Spectral matching techniques (SMTs) and automated cropland classification algorithms (ACCAs) for mapping croplands of Australia using MODIS 250-m time-series (2000–2015) data

Mapping croplands,including fallow areas,are an important measure to determine the quantity of food that is produced,where they are produced,and when they are produced(e.g.seasonality).Furthermore,croplands are known as water guzzlers by consuming anywhere between 70%and 90%of all human water use globally.Given these facts and the increase in global population to nearly 10 billion by the year 2050,the need for routine,rapid,and automated cropland mapping year-after-year and/or season-after-season is of great importance.The overarching goal of this study was to generate standard and routine cropland products,year-after-year,over very large areas through the use of two novel methods:(a)quantitative spectral matching techniques(QSMTs)applied at continental level and(b)rule-based Automated Cropland Classification Algorithm(ACCA)with the ability to hind-cast,now-cast,and future-cast.Australia was chosen for the study given its extensive croplands,rich history of agriculture,and yet nonexistent routine yearly generated cropland products using multi-temporal remote sensing.This research produced three distinct cropland products using Moderate Resolution Imaging Spectroradiometer(MODIS)250-m normalized difference vegetation index 16-day composite time-series data for 16 years:2000 through 2015.The products consisted of:(1)cropland extent/areas versus cropland fallow areas,(2)irrigated versus rainfed croplands,and(3)cropping intensities:single,double,and continuous cropping.An accurate reference cropland product(RCP)for the year 2014(RCP2014)produced using QSMT was used as a knowledge base to train and develop the ACCA algorithm that was then applied to the MODIS time-series data for the years 2000–2015.A comparison between the ACCA-derived cropland products(ACPs)for the year 2014(ACP2014)versus RCP2014 provided an overall agreement of 89.4%(kappa=0.814)with six classes:(a)producer’s accuracies varying between 72%and 90%and(b)user’s accuracies varying between 79%and 90%.ACPs for the individual years 2000–2013 and 2015(ACP2000–ACP2013,ACP2015)showed very strong similarities with several other studies.The extent and vigor of the Australian croplands versus cropland fallows were accurately captured by the ACCA algorithm for the years 2000–2015,thus highlighting the value of the study in food security analysis.

Mapping rice-fallow cropland areas for short-season grain legumes intensification in South Asia using MODIS 250 m time-series data

The goal of this study was to map rainfed and irrigated rice-fallow cropland areas across South Asia,using MODIS 250 m time-series data and identify where the farming system may be intensified by the inclusion of a short-season crop during the fallow period.Rice-fallow cropland areas are those areas where rice is grown during the kharif growing season(June–October),followed by a fallow during the rabi season(November–February).These cropland areas are not suitable for growing rabi-season rice due to their high water needs,but are suitable for a short-season(≤3 months),low water-consuming grain legumes such as chickpea(Cicer arietinum L.),black gram,green gram,and lentils.Intensification(double-cropping)in this manner can improve smallholder farmer’s incomes and soil health via rich nitrogen-fixation legume crops as well as address food security challenges of ballooning populations without having to expand croplands.Several grain legumes,primarily chickpea,are increasingly grown across Asia as a source of income for smallholder farmers and at the same time providing rich and cheap source of protein that can improve the nutritional quality of diets in the region.The suitability of rainfed and irrigated rice-fallow croplands for grain legume cultivation across South Asia were defined by these identifiers:(a)rice crop is grown during the primary(kharif)crop growing season or during the north-west monsoon season(June–October);(b)same croplands are left fallow during the second(rabi)season or during the south-east monsoon season(November–February);and(c)ability to support low water-consuming,short-growing season(≤3 months)grain legumes(chickpea,black gram,green gram,and lentils)during rabi season.Existing irrigated or rainfed crops such as rice or wheat that were grown during kharif were not considered suitable for growing during the rabi season,because the moisture/water demand of these crops is too high.The study established cropland classes based on the every 16-day 250 m normalized difference vegetation index(NDVI)time series for one year(June 2010–May 2011)of Moderate Resolution Imaging Spectroradiometer(MODIS)data,using spectral matching techniques(SMTs),and extensive field knowledge.Map accuracy was evaluated based on independent ground survey data as well as compared with available sub-national level statistics.The producers’and users’accuracies of the cropland fallow classes were between 75%and 82%.The overall accuracy and the kappa coefficient estimated for rice classes were 82%and 0.79,respectively.The analysis estimated approximately 22.3 Mha of suitable rice-fallow areas in South Asia,with 88.3%in India,0.5%in Pakistan,1.1%in Sri Lanka,8.7%in Bangladesh,1.4%in Nepal,and 0.02%in Bhutan.Decision-makers can target these areas for sustainable intensification of short-duration grain legumes.

A meta-analysis of global crop water productivity of three leading world crops(wheat,corn,and rice)in the irrigated areas over three decades

The overarching goal of this study was to perform a comprehensive metaanalysis of irrigated agricultural Crop Water Productivity(CWP)of the world’s three leading crops:wheat,corn,and rice based on three decades of remote sensing and non-remote sensing-based studies.Overall,CWP data from 148 crop growing study sites(60 wheat,43 corn,and 45 rice)spread across the world were gathered from published articles spanning 31 different countries.There was overwhelming evidence of a significant increase in CWP with an increase in latitude for predominately northern hemisphere datasets.For example,corn grown in latitude 40–50°had much higher mean CWP(2.45 kg/m^(3))compared to mean CWP of corn grown in other latitudes such as 30–40°(1.67 kg/m^(3))or 20–30°(0.94 kg/m^(3)).The same trend existed for wheat and rice as well.For soils,none of the CWP values,for any of the three crops,were statistically different.However,mean CWP in higher latitudes for the same soil was significantly higher than the mean CWP for the same soil in lower latitudes.This applied for all three crops studied.For wheat,the global CWP categories were low(≤0.75 kg/m^(3)),medium(>0.75 to<1.10 kg/m^(3)),and high CWP(≥1.10 kg/m^(3)).For corn the global CWP categories were low(≤1.25 kg/m^(3)),medium(>1.25 to≤1.75 kg/m^(3)),and high(>1.75 kg/m^(3)).For rice the global CWP categories were low(≤0.70 kg/m^(3)),medium(>0.70 to≤1.25 kg/m^(3)),and high(>1.25 kg/m^(3)).USA and China are the only two countries that have consistently high CWP for wheat,corn,and rice.Australia and India have medium CWP for wheat and rice.India’s corn,however,has low CWP.Egypt,Turkey,Netherlands,Mexico,and Israel have high CWP for wheat.Romania,Argentina,and Hungary have high CWP for corn,and Philippines has high CWP for rice.All other countries have either low or medium CWP for all three crops.Based on data in this study,the highest consumers of water for crop production also have the most potential for water savings.These countries are USA,India,and China for wheat;USA,China,and Brazil for corn;India,China,and Pakistan for rice.For example,even just a 10%increase in CWP of wheat grown in India can save 6974 billion liters of water.This is equivalent to creating 6974 lakes each of 100 m^(3)in volume that leads to many benefits such as acting as‘water banks’for lean season,recreation,and numerous ecological services.This study establishes the volume of water that can be saved for each crop in each country when there is an increase in CWP by 10%,20%,and 30%.