A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform

© 2018 The Author(s) Mapping high resolution (30-m or better) cropland extent over very large areas such as continents or large countries or regions accurately, precisely, repeatedly, and rapidly is of great importance for addressing the global food and water security challenges. Such cropland extent products capture individual farm fields, small or large, and are crucial for developing accurate higher-level cropland products such as cropping intensities, crop types, crop watering methods (irrigated or rainfed), crop productivity, and crop water productivity. It also brings many challenges that include handling massively large data volumes, computing power, and collecting resource intensive reference training and validation data over complex geographic and political boundaries. Thereby, this study developed a precise and accurate Landsat 30-m derived cropland extent product for two very important, distinct, diverse, and large countries: Australia and China. The study used of eight bands (blue, green, red, NIR, SWIR1, SWIR2, TIR1, and NDVI) of Landsat-8 every 16-day Operational Land Imager (OLI) data for the years 2013–2015. The classification was performed by using a pixel-based supervised random forest (RF) machine learning algorithm (MLA) executed on the Google Earth Engine (GEE) cloud computing platform. Each band was time-composited over 4–6 time-periods over a year using median value for various agro-ecological zones (AEZs) of Australia and China. This resulted in a 32–48-layer mega-file data-cube (MFDC) for each of the AEZs. Reference training and validation data were gathered from: (a) field visits, (b) sub-meter to 5-m very high spatial resolution imagery (VHRI) data, and (c) ancillary sources such as from the National agriculture bureaus. Croplands versus non-croplands knowledge base for training the RF algorithm were derived from MFDC using 958 reference-training samples for Australia and 2130 reference-training samples for China. The resulting 30-m cropland extent product was assessed for accuracies using independent validation samples: 900 for Australia and 1972 for China. The 30-m cropland extent product of Australia showed an overall accuracy of 97.6% with a producer's accuracy of 98.8% (errors of omissions = 1.2%), and user's accuracy of 79% (errors of commissions = 21%) for the cropland class. For China, overall accuracies were 94% with a producer's accuracy of 80% (errors of omissions = 20%), and user's accuracy of 84.2% (errors of commissions = 15.8%) for cropland class. Total cropland areas of Australia were estimated as 35.1 million hectares and 165.2 million hectares for China. These estimates were higher by 8.6% for Australia and 3.9% for China when compared with the traditionally derived national statistics. The cropland extent product further demonstrated the ability to estimate sub-national cropland areas accurately by providing an R2 value of 0.85 when compared with province-wise cropland areas of China. The study provides a paradigm-shift on how cropland maps are produced using multi-date remote sensing. These products can be browsed at www.croplands.org and made available for download at NASA's Land Processes Distributed Active Archive Center (LP DAAC) https://www.lpdaac.usgs.gov/node/1282

Hyperspectral Remote Sensing for Terrestrial Applications

Remote sensing data are considered hyperspectral when the data are gathered from numerous wavebands, contiguously over an entire range of the spectrum (e.g., 400–2500 nm). Goetz (1992) defines hyperspectral remote sensing as “The acquisition of images in hundreds of registered, contiguous spectral bands such that for each picture element of an image it is possible to derive a complete reflectance spectrum.” However, Jensen (2004) defines hyperspectral remote sensing as “The simultaneous acquisition of images in many relatively narrow, contiguous and/or non contiguous spectral bands throughout the ultraviolet, visible, and infrared portions of the electromagnetic spectrum.”..

