Figure 1: A) Landsat-7 image Marlborough-Tasman. B) Two images for Canterbury

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1 Prject Objectives Irrigatin represents a significant cst f prductin t pastures, arable and hrticultural crps in New Zealand. It is estimated that acrss New Zealand sme 650,000 ha f pasture and crpping land are under irrigatin. Therefre, techniques t imprve irrigatin efficiency culd have substantial financial and envirnmental benefits t the whle cuntry. An efficient irrigatin strategy shuld supply just enugh water, at the right time, t achieve the desired yields and crp quality. Excess irrigatin cmes at a financial and envirnmental cst. T reslve these prblems, Plant & Fd Research has carried ut a number f irrigatin research prjects fr grapes, apples, kiwifruit and lives, and lcal cnsultants are nw using results frm this past research t imprve their irrigatin scheduling services. Nw the majr challenge t irrigatrs and regulatrs is translate this knwledge f plant water requirements int water applicatin fr fields/rchards with variable sils and crp cnditins. This variability results in sme parts f the field receiving excess irrigatin, while ther parts are inadequately supplied. Current irrigatin management ften use scheduling services, which generally use a limited number f sil misture (SM) mnitring devices (e.g. Neutrn Prbe, Aquaflex r ther), in cnjunctin with a knwledge f crp evaptranspiratin. Hwever, extending data cllected frm a single pint (0.1 m 3 ) in a field/rchard/vineyard t the whle prperty requires infrmatin abut the variability f plant water needs n that prperty. Satellite imaging technlgy enables farmers and rchardists t understand the spatial variability f crp water status better. Using satellite remte sensing data, this wrk investigated whether it is pssible t extraplate in situ SM data frm SM prbes spatially t cver the whle field reliably and ptentially als neighburing fields. Apprach This wrk was a prf-f-cncept study t imprve ur understanding f crp water use by cmbining histrical SM data frm selected sites, with SM data btained frm mdels using satellite imaging during the (Canterbury) and grwing seasns fr Marlbrugh and Tasman (Figure 1). The cmbinatin f land-based measurements with satellite infrmatin was used t extraplate water use based n existing SM measurements t: All areas within the mnitred blck that have different sil prperties and crp develpment Other crps arund the mnitred blck, allwing fr infrmatin n changes in sil prperties but als fr differences in crps and crp cnditins. Satellite infrmatin: A Figure 1: A) Landsat-7 image Marlbrugh-Tasman. B) Tw images fr Canterbury B

2 The selectin f satellite image dates was based n the fllwing criteria: Images had t be clud free ver area f interest High number f available sil misture data pints The Landsat-7 satellite rbits nly every 16 days. Mst irrigatin mnitring is dne n a weekly basis (ut f sync), shwing the need fr daily measurements, r the use f data-lggers as fund in the Envirscan and Aquaflex sil misture measurement devices. Sil misture infrmatin: Sil misture infrmatin was btained frm bth Tasman/Marlbrugh (Fruitin Hrticulture) and Canterbury (Hydr Services). This infrmatin was cmpared with the eleaf satellite remte sensing images (i.e. visible, near infrared, thermal-infrared) in cnjunctin with mathematical mdels t determine actual crp SM and evaptranspiratin (ET). Sils infrmatin As very few f the grwers had infrmatin abut the sils r their sil water-hlding capacities, in mst cases this infrmatin had t be teased ut f infrmatin available frm the Natinal Sils Database ( Climate infrmatin Similarly, very few farms had access t climate data, which therefre had t be surced frm the clsest meterlgical statin (NIWA) r data were btained frm NIWA s virtual climate netwrk ( 2.1 The SEBAL mdel The Surface Energy Balance Algrithm fr Land (SEBAL) is an image-prcessing mdel that calculates the actual (ET) and ptential evaptranspiratin rates (ET pt ) as well as ther energy exchanges between land and atmsphere (Bastiaanssen et al. 1998a; Bastiaanssen et al. 1998b). The key input data fr SEBAL cnsist f raster values f spectral radiance in the visible, near infrared and thermal infra red part spectrum. Hence, the measurements are unique fr every pixel. The primary basis fr the SEBAL mdel is the surface energy balance (Figure 2). Figure 2: Surface Energy Balance fr different types f surfaces. 2.2 Triangle methd The Triangle methd, which was develped in the 1990s (Carlsn et al. 1994; Carlsn et al. 1995; Price 1990), is an apprach based n an interpretatin f the (satellite) image distributin in surface radiant temperature (T s ) and fractinal vegetatin cver (Fr). When a sufficient large number f pixels are present, the shape f all the pixels tgether resembles a triangle. A triangle shape emerges because the range f surface radiant temperature

