.04. Daqehleya, Gharbeya and Sharqeya ∆-parameter was calculated as 0.05. While Behera governorate ∆-parameter was estimated to be 0.07. While ∆--NDVI parameter for KafrElsheikh was 0.174, for Dumyat was 0.178, for Sharqeya was 0.18, for Gharbeya was 0.197, for Behera was 0.23, and for Daqhleya the ∆- NDVI parameter was 0.155. The developed rice-mapping algorithm was applied to the Delta region in Egypt to predict the rice cultivated areas in the year 2009. The resultant rice areas map was validated using randomly selected points, and local knowledge of rice planting practices, against very high-resolution (60 cm) imagery. The overall accuracy of the main land cover mapping was 90%. The rice areas map and probable transplanting dates conforms to local knowledge of rice planting practices. The results of this study indicate that the developed rice-mapping algorithm can be applied as an economic way for rice area mapping on a timely and frequent basis. However mapping rice fields prior to flooding would have been revealed more information for water management. More research should be directed to the early mapping of rice transplanting in the future

Spatio-temporal Analysis of Agriculture in the Vietnamese Mekong Delta using MODIS Imagery

New methodologies using MODIS time‒series imagery were developed for revealing spatio‒temporal changes of agricultural environments and land use patterns in the Vietnamese Mekong Delta. The following methodologies were proposed:a Wavelet based Filter for Crop Phenology (WFCP), a Wavelet‒based fi lter for evaluating the spatial distribution of Cropping Systems (WFCS), and a Wavelet‒based fi lter for detecting spatio‒temporal changes in Flood Inundation(WFFI). The WFCP algorithm involves smoothing the temporal profi le of the Enhanced Vegetation Index (EVI) using the wavelet transform approach. As a result of validation using the agricultural statistical data in Japan, it was shown that the WFCP was able to estimate rice growing stages, including transplanting date, heading date and harvesting date from the smoothed EVI data, with 9‒12 days accuracy(RMSE). The WFCS algorithm was developed for detecting rice‒cropping patterns in the Vietnamese Mekong delta based on WFCP. It was revealed that the spatial distribution of rice cropping seasons was characterized by both annual fl ood inundation around the upper region in the rainy season and salinity intrusion around the coastal region in the dry season. The WFFI algorithm was developed for estimating start and end dates of fl ood inundation by using time‒series Land Surface Water Index and EVI. Annual intensity of Mekong fl oods was evaluated from 2000 to 2004, at a regional scale. Applying a series of wavelet‒based methodologies to the MODIS data acquired from 2000 to 2006, it was confi rmed that the cropping season for the winter‒spring rice in the fl ood‒prone area fl uctuated depending on the annual change of fl ood scale. It was also confi rmed that the triple rice‒cropped area in the An Giang province expanded from 2000 to 2005, because the construction of a ring‒dike system and water‒resource infrastructure made it possible to sustain a third rice cropping season during the fl ood season. The proposed methodologies(WFCP, WFCS, WFFI) based on MODIS time‒series imagery made it clear that while the rice cropping in the Vietnamese Mekong Delta was quantitatively(annual fl ooding) and qualitatively(salinity intrusion) affected by water‒resource changes, there were some regions where the cultivation system was changed from double rice cropping to triple rice cropping because of the implementation of measures against fl ooding.日本の食料自給率 (2005年時の供給熱量ベース) は、40% と先進7カ国の中で最も低い。日本は、その食料海外依存度の高さから、世界的な食料価格の変動の影響を最も受け易い国と言える。近年の経済発展に伴う中国の大豆輸入量の増加や世界的なエネルギー政策の転換 (バイオエタノール政策) は、世界の穀物需給バランスを不安定にさせつつあり、世界的な問題となっている。さらに、地球温暖化による農業生産影響、増加し続ける世界人口、鈍化する穀物生産性を考えれば、世界の食料需給バランスが将来にわたって安定し続けると言うことはできないだろう。他方、食料増産・生産性向上を目的とした集約的農業の展開は、発展途上国の農業環境にさらなる負荷を与えるかもしれない。世界の食料生産と密接な関係にある日本は、自国の食料安全保障を議論する前提として、急速に変わり行く世界の農業生産現場やそれを取り巻く農業環境を客観的に理解し、世界の農業環境情報を独自の手法によって収集・整理する必要がある。そこで、筆者は、衛星リモートセンシング技術を活用することによって、地球規模の視点で、時間的・空間的な広がりを持って変わり行く農業生産活動とそれを取り巻く農業環境情報を把握・理解するための時系列衛星データ解析手法の確立を目指すこととした。本研究では、インドシナ半島南端に位置するベトナム・メコンデルタを調査対象領域とした。ベトナムは、タイに次ぐ世界第2位のコメ輸出国であり、その輸出米の9割近くが、ベトナム・メコンデルタで生産されたものである。筆者は、ベトナム・メコンデルタを世界の食料安全保障を考える上で重要な食料生産地帯の一つであると考え、本地域における農業環境及び土地利用パターンの時空間変化を明らかにするためのMODIS データを用いた新たな時系列解析手法の開発を行った。　本研究において提案する時系列解析手法は、次の三つである。1. Wavelet‒based Filter for Crop Phenology (WFCP) ,2. Wavelet‒based Filter for evaluating the spatial distribution of Cropping System (WFCS) , 3. Wavelet‒based Filterfor detecting spatio‒temporal changes in Flood Inundation (WFFI) . WFCP は、時系列植生指数 (EVI) を平滑化するためにウェーブレット変換手法を利用しており、日本の農業統計データを用いた検証結果から、水稲生育ステージ (田植日、出穂日、収獲日) を約9－12日 (RMSE) の精度で推定可能であることが示された。WFCP を基に改良されたWFCS は、水稲作付パターンの年次把握を可能にし、ベトナムメコンデルタにおける水稲作付時期の空間分布が、上流部において毎年雨期に発生する洪水と沿岸部において乾季に発生する塩水遡上によって特徴づけられていることを明らかにした。WFFI は、時系列水指数 (LSWI) と植生指数 (EVI) から、湛水期間、湛水開始日・湛水終息日を広域把握し、メコン川洪水強度の年次変化を地域スケールで評価することを可能にする。そして、ウェーブレット変換を利用した一連の手法を、2000～2006年までのMODIS 時系列画像に適用することによって、メコンデルタ上流部の洪水常襲地帯において、冬春米の作付時期が、年次変化する洪水規模に依存していることを明らかにした。また、An Giang 省において、堤防建設 (輪中) や水利施設の建設によって、洪水期における水稲三期作が可能になった地域が、2000～2005年にかけて拡大していることを明らかにした。本研究で提案したMODIS 時系列画像を利用した時系列解析手法 (WFCP、WFCS、WFFI) によって、ベトナムメコンデルタにおける水稲生産が水資源の量的 (洪水) ・質的 (塩水遡上) 変動影響を受ける一方、洪水対策の実施によって、栽培体系を二期作から三期作に変更している地域があることを明らかにした

