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

Assessment of MODIS spectral indices for determining rice paddy agricultural practices and hydroperiod

The aims of this study were to assess the dynamics of the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI(1) and NDWI(2)) and Shortwave Angle Slope Index (SASI) in relation to rice agricultural practices and hydroperiod, and (2) to assess the capability for these indices to detect phenometrics in rice under different flooding regimes

Mapping Irrigated and Rainfed Wheat Areas Using Multi-Temporal Satellite Data

Irrigation is crucial to agriculture in arid and semi-arid areas and significantly contributes to crop development, food diversity and the sustainability of agro-ecosystems. For a specific crop, the separation of its irrigated and rainfed areas is difficult, because their phenology is similar and therefore less distinguishable, especially when there are phenology shifts due to various factors, such as elevation and latitude. In this study, we present a simple, but robust method to map irrigated and rainfed wheat areas in a semi-arid region of China. We used the Normalized Difference Vegetation Index (NDVI) at a 30 × 30 m spatial resolution derived from the Chinese HJ-1A/B (HuanJing(HJ) means environment in Chinese) satellite to create a time series spanning the whole growth period of wheat from September 2010 to July 2011. The maximum NDVI and time-integrated NDVI (TIN) that usually exhibit significant differences between irrigated and rainfed wheat were selected to establish a classification model using a support vector machine (SVM) algorithm. The overall accuracy of the Google-Earth testing samples was 96.0%, indicating that the classification results are accurate. The estimated irrigated-to-rainfed ratio was 4.4:5.6, close to the estimates provided by the agricultural sector in Shanxi Province. Our results illustrate that the SVM classification model can effectively avoid empirical thresholds in supervised classification and realistically capture the magnitude and spatial patterns of rainfed and irrigated wheat areas. The approach in this study can be applied to map irrigated/rainfed areas in other regions when field observational data are available

Big Earth Data and Machine Learning for Sustainable and Resilient Agriculture

Big streams of Earth images from satellites or other platforms (e.g., drones and mobile phones) are becoming increasingly available at low or no cost and with enhanced spatial and temporal resolution. This thesis recognizes the unprecedented opportunities offered by the high quality and open access Earth observation data of our times and introduces novel machine learning and big data methods to properly exploit them towards developing applications for sustainable and resilient agriculture. The thesis addresses three distinct thematic areas, i.e., the monitoring of the Common Agricultural Policy (CAP), the monitoring of food security and applications for smart and resilient agriculture. The methodological innovations of the developments related to the three thematic areas address the following issues: i) the processing of big Earth Observation (EO) data, ii) the scarcity of annotated data for machine learning model training and iii) the gap between machine learning outputs and actionable advice. This thesis demonstrated how big data technologies such as data cubes, distributed learning, linked open data and semantic enrichment can be used to exploit the data deluge and extract knowledge to address real user needs. Furthermore, this thesis argues for the importance of semi-supervised and unsupervised machine learning models that circumvent the ever-present challenge of scarce annotations and thus allow for model generalization in space and time. Specifically, it is shown how merely few ground truth data are needed to generate high quality crop type maps and crop phenology estimations. Finally, this thesis argues there is considerable distance in value between model inferences and decision making in real-world scenarios and thereby showcases the power of causal and interpretable machine learning in bridging this gap.Comment: Phd thesi

The potential of satellite-observed crop phenology to enhance yield gap assessments in smallholder landscapes