3 decreases as the vegetatin cver increases, its narrw vertex (tp pint f the graph) attesting t the narrw range f surface radiant temperature ver dense vegetatin (Carlsn 2007). A B Figure 3 shws an example f SM infrmatin f all pixels within ne image. In ttal, SM data were cllected frm 32 blcks n three different dates. A Figure 3: Each black dt represents ne pixel within the image, while nly the big blue dts represent the pixels with measured sil misture. M = surface sil misture (M = 1 in wet sil and M =0 in dry sil), NDVI = Nrmalized Difference Vegetatin Index (NDVI_ = bare sil and NDVI_s = full vegetatin cver). A) Scatter plt f NDVI versus surface temperature taken frm a Landsat image. B) as in A, but f fractinal vegetatin cver (Fr, equatin 10) versus scaled surface temperature (T*). The main findings frm this prject Despite sme current shrtcmings in the applicatin f the methds (See 6: What wuld we d differently ), this was nly the first knwn study f this kind t have been undertaken in New Zealand. There are sme psitive lessns t take frm this wrk, especially if the bserved errrs can be crrected in the future. Many f the ptential causes f the pr relatinships between SM measurements and satellite image analysis can be easily reslved fr future wrk in this area: A large advantage f using the Landsat-7 images is the high reslutin f pixels in bth visible and thermal wavelengths. Hwever, this satellite is quite ld and will be replaced very sn. This wrk was started as a pilt study with a restricted budget, using histrical satellite data and histrical SM data frm existing installed prbes. Retrspectively, this created a number f challenges: Many f the existing SM prbes were in inapprpriate psitins in the field (e.g. alngside rads, under rchard nets). Prbes were fund nly in irrigated fields, reducing the spread f SMs used in this trial, which severely reduced the ptential fr gd crrelatins t be fund. B

4 Only a few fields were measured fr SM n a daily basis, which caused the 16- day cycle satellite verpasses t be ut f sync with mst weekly SM measurements. SM measurements were nt made at a cnsistent depth in relatin t the rting depth f the crp. Crp grwth stages (and therefre their water needs and transpiratin) were insufficiently identified. Insufficient infrmatin abut sil water-hlding capacity was available fr mst blcks studied. These issues can easily be addressed in any future wrk by using live satellite images, ensuring that SM measurements are made n the day f the satellite verpass, and that prbes are psitined centrally in the measured blcks as in rads, tracks, ditches, gravel pits, etc and s n. What is needed nw is further in depth and detailed research (as described abve) in rder t imprve the mdels that describe the relatinship between the satellite images and the in situ SM. These will assist the grwer by prviding rbust data t allw him t irrigate the ther blcks n his prperty accurately, withut having t install SM prbes in every individual irrigated blck. Differences this prject made t yur grup / cmmunity f interest / industry Lking at the satellite images already gives grwers a gd impressin f the relative misture f all the individual blcks cmpared with the blck that actually cntains the SM prbe. Irrigatin reduces measurable SM differences in fields and makes them less, if at all, visible in the images. Hwever, it amplifies the differences between fields with different irrigatin regimes. Unfrtunately at this stage, the cst f the use f this technlgy wuld be t high if it can distinguish nly between bviusly wet and dry fields. In rder t becme cst-effective fr grwers, this satellite technlgy needs t be able t imprve water use efficiency. Fr the wider cmmunity, the satellite image data can already be used t gain a gd verall indicatin f water use in the regin n a regular basis. This infrmatin can be used fr finding bttlenecks in irrigatin applicatins and distributin f water rights, as currently under investigatin in Canterbury. Cst/benefit calculatins: In light f the current applicatin handicaps f this wrk, a cst/benefit calculatin is difficult. The aim was always t use existing sil misture mnitring services fr the SM data fr each mnitred field. This cst wuld remain the same. The questin is nly whether the additinal cst f the satellite image analysis is justified by the additinal sil misture infrmatin n the variability in the field and f the surrunding fields. The calculatin in the fllwing table calculates the cst fr btaining that infrmatin fr additinal fields, but des nt take int accunt the ptential benefit f a better understanding f sil misture distributins within fields. Sme simple calculatins f the csts fr using the satellite images and mdels used by eleaf: Eurpean situatin This prpsal is based n the DMC satellite, which des nt have a thermal band, i.e. n surface temperature. Nt suited fr Triangle methd. Ptential adjustments t New Zealand situatin DMC is nt well suited because thermal images are required fr Triangle methd. Only ptins are nw ASTER and Landsat 7 images (Landsat 8 will be launched end f 2012/beginning f 2013).