Combining remote sensing and ground census data to develop new maps of the distribution of rice agriculture in China

Large-scale assessments of the potential for food production and its impact on biogeochemical cycling require the best possible information on the distribution of cropland. This information can come from ground-based agricultural census data sets and/or spaceborne remote sensing products, both with strengths and weaknesses. Official cropland statistics for China contain much information on the distribution of crop types, but are known to significantly underestimate total cropland areas and are generally at coarse spatial resolution. Remote sensing products can provide moderate to fine spatial resolution estimates of cropland location and extent, but supply little information on crop type or management. We combined county-scale agricultural census statistics on total cropland area and sown area of 17 major crops in 1990 with a fine-resolution land-cover map derived from 1995–1996 optical remote sensing (Landsat) data to generate 0.5° resolution maps of the distribution of rice agriculture in mainland China. Agricultural census data were used to determine the fraction of crop area in each 0.5° grid cell that was in single rice and each of 10 different multicrop paddy rice rotations (e.g., winter wheat/rice), while the remote sensing land-cover product was used to determine the spatial distribution and extent of total cropland in China. We estimate that there were 0.30 million km2 of paddy rice cropland; 75% of this paddy land was multicropped, and 56% had two rice plantings per year. Total sown area for paddy rice was 0.47 million km2. Paddy rice agriculture occurred on 23% of all cultivated land in China

Mapping of multitemporal rice (Oryza sativa L.) growth stages using remote sensing with multi-sensor and machine learning : a thesis dissertation presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Earth Science at Massey University, Manawatū, New Zealand