Agricultural cropland extent and areas of South Asia derived using Landsat satellite 30-m time-series big-data using random forest machine learning algorithms on the Google Earth Engine cloud

The South Asia (India, Pakistan, Bangladesh, Nepal, Sri Lanka and Bhutan) has a staggering 900 million people (~43% of the population) who face food insecurity or severe food insecurity as per United Nations, Food and Agriculture Organization’s (FAO) the Food Insecurity Experience Scale (FIES). The existing coarse-resolution (≥250-m) cropland maps lack precision in geo-location of individual farms and have low map accuracies. This also results in uncertainties in cropland areas calculated fromsuch products. Thereby, the overarching goal of this study was to develop a high spatial resolution (30-m or better) baseline cropland extent product of South Asia for the year 2015 using Landsat satellite time-series big-data and machine learning algorithms (MLAs) on the Google Earth Engine (GEE) cloud computing platform. To eliminate the impact of clouds, 10 time-composited Landsat bands (blue, green, red, NIR, SWIR1, SWIR2, Thermal, EVI, NDVI, NDWI) were derived for each of the three timeperiods over 12 months (monsoon: Days of the Year (DOY) 151–300; winter: DOY 301–365 plus 1–60; and summer: DOY 61–150), taking the every 8-day data from Landsat-8 and 7 for the years 2013–2015, for a total of 30-bands plus global digital elevation model (GDEM) derived slope band. This 31-band mega-file big data-cube was composed for each of the five agro-ecological zones (AEZ’s) of South Asia and formed a baseline data for image classification and analysis. Knowledgebase for the Random Forest (RF) MLAs were developed using spatially well spread-out reference training data (N = 2179) in five AEZs. The classification was performed on GEE for each of the five AEZs using well-established knowledge-base and RF MLAs on the cloud. Map accuracies were measured using independent validation data (N = 1185). The survey showed that the South Asia cropland product had a producer’s accuracy of 89.9% (errors of omissions of 10.1%), user’s accuracy of 95.3% (errors of commission of 4.7%) and an overall accuracy of 88.7%. The National and sub-national (districts) areas computed from this cropland extent product explained 80-96% variability when compared with the National statistics of the South Asian Countries. The full-resolution imagery can be viewed at full-resolution, by zooming-in to any location in South Asia or the world, atwww.croplands. org and the cropland products of South Asia downloaded from The Land Processes Distributed Active Archive Center (LP DAAC) of National Aeronautics and Space Administration (NASA) and the United States Geological Survey (USGS): https://lpdaac.usgs.gov/products/gfsad30saafgircev001/

Hyperspectral Remote Sensing of Vegetation and Agricultural Crops

There are now over 40 years of research in hyperspectral remote sensing (or imaging spectroscopy) of vegetation and agricultural crops (Thenkabail et al., 2011a). Even though much of the early research in hyperspectral remote sensing was overwhelmingly focused on minerals, now there is substantial literature in characterization, monitoring, modeling, and mapping of vegetation and agricultural crops using ground-based, platform-mounted, airborne, Unmanned Aerial Vehicle (UAV) mounted, and spaceborne hyperspectral remote sensing (Swatantran et al., 2011; Atzberger, 2013; Middleton et al., 2013; Schlemmer et al., 2013; Thenkabail et al., 2013; Udelhoven et al., 2013; Zhang et al., 2013). The state-of-the-art in hyperspectral remote sensing of vegetation and agriculture shows significant enhancement over conventional remote sensing, leading to improved and targeted modeling and mapping of specific agricultural characteristics such as: (a) biophysical and biochemical quantities (Galvão, 2011; Clark and Roberts, 2012), (b) crop type\species (Thenkabail et al., 2013), (c) management and stress factors such as nitrogen deficiency, moisture deficiency, or drought conditions (Delalieux et al., 2009; Gitelson, 2013; Slonecker et al., 2013), and (d) water use and water productivities (Thenkabail et al., 2013). At the same time, overcoming Hughes’ phenomenon or curse of dimensionality of data and data redundancy (Plaza et al., 2009) is of great importance to make rapid advances in a much wider utilization of hyperspectral data. This is because, for a specific application, a large number of hyperspectral bands are redundant (Thenkabail et al., 2013). Selecting the relevant bands will require the use of data mining techniques (Burger and Gowen, 2011) to focus on utilizing the optimal or best ones to maximize the efficiency of data use and reduce unnecessary computing..

