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

High resolution Sentinel-2 crop type mapping 2018-2019 A case study in Ahmednagar district

Ahmednagar is the largest district of Maharashtra in terms of area and population. It lies in the central part of the state of Maharashtra which is having common boundaries with seven adjoining Districts. The total geographical area of the district is 17.41 lakh ha. The net cropped area is 12,56,500 ha, out of which an area of 3,30,000 ha. (26.27 %) is under canal (84,000 ha) and well irrigation. About 9,26,500 ha. (73.73 %) area is rain fed. The area under Kharif crops is 4,60,000 ha. (36.6 per cent) while 7,58,000 ha (60.32 per cent) area is under Rabi crops. A multiple cropping system is followed on 1,10,500 ha area. A total of 8.73 per cent area of the district is under forest. The climate of the district is hot and dry, on whole extremely genial and is characterized by a hot summer and general dryness during major part of the year except during south-west monsoon season. Ahmednagar district receives average 566 mm. rainfall. The major rainfall received during month of June to September. The average temperature ranges between 9 0c (during Dec.) to 41 0C (during April and May). The soil types of the district are broadly divided into four categories namely coarse shallow soil; medium black soil; deep black soil and reddish soil occupying about 38, 41, 13 and 8 percent of the cultivated area respectively. In the first two categories, soil moisture is the predominant limiting factor affecting productivity of crops particularly under rainfed condition. Godavari and Bhima are the major rivers in the district. Godavari river flows through the northern border of Ahmednagar district. Major Kharif crops grown in the district are Cotton, Maize, Bajra, Sugarcane, and Soybean and during Rabi season are Jowar, Wheat, Soybean and Pulses

Crop type mapping using high-resolution Sentinel-2 Satellite Data– A case study on Gujarat State

Mapping following products of Gujarat state using high-resolution Sentinel-2 satellite data 1. Crop type 2. Irrigated and Rainfed area Major Activities • Satellite imagery acquisition/ procurement and pre-processing (Sentinel-2, 10&20 m spatial resolution). • Field information (ground reference data) and farmer interviews at selected locations and collection of validation points. • Satellite Imagery analysis and interpretation for land use / land cover areas including irrigated and rainfed cultivated areas

NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 Southeast Asia 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 over Southeast and Northeast Asia for nominal year 2015 at 30 meter resolution (GFSAD30SEACE). 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 GFSAD30SEACE data product uses the pixel-based supervised classifiers Random Forest (RF) to retrieve cropland extent from a combination of Landsat 8 Operational Land Imager (OLI), Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and elevation derived from the Shuttle Radar Topography Mission (SRTM) Version 3 data products. Each GFSAD30SEACE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10⁰ by 10⁰ area

NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 Africa 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 over the continent of Africa for nominal year 2015 at 30 meter resolution (GFSAD30AFCE). 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 GFSAD30AFCE data product uses two pixel-based supervised classifiers—Random Forest (RF) and Support Vector Machine (SVM)—and one object-oriented classifier—Recursive Hierarchical Image Segmentation (RHSEG)—to retrieve cropland extent from a combination of Landsat 8 Operational Land Imager (OLI) and Sentinel-2 MultiSpectral Instrument (MSI) data and elevation derived from the Shuttle Radar Topography Mission (SRTM) Version 3 data products. Each GFSAD30AFCE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10⁰ by 10⁰ area

NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 Australia, New Zealand, China, Mongolia 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 over Australia, New Zealand, China, and Mongolia for nominal year 2015 at 30 meter resolution (GFSAD30AUNZCNMOCE). 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 GFSAD30AUNZCNMOCE data product uses the pixel-based supervised classifier Random Forest (RF) to retrieve cropland extent from a combination of Landsat 8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data. Each GFSAD30AUNZCNMOCE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10⁰ by 10⁰ area

Tungabhadra Left Bank Canal (TLBC) irrigation modernization: Detailed command area mapping using remote sensing, (November 2018 - March 2019)

Conventional surveys and assessments have merged with technology to help decision makers take timely action and prevent losses in agricultural production. Remote sensing is one such technology-driven tool that can produce accurate results economically. So is satellite imagery with high temporal and spatial resolutions specifically suited to agriculture. Given Karnataka state’s diverse agro-ecosystems, knowing the spatial distribution of such systems and their constraints to production is essential so that abiotic stresses like drought can be mapped and information disseminated to decision makers for contingency planning. The natural resource base of an area is instrumental in its agricultural development and also linked to improvement of livelihoods. Mapping the natural resource base and crop domains provides insights into the status of and trends in land and water utilization, development of water resources over time, and the impacts of cropping systems on water resource systems (Gumma et al. 2016a; Gumma et al. 2011a). Integrating remote sensing with spatial analyses like crop and economic models provides the much needed knowledge for decision making for resource-based planning of lower risk and profitable cropping systems, efficient allocation of water at basin and sub-basin levels and other crop management interventions in targeting technologies appropriate for different locations. Remote sensing provides such information in a time and cost effective manner by mapping major cropped areas using open datasets such as MODIS, LANDSAT, along with high resolution datasets from Indian remote sensing satellites, and sensors such as LISS IV and RiSAT-1 which provide reliable information on spatial distribution, acreage estimation and crop growth stages (during planting and harvesting)..

Pilot studies on GP Crop yield estimation using Technology (Kharif 2019) using SENTINEL- 2 satellite data (in Andhra Pradesh, Telangana and Odisha States (Five Districts)) for Groundnut, Chickpea, Maize and Rice

The Government of India plans to optimize Crop Cutting Experiments (CCEs) using different technologies including satellite derived metrics on crop performance and spatial variability to guide the selection and number of ground data sites. This requires the development of an approach for different crops for the different agro-climatic regions of India. The present study plans to develop an approach for following crops viz., Groundnut, Chickpea, Rice and Maize. The above crops will be studied in five districts of three states viz. Andhra Pradesh, Telangana and Odisha. The study will use comprehensive and existing environmental, weather and management data along with satellite derived crop spatial data. This information will be modelled using statistical optimization techniques to assess the optimal numbers of CCE’s that can be undertaken

NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 South Asia, Afghanistan, Iran 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 over South Asia, Afghanistan, and Iran for nominal year 2015 at 30 meter resolution (GFSAD30SAAFGIRCE). 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 GFSAD30SAAFGIRCE data product uses the pixel-based supervised classifier Random Forest (RF) to retrieve cropland extent from a combination of Landsat 8 Operational Land Imager (OLI) and elevation derived from the Shuttle Radar Topography Mission (SRTM) Version 3 data products. Each GFSAD30SAAFGIRCE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10⁰ by 10⁰ area