Many of the undernourished people on the planet obtain their entitlements to food via agricultural-based livelihood strategies, often on underperforming croplands and smallholdings. In this context, expanding cropland extent is not a viable strategy for smallholders to meet their food needs. Therefore, attention must shift to increasing productivity on existing plots and ensuring yield gaps do not widen. Thus, supporting smallholder farmers to sustainably increase the productivity of their lands is one part of a complex solution to realizing universal food security. However, the information (e.g., location and causes of cropland underperformance) required to support measures to close yield gaps in smallholder landscapes are often not available. This paper reviews the potential of crop phenology, observed from satellites carrying remote sensing sensors, to fill this information gap. It is suggested that on a theoretical level phenological approaches can reveal greater intra-cropland thematic detail, and increase the accuracy of crop extent maps and crop yield estimates. However, on a practical level the spatial mismatch between the resolution at which crop phenology can be estimated from satellite remote sensing data and the scale of yield variability in smallholder croplands inhibits its use in this context. Similarly, the spatial coverage of remote sensing-derived phenology offers potential for integration with ancillary spatial datasets to identify causes of yield gaps. To reflect the complexity of smallholder cropping systems requires ancillary datasets at fine spatial resolutions which, often, are not available. This further precludes the use of crop phenology in attempts to unpick the causes of yield gaps. Research agendas should focus on generating fine spatial resolution crop phenology, either via data fusion or through new sensors (e.g., Sentinel-2) in smallholder croplands. This has potential to transform the applied use of remote sensing in this context

PhenoRice:A method for automatic extraction of spatio-temporal information on rice crops using satellite data time series

Agricultural monitoring systems require spatio-temporal information on widely cultivated staple crops like rice. More emphasis has been made on area estimation and crop detection than on the temporal aspects of crop cultivation, but seasonal and temporal information such as i) crop duration, ii) date of crop establishment and iii) cropping intensity are as important as area for understanding crop production. Rice cropping systems are diverse because genetic, environmental and management factors (G × E × M combinations) influence the spatio-temporal patterns of cultivation. We present a rule based algorithm called PhenoRice for automatic extraction of temporal information on the rice crop using moderate resolution hypertemporal optical imagery from MODIS. Performance of PhenoRice against spatially and temporally explicit reference information was tested in three diverse sites: rice-fallow (Italy), rice-other crop (India) and rice-rice (Philippines) systems. Regional product accuracy assessments showed that PhenoRice made a conservative, spatially representative and robust detection of rice cultivation in all sites (r2 between 0.75 and 0.92) and crop establishment dates were in close agreement with the reference data (r2 = 0.98, Mean Error = 4.07 days, Mean Absolute Error = 9.95 days, p < 0.01). Variability in algorithm performance in different conditions in each site (irrigated vs rainfed, direct seeding vs transplanting, fragmented vs clustered rice landscapes and the impact of cloud contamination) was analysed and discussed. Analysis of the maps revealed that cropping intensity and season length per site matched well with local information on agro-practices and cultivated varieties. The results show that PhenoRice is robust for deriving essential temporal descriptions of rice systems in both temperate and tropical regions at a level of spatial and temporal detail that is suitable for regional crop monitoring on a seasonal basis

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

Wavelet analysis of a Sentinel-2 time series to detect land use changes in agriculture in the Vega Alta of the Guadalquivir River: Cantillana case study (Seville)

Historically, the Vega Alta of the Guadalquivir River (southern Spain) has been an anthropized space. Over time, the dominance of latifundia agriculture has evolved towards more intensive citrus-based agriculture. In this study, wavelet algorithms applied to Sentinel-2 time series were used to determine both the expansion of citrus plantations and the level of intensification of these plantations within the municipality of Cantillana. Sentinel-2 provides comprehensive global coverage from March 2017 to the present. Our study applied a 90% power wavelet transformation for the creation of a wavelet-smoothed time series for four years of Sentinel-2 NDVI data. Based on the data, it can be stated that within our research region covering 5000 hectares of agricultural land, over a span of four years (2017 to 2020), more than 980 hectares of native vegetation and pasture were transformed into citrus orchards, giving rise, at the end of 2020, to a total area of 3250 ha. Analyzing unique spatial patterns within a wavelet-smoothed time series data is very useful for land management, as it allows land use changes to be controlled. For this reason, it becomes feasible to assess the reliability of the wavelet method using both remote sensing and GIS tools