5 Delivery: weekly maps f sil misture indicatr. Grwing seasn: ~26 weeks DMC satellite images: ~1200 per image per week (size 60 x 60 km) (satellite dependent!), thus fr 26 weeks: 31,000. Set up: takes abut 2 weeks: 8,000 Weekly image prcessing: 1 image takes abut 14 hurs (ge-crrectin, atmspheric crrectin, SEBAL mdelling (satellite dependent!): s fr the entire grwing seasn: 36,000 Delivery: frtnightly maps f sil misture indicatr with satellite revisit time f 2 weeks. The images are slightly cheaper. One image cvers 185 x 185 km. At the same image price, the cst wuld be abut 15,000 (±NZ$30,000) fr 9x the area cvered. Ditt (±NZ$16,000) Ditt, but nly nce every tw weeks, halving the cst t 18,000 (±NZ$36,000). Only a cst when there is a usable image. What d the farmers want if there are n images fr 1 mnth because f cludy cnditins? With DMC, mre chances t cllect a gd image because f weekly images. Alternative: Irrigatin planner that we prvide (prvides daily sil misture values fr field (average) since start f grwing seasn + frecast f 5 days). Requires feedback frm farmers regarding irrigatin applicatins (in this case using a website). Ttal csts will be abut 85,000, s fr 30 fields that is ~3,000 fr the seasn. Assuming all 30 fields lie within the image (see 3). Nt included in prpsal: csts t set up website, subscriptins fr weather data (frecasts, etc). Hw t reduce csts per field? Mre fields. In case we use DMC + field irrigatin planner, we can g frm weekly t 2-weekly images, as the NDVI and surface albed remain quite cnstant. What d the farmers want if there are n images fr 1 mnth because f cludy cnditins? Past experience has shwn that n average clud cver f the satellite image is less f an issue here than in many parts f Eurpe. Only tw chances fr an image per mnth fr ASTER and Landsat. Part f the NZ plan wuld be t have sil misture measurement systems in place that wuld nly be cmplemented with satellite data. Cst fr weekly SM - $600 per seasn. Fr cntinuus measurements - $1200 per seasn Ttal cst abut 45,000 r $90,000 annually per image cvering the whle f Marlbrugh/Tasman r half f Canterbury. Weather data available free f charge frm NIWA Hw t reduce csts per field? Mre fields! Each in situ SM shuld be extraplated by the satellite image t at least anther 10 fields n the same prperty, representing minimally a cst f $ mnitred fields r e.g. 15 grwers with 10 fields each wuld cver the cst f the satellite image mdel use at the same cst what is being paid at the mment per field mnitred. That is acrss the whle f the primary industry in the almst 35,000 ha f area cvered by ne image. Additinal fields per farm/rchard and additinal grwers wuld reduce the cst per field t well belw actual sil measurement csts. This shuld be easily achievable within the area cvered by the image.

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