Figure 2.1 is adapted and re-used under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.Rice (Oryza Sativa) plays a pivotal role in food security for Asian countries, especially in Indonesia. Due to the increasing pressure of environmental changes, such as land use and climate, rice cultivation areas need to be monitored regularly and spatially to ensure sustainable rice production. Moreover, timely information of rice growth stages (RGS) can lead to more efficient of inputs distribution from water, seed, fertilizer, and pesticide. One of the efficient solutions for regularly mapping the rice crop is using Earth observation satellites. Moreover, the increasing availability of open access satellite images such as Landsat-8, Sentinel-1, and Sentinel-2 provides ample opportunities to map continuous and high-resolution rice growth stages with greater accuracy. The majority of the literature has focused on mapping rice area, cropping patterns and relied mainly on the phenology of vegetation. However, the mapping process of RGS was difficult to assess the accuracy, time-consuming, and depended on only one sensor. In this work, we discuss the use of machine learning algorithms (MLA) for mapping paddy RGS with multiple remote sensing data in near-real-time. The study area was Java Island, which is the primary rice producer in Indonesia. This study has investigated: (1) the mapping of RGS using Landsat-8 imagery and different MLAs, and their rigorous performance was evaluated by conducting a multitemporal analysis; (2) the temporal consistency of predicting RGS using Sentinel-2, MOD13Q1, and Sentinel-1 data; (3) evaluating the correlation of local statistics data and paddy RGS using Sentinel-2, PROBA-V, and Sentinel-1 with MLAs. The ground truth datasets were collected from multi-year web camera data (2014-2016) and three months of the field campaign in different regions of Java (2018). The study considered the RGS in the analysis to be vegetative, reproductive, ripening, bare land, and flooding, and MLAs such as support vector machines (SVMs), random forest (RF), and artificial neural network (ANN) were used. The temporal consistency matrix was used to compare the classification maps within three sensor datasets (Landsat-8 OLI, Sentinel-2, and Sentinel-2, MOD13Q1, Sentinel-1) and in four periods (5, 10, 15, 16 days). Moreover, the result of the RGS map was also compared with monthly data from local statistics within each sub-district using cross-correlation analysis. The result from the analysis shows that SVM with a radial base function outperformed the RF and ANN and proved to be a robust method for small-size datasets (< 1,000 points). Compared to Sentinel-2, Landsat-8 OLI gives less accuracy due to the lack of a red-edge band and larger pixel size (30 x 30 m). Integration of Sentinel-2, MOD13Q1, and Sentinel-1 improved the classification performance and increased the temporal availability of cloud-free maps. The integration of PROBA-V and Sentinel-1 improved the classification accuracy from the Landsat-8 result, consistent with the monthly rice planting area statistics at the sub-district level. The western area of Java has the highest accuracy and consistency since the cropping pattern only relied on rice cultivation. In contrast, less accuracy was noticed in the eastern area because of upland rice cultivation due to limited irrigation facilities and mixed cropping. In addition, the cultivation of shallots to the north of Nganjuk Regency interferes with the model predictions because the cultivation of shallots resembles the vegetative phase due to the water banks. One future research idea is the auto-detection of the cropping index in the complex landscape to be able to use it for mapping RGS on a global scale. Detection of the rice area and RGS using Google Earth Engine (GEE) can be an action plan to disseminate the information quickly on a planetary scale. Our results show that the multitemporal Sentinel-1 combined with RF can detect rice areas with high accuracy (>91%). Similarly, accurate RGS maps can be detected by integrating multiple remote sensing (Sentinel-2, Landsat-8 OLI, and MOD13Q1) data with acceptable accuracy (76.4%), with high temporal frequency and lower cloud interference (every 16 days). Overall, this study shows that remote sensing combined with the machine learning methodology can deliver information on RGS in a timely fashion, which is easy to scale up and consistent both in time and space and matches the local statistics. This thesis is also in line with the existing rice monitoring projects such as Crop Monitor, Crop Watch, AMIS, and Sen4Agri to support disseminating information over a large area. To sum up, the proposed workflow and detailed map provide a more accurate method and information in near real-time for stakeholders, such as governmental agencies against the existing mapping method. This method can be introduced to provide accurate information to rice farmers promptly with sufficient inputs such as irrigation, seeds, and fertilisers for ensuring national food security from the shifting planting time due to climate change

Paddy field classification with MODIS-terra multi-temporal image transformation using phenological approach in Java Island

This paper presents the paddy field classification model using the approach based on periodic plant life cycle events and how these elevations in climate as well as habitat factors, such as elevation. The data used are MODIS-Terra two tiles of H28v09 and H29v09 of 2016, consist of 46 series of 8-daily data, with 500 meter resolution in Java region. The paddy field classification method based on the phenological model is done by Maximum Likelihood on the transformed annual multi-temporal image of the reflectance data, index data, and the combination of reflectance and index data. The results of the study showed that, with the reference of the Paddy Field Map from the Ministry of Agriculture (MoA), the overall accuracies of the paddy field classification results using the combination of reflectance and index data provide the highest (85.4%) among the reflectance data (83.5%) and index data (81.7%). The accuracy levels were varied; these depend on the slope and the types of paddy fields. Paddy fields on the slopes of 0-2% could be well identified by MODIS-Terra data, whereas it was difficult to identify the paddy fields on the slope &gt;2%. Rain-fed lowland paddy field type has a lower user accuracy than irrigated paddy fields. This study also performed correlation (r2) between the analysis results and the statistical data based on district and provincial boundaries were &gt;0.85 and &gt;0.99 respectively. These correlations were much higher than the previous study results, which reached 0.49-0.65 (hilly-flat areas of county-level), and 0.80-0.88 (hilly-flat areas of provincial level) for China, and reached 0.44 for Indonesia

The Economics of Desertification, Land Degradation, and Drought; Toward an Integrated Global Assessment

Land degradation has not been comprehensively addressed at the global level or in developing countries. A suitable economic framework that could guide investments and institutional action is lacking. This study aims to overcome this deficiency and to provide a framework for a global assessment based on a consideration of the costs of action versus inaction regarding desertification, land degradation, and drought (DLDD). Most of the studies on the costs of land degradation (mainly limited to soil erosion) give cost estimates of less than 1 percent up to about 10 percent of the agricultural gross domestic product (GDP) for various countries worldwide. But the indirect costs of DLDD on the economy (national income), as well as their socioeconomic consequences (particularly poverty impacts), must be accounted for, too. Despite the numerous challenges, a global assessment of the costs of action and inaction against DLDD is possible, urgent, and necessary. This study provides a framework for such a global assessment and provides insights from some related country studies.Agricultural Finance, Crop Production/Industries, Environmental Economics and Policy, Land Economics/Use, Resource /Energy Economics and Policy,