Relating Trends in Streamflow to Anthropogenic Influences: A Case Study of Himayat Sagar Catchment, India

Catchment development has been identified as a potentially major cause of streamflow change in many river basins in India. This research aims to understand changes in the Himayat Sagar catchment (HSC), India, where significant reductions in streamflow have been observed. Rainfall and streamflow trend analysis for 1980–2004 shows a decline in streamflow without significant changes in rainfall. A regression model was used to quantify changes in the rainfall-runoff relationship over the study period. We relate these streamflow trends to anthropogenic changes in land use, groundwater abstraction and watershed development that lead to increased ET (Evapotranspiration) in the catchment. Streamflow has declined at a rate of 3.6 mm/y. Various estimates of changes in evapotranspiration/irrigation water use were made. Well inventories suggested an increase of 7.2 mm/y in groundwater extractions whereas typical irrigation practices suggests applied water increased by 9.0 mm/y, while estimates of evapotranspiration using remote sensing data showed an increasing rate of 4.1 mm/y. Surface water storage capacity of various small watershed development structures increased by 2 mm over 7 years. It is concluded that the dominant hydrological process responsible for streamflow reduction is the increase in evapotranspiration associated with irrigation development, however, most of the anthropogenic changes examined are interrelated and occurred simultaneously, making separating out individual impacts very difficult

Inland Valley Wetland Cultivation and Preservation for Africa’s Green and Blue Revolution Using Multi-Sensor Remote Sensing

Africa is the second largest continent after Asia with a total area of 30.22 million km2 (including the adjacent islands). It has great rivers such as the River Nile, which is the longest in the world and flows a distance of 6650 km, and the River Congo, which is the deepest in the world, as well as the second largest in the world in terms of water availability. Yet, Africa also has vast stretches of arid, semiarid, and desert lands with little or no water. Further, Africa’s population is projected to increase by four times by the year 2100, reaching about four billion from the current population of little over one billion. Food insecurity and malnutrition are already highest in Africa (Heidhues et al., 2004) and the challenge of meeting the food security needs of the fastest-growing continent in the twenty-first century is daunting. So, many solutions are thought of to ensure food security in Africa. These ideas include such measures as increasing irrigation in a continent that currently has just about 2% of the global irrigated areas (Thenkabail et al., 2009a, 2010), improving crop productivity (kg m−2), and increasing water productivity (kg m−3). However, an overwhelming proportion of Africa’s agriculture now takes place on uplands that have poor soil fertility and water availability (Scholes, 1990). Thereby, the interest in developing sustainable agriculture in Africa’s lowland wetlands, considered by some as the “new frontier” in agriculture, has swiftly increased in recent years. The lowland wetland systems include the big wetland systems that are prominent and widely recognized (Figure 9.1) as well as the less prominent, but more widespread, inland valley (IV) wetlands (Figures 9.2 through 9.8) that are all along the first to highest order river systems..

Monitoring of Spatio-temporal Dynamics of Rabi Rice Fallows in South-Asia using Remote Sensing

Cereals and grain legumes are the most important part of human diet and nutrition. The rural population of low income groups in dry land areas of South Asia depends on these staples. Expansion of grain legumes with improved productivity to cater the growing population’s nutritional security is of prime importance and need of the hour. Rice-fallows are best niche areas with residual moisture to grow short duration legumes there by achieving intensification. Identifying suitable areas for grain legumes and cereal grains is important in this region. In this context, the goal of this study was to map fallow lands followed by rainy season (kharif) rice cultivation or post rainy (rabi) fallows in rice growing environments between 2005 and 2015 using temporal moderate-resolution imaging Spectroradiometer (MODIS) data applying Spectral matching techniques. This study was conducted in South Asia where different rice eco-systems exist. MODIS 16-days normalized difference vegetation index (NDVI) at 250m spatial resolution and season wise intensive ground survey data were used to map rice systems and the fallows thereafter (rabi-fallows) in South Asia. The rice maps were validated with independent ground survey data and compared with available sub-national level statistics. Overall accuracy and kappa coefficient estimated for rice classes were 81.5% and 0.79 respectively with ground survey data. The derived physical rice area and irrigated areas were highly correlated with the sub-national statistics with R2 values of 84% at the district level for the year 2005-06 and 2015-16. Results clearly show that rice-fallows areas increased from 2005 to 2015. The results show spatial distribution of rice-fallows in South Asia which are identified as target domains for sustainable intensification of short duration grain legumes, fixing the soil nitrogen and increasing incomes of small holder farmers

Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 Validation 30 m V001

The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data of the globe for nominal year 2015 at 30 meter resolution. The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30 Validation (GFSAD30VAL) data product provides a thorough and independent accuracy assessment and validation of the cropland extent products produced for each of the seven regions. Each GFSAD30VAL shapefile contains information on sample locations, presence of cropland or no cropland, and the zones that were randomly selected for accuracy assessment across the globe

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

Automated cropland mapping of continental Africa using Google Earth Engine cloud computing

The automation of agricultural mapping using satellite-derived remotely sensed data remains a challenge in Africa because of the heterogeneous and fragmental landscape, complex crop cycles, and limited access to local knowledge. Currently, consistent, continent-wide routine cropland mapping of Africa does not exist, with most studies focused either on certain portions of the continent or at most a one-time effort at mapping the continent at coarse resolution remote sensing. In this research, we addressed these limitations by applying an automated cropland mapping algorithm (ACMA) that captures extensive knowledge on the croplands of Africa available through: (a) ground-based training samples, (b) very high (sub-meter to five-meter) resolution imagery (VHRI), and (c) local knowledge captured during field visits and/or sourced from country reports and literature. The study used 16-day time-series of Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) composited data at 250-m resolution for the entire African continent. Based on these data, the study first produced accurate reference cropland layers or RCLs (cropland extent/areas, irrigation versus rainfed, cropping intensities, crop dominance, and croplands versus cropland fallows) for the year 2014 that provided an overall accuracy of around 90% for crop extent in different agro-ecological zones (AEZs). The RCLs for the year 2014 (RCL2014) were then used in the development of the ACMA algorithm to create ACMA-derived cropland layers for 2014 (ACL2014). ACL2014 when compared pixel-by-pixel with the RCL2014 had an overall similarity greater than 95%. Based on the ACL2014, the African continent had 296 Mha of net cropland areas (260 Mha cultivated plus 36 Mha fallows) and 330 Mha of gross cropland areas. Of the 260 Mha of net cropland areas cultivated during 2014, 90.6% (236 Mha) was rainfed and just 9.4% (24 Mha) was irrigated. Africa has about 15% of the world’s population, but only about 6% of world’s irrigation. Net cropland area distribution was 95 Mha during season 1, 117 Mha during season 2, and 84 Mha continuous. About 58% of the rainfed and 39% of the irrigated were single crops (net cropland area without cropland fallows) cropped during either season 1 (January-May) or season 2 (June-September). The ACMA algorithm was deployed on Google Earth Engine (GEE) cloud computing platform and applied on MODIS time-series data from 2003 through 2014 to obtain ACMA-derived cropland layers for these years (ACL2003 to ACL2014). The results indicated that over these twelve years, on average: (a) croplands increased by 1 Mha/yr, and (b) cropland fallows decreased by 1 Mha/year. Cropland areas computed from ACL2014 for the 55 African countries were largely underestimated when compared with an independent source of census-based cropland data, with a root-mean-square error (RMSE) of 3.5 Mha. ACMA demonstrated the ability to hind-cast (past years), now-cast (present year), and forecast (future years) cropland products using MODIS 250-m time-series data rapidly, but currently, insufficient reference data exist to rigorously report trends from these results